BasePandasDataset¶
The class implements functionality that is common to Modin’s pandas API for both DataFrame and Series classes.
Public API¶
- class modin.pandas.base.BasePandasDataset
Implement most of the common code that exists in DataFrame/Series.
Since both objects share the same underlying representation, and the algorithms are the same, we use this object to define the general behavior of those objects and then use those objects to define the output type.
Notes
See pandas API documentation for pandas.DataFrame for more.
- abs()
Return a Series/DataFrame with absolute numeric value of each element.
This function only applies to elements that are all numeric.
- Returns
Series/DataFrame containing the absolute value of each element.
- Return type
abs
See also
numpy.absoluteCalculate the absolute value element-wise.
Notes
See pandas API documentation for pandas.DataFrame.abs for more. For
complexinputs,1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).Examples
Absolute numeric values in a Series.
>>> s = pd.Series([-1.10, 2, -3.33, 4]) >>> s.abs() 0 1.10 1 2.00 2 3.33 3 4.00 dtype: float64
Absolute numeric values in a Series with complex numbers.
>>> s = pd.Series([1.2 + 1j]) >>> s.abs() 0 1.56205 dtype: float64
Absolute numeric values in a Series with a Timedelta element.
>>> s = pd.Series([pd.Timedelta('1 days')]) >>> s.abs() 0 1 days dtype: timedelta64[ns]
Select rows with data closest to certain value using argsort (from StackOverflow).
>>> df = pd.DataFrame({ ... 'a': [4, 5, 6, 7], ... 'b': [10, 20, 30, 40], ... 'c': [100, 50, -30, -50] ... }) >>> df a b c 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50 >>> df.loc[(df.c - 43).abs().argsort()] a b c 1 5 20 50 0 4 10 100 2 6 30 -30 3 7 40 -50
- add(other, axis='columns', level=None, fill_value=None)
Get Addition of dataframe and other, element-wise (binary operator add).
Equivalent to
dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.add for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- agg(func=None, axis=0, *args, **kwargs)
Aggregate using one or more operations over the specified axis.
- Parameters
func (function, str, list or dict) –
Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.
Accepted combinations are:
function
string function name
list of functions and/or function names, e.g.
[np.sum, 'mean']dict of axis labels -> functions, function names or list of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.
- Returns
scalar, Series or DataFrame – The return can be:
scalar : when Series.agg is called with single function
Series : when DataFrame.agg is called with a single function
DataFrame : when DataFrame.agg is called with several functions
Return scalar, Series or DataFrame.
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
numpy aggregation functions (mean, median, prod, sum, std,
var), where the default is to compute the aggregation of the flattened
array, e.g.,
numpy.mean(arr_2d)as opposed tonumpy.mean(arr_2d, axis=0).agg is an alias for aggregate. Use the alias.
See also
DataFrame.applyPerform any type of operations.
DataFrame.transformPerform transformation type operations.
core.groupby.GroupByPerform operations over groups.
core.resample.ResamplerPerform operations over resampled bins.
core.window.RollingPerform operations over rolling window.
core.window.ExpandingPerform operations over expanding window.
core.window.ExponentialMovingWindowPerform operation over exponential weighted window.
Notes
See pandas API documentation for pandas.DataFrame.aggregate for more. agg is an alias for aggregate. Use the alias.
Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.
A passed user-defined-function will be passed a Series for evaluation.
Examples
>>> df = pd.DataFrame([[1, 2, 3], ... [4, 5, 6], ... [7, 8, 9], ... [np.nan, np.nan, np.nan]], ... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min']) A B C sum 12.0 15.0 18.0 min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}) A B sum 12.0 NaN min 1.0 2.0 max NaN 8.0
Aggregate different functions over the columns and rename the index of the resulting DataFrame.
>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean)) A B C x 7.0 NaN NaN y NaN 2.0 NaN z NaN NaN 6.0
Aggregate over the columns.
>>> df.agg("mean", axis="columns") 0 2.0 1 5.0 2 8.0 3 NaN dtype: float64
- aggregate(func=None, axis=0, *args, **kwargs)
Aggregate using one or more operations over the specified axis.
- Parameters
func (function, str, list or dict) –
Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.
Accepted combinations are:
function
string function name
list of functions and/or function names, e.g.
[np.sum, 'mean']dict of axis labels -> functions, function names or list of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.
- Returns
scalar, Series or DataFrame – The return can be:
scalar : when Series.agg is called with single function
Series : when DataFrame.agg is called with a single function
DataFrame : when DataFrame.agg is called with several functions
Return scalar, Series or DataFrame.
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
numpy aggregation functions (mean, median, prod, sum, std,
var), where the default is to compute the aggregation of the flattened
array, e.g.,
numpy.mean(arr_2d)as opposed tonumpy.mean(arr_2d, axis=0).agg is an alias for aggregate. Use the alias.
See also
DataFrame.applyPerform any type of operations.
DataFrame.transformPerform transformation type operations.
core.groupby.GroupByPerform operations over groups.
core.resample.ResamplerPerform operations over resampled bins.
core.window.RollingPerform operations over rolling window.
core.window.ExpandingPerform operations over expanding window.
core.window.ExponentialMovingWindowPerform operation over exponential weighted window.
Notes
See pandas API documentation for pandas.DataFrame.aggregate for more. agg is an alias for aggregate. Use the alias.
Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.
A passed user-defined-function will be passed a Series for evaluation.
Examples
>>> df = pd.DataFrame([[1, 2, 3], ... [4, 5, 6], ... [7, 8, 9], ... [np.nan, np.nan, np.nan]], ... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min']) A B C sum 12.0 15.0 18.0 min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']}) A B sum 12.0 NaN min 1.0 2.0 max NaN 8.0
Aggregate different functions over the columns and rename the index of the resulting DataFrame.
>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean)) A B C x 7.0 NaN NaN y NaN 2.0 NaN z NaN NaN 6.0
Aggregate over the columns.
>>> df.agg("mean", axis="columns") 0 2.0 1 5.0 2 8.0 3 NaN dtype: float64
- align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
- Parameters
join ({'outer', 'inner', 'left', 'right'}, default 'outer') –
axis (allowed axis of the other object, default None) – Align on index (0), columns (1), or both (None).
level (int or level name, default None) – Broadcast across a level, matching Index values on the passed MultiIndex level.
copy (bool, default True) – Always returns new objects. If copy=False and no reindexing is required then original objects are returned.
fill_value (scalar, default np.NaN) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.
method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) –
Method to use for filling holes in reindexed Series:
pad / ffill: propagate last valid observation forward to next valid.
backfill / bfill: use NEXT valid observation to fill gap.
limit (int, default None) – If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
fill_axis ({0 or 'index', 1 or 'columns'}, default 0) – Filling axis, method and limit.
broadcast_axis ({0 or 'index', 1 or 'columns'}, default None) – Broadcast values along this axis, if aligning two objects of different dimensions.
- Returns
(left, right) – Aligned objects.
- Return type
(DataFrame, type of other)
Notes
See pandas API documentation for pandas.DataFrame.align for more.
- all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)
Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).
- Parameters
axis ({0 or 'index', 1 or 'columns', None}, default 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
bool_only (bool, default None) – Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
**kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
If level is specified, then, DataFrame is returned; otherwise, Series is returned.
- Return type
See also
Series.allReturn True if all elements are True.
DataFrame.anyReturn True if one (or more) elements are True.
Examples
Series
>>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False >>> pd.Series([], dtype="float64").all() True >>> pd.Series([np.nan]).all() True >>> pd.Series([np.nan]).all(skipna=False) True
DataFrames
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False
Default behaviour checks if column-wise values all return True.
>>> df.all() col1 True col2 False dtype: bool
Specify
axis='columns'to check if row-wise values all return True.>>> df.all(axis='columns') 0 True 1 False dtype: bool
Or
axis=Nonefor whether every value is True.>>> df.all(axis=None) False
Notes
See pandas API documentation for pandas.DataFrame.all for more.
- any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)
Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
- Parameters
axis ({0 or 'index', 1 or 'columns', None}, default 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
bool_only (bool, default None) – Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
**kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
If level is specified, then, DataFrame is returned; otherwise, Series is returned.
- Return type
See also
numpy.anyNumpy version of this method.
Series.anyReturn whether any element is True.
Series.allReturn whether all elements are True.
DataFrame.anyReturn whether any element is True over requested axis.
DataFrame.allReturn whether all elements are True over requested axis.
Examples
Series
For Series input, the output is a scalar indicating whether any element is True.
>>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([], dtype="float64").any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True
DataFrame
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0
>>> df.any() A True B True C False dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2
>>> df.any(axis='columns') 0 True 1 True dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0
>>> df.any(axis='columns') 0 True 1 False dtype: bool
Aggregating over the entire DataFrame with
axis=None.>>> df.any(axis=None) True
any for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any() Series([], dtype: bool)
Notes
See pandas API documentation for pandas.DataFrame.any for more.
- apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, convert_dtype=True, args=(), **kwds)
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is either the DataFrame’s index (
axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.- Parameters
func (function) – Function to apply to each column or row.
axis ({0 or 'index', 1 or 'columns'}, default 0) –
Axis along which the function is applied:
0 or ‘index’: apply function to each column.
1 or ‘columns’: apply function to each row.
raw (bool, default False) –
Determines if row or column is passed as a Series or ndarray object:
False: passes each row or column as a Series to the function.True: the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
result_type ({'expand', 'reduce', 'broadcast', None}, default None) –
These only act when
axis=1(columns):’expand’ : list-like results will be turned into columns.
’reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.
’broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.
args (tuple) – Positional arguments to pass to func in addition to the array/series.
**kwargs – Additional keyword arguments to pass as keywords arguments to func.
- Returns
Result of applying
funcalong the given axis of the DataFrame.- Return type
See also
DataFrame.applymapFor elementwise operations.
DataFrame.aggregateOnly perform aggregating type operations.
DataFrame.transformOnly perform transforming type operations.
Notes
See pandas API documentation for pandas.DataFrame.apply for more. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.
Examples
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B']) >>> df A B 0 4 9 1 4 9 2 4 9
Using a numpy universal function (in this case the same as
np.sqrt(df)):>>> df.apply(np.sqrt) A B 0 2.0 3.0 1 2.0 3.0 2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0) A 12 B 27 dtype: int64
>>> df.apply(np.sum, axis=1) 0 13 1 13 2 13 dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1) 0 [1, 2] 1 [1, 2] 2 [1, 2] dtype: object
Passing
result_type='expand'will expand list-like results to columns of a Dataframe>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand') 0 1 0 1 2 1 1 2 2 1 2
Returning a Series inside the function is similar to passing
result_type='expand'. The resulting column names will be the Series index.>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1) foo bar 0 1 2 1 1 2 2 1 2
Passing
result_type='broadcast'will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast') A B 0 1 2 1 1 2 2 1 2
- asfreq(freq, method=None, how=None, normalize=False, fill_value=None)
Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified frequency.
If the index of this DataFrame is a
PeriodIndex, the new index is the result of transforming the original index withPeriodIndex.asfreq(so the original index will map one-to-one to the new index).Otherwise, the new index will be equivalent to
pd.date_range(start, end, freq=freq)wherestartandendare, respectively, the first and last entries in the original index (seepandas.date_range()). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see themethodparameter below).The
resample()method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.- Parameters
freq (DateOffset or str) – Frequency DateOffset or string.
method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) –
Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):
’pad’ / ‘ffill’: propagate last valid observation forward to next valid
’backfill’ / ‘bfill’: use NEXT valid observation to fill.
how ({'start', 'end'}, default end) – For PeriodIndex only (see PeriodIndex.asfreq).
normalize (bool, default False) – Whether to reset output index to midnight.
fill_value (scalar, optional) – Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).
- Returns
DataFrame object reindexed to the specified frequency.
- Return type
See also
reindexConform DataFrame to new index with optional filling logic.
Notes
See pandas API documentation for pandas.DataFrame.asfreq for more. To learn more about the frequency strings, please see this link.
Examples
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T') >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index) >>> df = pd.DataFrame({'s': series}) >>> df s 2000-01-01 00:00:00 0.0 2000-01-01 00:01:00 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 NaN 2000-01-01 00:03:00 3.0
Upsample again, providing a
fill value.>>> df.asfreq(freq='30S', fill_value=9.0) s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 9.0 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 9.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 9.0 2000-01-01 00:03:00 3.0
Upsample again, providing a
method.>>> df.asfreq(freq='30S', method='bfill') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 2.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 3.0 2000-01-01 00:03:00 3.0
- asof(where, subset=None)
Return the last row(s) without any NaNs before where.
The last row (for each element in where, if list) without any NaN is taken. In case of a
DataFrame, the last row without NaN considering only the subset of columns (if not None)If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame
- Parameters
where (date or array-like of dates) – Date(s) before which the last row(s) are returned.
subset (str or array-like of str, default None) – For DataFrame, if not None, only use these columns to check for NaNs.
- Returns
The return can be:
scalar : when self is a Series and where is a scalar
Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar
DataFrame : when self is a DataFrame and where is an array-like
Return scalar, Series, or DataFrame.
- Return type
See also
merge_asofPerform an asof merge. Similar to left join.
Notes
See pandas API documentation for pandas.DataFrame.asof for more. Dates are assumed to be sorted. Raises if this is not the case.
Examples
A Series and a scalar where.
>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40]) >>> s 10 1.0 20 2.0 30 NaN 40 4.0 dtype: float64
>>> s.asof(20) 2.0
For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.
>>> s.asof([5, 20]) 5 NaN 20 2.0 dtype: float64
Missing values are not considered. The following is
2.0, not NaN, even though NaN is at the index location for30.>>> s.asof(30) 2.0
Take all columns into consideration
>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50], ... 'b': [None, None, None, None, 500]}, ... index=pd.DatetimeIndex(['2018-02-27 09:01:00', ... '2018-02-27 09:02:00', ... '2018-02-27 09:03:00', ... '2018-02-27 09:04:00', ... '2018-02-27 09:05:00'])) >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30'])) a b 2018-02-27 09:03:30 NaN NaN 2018-02-27 09:04:30 NaN NaN
Take a single column into consideration
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30']), ... subset=['a']) a b 2018-02-27 09:03:30 30.0 NaN 2018-02-27 09:04:30 40.0 NaN
- astype(dtype, copy=True, errors='raise')
Cast a pandas object to a specified dtype
dtype.- Parameters
dtype (data type, or dict of column name -> data type) – Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
copy (bool, default True) – Return a copy when
copy=True(be very careful settingcopy=Falseas changes to values then may propagate to other pandas objects).errors ({'raise', 'ignore'}, default 'raise') –
Control raising of exceptions on invalid data for provided dtype.
raise: allow exceptions to be raisedignore: suppress exceptions. On error return original object.
- Returns
casted
- Return type
same type as caller
See also
to_datetimeConvert argument to datetime.
to_timedeltaConvert argument to timedelta.
to_numericConvert argument to a numeric type.
numpy.ndarray.astypeCast a numpy array to a specified type.
Notes
See pandas API documentation for pandas.DataFrame.astype for more. .. deprecated:: 1.3.0
Using
astypeto convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. UseSeries.dt.tz_localize()instead.Examples
Create a DataFrame:
>>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df = pd.DataFrame(data=d) >>> df.dtypes col1 int64 col2 int64 dtype: object
Cast all columns to int32:
>>> df.astype('int32').dtypes col1 int32 col2 int32 dtype: object
Cast col1 to int32 using a dictionary:
>>> df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object
Create a series:
>>> ser = pd.Series([1, 2], dtype='int32') >>> ser 0 1 1 2 dtype: int32 >>> ser.astype('int64') 0 1 1 2 dtype: int64
Convert to categorical type:
>>> ser.astype('category') 0 1 1 2 dtype: category Categories (2, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> from pandas.api.types import CategoricalDtype >>> cat_dtype = CategoricalDtype( ... categories=[2, 1], ordered=True) >>> ser.astype(cat_dtype) 0 1 1 2 dtype: category Categories (2, int64): [2 < 1]
Note that using
copy=Falseand changing data on a new pandas object may propagate changes:>>> s1 = pd.Series([1, 2]) >>> s2 = s1.astype('int64', copy=False) >>> s2[0] = 10 >>> s1 # note that s1[0] has changed too 0 10 1 2 dtype: int64
Create a series of dates:
>>> ser_date = pd.Series(pd.date_range('20200101', periods=3)) >>> ser_date 0 2020-01-01 1 2020-01-02 2 2020-01-03 dtype: datetime64[ns]
- property at
Access a single value for a row/column label pair.
Similar to
loc, in that both provide label-based lookups. Useatif you only need to get or set a single value in a DataFrame or Series.- Raises
KeyError – If ‘label’ does not exist in DataFrame.
See also
DataFrame.iatAccess a single value for a row/column pair by integer position.
DataFrame.locAccess a group of rows and columns by label(s).
Series.atAccess a single value using a label.
Examples
>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], ... index=[4, 5, 6], columns=['A', 'B', 'C']) >>> df A B C 4 0 2 3 5 0 4 1 6 10 20 30
Get value at specified row/column pair
>>> df.at[4, 'B'] 2
Set value at specified row/column pair
>>> df.at[4, 'B'] = 10 >>> df.at[4, 'B'] 10
Get value within a Series
>>> df.loc[5].at['B'] 4
Notes
See pandas API documentation for pandas.DataFrame.at for more.
- at_time(time, asof=False, axis=None)
Select values at particular time of day (e.g., 9:30AM).
- Parameters
time (datetime.time or str) –
axis ({0 or 'index', 1 or 'columns'}, default 0) –
- Returns
- Return type
- Raises
TypeError – If the index is not a
DatetimeIndex
See also
between_timeSelect values between particular times of the day.
firstSelect initial periods of time series based on a date offset.
lastSelect final periods of time series based on a date offset.
DatetimeIndex.indexer_at_timeGet just the index locations for values at particular time of the day.
Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-09 12:00:00 2 2018-04-10 00:00:00 3 2018-04-10 12:00:00 4
>>> ts.at_time('12:00') A 2018-04-09 12:00:00 2 2018-04-10 12:00:00 4
Notes
See pandas API documentation for pandas.DataFrame.at_time for more.
- backfill(axis=None, inplace=False, limit=None, downcast=None)
Synonym for
DataFrame.fillna()withmethod='bfill'.- Returns
Object with missing values filled or None if
inplace=True.- Return type
Series/DataFrame or None
Notes
See pandas API documentation for pandas.DataFrame.backfill for more.
- between_time(start_time, end_time, include_start=True, include_end=True, axis=None)
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting
start_timeto be later thanend_time, you can get the times that are not between the two times.- Parameters
start_time (datetime.time or str) – Initial time as a time filter limit.
end_time (datetime.time or str) – End time as a time filter limit.
include_start (bool, default True) – Whether the start time needs to be included in the result.
include_end (bool, default True) – Whether the end time needs to be included in the result.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Determine range time on index or columns value.
- Returns
Data from the original object filtered to the specified dates range.
- Return type
- Raises
TypeError – If the index is not a
DatetimeIndex
See also
at_timeSelect values at a particular time of the day.
firstSelect initial periods of time series based on a date offset.
lastSelect final periods of time series based on a date offset.
DatetimeIndex.indexer_between_timeGet just the index locations for values between particular times of the day.
Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45') A 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3
You get the times that are not between two times by setting
start_timelater thanend_time:>>> ts.between_time('0:45', '0:15') A 2018-04-09 00:00:00 1 2018-04-12 01:00:00 4
Notes
See pandas API documentation for pandas.DataFrame.between_time for more.
- bfill(axis=None, inplace=False, limit=None, downcast=None)
Synonym for
DataFrame.fillna()withmethod='bfill'.- Returns
Object with missing values filled or None if
inplace=True.- Return type
Series/DataFrame or None
Notes
See pandas API documentation for pandas.DataFrame.backfill for more.
- bool()
Return the bool of a single element Series or DataFrame.
This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).
- Returns
The value in the Series or DataFrame.
- Return type
bool
See also
Series.astypeChange the data type of a Series, including to boolean.
DataFrame.astypeChange the data type of a DataFrame, including to boolean.
numpy.bool_NumPy boolean data type, used by pandas for boolean values.
Examples
The method will only work for single element objects with a boolean value:
>>> pd.Series([True]).bool() True >>> pd.Series([False]).bool() False
>>> pd.DataFrame({'col': [True]}).bool() True >>> pd.DataFrame({'col': [False]}).bool() False
Notes
See pandas API documentation for pandas.DataFrame.bool for more.
- combine(other, func, fill_value=None, **kwargs)
Perform column-wise combine with another DataFrame.
Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.
- Parameters
other (DataFrame) – The DataFrame to merge column-wise.
func (function) – Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_value (scalar value, default None) – The value to fill NaNs with prior to passing any column to the merge func.
overwrite (bool, default True) – If True, columns in self that do not exist in other will be overwritten with NaNs.
- Returns
Combination of the provided DataFrames.
- Return type
See also
DataFrame.combine_firstCombine two DataFrame objects and default to non-null values in frame calling the method.
Examples
Combine using a simple function that chooses the smaller column.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2 >>> df1.combine(df2, take_smaller) A B 0 0 3 1 0 3
Example using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, np.minimum) A B 0 1 2 1 0 3
Using fill_value fills Nones prior to passing the column to the merge function.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 4.0
However, if the same element in both dataframes is None, that None is preserved
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 3.0
Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2]) >>> df1.combine(df2, take_smaller) A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2]) >>> df2.combine(df1, take_smaller) A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0
Notes
See pandas API documentation for pandas.DataFrame.combine for more.
- combine_first(other)
Update null elements with value in the same location in other.
Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two.
- Parameters
other (DataFrame) – Provided DataFrame to use to fill null values.
- Returns
The result of combining the provided DataFrame with the other object.
- Return type
See also
DataFrame.combinePerform series-wise operation on two DataFrames using a given function.
Examples
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine_first(df2) A B 0 1.0 3.0 1 0.0 4.0
Null values still persist if the location of that null value does not exist in other
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2]) >>> df1.combine_first(df2) A B C 0 NaN 4.0 NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0
Notes
See pandas API documentation for pandas.DataFrame.combine_first for more.
- convert_dtypes(infer_objects: modin.pandas.base.BasePandasDataset.bool = True, convert_string: modin.pandas.base.BasePandasDataset.bool = True, convert_integer: modin.pandas.base.BasePandasDataset.bool = True, convert_boolean: modin.pandas.base.BasePandasDataset.bool = True, convert_floating: modin.pandas.base.BasePandasDataset.bool = True)
Convert columns to best possible dtypes using dtypes supporting
pd.NA.New in version 1.0.0.
- Parameters
infer_objects (bool, default True) – Whether object dtypes should be converted to the best possible types.
convert_string (bool, default True) – Whether object dtypes should be converted to
StringDtype().convert_integer (bool, default True) – Whether, if possible, conversion can be done to integer extension types.
convert_boolean (bool, defaults True) – Whether object dtypes should be converted to
BooleanDtypes().convert_floating (bool, defaults True) –
Whether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.
New in version 1.2.0.
- Returns
Copy of input object with new dtype.
- Return type
See also
infer_objectsInfer dtypes of objects.
to_datetimeConvert argument to datetime.
to_timedeltaConvert argument to timedelta.
to_numericConvert argument to a numeric type.
Notes
See pandas API documentation for pandas.DataFrame.convert_dtypes for more. By default,
convert_dtypeswill attempt to convert a Series (or each Series in a DataFrame) to dtypes that supportpd.NA. By using the optionsconvert_string,convert_integer,convert_booleanandconvert_boolean, it is possible to turn off individual conversions toStringDtype, the integer extension types,BooleanDtypeor floating extension types, respectively.For object-dtyped columns, if
infer_objectsisTrue, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert toStringDtype,BooleanDtypeor an appropriate integer or floating extension type, otherwise leave asobject.If the dtype is integer, convert to an appropriate integer extension type.
If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.
Changed in version 1.2: Starting with pandas 1.2, this method also converts float columns to the nullable floating extension type.
In the future, as new dtypes are added that support
pd.NA, the results of this method will change to support those new dtypes.Examples
>>> df = pd.DataFrame( ... { ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), ... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), ... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")), ... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")), ... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")), ... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), ... } ... )
Start with a DataFrame with default dtypes.
>>> df a b c d e f 0 1 x True h 10.0 NaN 1 2 y False i NaN 100.5 2 3 z NaN NaN 20.0 200.0
>>> df.dtypes a int32 b object c object d object e float64 f float64 dtype: object
Convert the DataFrame to use best possible dtypes.
>>> dfn = df.convert_dtypes() >>> dfn a b c d e f 0 1 x True h 10 <NA> 1 2 y False i <NA> 100.5 2 3 z <NA> <NA> 20 200.0
>>> dfn.dtypes a Int32 b string c boolean d string e Int64 f Float64 dtype: object
Start with a Series of strings and missing data represented by
np.nan.>>> s = pd.Series(["a", "b", np.nan]) >>> s 0 a 1 b 2 NaN dtype: object
Obtain a Series with dtype
StringDtype.>>> s.convert_dtypes() 0 a 1 b 2 <NA> dtype: string
- copy(deep=True)
Make a copy of this object’s indices and data.
When
deep=True(default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).When
deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa).- Parameters
deep (bool, default True) – Make a deep copy, including a copy of the data and the indices. With
deep=Falseneither the indices nor the data are copied.- Returns
copy – Object type matches caller.
- Return type
Notes
See pandas API documentation for pandas.DataFrame.copy for more. When
deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).While
Indexobjects are copied whendeep=True, the underlying numpy array is not copied for performance reasons. SinceIndexis immutable, the underlying data can be safely shared and a copy is not needed.Examples
>>> s = pd.Series([1, 2], index=["a", "b"]) >>> s a 1 b 2 dtype: int64
>>> s_copy = s.copy() >>> s_copy a 1 b 2 dtype: int64
Shallow copy versus default (deep) copy:
>>> s = pd.Series([1, 2], index=["a", "b"]) >>> deep = s.copy() >>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.
>>> s is shallow False >>> s.values is shallow.values and s.index is shallow.index True
Deep copy has own copy of data and index.
>>> s is deep False >>> s.values is deep.values or s.index is deep.index False
Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged.
>>> s[0] = 3 >>> shallow[1] = 4 >>> s a 3 b 4 dtype: int64 >>> shallow a 3 b 4 dtype: int64 >>> deep a 1 b 2 dtype: int64
Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy.
>>> s = pd.Series([[1, 2], [3, 4]]) >>> deep = s.copy() >>> s[0][0] = 10 >>> s 0 [10, 2] 1 [3, 4] dtype: object >>> deep 0 [10, 2] 1 [3, 4] dtype: object
- count(axis=0, level=None, numeric_only=False)
Count non-NA cells for each column or row.
The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
level (int or str, optional) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame. A str specifies the level name.
numeric_only (bool, default False) – Include only float, int or boolean data.
- Returns
For each column/row the number of non-NA/null entries. If level is specified returns a DataFrame.
- Return type
See also
Series.countNumber of non-NA elements in a Series.
DataFrame.value_countsCount unique combinations of columns.
DataFrame.shapeNumber of DataFrame rows and columns (including NA elements).
DataFrame.isnaBoolean same-sized DataFrame showing places of NA elements.
Examples
Constructing DataFrame from a dictionary:
>>> df = pd.DataFrame({"Person": ... ["John", "Myla", "Lewis", "John", "Myla"], ... "Age": [24., np.nan, 21., 33, 26], ... "Single": [False, True, True, True, False]}) >>> df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 Lewis 21.0 True 3 John 33.0 True 4 Myla 26.0 False
Notice the uncounted NA values:
>>> df.count() Person 5 Age 4 Single 5 dtype: int64
Counts for each row:
>>> df.count(axis='columns') 0 3 1 2 2 3 3 3 4 3 dtype: int64
Notes
See pandas API documentation for pandas.DataFrame.count for more.
- cummax(axis=None, skipna=True, *args, **kwargs)
Return cumulative maximum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative maximum.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
Return cumulative maximum of Series or DataFrame.
- Return type
See also
core.window.Expanding.maxSimilar functionality but ignores
NaNvalues.DataFrame.maxReturn the maximum over DataFrame axis.
DataFrame.cummaxReturn cumulative maximum over DataFrame axis.
DataFrame.cumminReturn cumulative minimum over DataFrame axis.
DataFrame.cumsumReturn cumulative sum over DataFrame axis.
DataFrame.cumprodReturn cumulative product over DataFrame axis.
Examples
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64
By default, NA values are ignored.
>>> s.cummax() 0 2.0 1 NaN 2 5.0 3 5.0 4 5.0 dtype: float64
To include NA values in the operation, use
skipna=False>>> s.cummax(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0
By default, iterates over rows and finds the maximum in each column. This is equivalent to
axis=Noneoraxis='index'.>>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0
To iterate over columns and find the maximum in each row, use
axis=1>>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0
Notes
See pandas API documentation for pandas.DataFrame.cummax for more.
- cummin(axis=None, skipna=True, *args, **kwargs)
Return cumulative minimum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative minimum.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
Return cumulative minimum of Series or DataFrame.
- Return type
See also
core.window.Expanding.minSimilar functionality but ignores
NaNvalues.DataFrame.minReturn the minimum over DataFrame axis.
DataFrame.cummaxReturn cumulative maximum over DataFrame axis.
DataFrame.cumminReturn cumulative minimum over DataFrame axis.
DataFrame.cumsumReturn cumulative sum over DataFrame axis.
DataFrame.cumprodReturn cumulative product over DataFrame axis.
Examples
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64
By default, NA values are ignored.
>>> s.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64
To include NA values in the operation, use
skipna=False>>> s.cummin(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0
By default, iterates over rows and finds the minimum in each column. This is equivalent to
axis=Noneoraxis='index'.>>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0
To iterate over columns and find the minimum in each row, use
axis=1>>> df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0
Notes
See pandas API documentation for pandas.DataFrame.cummin for more.
- cumprod(axis=None, skipna=True, *args, **kwargs)
Return cumulative product over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative product.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
Return cumulative product of Series or DataFrame.
- Return type
See also
core.window.Expanding.prodSimilar functionality but ignores
NaNvalues.DataFrame.prodReturn the product over DataFrame axis.
DataFrame.cummaxReturn cumulative maximum over DataFrame axis.
DataFrame.cumminReturn cumulative minimum over DataFrame axis.
DataFrame.cumsumReturn cumulative sum over DataFrame axis.
DataFrame.cumprodReturn cumulative product over DataFrame axis.
Examples
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64
By default, NA values are ignored.
>>> s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64
To include NA values in the operation, use
skipna=False>>> s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0
By default, iterates over rows and finds the product in each column. This is equivalent to
axis=Noneoraxis='index'.>>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0
To iterate over columns and find the product in each row, use
axis=1>>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0
Notes
See pandas API documentation for pandas.DataFrame.cumprod for more.
- cumsum(axis=None, skipna=True, *args, **kwargs)
Return cumulative sum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative sum.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.
- Returns
Return cumulative sum of Series or DataFrame.
- Return type
See also
core.window.Expanding.sumSimilar functionality but ignores
NaNvalues.DataFrame.sumReturn the sum over DataFrame axis.
DataFrame.cummaxReturn cumulative maximum over DataFrame axis.
DataFrame.cumminReturn cumulative minimum over DataFrame axis.
DataFrame.cumsumReturn cumulative sum over DataFrame axis.
DataFrame.cumprodReturn cumulative product over DataFrame axis.
Examples
Series
>>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64
By default, NA values are ignored.
>>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64
To include NA values in the operation, use
skipna=False>>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64
DataFrame
>>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0
By default, iterates over rows and finds the sum in each column. This is equivalent to
axis=Noneoraxis='index'.>>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0
To iterate over columns and find the sum in each row, use
axis=1>>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0
Notes
See pandas API documentation for pandas.DataFrame.cumsum for more.
- describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)
Generate descriptive statistics.
Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding
NaNvalues.Analyzes both numeric and object series, as well as
DataFramecolumn sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.- Parameters
percentiles (list-like of numbers, optional) – The percentiles to include in the output. All should fall between 0 and 1. The default is
[.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.include ('all', list-like of dtypes or None (default), optional) –
A white list of data types to include in the result. Ignored for
Series. Here are the options:’all’ : All columns of the input will be included in the output.
A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit
numpy.number. To limit it instead to object columns submit thenumpy.objectdata type. Strings can also be used in the style ofselect_dtypes(e.g.df.describe(include=['O'])). To select pandas categorical columns, use'category'None (default) : The result will include all numeric columns.
exclude (list-like of dtypes or None (default), optional,) –
A black list of data types to omit from the result. Ignored for
Series. Here are the options:A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit
numpy.number. To exclude object columns submit the data typenumpy.object. Strings can also be used in the style ofselect_dtypes(e.g.df.describe(include=['O'])). To exclude pandas categorical columns, use'category'None (default) : The result will exclude nothing.
datetime_is_numeric (bool, default False) –
Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.
New in version 1.1.0.
- Returns
Summary statistics of the Series or Dataframe provided.
- Return type
See also
DataFrame.countCount number of non-NA/null observations.
DataFrame.maxMaximum of the values in the object.
DataFrame.minMinimum of the values in the object.
DataFrame.meanMean of the values.
DataFrame.stdStandard deviation of the observations.
DataFrame.select_dtypesSubset of a DataFrame including/excluding columns based on their dtype.
Notes
See pandas API documentation for pandas.DataFrame.describe for more. For numeric data, the result’s index will include
count,mean,std,min,maxas well as lower,50and upper percentiles. By default the lower percentile is25and the upper percentile is75. The50percentile is the same as the median.For object data (e.g. strings or timestamps), the result’s index will include
count,unique,top, andfreq. Thetopis the most common value. Thefreqis the most common value’s frequency. Timestamps also include thefirstandlastitems.If multiple object values have the highest count, then the
countandtopresults will be arbitrarily chosen from among those with the highest count.For mixed data types provided via a
DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. Ifinclude='all'is provided as an option, the result will include a union of attributes of each type.The include and exclude parameters can be used to limit which columns in a
DataFrameare analyzed for the output. The parameters are ignored when analyzing aSeries.Examples
Describing a numeric
Series.>>> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64
Describing a categorical
Series.>>> s = pd.Series(['a', 'a', 'b', 'c']) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object
Describing a timestamp
Series.>>> s = pd.Series([ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01") ... ]) >>> s.describe(datetime_is_numeric=True) count 3 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object
Describing a
DataFrame. By default only numeric fields are returned.>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']), ... 'numeric': [1, 2, 3], ... 'object': ['a', 'b', 'c'] ... }) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0
Describing all columns of a
DataFrameregardless of data type.>>> df.describe(include='all') categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN
Describing a column from a
DataFrameby accessing it as an attribute.>>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64
Including only numeric columns in a
DataFramedescription.>>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0
Including only string columns in a
DataFramedescription.>>> df.describe(include=[object]) object count 3 unique 3 top a freq 1
Including only categorical columns from a
DataFramedescription.>>> df.describe(include=['category']) categorical count 3 unique 3 top d freq 1
Excluding numeric columns from a
DataFramedescription.>>> df.describe(exclude=[np.number]) categorical object count 3 3 unique 3 3 top f a freq 1 1
Excluding object columns from a
DataFramedescription.>>> df.describe(exclude=[object]) categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0
- diff(periods=1, axis=0)
First discrete difference of element.
Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).
- Parameters
periods (int, default 1) – Periods to shift for calculating difference, accepts negative values.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Take difference over rows (0) or columns (1).
- Returns
First differences of the Series.
- Return type
Dataframe
See also
Dataframe.pct_changePercent change over given number of periods.
Dataframe.shiftShift index by desired number of periods with an optional time freq.
Series.diffFirst discrete difference of object.
Notes
See pandas API documentation for pandas.DataFrame.diff for more. For boolean dtypes, this uses
operator.xor()rather thanoperator.sub(). The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.Examples
Difference with previous row
>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], ... 'b': [1, 1, 2, 3, 5, 8], ... 'c': [1, 4, 9, 16, 25, 36]}) >>> df a b c 0 1 1 1 1 2 1 4 2 3 2 9 3 4 3 16 4 5 5 25 5 6 8 36
>>> df.diff() a b c 0 NaN NaN NaN 1 1.0 0.0 3.0 2 1.0 1.0 5.0 3 1.0 1.0 7.0 4 1.0 2.0 9.0 5 1.0 3.0 11.0
Difference with previous column
>>> df.diff(axis=1) a b c 0 NaN 0 0 1 NaN -1 3 2 NaN -1 7 3 NaN -1 13 4 NaN 0 20 5 NaN 2 28
Difference with 3rd previous row
>>> df.diff(periods=3) a b c 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 3.0 2.0 15.0 4 3.0 4.0 21.0 5 3.0 6.0 27.0
Difference with following row
>>> df.diff(periods=-1) a b c 0 -1.0 0.0 -3.0 1 -1.0 -1.0 -5.0 2 -1.0 -1.0 -7.0 3 -1.0 -2.0 -9.0 4 -1.0 -3.0 -11.0 5 NaN NaN NaN
Overflow in input dtype
>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8) >>> df.diff() a 0 NaN 1 255.0
- div(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator truediv).
Equivalent to
dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.truediv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- divide(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator truediv).
Equivalent to
dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.truediv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.
- Parameters
labels (single label or list-like) – Index or column labels to drop.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
index (single label or list-like) – Alternative to specifying axis (
labels, axis=0is equivalent toindex=labels).columns (single label or list-like) – Alternative to specifying axis (
labels, axis=1is equivalent tocolumns=labels).level (int or level name, optional) – For MultiIndex, level from which the labels will be removed.
inplace (bool, default False) – If False, return a copy. Otherwise, do operation inplace and return None.
errors ({'ignore', 'raise'}, default 'raise') – If ‘ignore’, suppress error and only existing labels are dropped.
- Returns
DataFrame without the removed index or column labels or None if
inplace=True.- Return type
DataFrame or None
- Raises
KeyError – If any of the labels is not found in the selected axis.
See also
DataFrame.locLabel-location based indexer for selection by label.
DataFrame.dropnaReturn DataFrame with labels on given axis omitted where (all or any) data are missing.
DataFrame.drop_duplicatesReturn DataFrame with duplicate rows removed, optionally only considering certain columns.
Series.dropReturn Series with specified index labels removed.
Examples
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4), ... columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11
>>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11
Drop a row by index
>>> df.drop([0, 1]) A B C D 2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20], ... [250, 150], [1.5, 0.8], [320, 250], ... [1, 0.8], [0.3, 0.2]]) >>> df big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2
>>> df.drop(index='cow', columns='small') big lama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3
>>> df.drop(index='length', level=1) big small lama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8
Notes
See pandas API documentation for pandas.DataFrame.drop for more.
- drop_duplicates(keep='first', inplace=False, **kwargs)
Return DataFrame with duplicate rows removed.
Considering certain columns is optional. Indexes, including time indexes are ignored.
- Parameters
subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all of the columns.
keep ({'first', 'last', False}, default 'first') – Determines which duplicates (if any) to keep. -
first: Drop duplicates except for the first occurrence. -last: Drop duplicates except for the last occurrence. - False : Drop all duplicates.inplace (bool, default False) – Whether to drop duplicates in place or to return a copy.
ignore_index (bool, default False) –
If True, the resulting axis will be labeled 0, 1, …, n - 1.
New in version 1.0.0.
- Returns
DataFrame with duplicates removed or None if
inplace=True.- Return type
DataFrame or None
See also
DataFrame.value_countsCount unique combinations of columns.
Examples
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
By default, it removes duplicate rows based on all columns.
>>> df.drop_duplicates() brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
To remove duplicates on specific column(s), use
subset.>>> df.drop_duplicates(subset=['brand']) brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5
To remove duplicates and keep last occurrences, use
keep.>>> df.drop_duplicates(subset=['brand', 'style'], keep='last') brand style rating 1 Yum Yum cup 4.0 2 Indomie cup 3.5 4 Indomie pack 5.0
Notes
See pandas API documentation for pandas.DataFrame.drop_duplicates for more.
- droplevel(level, axis=0)
Return Series/DataFrame with requested index / column level(s) removed.
- Parameters
level (int, str, or list-like) – If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
axis ({0 or 'index', 1 or 'columns'}, default 0) –
Axis along which the level(s) is removed:
0 or ‘index’: remove level(s) in column.
1 or ‘columns’: remove level(s) in row.
- Returns
Series/DataFrame with requested index / column level(s) removed.
- Return type
Series/DataFrame
Examples
>>> df = pd.DataFrame([ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12] ... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([ ... ('c', 'e'), ('d', 'f') ... ], names=['level_1', 'level_2'])
>>> df level_1 c d level_2 e f a b 1 2 3 4 5 6 7 8 9 10 11 12
>>> df.droplevel('a') level_1 c d level_2 e f b 2 3 4 6 7 8 10 11 12
>>> df.droplevel('level_2', axis=1) level_1 c d a b 1 2 3 4 5 6 7 8 9 10 11 12
Notes
See pandas API documentation for pandas.DataFrame.droplevel for more.
- dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
Remove missing values.
See the User Guide for more on which values are considered missing, and how to work with missing data.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) –
Determine if rows or columns which contain missing values are removed.
0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.
Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. Only a single axis is allowed.
how ({'any', 'all'}, default 'any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
’any’ : If any NA values are present, drop that row or column.
’all’ : If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non-NA values.
subset (array-like, optional) – Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.
inplace (bool, default False) – If True, do operation inplace and return None.
- Returns
DataFrame with NA entries dropped from it or None if
inplace=True.- Return type
DataFrame or None
See also
DataFrame.isnaIndicate missing values.
DataFrame.notnaIndicate existing (non-missing) values.
DataFrame.fillnaReplace missing values.
Series.dropnaDrop missing values.
Index.dropnaDrop missing indices.
Examples
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], ... "toy": [np.nan, 'Batmobile', 'Bullwhip'], ... "born": [pd.NaT, pd.Timestamp("1940-04-25"), ... pd.NaT]}) >>> df name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT
Drop the rows where at least one element is missing.
>>> df.dropna() name toy born 1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns') name 0 Alfred 1 Batman 2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all') name toy born 0 Alfred NaN NaT 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'toy']) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT
Keep the DataFrame with valid entries in the same variable.
>>> df.dropna(inplace=True) >>> df name toy born 1 Batman Batmobile 1940-04-25
Notes
See pandas API documentation for pandas.DataFrame.dropna for more.
- eq(other, axis='columns', level=None)
Get Equal to of dataframe and other, element-wise (binary operator eq).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.eq for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None)
Provide exponential weighted (EW) functions.
Available EW functions:
mean(),var(),std(),corr(),cov().Exactly one parameter:
com,span,halflife, oralphamust be provided.- Parameters
com (float, optional) – Specify decay in terms of center of mass, \(\alpha = 1 / (1 + com)\), for \(com \geq 0\).
span (float, optional) – Specify decay in terms of span, \(\alpha = 2 / (span + 1)\), for \(span \geq 1\).
halflife (float, str, timedelta, optional) –
Specify decay in terms of half-life, \(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\).
If
timesis specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable tomean()and halflife value will not apply to the other functions.New in version 1.1.0.
alpha (float, optional) – Specify smoothing factor \(\alpha\) directly, \(0 < \alpha \leq 1\).
min_periods (int, default 0) – Minimum number of observations in window required to have a value (otherwise result is NA).
adjust (bool, default True) –
Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).
When
adjust=True(default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be:
\[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\]When
adjust=False, the exponentially weighted function is calculated recursively:
\[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]ignore_na (bool, default False) –
Ignore missing values when calculating weights; specify
Trueto reproduce pre-0.15.0 behavior.When
ignore_na=False(default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) ifadjust=True, and \((1-\alpha)^2\) and \(\alpha\) ifadjust=False.When
ignore_na=True(reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) ifadjust=True, and \(1-\alpha\) and \(\alpha\) ifadjust=False.
axis ({0, 1}, default 0) – The axis to use. The value 0 identifies the rows, and 1 identifies the columns.
times (str, np.ndarray, Series, default None) –
New in version 1.1.0.
Times corresponding to the observations. Must be monotonically increasing and
datetime64[ns]dtype.If str, the name of the column in the DataFrame representing the times.
If 1-D array like, a sequence with the same shape as the observations.
Only applicable to
mean().
- Returns
A Window sub-classed for the particular operation.
- Return type
See also
rollingProvides rolling window calculations.
expandingProvides expanding transformations.
Notes
See pandas API documentation for pandas.DataFrame.ewm for more.
More details can be found at: Exponentially weighted windows.
Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0
>>> df.ewm(com=0.5).mean() B 0 0.000000 1 0.750000 2 1.615385 3 1.615385 4 3.670213
Specifying
timeswith a timedeltahalflifewhen computing mean.>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17'] >>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean() B 0 0.000000 1 0.585786 2 1.523889 3 1.523889 4 3.233686
- expanding(min_periods=1, center=None, axis=0, method='single')
Provide expanding transformations.
- Parameters
min_periods (int, default 1) – Minimum number of observations in window required to have a value (otherwise result is NA).
center (bool, default False) – Set the labels at the center of the window.
axis (int or str, default 0) –
method (str {'single', 'table'}, default 'single') –
Execute the rolling operation per single column or row (
'single') or over the entire object ('table').This argument is only implemented when specifying
engine='numba'in the method call.New in version 1.3.0.
- Returns
- Return type
a Window sub-classed for the particular operation
See also
rollingProvides rolling window calculations.
ewmProvides exponential weighted functions.
Notes
See pandas API documentation for pandas.DataFrame.expanding for more. By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting
center=True.Examples
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0
>>> df.expanding(2).sum() B 0 NaN 1 1.0 2 3.0 3 3.0 4 7.0
- ffill(axis=None, inplace=False, limit=None, downcast=None)
Synonym for
DataFrame.fillna()withmethod='ffill'.- Returns
Object with missing values filled or None if
inplace=True.- Return type
Series/DataFrame or None
Notes
See pandas API documentation for pandas.DataFrame.pad for more.
- filter(items=None, like=None, regex=None, axis=None)
Subset the dataframe rows or columns according to the specified index labels.
Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.
- Parameters
items (list-like) – Keep labels from axis which are in items.
like (str) – Keep labels from axis for which “like in label == True”.
regex (str (regular expression)) – Keep labels from axis for which re.search(regex, label) == True.
axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) – The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, ‘index’ for Series, ‘columns’ for DataFrame.
- Returns
- Return type
same type as input object
See also
DataFrame.locAccess a group of rows and columns by label(s) or a boolean array.
Notes
See pandas API documentation for pandas.DataFrame.filter for more. The
items,like, andregexparameters are enforced to be mutually exclusive.axisdefaults to the info axis that is used when indexing with[].Examples
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])), ... index=['mouse', 'rabbit'], ... columns=['one', 'two', 'three']) >>> df one two three mouse 1 2 3 rabbit 4 5 6
>>> # select columns by name >>> df.filter(items=['one', 'three']) one three mouse 1 3 rabbit 4 6
>>> # select columns by regular expression >>> df.filter(regex='e$', axis=1) one three mouse 1 3 rabbit 4 6
>>> # select rows containing 'bbi' >>> df.filter(like='bbi', axis=0) one two three rabbit 4 5 6
- first(offset)
Select initial periods of time series data based on a date offset.
When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.
- Parameters
offset (str, DateOffset or dateutil.relativedelta) – The offset length of the data that will be selected. For instance, ‘1M’ will display all the rows having their index within the first month.
- Returns
A subset of the caller.
- Return type
- Raises
TypeError – If the index is not a
DatetimeIndex
See also
lastSelect final periods of time series based on a date offset.
at_timeSelect values at a particular time of the day.
between_timeSelect values between particular times of the day.
Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4
Get the rows for the first 3 days:
>>> ts.first('3D') A 2018-04-09 1 2018-04-11 2
Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.
Notes
See pandas API documentation for pandas.DataFrame.first for more.
- first_valid_index()
Return index for first non-NA value or None, if no NA value is found.
- Returns
scalar
- Return type
type of index
Notes
See pandas API documentation for pandas.DataFrame.first_valid_index for more. If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.
- property flags
Get the properties associated with this pandas object.
The available flags are
Flags.allows_duplicate_labels
See also
FlagsFlags that apply to pandas objects.
DataFrame.attrsGlobal metadata applying to this dataset.
Notes
See pandas API documentation for pandas.DataFrame.flags for more. “Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in
DataFrame.attrs.Examples
>>> df = pd.DataFrame({"A": [1, 2]}) >>> df.flags <Flags(allows_duplicate_labels=True)>
Flags can be get or set using
.>>> df.flags.allows_duplicate_labels True >>> df.flags.allows_duplicate_labels = False
Or by slicing with a key
>>> df.flags["allows_duplicate_labels"] False >>> df.flags["allows_duplicate_labels"] = True
- floordiv(other, axis='columns', level=None, fill_value=None)
Get Integer division of dataframe and other, element-wise (binary operator floordiv).
Equivalent to
dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rfloordiv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.floordiv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- ge(other, axis='columns', level=None)
Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.ge for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- get(key, default=None)
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
- Parameters
key (object) –
- Returns
value
- Return type
same type as items contained in object
Notes
See pandas API documentation for pandas.DataFrame.get for more.
- gt(other, axis='columns', level=None)
Get Greater than of dataframe and other, element-wise (binary operator gt).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.gt for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- head(n=5)
Return the first n rows.
This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.
For negative values of n, this function returns all rows except the last n rows, equivalent to
df[:-n].- Parameters
n (int, default 5) – Number of rows to select.
- Returns
The first n rows of the caller object.
- Return type
same type as caller
See also
DataFrame.tailReturns the last n rows.
Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra
Viewing the first 5 lines
>>> df.head() animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey
Viewing the first n lines (three in this case)
>>> df.head(3) animal 0 alligator 1 bee 2 falcon
For negative values of n
>>> df.head(-3) animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot
Notes
See pandas API documentation for pandas.DataFrame.head for more.
- property iat
Access a single value for a row/column pair by integer position.
Similar to
iloc, in that both provide integer-based lookups. Useiatif you only need to get or set a single value in a DataFrame or Series.- Raises
IndexError – When integer position is out of bounds.
See also
DataFrame.atAccess a single value for a row/column label pair.
DataFrame.locAccess a group of rows and columns by label(s).
DataFrame.ilocAccess a group of rows and columns by integer position(s).
Examples
>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]], ... columns=['A', 'B', 'C']) >>> df A B C 0 0 2 3 1 0 4 1 2 10 20 30
Get value at specified row/column pair
>>> df.iat[1, 2] 1
Set value at specified row/column pair
>>> df.iat[1, 2] = 10 >>> df.iat[1, 2] 10
Get value within a series
>>> df.loc[0].iat[1] 2
Notes
See pandas API documentation for pandas.DataFrame.iat for more.
- idxmax(axis=0, skipna=True)
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
- Returns
Indexes of maxima along the specified axis.
- Return type
- Raises
ValueError –
If the row/column is empty
See also
Series.idxmaxReturn index of the maximum element.
Notes
See pandas API documentation for pandas.DataFrame.idxmax for more. This method is the DataFrame version of
ndarray.argmax.Examples
Consider a dataset containing food consumption in Argentina.
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48], ... 'co2_emissions': [37.2, 19.66, 1712]}, ... index=['Pork', 'Wheat Products', 'Beef'])
>>> df consumption co2_emissions Pork 10.51 37.20 Wheat Products 103.11 19.66 Beef 55.48 1712.00
By default, it returns the index for the maximum value in each column.
>>> df.idxmax() consumption Wheat Products co2_emissions Beef dtype: object
To return the index for the maximum value in each row, use
axis="columns".>>> df.idxmax(axis="columns") Pork co2_emissions Wheat Products consumption Beef co2_emissions dtype: object
- idxmin(axis=0, skipna=True)
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
- Returns
Indexes of minima along the specified axis.
- Return type
- Raises
ValueError –
If the row/column is empty
See also
Series.idxminReturn index of the minimum element.
Notes
See pandas API documentation for pandas.DataFrame.idxmin for more. This method is the DataFrame version of
ndarray.argmin.Examples
Consider a dataset containing food consumption in Argentina.
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48], ... 'co2_emissions': [37.2, 19.66, 1712]}, ... index=['Pork', 'Wheat Products', 'Beef'])
>>> df consumption co2_emissions Pork 10.51 37.20 Wheat Products 103.11 19.66 Beef 55.48 1712.00
By default, it returns the index for the minimum value in each column.
>>> df.idxmin() consumption Pork co2_emissions Wheat Products dtype: object
To return the index for the minimum value in each row, use
axis="columns".>>> df.idxmin(axis="columns") Pork consumption Wheat Products co2_emissions Beef consumption dtype: object
- property iloc
Purely integer-location based indexing for selection by position.
.iloc[]is primarily integer position based (from0tolength-1of the axis), but may also be used with a boolean array.Allowed inputs are:
An integer, e.g.
5.A list or array of integers, e.g.
[4, 3, 0].A slice object with ints, e.g.
1:7.A boolean array.
A
callablefunction with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.
.ilocwill raiseIndexErrorif a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).See more at Selection by Position.
See also
DataFrame.iatFast integer location scalar accessor.
DataFrame.locPurely label-location based indexer for selection by label.
Series.ilocPurely integer-location based indexing for selection by position.
Examples
>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4}, ... {'a': 100, 'b': 200, 'c': 300, 'd': 400}, ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }] >>> df = pd.DataFrame(mydict) >>> df a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000
Indexing just the rows
With a scalar integer.
>>> type(df.iloc[0]) <class 'pandas.core.series.Series'> >>> df.iloc[0] a 1 b 2 c 3 d 4 Name: 0, dtype: int64
With a list of integers.
>>> df.iloc[[0]] a b c d 0 1 2 3 4 >>> type(df.iloc[[0]]) <class 'pandas.core.frame.DataFrame'>
>>> df.iloc[[0, 1]] a b c d 0 1 2 3 4 1 100 200 300 400
With a slice object.
>>> df.iloc[:3] a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000
With a boolean mask the same length as the index.
>>> df.iloc[[True, False, True]] a b c d 0 1 2 3 4 2 1000 2000 3000 4000
With a callable, useful in method chains. The x passed to the
lambdais the DataFrame being sliced. This selects the rows whose index label even.>>> df.iloc[lambda x: x.index % 2 == 0] a b c d 0 1 2 3 4 2 1000 2000 3000 4000
Indexing both axes
You can mix the indexer types for the index and columns. Use
:to select the entire axis.With scalar integers.
>>> df.iloc[0, 1] 2
With lists of integers.
>>> df.iloc[[0, 2], [1, 3]] b d 0 2 4 2 2000 4000
With slice objects.
>>> df.iloc[1:3, 0:3] a b c 1 100 200 300 2 1000 2000 3000
With a boolean array whose length matches the columns.
>>> df.iloc[:, [True, False, True, False]] a c 0 1 3 1 100 300 2 1000 3000
With a callable function that expects the Series or DataFrame.
>>> df.iloc[:, lambda df: [0, 2]] a c 0 1 3 1 100 300 2 1000 3000
Notes
See pandas API documentation for pandas.DataFrame.iloc for more.
- property index
Get the index for this DataFrame.
- Returns
The union of all indexes across the partitions.
- Return type
pandas.Index
- infer_objects()
Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.
- Returns
converted
- Return type
same type as input object
See also
to_datetimeConvert argument to datetime.
to_timedeltaConvert argument to timedelta.
to_numericConvert argument to numeric type.
convert_dtypesConvert argument to best possible dtype.
Examples
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]}) >>> df = df.iloc[1:] >>> df A 1 1 2 2 3 3
>>> df.dtypes A object dtype: object
>>> df.infer_objects().dtypes A int64 dtype: object
Notes
See pandas API documentation for pandas.DataFrame.infer_objects for more.
- isin(values)
Whether each element in the DataFrame is contained in values.
- Parameters
values (iterable, Series, DataFrame or dict) – The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.
- Returns
DataFrame of booleans showing whether each element in the DataFrame is contained in values.
- Return type
See also
DataFrame.eqEquality test for DataFrame.
Series.isinEquivalent method on Series.
Series.str.containsTest if pattern or regex is contained within a string of a Series or Index.
Examples
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]}, ... index=['falcon', 'dog']) >>> df num_legs num_wings falcon 2 2 dog 4 0
When
valuesis a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)>>> df.isin([0, 2]) num_legs num_wings falcon True True dog False True
When
valuesis a dict, we can pass values to check for each column separately:>>> df.isin({'num_wings': [0, 3]}) num_legs num_wings falcon False False dog False True
When
valuesis a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in df2.>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]}, ... index=['spider', 'falcon']) >>> df.isin(other) num_legs num_wings falcon True True dog False False
Notes
See pandas API documentation for pandas.DataFrame.isin for more.
- isna()
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as None or
numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings''ornumpy.infare not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True).- Returns
Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
- Return type
See also
DataFrame.isnullAlias of isna.
DataFrame.notnaBoolean inverse of isna.
DataFrame.dropnaOmit axes labels with missing values.
isnaTop-level isna.
Examples
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker
>>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.isna() 0 False 1 False 2 True dtype: bool
Notes
See pandas API documentation for pandas.DataFrame.isna for more.
- isnull()
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as None or
numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings''ornumpy.infare not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True).- Returns
Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
- Return type
See also
DataFrame.isnullAlias of isna.
DataFrame.notnaBoolean inverse of isna.
DataFrame.dropnaOmit axes labels with missing values.
isnaTop-level isna.
Examples
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker
>>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.isna() 0 False 1 False 2 True dtype: bool
Notes
See pandas API documentation for pandas.DataFrame.isna for more.
- kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.kurt for more.
- kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.kurt for more.
- last(offset)
Select final periods of time series data based on a date offset.
For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.
- Parameters
offset (str, DateOffset, dateutil.relativedelta) – The offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days.
- Returns
A subset of the caller.
- Return type
- Raises
TypeError – If the index is not a
DatetimeIndex
See also
firstSelect initial periods of time series based on a date offset.
at_timeSelect values at a particular time of the day.
between_timeSelect values between particular times of the day.
Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4
Get the rows for the last 3 days:
>>> ts.last('3D') A 2018-04-13 3 2018-04-15 4
Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.
Notes
See pandas API documentation for pandas.DataFrame.last for more.
- last_valid_index()
Return index for last non-NA value or None, if no NA value is found.
- Returns
scalar
- Return type
type of index
Notes
See pandas API documentation for pandas.DataFrame.last_valid_index for more. If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.
- le(other, axis='columns', level=None)
Get Less than or equal to of dataframe and other, element-wise (binary operator le).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.le for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- property loc
Access a group of rows and columns by label(s) or a boolean array.
.loc[]is primarily label based, but may also be used with a boolean array.Allowed inputs are:
A single label, e.g.
5or'a', (note that5is interpreted as a label of the index, and never as an integer position along the index).A list or array of labels, e.g.
['a', 'b', 'c'].A slice object with labels, e.g.
'a':'f'.Warning
Note that contrary to usual python slices, both the start and the stop are included
A boolean array of the same length as the axis being sliced, e.g.
[True, False, True].An alignable boolean Series. The index of the key will be aligned before masking.
An alignable Index. The Index of the returned selection will be the input.
A
callablefunction with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
See more at Selection by Label.
- Raises
KeyError – If any items are not found.
IndexingError – If an indexed key is passed and its index is unalignable to the frame index.
See also
DataFrame.atAccess a single value for a row/column label pair.
DataFrame.ilocAccess group of rows and columns by integer position(s).
DataFrame.xsReturns a cross-section (row(s) or column(s)) from the Series/DataFrame.
Series.locAccess group of values using labels.
Examples
Getting values
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], ... index=['cobra', 'viper', 'sidewinder'], ... columns=['max_speed', 'shield']) >>> df max_speed shield cobra 1 2 viper 4 5 sidewinder 7 8
Single label. Note this returns the row as a Series.
>>> df.loc['viper'] max_speed 4 shield 5 Name: viper, dtype: int64
List of labels. Note using
[[]]returns a DataFrame.>>> df.loc[['viper', 'sidewinder']] max_speed shield viper 4 5 sidewinder 7 8
Single label for row and column
>>> df.loc['cobra', 'shield'] 2
Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc['cobra':'viper', 'max_speed'] cobra 1 viper 4 Name: max_speed, dtype: int64
Boolean list with the same length as the row axis
>>> df.loc[[False, False, True]] max_speed shield sidewinder 7 8
Alignable boolean Series:
>>> df.loc[pd.Series([False, True, False], ... index=['viper', 'sidewinder', 'cobra'])] max_speed shield sidewinder 7 8
Index (same behavior as
df.reindex)>>> df.loc[pd.Index(["cobra", "viper"], name="foo")] max_speed shield foo cobra 1 2 viper 4 5
Conditional that returns a boolean Series
>>> df.loc[df['shield'] > 6] max_speed shield sidewinder 7 8
Conditional that returns a boolean Series with column labels specified
>>> df.loc[df['shield'] > 6, ['max_speed']] max_speed sidewinder 7
Callable that returns a boolean Series
>>> df.loc[lambda df: df['shield'] == 8] max_speed shield sidewinder 7 8
Setting values
Set value for all items matching the list of labels
>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50 >>> df max_speed shield cobra 1 2 viper 4 50 sidewinder 7 50
Set value for an entire row
>>> df.loc['cobra'] = 10 >>> df max_speed shield cobra 10 10 viper 4 50 sidewinder 7 50
Set value for an entire column
>>> df.loc[:, 'max_speed'] = 30 >>> df max_speed shield cobra 30 10 viper 30 50 sidewinder 30 50
Set value for rows matching callable condition
>>> df.loc[df['shield'] > 35] = 0 >>> df max_speed shield cobra 30 10 viper 0 0 sidewinder 0 0
Getting values on a DataFrame with an index that has integer labels
Another example using integers for the index
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], ... index=[7, 8, 9], columns=['max_speed', 'shield']) >>> df max_speed shield 7 1 2 8 4 5 9 7 8
Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc[7:9] max_speed shield 7 1 2 8 4 5 9 7 8
Getting values with a MultiIndex
A number of examples using a DataFrame with a MultiIndex
>>> tuples = [ ... ('cobra', 'mark i'), ('cobra', 'mark ii'), ... ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'), ... ('viper', 'mark ii'), ('viper', 'mark iii') ... ] >>> index = pd.MultiIndex.from_tuples(tuples) >>> values = [[12, 2], [0, 4], [10, 20], ... [1, 4], [7, 1], [16, 36]] >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index) >>> df max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Single label. Note this returns a DataFrame with a single index.
>>> df.loc['cobra'] max_speed shield mark i 12 2 mark ii 0 4
Single index tuple. Note this returns a Series.
>>> df.loc[('cobra', 'mark ii')] max_speed 0 shield 4 Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this returns a Series.
>>> df.loc['cobra', 'mark i'] max_speed 12 shield 2 Name: (cobra, mark i), dtype: int64
Single tuple. Note using
[[]]returns a DataFrame.>>> df.loc[[('cobra', 'mark ii')]] max_speed shield cobra mark ii 0 4
Single tuple for the index with a single label for the column
>>> df.loc[('cobra', 'mark i'), 'shield'] 2
Slice from index tuple to single label
>>> df.loc[('cobra', 'mark i'):'viper'] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Slice from index tuple to index tuple
>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1
Notes
See pandas API documentation for pandas.DataFrame.loc for more.
- lt(other, axis='columns', level=None)
Get Less than of dataframe and other, element-wise (binary operator lt).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.lt for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- mad(axis=None, skipna=None, level=None)
Return the mean absolute deviation of the values over the requested axis.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default None) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.mad for more.
- max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return the maximum of the values over the requested axis.
If you want the index of the maximum, use
idxmax. This is the equivalent of thenumpy.ndarraymethodargmax.- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
See also
Series.sumReturn the sum.
Series.minReturn the minimum.
Series.maxReturn the maximum.
Series.idxminReturn the index of the minimum.
Series.idxmaxReturn the index of the maximum.
DataFrame.sumReturn the sum over the requested axis.
DataFrame.minReturn the minimum over the requested axis.
DataFrame.maxReturn the maximum over the requested axis.
DataFrame.idxminReturn the index of the minimum over the requested axis.
DataFrame.idxmaxReturn the index of the maximum over the requested axis.
Examples
>>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64
>>> s.max() 8
Notes
See pandas API documentation for pandas.DataFrame.max for more.
- mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return the mean of the values over the requested axis.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.mean for more.
- median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return the median of the values over the requested axis.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.median for more.
- memory_usage(index=True, deep=False)
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of the index and elements of object dtype.
This value is displayed in DataFrame.info by default. This can be suppressed by setting
pandas.options.display.memory_usageto False.- Parameters
index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If
index=True, the memory usage of the index is the first item in the output.deep (bool, default False) – If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.
- Returns
A Series whose index is the original column names and whose values is the memory usage of each column in bytes.
- Return type
See also
numpy.ndarray.nbytesTotal bytes consumed by the elements of an ndarray.
Series.memory_usageBytes consumed by a Series.
CategoricalMemory-efficient array for string values with many repeated values.
DataFrame.infoConcise summary of a DataFrame.
Examples
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool'] >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t)) ... for t in dtypes]) >>> df = pd.DataFrame(data) >>> df.head() int64 float64 complex128 object bool 0 1 1.0 1.0+0.0j 1 True 1 1 1.0 1.0+0.0j 1 True 2 1 1.0 1.0+0.0j 1 True 3 1 1.0 1.0+0.0j 1 True 4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage() Index 128 int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64
>>> df.memory_usage(index=False) int64 40000 float64 40000 complex128 80000 object 40000 bool 5000 dtype: int64
The memory footprint of object dtype columns is ignored by default:
>>> df.memory_usage(deep=True) Index 128 int64 40000 float64 40000 complex128 80000 object 180000 bool 5000 dtype: int64
Use a Categorical for efficient storage of an object-dtype column with many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True) 5244
Notes
See pandas API documentation for pandas.DataFrame.memory_usage for more.
- min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return the minimum of the values over the requested axis.
If you want the index of the minimum, use
idxmin. This is the equivalent of thenumpy.ndarraymethodargmin.- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
See also
Series.sumReturn the sum.
Series.minReturn the minimum.
Series.maxReturn the maximum.
Series.idxminReturn the index of the minimum.
Series.idxmaxReturn the index of the maximum.
DataFrame.sumReturn the sum over the requested axis.
DataFrame.minReturn the minimum over the requested axis.
DataFrame.maxReturn the maximum over the requested axis.
DataFrame.idxminReturn the index of the minimum over the requested axis.
DataFrame.idxmaxReturn the index of the maximum over the requested axis.
Examples
>>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64
>>> s.min() 0
Notes
See pandas API documentation for pandas.DataFrame.min for more.
- mod(other, axis='columns', level=None, fill_value=None)
Get Modulo of dataframe and other, element-wise (binary operator mod).
Equivalent to
dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.mod for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- mode(axis=0, numeric_only=False, dropna=True)
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often. It can be multiple values.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) –
The axis to iterate over while searching for the mode:
0 or ‘index’ : get mode of each column
1 or ‘columns’ : get mode of each row.
numeric_only (bool, default False) – If True, only apply to numeric columns.
dropna (bool, default True) – Don’t consider counts of NaN/NaT.
- Returns
The modes of each column or row.
- Return type
See also
Series.modeReturn the highest frequency value in a Series.
Series.value_countsReturn the counts of values in a Series.
Examples
>>> df = pd.DataFrame([('bird', 2, 2), ... ('mammal', 4, np.nan), ... ('arthropod', 8, 0), ... ('bird', 2, np.nan)], ... index=('falcon', 'horse', 'spider', 'ostrich'), ... columns=('species', 'legs', 'wings')) >>> df species legs wings falcon bird 2 2.0 horse mammal 4 NaN spider arthropod 8 0.0 ostrich bird 2 NaN
By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of
speciesandlegscontainsNaN.>>> df.mode() species legs wings 0 bird 2.0 0.0 1 NaN NaN 2.0
Setting
dropna=FalseNaNvalues are considered and they can be the mode (like for wings).>>> df.mode(dropna=False) species legs wings 0 bird 2 NaN
Setting
numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.>>> df.mode(numeric_only=True) legs wings 0 2.0 0.0 1 NaN 2.0
To compute the mode over columns and not rows, use the axis parameter:
>>> df.mode(axis='columns', numeric_only=True) 0 1 falcon 2.0 NaN horse 4.0 NaN spider 0.0 8.0 ostrich 2.0 NaN
Notes
See pandas API documentation for pandas.DataFrame.mode for more.
- mul(other, axis='columns', level=None, fill_value=None)
Get Multiplication of dataframe and other, element-wise (binary operator mul).
Equivalent to
dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.mul for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- multiply(other, axis='columns', level=None, fill_value=None)
Get Multiplication of dataframe and other, element-wise (binary operator mul).
Equivalent to
dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.mul for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- ne(other, axis='columns', level=None)
Get Not equal to of dataframe and other, element-wise (binary operator ne).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
- Returns
Result of the comparison.
- Return type
DataFrame of bool
See also
DataFrame.eqCompare DataFrames for equality elementwise.
DataFrame.neCompare DataFrames for inequality elementwise.
DataFrame.leCompare DataFrames for less than inequality or equality elementwise.
DataFrame.ltCompare DataFrames for strictly less than inequality elementwise.
DataFrame.geCompare DataFrames for greater than inequality or equality elementwise.
DataFrame.gtCompare DataFrames for strictly greater than inequality elementwise.
Notes
See pandas API documentation for pandas.DataFrame.ne for more. Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).
Examples
>>> df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) >>> df cost revenue A 250 100 B 150 250 C 100 300
Comparison with a scalar, using either the operator or method:
>>> df == 100 cost revenue A False True B False False C True False
>>> df.eq(100) cost revenue A False True B False False C True False
When other is a
Series, the columns of a DataFrame are aligned with the index of other and broadcast:>>> df != pd.Series([100, 250], index=["cost", "revenue"]) cost revenue A True True B True False C False True
Use the method to control the broadcast axis:
>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') cost revenue A True False B True True C True True D True True
When comparing to an arbitrary sequence, the number of columns must match the number elements in other:
>>> df == [250, 100] cost revenue A True True B False False C False False
Use the method to control the axis:
>>> df.eq([250, 250, 100], axis='index') cost revenue A True False B False True C True False
Compare to a DataFrame of different shape.
>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) >>> other revenue A 300 B 250 C 100 D 150
>>> df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B', 'C']]) >>> df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
>>> df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False
- notna()
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings
''ornumpy.infare not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True). NA values, such as None ornumpy.NaN, get mapped to False values.- Returns
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
- Return type
See also
DataFrame.notnullAlias of notna.
DataFrame.isnaBoolean inverse of notna.
DataFrame.dropnaOmit axes labels with missing values.
notnaTop-level notna.
Examples
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker
>>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.notna() 0 True 1 True 2 False dtype: bool
Notes
See pandas API documentation for pandas.DataFrame.notna for more.
- notnull()
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings
''ornumpy.infare not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True). NA values, such as None ornumpy.NaN, get mapped to False values.- Returns
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
- Return type
See also
DataFrame.notnullAlias of notna.
DataFrame.isnaBoolean inverse of notna.
DataFrame.dropnaOmit axes labels with missing values.
notnaTop-level notna.
Examples
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker
>>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.notna() 0 True 1 True 2 False dtype: bool
Notes
See pandas API documentation for pandas.DataFrame.notna for more.
- nunique(axis=0, dropna=True)
Count number of distinct elements in specified axis.
Return Series with number of distinct elements. Can ignore NaN values.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
dropna (bool, default True) – Don’t include NaN in the counts.
- Returns
- Return type
See also
Series.nuniqueMethod nunique for Series.
DataFrame.countCount non-NA cells for each column or row.
Examples
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]}) >>> df.nunique() A 3 B 2 dtype: int64
>>> df.nunique(axis=1) 0 1 1 2 2 2 dtype: int64
Notes
See pandas API documentation for pandas.DataFrame.nunique for more.
- pad(axis=None, inplace=False, limit=None, downcast=None)
Synonym for
DataFrame.fillna()withmethod='ffill'.- Returns
Object with missing values filled or None if
inplace=True.- Return type
Series/DataFrame or None
Notes
See pandas API documentation for pandas.DataFrame.pad for more.
- pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)
Percentage change between the current and a prior element.
Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements.
- Parameters
periods (int, default 1) – Periods to shift for forming percent change.
fill_method (str, default 'pad') – How to handle NAs before computing percent changes.
limit (int, default None) – The number of consecutive NAs to fill before stopping.
freq (DateOffset, timedelta, or str, optional) – Increment to use from time series API (e.g. ‘M’ or BDay()).
**kwargs – Additional keyword arguments are passed into DataFrame.shift or Series.shift.
- Returns
chg – The same type as the calling object.
- Return type
See also
Series.diffCompute the difference of two elements in a Series.
DataFrame.diffCompute the difference of two elements in a DataFrame.
Series.shiftShift the index by some number of periods.
DataFrame.shiftShift the index by some number of periods.
Examples
Series
>>> s = pd.Series([90, 91, 85]) >>> s 0 90 1 91 2 85 dtype: int64
>>> s.pct_change() 0 NaN 1 0.011111 2 -0.065934 dtype: float64
>>> s.pct_change(periods=2) 0 NaN 1 NaN 2 -0.055556 dtype: float64
See the percentage change in a Series where filling NAs with last valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85]) >>> s 0 90.0 1 91.0 2 NaN 3 85.0 dtype: float64
>>> s.pct_change(fill_method='ffill') 0 NaN 1 0.011111 2 0.000000 3 -0.065934 dtype: float64
DataFrame
Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({ ... 'FR': [4.0405, 4.0963, 4.3149], ... 'GR': [1.7246, 1.7482, 1.8519], ... 'IT': [804.74, 810.01, 860.13]}, ... index=['1980-01-01', '1980-02-01', '1980-03-01']) >>> df FR GR IT 1980-01-01 4.0405 1.7246 804.74 1980-02-01 4.0963 1.7482 810.01 1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change() FR GR IT 1980-01-01 NaN NaN NaN 1980-02-01 0.013810 0.013684 0.006549 1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.
>>> df = pd.DataFrame({ ... '2016': [1769950, 30586265], ... '2015': [1500923, 40912316], ... '2014': [1371819, 41403351]}, ... index=['GOOG', 'APPL']) >>> df 2016 2015 2014 GOOG 1769950 1500923 1371819 APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns', periods=-1) 2016 2015 2014 GOOG 0.179241 0.094112 NaN APPL -0.252395 -0.011860 NaN
Notes
See pandas API documentation for pandas.DataFrame.pct_change for more.
- pipe(func, *args, **kwargs)
Apply func(self, *args, **kwargs).
- Parameters
func (function) – Function to apply to the Series/DataFrame.
args, andkwargsare passed intofunc. Alternatively a(callable, data_keyword)tuple wheredata_keywordis a string indicating the keyword ofcallablethat expects the Series/DataFrame.args (iterable, optional) – Positional arguments passed into
func.kwargs (mapping, optional) – A dictionary of keyword arguments passed into
func.
- Returns
object
- Return type
the return type of
func.
See also
DataFrame.applyApply a function along input axis of DataFrame.
DataFrame.applymapApply a function elementwise on a whole DataFrame.
Series.mapApply a mapping correspondence on a
Series.
Notes
See pandas API documentation for pandas.DataFrame.pipe for more. Use
.pipewhen chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing>>> func(g(h(df), arg1=a), arg2=b, arg3=c)
You can write
>>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe(func, arg2=b, arg3=c) ... )
If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose
ftakes its data asarg2:>>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe((func, 'arg2'), arg1=a, arg3=c) ... )
- pop(item)
Return item and drop from frame. Raise KeyError if not found.
- Parameters
item (label) – Label of column to be popped.
- Returns
- Return type
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=('name', 'class', 'max_speed')) >>> df name class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN
>>> df.pop('class') 0 bird 1 bird 2 mammal 3 mammal Name: class, dtype: object
>>> df name max_speed 0 falcon 389.0 1 parrot 24.0 2 lion 80.5 3 monkey NaN
Notes
See pandas API documentation for pandas.DataFrame.pop for more.
- pow(other, axis='columns', level=None, fill_value=None)
Get Exponential power of dataframe and other, element-wise (binary operator pow).
Equivalent to
dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.pow for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')
Return values at the given quantile over requested axis.
- Parameters
q (float or array-like, default 0.5 (50% quantile)) – Value between 0 <= q <= 1, the quantile(s) to compute.
axis ({0, 1, 'index', 'columns'}, default 0) – Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
numeric_only (bool, default True) – If False, the quantile of datetime and timedelta data will be computed as well.
interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –
This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:
linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
lower: i.
higher: j.
nearest: i or j whichever is nearest.
midpoint: (i + j) / 2.
- Returns
- If
qis an array, a DataFrame will be returned where the index is
q, the columns are the columns of self, and the values are the quantiles.- If
qis a float, a Series will be returned where the index is the columns of self and the values are the quantiles.
- If
- Return type
See also
core.window.Rolling.quantileRolling quantile.
numpy.percentileNumpy function to compute the percentile.
Examples
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), ... columns=['a', 'b']) >>> df.quantile(.1) a 1.3 b 3.7 Name: 0.1, dtype: float64 >>> df.quantile([.1, .5]) a b 0.1 1.3 3.7 0.5 2.5 55.0
Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2], ... 'B': [pd.Timestamp('2010'), ... pd.Timestamp('2011')], ... 'C': [pd.Timedelta('1 days'), ... pd.Timedelta('2 days')]}) >>> df.quantile(0.5, numeric_only=False) A 1.5 B 2010-07-02 12:00:00 C 1 days 12:00:00 Name: 0.5, dtype: object
Notes
See pandas API documentation for pandas.DataFrame.quantile for more.
- radd(other, axis='columns', level=None, fill_value=None)
Get Addition of dataframe and other, element-wise (binary operator add).
Equivalent to
dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.add for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)
Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the ranks of those values.
- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – Index to direct ranking.
method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') –
How to rank the group of records that have the same value (i.e. ties):
average: average rank of the group
min: lowest rank in the group
max: highest rank in the group
first: ranks assigned in order they appear in the array
dense: like ‘min’, but rank always increases by 1 between groups.
numeric_only (bool, optional) – For DataFrame objects, rank only numeric columns if set to True.
na_option ({'keep', 'top', 'bottom'}, default 'keep') –
How to rank NaN values:
keep: assign NaN rank to NaN values
top: assign lowest rank to NaN values
bottom: assign highest rank to NaN values
ascending (bool, default True) – Whether or not the elements should be ranked in ascending order.
pct (bool, default False) – Whether or not to display the returned rankings in percentile form.
- Returns
Return a Series or DataFrame with data ranks as values.
- Return type
same type as caller
See also
core.groupby.GroupBy.rankRank of values within each group.
Examples
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN
The following example shows how the method behaves with the above parameters:
default_rank: this is the default behaviour obtained without using any parameter.
max_rank: setting
method = 'max'the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)NA_bottom: choosing
na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.pct_rank: when setting
pct = True, the ranking is expressed as percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank() >>> df['max_rank'] = df['Number_legs'].rank(method='max') >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom') >>> df['pct_rank'] = df['Number_legs'].rank(pct=True) >>> df Animal Number_legs default_rank max_rank NA_bottom pct_rank 0 cat 4.0 2.5 3.0 2.5 0.625 1 penguin 2.0 1.0 1.0 1.0 0.250 2 dog 4.0 2.5 3.0 2.5 0.625 3 spider 8.0 4.0 4.0 4.0 1.000 4 snake NaN NaN NaN 5.0 NaN
Notes
See pandas API documentation for pandas.DataFrame.rank for more.
- rdiv(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
Equivalent to
other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rtruediv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- reindex(index=None, columns=None, copy=True, **kwargs)
Conform Series/DataFrame to new index with optional filling logic.
Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and
copy=False.- Parameters
axes (keywords for) – New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data.
method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –
Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
None (default): don’t fill gaps
pad / ffill: Propagate last valid observation forward to next valid.
backfill / bfill: Use next valid observation to fill gap.
nearest: Use nearest valid observations to fill gap.
copy (bool, default True) – Return a new object, even if the passed indexes are the same.
level (int or name) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (scalar, default np.NaN) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.
limit (int, default None) – Maximum number of consecutive elements to forward or backward fill.
tolerance (optional) –
Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation
abs(index[indexer] - target) <= tolerance.Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.
- Returns
- Return type
Series/DataFrame with changed index.
See also
DataFrame.set_indexSet row labels.
DataFrame.reset_indexRemove row labels or move them to new columns.
DataFrame.reindex_likeChange to same indices as other DataFrame.
Examples
DataFrame.reindexsupports two calling conventions(index=index_labels, columns=column_labels, ...)(labels, axis={'index', 'columns'}, ...)
We highly recommend using keyword arguments to clarify your intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror'] >>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301], ... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}, ... index=index) >>> df http_status response_time Firefox 200 0.04 Chrome 200 0.02 Safari 404 0.07 IE10 404 0.08 Konqueror 301 1.00
Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned
NaN.>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10', ... 'Chrome'] >>> df.reindex(new_index) http_status response_time Safari 404.0 0.07 Iceweasel NaN NaN Comodo Dragon NaN NaN IE10 404.0 0.08 Chrome 200.0 0.02
We can fill in the missing values by passing a value to the keyword
fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keywordmethodto fill theNaNvalues.>>> df.reindex(new_index, fill_value=0) http_status response_time Safari 404 0.07 Iceweasel 0 0.00 Comodo Dragon 0 0.00 IE10 404 0.08 Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing') http_status response_time Safari 404 0.07 Iceweasel missing missing Comodo Dragon missing missing IE10 404 0.08 Chrome 200 0.02
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent']) http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN
Or we can use “axis-style” keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns") http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN
To further illustrate the filling functionality in
reindex, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D') >>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]}, ... index=date_index) >>> df2 prices 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D') >>> df2.reindex(date_index2) prices 2009-12-29 NaN 2009-12-30 NaN 2009-12-31 NaN 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN
The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with
NaN. If desired, we can fill in the missing values using one of several options.For example, to back-propagate the last valid value to fill the
NaNvalues, passbfillas an argument to themethodkeyword.>>> df2.reindex(date_index2, method='bfill') prices 2009-12-29 100.0 2009-12-30 100.0 2009-12-31 100.0 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN
Please note that the
NaNvalue present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in theNaNvalues present in the original dataframe, use thefillna()method.See the user guide for more.
Notes
See pandas API documentation for pandas.DataFrame.reindex for more.
- reindex_like(other, method=None, copy=True, limit=None, tolerance=None)
Return an object with matching indices as other object.
Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.
- Parameters
other (Object of the same data type) – Its row and column indices are used to define the new indices of this object.
method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –
Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
None (default): don’t fill gaps
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use next valid observation to fill gap
nearest: use nearest valid observations to fill gap.
copy (bool, default True) – Return a new object, even if the passed indexes are the same.
limit (int, default None) – Maximum number of consecutive labels to fill for inexact matches.
tolerance (optional) –
Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation
abs(index[indexer] - target) <= tolerance.Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.
- Returns
Same type as caller, but with changed indices on each axis.
- Return type
See also
DataFrame.set_indexSet row labels.
DataFrame.reset_indexRemove row labels or move them to new columns.
DataFrame.reindexChange to new indices or expand indices.
Notes
See pandas API documentation for pandas.DataFrame.reindex_like for more. Same as calling
.reindex(index=other.index, columns=other.columns,...).Examples
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'], ... [31, 87.8, 'high'], ... [22, 71.6, 'medium'], ... [35, 95, 'medium']], ... columns=['temp_celsius', 'temp_fahrenheit', ... 'windspeed'], ... index=pd.date_range(start='2014-02-12', ... end='2014-02-15', freq='D'))
>>> df1 temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium
>>> df2 = pd.DataFrame([[28, 'low'], ... [30, 'low'], ... [35.1, 'medium']], ... columns=['temp_celsius', 'windspeed'], ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13', ... '2014-02-15']))
>>> df2 temp_celsius windspeed 2014-02-12 28.0 low 2014-02-13 30.0 low 2014-02-15 35.1 medium
>>> df2.reindex_like(df1) temp_celsius temp_fahrenheit windspeed 2014-02-12 28.0 NaN low 2014-02-13 30.0 NaN low 2014-02-14 NaN NaN NaN 2014-02-15 35.1 NaN medium
- rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)
Set the name of the axis for the index or columns.
- Parameters
mapper (scalar, list-like, optional) – Value to set the axis name attribute.
index (scalar, list-like, dict-like or function, optional) –
A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the
columnsparameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.Use either
mapperandaxisto specify the axis to target withmapper, orindexand/orcolumns.columns (scalar, list-like, dict-like or function, optional) –
A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the
columnsparameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.Use either
mapperandaxisto specify the axis to target withmapper, orindexand/orcolumns.axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to rename.
copy (bool, default True) – Also copy underlying data.
inplace (bool, default False) – Modifies the object directly, instead of creating a new Series or DataFrame.
- Returns
The same type as the caller or None if
inplace=True.- Return type
See also
Series.renameAlter Series index labels or name.
DataFrame.renameAlter DataFrame index labels or name.
Index.renameSet new names on index.
Notes
See pandas API documentation for pandas.DataFrame.rename_axis for more.
DataFrame.rename_axissupports two calling conventions(index=index_mapper, columns=columns_mapper, ...)(mapper, axis={'index', 'columns'}, ...)
The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter
copyis ignored.The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.
We highly recommend using keyword arguments to clarify your intent.
Examples
Series
>>> s = pd.Series(["dog", "cat", "monkey"]) >>> s 0 dog 1 cat 2 monkey dtype: object >>> s.rename_axis("animal") animal 0 dog 1 cat 2 monkey dtype: object
DataFrame
>>> df = pd.DataFrame({"num_legs": [4, 4, 2], ... "num_arms": [0, 0, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs num_arms dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("animal") >>> df num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("limbs", axis="columns") >>> df limbs num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2
MultiIndex
>>> df.index = pd.MultiIndex.from_product([['mammal'], ... ['dog', 'cat', 'monkey']], ... names=['type', 'name']) >>> df limbs num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2
>>> df.rename_axis(index={'type': 'class'}) limbs num_legs num_arms class name mammal dog 4 0 cat 4 0 monkey 2 2
>>> df.rename_axis(columns=str.upper) LIMBS num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2
- reorder_levels(order, axis=0)
Rearrange index levels using input order. May not drop or duplicate levels.
- Parameters
order (list of int or list of str) – List representing new level order. Reference level by number (position) or by key (label).
axis ({0 or 'index', 1 or 'columns'}, default 0) – Where to reorder levels.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.reorder_levels for more.
- resample(rule, axis=0, closed=None, label=None, convention='start', kind=None, loffset=None, base: Optional[int] = None, on=None, level=None, origin: Union[str, Timestamp, datetime.datetime, numpy.datetime64, int, numpy.int64, float] = 'start_day', offset: Optional[Union[Timedelta, datetime.timedelta, numpy.timedelta64, int, numpy.int64, float, str]] = None)
Resample time-series data.
Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the
on/levelkeyword parameter.- Parameters
rule (DateOffset, Timedelta or str) – The offset string or object representing target conversion.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Which axis to use for up- or down-sampling. For Series this will default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.
closed ({'right', 'left'}, default None) – Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.
label ({'right', 'left'}, default None) – Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.
convention ({'start', 'end', 's', 'e'}, default 'start') – For PeriodIndex only, controls whether to use the start or end of rule.
kind ({'timestamp', 'period'}, optional, default None) – Pass ‘timestamp’ to convert the resulting index to a DateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.
loffset (timedelta, default None) –
Adjust the resampled time labels.
Deprecated since version 1.1.0: You should add the loffset to the df.index after the resample. See below.
base (int, default 0) –
For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0.
Deprecated since version 1.1.0: The new arguments that you should use are ‘offset’ or ‘origin’.
on (str, optional) – For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.
level (str or int, optional) – For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.
origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, Timestamp) –
or str, default ‘start_day’ The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported:
’epoch’: origin is 1970-01-01
’start’: origin is the first value of the timeseries
’start_day’: origin is the first day at midnight of the timeseries
New in version 1.1.0.
’end’: origin is the last value of the timeseries
’end_day’: origin is the ceiling midnight of the last day
New in version 1.3.0.
offset (Timedelta or str, default is None) –
An offset timedelta added to the origin.
New in version 1.1.0.
- Returns
Resamplerobject.- Return type
pandas.core.Resampler
See also
Series.resampleResample a Series.
DataFrame.resampleResample a DataFrame.
groupbyGroup DataFrame by mapping, function, label, or list of labels.
asfreqReindex a DataFrame with the given frequency without grouping.
Notes
See pandas API documentation for pandas.DataFrame.resample for more. See the user guide for more.
To learn more about the offset strings, please see this link.
Examples
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='T') >>> series = pd.Series(range(9), index=index) >>> series 2000-01-01 00:00:00 0 2000-01-01 00:01:00 1 2000-01-01 00:02:00 2 2000-01-01 00:03:00 3 2000-01-01 00:04:00 4 2000-01-01 00:05:00 5 2000-01-01 00:06:00 6 2000-01-01 00:07:00 7 2000-01-01 00:08:00 8 Freq: T, dtype: int64
Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.
>>> series.resample('3T').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket
2000-01-01 00:03:00contains the value 3, but the summed value in the resampled bucket with the label2000-01-01 00:03:00does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.>>> series.resample('3T', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but close the right side of the bin interval.
>>> series.resample('3T', label='right', closed='right').sum() 2000-01-01 00:00:00 0 2000-01-01 00:03:00 6 2000-01-01 00:06:00 15 2000-01-01 00:09:00 15 Freq: 3T, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30S').asfreq()[0:5] # Select first 5 rows 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1.0 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 Freq: 30S, dtype: float64
Upsample the series into 30 second bins and fill the
NaNvalues using thepadmethod.>>> series.resample('30S').pad()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 1 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64
Upsample the series into 30 second bins and fill the
NaNvalues using thebfillmethod.>>> series.resample('30S').bfill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 1 2000-01-01 00:01:00 1 2000-01-01 00:01:30 2 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64
Pass a custom function via
apply>>> def custom_resampler(arraylike): ... return np.sum(arraylike) + 5 ... >>> series.resample('3T').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3T, dtype: int64
For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.
Resample a year by quarter using ‘start’ convention. Values are assigned to the first quarter of the period.
>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01', ... freq='A', ... periods=2)) >>> s 2012 1 2013 2 Freq: A-DEC, dtype: int64 >>> s.resample('Q', convention='start').asfreq() 2012Q1 1.0 2012Q2 NaN 2012Q3 NaN 2012Q4 NaN 2013Q1 2.0 2013Q2 NaN 2013Q3 NaN 2013Q4 NaN Freq: Q-DEC, dtype: float64
Resample quarters by month using ‘end’ convention. Values are assigned to the last month of the period.
>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01', ... freq='Q', ... periods=4)) >>> q 2018Q1 1 2018Q2 2 2018Q3 3 2018Q4 4 Freq: Q-DEC, dtype: int64 >>> q.resample('M', convention='end').asfreq() 2018-03 1.0 2018-04 NaN 2018-05 NaN 2018-06 2.0 2018-07 NaN 2018-08 NaN 2018-09 3.0 2018-10 NaN 2018-11 NaN 2018-12 4.0 Freq: M, dtype: float64
For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.
>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]} >>> df = pd.DataFrame(d) >>> df['week_starting'] = pd.date_range('01/01/2018', ... periods=8, ... freq='W') >>> df price volume week_starting 0 10 50 2018-01-07 1 11 60 2018-01-14 2 9 40 2018-01-21 3 13 100 2018-01-28 4 14 50 2018-02-04 5 18 100 2018-02-11 6 17 40 2018-02-18 7 19 50 2018-02-25 >>> df.resample('M', on='week_starting').mean() price volume week_starting 2018-01-31 10.75 62.5 2018-02-28 17.00 60.0
For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.
>>> days = pd.date_range('1/1/2000', periods=4, freq='D') >>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]} >>> df2 = pd.DataFrame( ... d2, ... index=pd.MultiIndex.from_product( ... [days, ['morning', 'afternoon']] ... ) ... ) >>> df2 price volume 2000-01-01 morning 10 50 afternoon 11 60 2000-01-02 morning 9 40 afternoon 13 100 2000-01-03 morning 14 50 afternoon 18 100 2000-01-04 morning 17 40 afternoon 19 50 >>> df2.resample('D', level=0).sum() price volume 2000-01-01 21 110 2000-01-02 22 140 2000-01-03 32 150 2000-01-04 36 90
If you want to adjust the start of the bins based on a fixed timestamp:
>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00' >>> rng = pd.date_range(start, end, freq='7min') >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7T, dtype: int64
>>> ts.resample('17min').sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17T, dtype: int64
If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:
>>> ts.resample('17min', origin='start').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64
If you want to take the largest Timestamp as the end of the bins:
>>> ts.resample('17min', origin='end').sum() 2000-10-01 23:35:00 0 2000-10-01 23:52:00 18 2000-10-02 00:09:00 27 2000-10-02 00:26:00 63 Freq: 17T, dtype: int64
In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:
>>> ts.resample('17min', origin='end_day').sum() 2000-10-01 23:38:00 3 2000-10-01 23:55:00 15 2000-10-02 00:12:00 45 2000-10-02 00:29:00 45 Freq: 17T, dtype: int64
To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:
>>> ts.resample('17min', offset='2min').sum() 2000-10-01 23:16:00 0 2000-10-01 23:33:00 9 2000-10-01 23:50:00 36 2000-10-02 00:07:00 39 2000-10-02 00:24:00 24 Freq: 17T, dtype: int64
To replace the use of the deprecated loffset argument:
>>> from pandas.tseries.frequencies import to_offset >>> loffset = '19min' >>> ts_out = ts.resample('17min').sum() >>> ts_out.index = ts_out.index + to_offset(loffset) >>> ts_out 2000-10-01 23:33:00 0 2000-10-01 23:50:00 9 2000-10-02 00:07:00 21 2000-10-02 00:24:00 54 2000-10-02 00:41:00 24 Freq: 17T, dtype: int64
- reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.
- Parameters
level (int, str, tuple, or list, default None) – Only remove the given levels from the index. Removes all levels by default.
drop (bool, default False) – Do not try to insert index into dataframe columns. This resets the index to the default integer index.
inplace (bool, default False) – Modify the DataFrame in place (do not create a new object).
col_level (int or str, default 0) – If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.
col_fill (object, default '') – If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.
- Returns
DataFrame with the new index or None if
inplace=True.- Return type
DataFrame or None
See also
DataFrame.set_indexOpposite of reset_index.
DataFrame.reindexChange to new indices or expand indices.
DataFrame.reindex_likeChange to same indices as other DataFrame.
Examples
>>> df = pd.DataFrame([('bird', 389.0), ... ('bird', 24.0), ... ('mammal', 80.5), ... ('mammal', np.nan)], ... index=['falcon', 'parrot', 'lion', 'monkey'], ... columns=('class', 'max_speed')) >>> df class max_speed falcon bird 389.0 parrot bird 24.0 lion mammal 80.5 monkey mammal NaN
When we reset the index, the old index is added as a column, and a new sequential index is used:
>>> df.reset_index() index class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN
We can use the drop parameter to avoid the old index being added as a column:
>>> df.reset_index(drop=True) class max_speed 0 bird 389.0 1 bird 24.0 2 mammal 80.5 3 mammal NaN
You can also use reset_index with MultiIndex.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'), ... ('bird', 'parrot'), ... ('mammal', 'lion'), ... ('mammal', 'monkey')], ... names=['class', 'name']) >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'), ... ('species', 'type')]) >>> df = pd.DataFrame([(389.0, 'fly'), ... ( 24.0, 'fly'), ... ( 80.5, 'run'), ... (np.nan, 'jump')], ... index=index, ... columns=columns) >>> df speed species max type class name bird falcon 389.0 fly parrot 24.0 fly mammal lion 80.5 run monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class') class speed species max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1) speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump
When the index is inserted under another level, we can specify under which one with the parameter col_fill:
>>> df.reset_index(level='class', col_level=1, col_fill='species') species speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump
If we specify a nonexistent level for col_fill, it is created:
>>> df.reset_index(level='class', col_level=1, col_fill='genus') genus speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump
Notes
See pandas API documentation for pandas.DataFrame.reset_index for more.
- rfloordiv(other, axis='columns', level=None, fill_value=None)
Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
Equivalent to
other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rfloordiv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rmod(other, axis='columns', level=None, fill_value=None)
Get Modulo of dataframe and other, element-wise (binary operator rmod).
Equivalent to
other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rmod for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rmul(other, axis='columns', level=None, fill_value=None)
Get Multiplication of dataframe and other, element-wise (binary operator mul).
Equivalent to
dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.mul for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')
Provide rolling window calculations.
- Parameters
window (int, offset, or BaseIndexer subclass) –
Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.
If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.
If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined
get_window_boundsmethod. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.min_periods (int, default None) – Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.
center (bool, default False) – Set the labels at the center of the window.
win_type (str, default None) – Provide a window type. If
None, all points are evenly weighted. See the notes below for further information.on (str, optional) – For a DataFrame, a datetime-like column or Index level on which to calculate the rolling window, rather than the DataFrame’s index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.
axis (int or str, default 0) –
closed (str, default None) –
Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. Defaults to ‘right’.
Changed in version 1.2.0: The closed parameter with fixed windows is now supported.
method (str {'single', 'table'}, default 'single') –
Execute the rolling operation per single column or row (
'single') or over the entire object ('table').This argument is only implemented when specifying
engine='numba'in the method call.New in version 1.3.0.
- Returns
- Return type
a Window or Rolling sub-classed for the particular operation
See also
expandingProvides expanding transformations.
ewmProvides exponential weighted functions.
Notes
See pandas API documentation for pandas.DataFrame.rolling for more. By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting
center=True.To learn more about the offsets & frequency strings, please see this link.
If
win_type=None, all points are evenly weighted; otherwise,win_typecan accept a string of any scipy.signal window function.Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. Please see the third example below on how to add the additional parameters.
Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0
Rolling sum with a window length of 2, using the ‘triang’ window type.
>>> df.rolling(2, win_type='triang').sum() B 0 NaN 1 0.5 2 1.5 3 NaN 4 NaN
Rolling sum with a window length of 2, using the ‘gaussian’ window type (note how we need to specify std).
>>> df.rolling(2, win_type='gaussian').sum(std=3) B 0 NaN 1 0.986207 2 2.958621 3 NaN 4 NaN
Rolling sum with a window length of 2, min_periods defaults to the window length.
>>> df.rolling(2).sum() B 0 NaN 1 1.0 2 3.0 3 NaN 4 NaN
Same as above, but explicitly set the min_periods
>>> df.rolling(2, min_periods=1).sum() B 0 0.0 1 1.0 2 3.0 3 2.0 4 4.0
Same as above, but with forward-looking windows
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2) >>> df.rolling(window=indexer, min_periods=1).sum() B 0 1.0 1 3.0 2 2.0 3 4.0 4 4.0
A ragged (meaning not-a-regular frequency), time-indexed DataFrame
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, ... index = [pd.Timestamp('20130101 09:00:00'), ... pd.Timestamp('20130101 09:00:02'), ... pd.Timestamp('20130101 09:00:03'), ... pd.Timestamp('20130101 09:00:05'), ... pd.Timestamp('20130101 09:00:06')])
>>> df B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 2.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0
Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.
>>> df.rolling('2s').sum() B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0
- round(decimals=0, *args, **kwargs)
Round a DataFrame to a variable number of decimal places.
- Parameters
decimals (int, dict, Series) – Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
*args – Additional keywords have no effect but might be accepted for compatibility with numpy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with numpy.
- Returns
A DataFrame with the affected columns rounded to the specified number of decimal places.
- Return type
See also
numpy.aroundRound a numpy array to the given number of decimals.
Series.roundRound a Series to the given number of decimals.
Examples
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)], ... columns=['dogs', 'cats']) >>> df dogs cats 0 0.21 0.32 1 0.01 0.67 2 0.66 0.03 3 0.21 0.18
By providing an integer each column is rounded to the same number of decimal places
>>> df.round(1) dogs cats 0 0.2 0.3 1 0.0 0.7 2 0.7 0.0 3 0.2 0.2
With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value
>>> df.round({'dogs': 1, 'cats': 0}) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0
Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs']) >>> df.round(decimals) dogs cats 0 0.2 0.0 1 0.0 1.0 2 0.7 0.0 3 0.2 0.0
Notes
See pandas API documentation for pandas.DataFrame.round for more.
- rpow(other, axis='columns', level=None, fill_value=None)
Get Exponential power of dataframe and other, element-wise (binary operator rpow).
Equivalent to
other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rpow for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rsub(other, axis='columns', level=None, fill_value=None)
Get Subtraction of dataframe and other, element-wise (binary operator rsub).
Equivalent to
other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rsub for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- rtruediv(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
Equivalent to
other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.rtruediv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)
Return a random sample of items from an axis of object.
You can use random_state for reproducibility.
- Parameters
n (int, optional) – Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
frac (float, optional) – Fraction of axis items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the same row more than once.
weights (str or ndarray-like, optional) – Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
random_state (int, array-like, BitGenerator, np.random.RandomState, optional) –
If int, array-like, or BitGenerator (NumPy>=1.17), seed for random number generator If np.random.RandomState, use as numpy RandomState object.
Changed in version 1.1.0: array-like and BitGenerator (for NumPy>=1.17) object now passed to np.random.RandomState() as seed
axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) – Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames).
ignore_index (bool, default False) –
If True, the resulting index will be labeled 0, 1, …, n - 1.
New in version 1.3.0.
- Returns
A new object of same type as caller containing n items randomly sampled from the caller object.
- Return type
See also
DataFrameGroupBy.sampleGenerates random samples from each group of a DataFrame object.
SeriesGroupBy.sampleGenerates random samples from each group of a Series object.
numpy.random.choiceGenerates a random sample from a given 1-D numpy array.
Notes
See pandas API documentation for pandas.DataFrame.sample for more. If frac > 1, replacement should be set to True.
Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0], ... 'num_wings': [2, 0, 0, 0], ... 'num_specimen_seen': [10, 2, 1, 8]}, ... index=['falcon', 'dog', 'spider', 'fish']) >>> df num_legs num_wings num_specimen_seen falcon 2 2 10 dog 4 0 2 spider 8 0 1 fish 0 0 8
Extract 3 random elements from the
Seriesdf['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.>>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64
A random 50% sample of the
DataFramewith replacement:>>> df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8
An upsample sample of the
DataFramewith replacement: Note that replace parameter has to be True for frac parameter > 1.>>> df.sample(frac=2, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 falcon 2 2 10 falcon 2 2 10 fish 0 0 8 dog 4 0 2 fish 0 0 8 dog 4 0 2
Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.
>>> df.sample(n=2, weights='num_specimen_seen', random_state=1) num_legs num_wings num_specimen_seen falcon 2 2 10 fish 0 0 8
- sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)
Return unbiased standard error of the mean over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
- Parameters
axis ({index (0), columns (1)}) –
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.sem for more. To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)
- set_axis(labels, axis=0, inplace=False)
Assign desired index to given axis.
Indexes for column or row labels can be changed by assigning a list-like or Index.
- Parameters
labels (list-like, Index) – The values for the new index.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to update. The value 0 identifies the rows, and 1 identifies the columns.
inplace (bool, default False) – Whether to return a new DataFrame instance.
- Returns
renamed – An object of type DataFrame or None if
inplace=True.- Return type
DataFrame or None
See also
DataFrame.rename_axisAlter the name of the index or columns. Examples ——– >>> df = pd.DataFrame({“A”: [1, 2, 3], “B”: [4, 5, 6]}) Change the row labels. >>> df.set_axis([‘a’, ‘b’, ‘c’], axis=’index’) A B a 1 4 b 2 5 c 3 6 Change the column labels. >>> df.set_axis([‘I’, ‘II’], axis=’columns’) I II 0 1 4 1 2 5 2 3 6 Now, update the labels inplace. >>> df.set_axis([‘i’, ‘ii’], axis=’columns’, inplace=True) >>> df i ii 0 1 4 1 2 5 2 3 6
Notes
See pandas API documentation for pandas.DataFrame.set_axis for more.
- set_flags(*, copy: modin.pandas.base.BasePandasDataset.bool = False, allows_duplicate_labels: Optional[modin.pandas.base.BasePandasDataset.bool] = None)
Return a new object with updated flags.
- Parameters
allows_duplicate_labels (bool, optional) – Whether the returned object allows duplicate labels.
- Returns
The same type as the caller.
- Return type
See also
DataFrame.attrsGlobal metadata applying to this dataset.
DataFrame.flagsGlobal flags applying to this object.
Notes
See pandas API documentation for pandas.DataFrame.set_flags for more. This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other.
This method is intended to be used in method chains.
“Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in
DataFrame.attrs.Examples
>>> df = pd.DataFrame({"A": [1, 2]}) >>> df.flags.allows_duplicate_labels True >>> df2 = df.set_flags(allows_duplicate_labels=False) >>> df2.flags.allows_duplicate_labels False
- shift(periods=1, freq=None, axis=0, fill_value=NoDefault.no_default)
Shift index by desired number of periods with an optional time freq.
When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.
- Parameters
periods (int) – Number of periods to shift. Can be positive or negative.
freq (DateOffset, tseries.offsets, timedelta, or str, optional) – Offset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
axis ({0 or 'index', 1 or 'columns', None}, default None) – Shift direction.
fill_value (object, optional) –
The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data,
np.nanis used. For datetime, timedelta, or period data, etc.NaTis used. For extension dtypes,self.dtype.na_valueis used.Changed in version 1.1.0.
- Returns
Copy of input object, shifted.
- Return type
See also
Index.shiftShift values of Index.
DatetimeIndex.shiftShift values of DatetimeIndex.
PeriodIndex.shiftShift values of PeriodIndex.
tshiftShift the time index, using the index’s frequency if available.
Examples
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45], ... "Col2": [13, 23, 18, 33, 48], ... "Col3": [17, 27, 22, 37, 52]}, ... index=pd.date_range("2020-01-01", "2020-01-05")) >>> df Col1 Col2 Col3 2020-01-01 10 13 17 2020-01-02 20 23 27 2020-01-03 15 18 22 2020-01-04 30 33 37 2020-01-05 45 48 52
>>> df.shift(periods=3) Col1 Col2 Col3 2020-01-01 NaN NaN NaN 2020-01-02 NaN NaN NaN 2020-01-03 NaN NaN NaN 2020-01-04 10.0 13.0 17.0 2020-01-05 20.0 23.0 27.0
>>> df.shift(periods=1, axis="columns") Col1 Col2 Col3 2020-01-01 NaN 10 13 2020-01-02 NaN 20 23 2020-01-03 NaN 15 18 2020-01-04 NaN 30 33 2020-01-05 NaN 45 48
>>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 2020-01-01 0 0 0 2020-01-02 0 0 0 2020-01-03 0 0 0 2020-01-04 10 13 17 2020-01-05 20 23 27
>>> df.shift(periods=3, freq="D") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52
>>> df.shift(periods=3, freq="infer") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52
Notes
See pandas API documentation for pandas.DataFrame.shift for more.
- property size
Return an int representing the number of elements in this object.
Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.
See also
ndarray.sizeNumber of elements in the array.
Examples
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.size 3
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.size 4
Notes
See pandas API documentation for pandas.DataFrame.size for more.
- skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Return unbiased skew over requested axis.
Normalized by N-1.
- Parameters
axis ({index (0), columns (1)}) – Axis for the function to be applied on.
skipna (bool, default True) – Exclude NA/null values when computing the result.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
**kwargs – Additional keyword arguments to be passed to the function.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.skew for more.
- sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index: modin.pandas.base.BasePandasDataset.bool = False, key: Optional[Callable[[Index], Union[Index, ExtensionArray, numpy.ndarray, Series]]] = None)
Sort object by labels (along an axis).
Returns a new DataFrame sorted by label if inplace argument is
False, otherwise updates the original DataFrame and returns None.- Parameters
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
level (int or level name or list of ints or list of level names) – If not None, sort on values in specified index level(s).
ascending (bool or list-like of bools, default True) – Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
inplace (bool, default False) – If True, perform operation in-place.
kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') – Choice of sorting algorithm. See also
numpy.sort()for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex.
sort_remaining (bool, default True) – If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
ignore_index (bool, default False) –
If True, the resulting axis will be labeled 0, 1, …, n - 1.
New in version 1.0.0.
key (callable, optional) –
If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin
sorted()function, with the notable difference that this key function should be vectorized. It should expect anIndexand return anIndexof the same shape. For MultiIndex inputs, the key is applied per level.New in version 1.1.0.
- Returns
The original DataFrame sorted by the labels or None if
inplace=True.- Return type
DataFrame or None
See also
Series.sort_indexSort Series by the index.
DataFrame.sort_valuesSort DataFrame by the value.
Series.sort_valuesSort Series by the value.
Examples
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], ... columns=['A']) >>> df.sort_index() A 1 4 29 2 100 1 150 5 234 3
By default, it sorts in ascending order, to sort in descending order, use
ascending=False>>> df.sort_index(ascending=False) A 234 3 150 5 100 1 29 2 1 4
A key function can be specified which is applied to the index before sorting. For a
MultiIndexthis is applied to each level separately.>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd']) >>> df.sort_index(key=lambda x: x.str.lower()) a A 1 b 2 C 3 d 4
Notes
See pandas API documentation for pandas.DataFrame.sort_index for more.
- sort_values(by, axis=0, ascending=True, inplace: modin.pandas.base.BasePandasDataset.bool = False, kind='quicksort', na_position='last', ignore_index: modin.pandas.base.BasePandasDataset.bool = False, key: Optional[Callable[[Index], Union[Index, ExtensionArray, numpy.ndarray, Series]]] = None)
Sort by the values along either axis.
- Parameters
by (str or list of str) –
Name or list of names to sort by.
if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.
- axis{0 or ‘index’, 1 or ‘columns’}, default 0
Axis to be sorted.
- ascendingbool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
- inplacebool, default False
If True, perform operation in-place.
- kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’
Choice of sorting algorithm. See also
numpy.sort()for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.- na_position{‘first’, ‘last’}, default ‘last’
Puts NaNs at the beginning if first; last puts NaNs at the end.
- ignore_indexbool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
New in version 1.0.0.
- keycallable, optional
Apply the key function to the values before sorting. This is similar to the key argument in the builtin
sorted()function, with the notable difference that this key function should be vectorized. It should expect aSeriesand return a Series with the same shape as the input. It will be applied to each column in by independently.New in version 1.1.0.
- Returns
DataFrame with sorted values or None if
inplace=True.- Return type
DataFrame or None
See also
DataFrame.sort_indexSort a DataFrame by the index.
Series.sort_valuesSimilar method for a Series.
Examples
>>> df = pd.DataFrame({ ... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'], ... 'col2': [2, 1, 9, 8, 7, 4], ... 'col3': [0, 1, 9, 4, 2, 3], ... 'col4': ['a', 'B', 'c', 'D', 'e', 'F'] ... }) >>> df col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F
Sort by col1
>>> df.sort_values(by=['col1']) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2']) col1 col2 col3 col4 1 A 1 1 B 0 A 2 0 a 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D
Sort Descending
>>> df.sort_values(by='col1', ascending=False) col1 col2 col3 col4 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B 3 NaN 8 4 D
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first') col1 col2 col3 col4 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B
Sorting with a key function
>>> df.sort_values(by='col4', key=lambda col: col.str.lower()) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F
Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.
>>> df = pd.DataFrame({ ... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'], ... "value": [10, 20, 30, 40, 50] ... }) >>> df time value 0 0hr 10 1 128hr 20 2 72hr 30 3 48hr 40 4 96hr 50 >>> from natsort import index_natsorted >>> df.sort_values( ... by="time", ... key=lambda x: np.argsort(index_natsorted(df["time"])) ... ) time value 0 0hr 10 3 48hr 40 2 72hr 30 4 96hr 50 1 128hr 20
Notes
See pandas API documentation for pandas.DataFrame.sort_values for more.
- std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)
Return sample standard deviation over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
- Parameters
axis ({index (0), columns (1)}) –
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.std for more. To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)
- sub(other, axis='columns', level=None, fill_value=None)
Get Subtraction of dataframe and other, element-wise (binary operator sub).
Equivalent to
dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.sub for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- subtract(other, axis='columns', level=None, fill_value=None)
Get Subtraction of dataframe and other, element-wise (binary operator sub).
Equivalent to
dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.sub for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- swapaxes(axis1, axis2, copy=True)
Interchange axes and swap values axes appropriately.
- Returns
y
- Return type
same as input
Notes
See pandas API documentation for pandas.DataFrame.swapaxes for more.
- swaplevel(i=- 2, j=- 1, axis=0)
Swap levels i and j in a
MultiIndex.Default is to swap the two innermost levels of the index.
- Parameters
i (int or str) – Levels of the indices to be swapped. Can pass level name as string.
j (int or str) – Levels of the indices to be swapped. Can pass level name as string.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to swap levels on. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
- Returns
DataFrame – DataFrame with levels swapped in MultiIndex.
Examples – ——– >>> df = pd.DataFrame( … {“Grade”: [“A”, “B”, “A”, “C”]}, … index=[ … [“Final exam”, “Final exam”, “Coursework”, “Coursework”], … [“History”, “Geography”, “History”, “Geography”], … [“January”, “February”, “March”, “April”], … ], … ) >>> df
Grade
- Final exam History January A
Geography February B
- Coursework History March A
Geography April C
In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.
>>> df.swaplevel() Grade Final exam January History A February Geography B Coursework March History A April Geography C
By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.
>>> df.swaplevel(0) Grade January History Final exam A February Geography Final exam B March History Coursework A April Geography Coursework C
We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.
>>> df.swaplevel(0, 1) Grade History Final exam January A Geography Final exam February B History Coursework March A Geography Coursework April C
Notes
See pandas API documentation for pandas.DataFrame.swaplevel for more.
- tail(n=5)
Return the last n rows.
This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.
For negative values of n, this function returns all rows except the first n rows, equivalent to
df[n:].- Parameters
n (int, default 5) – Number of rows to select.
- Returns
The last n rows of the caller object.
- Return type
type of caller
See also
DataFrame.headThe first n rows of the caller object.
Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra
Viewing the last 5 lines
>>> df.tail() animal 4 monkey 5 parrot 6 shark 7 whale 8 zebra
Viewing the last n lines (three in this case)
>>> df.tail(3) animal 6 shark 7 whale 8 zebra
For negative values of n
>>> df.tail(-3) animal 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra
Notes
See pandas API documentation for pandas.DataFrame.tail for more.
- take(indices, axis=0, is_copy=None, **kwargs)
Return the elements in the given positional indices along an axis.
This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.
- Parameters
indices (array-like) – An array of ints indicating which positions to take.
axis ({0 or 'index', 1 or 'columns', None}, default 0) – The axis on which to select elements.
0means that we are selecting rows,1means that we are selecting columns.is_copy (bool) –
Before pandas 1.0,
is_copy=Falsecan be specified to ensure that the return value is an actual copy. Starting with pandas 1.0,takealways returns a copy, and the keyword is therefore deprecated.Deprecated since version 1.0.0.
**kwargs – For compatibility with
numpy.take(). Has no effect on the output.
- Returns
taken – An array-like containing the elements taken from the object.
- Return type
same type as caller
See also
DataFrame.locSelect a subset of a DataFrame by labels.
DataFrame.ilocSelect a subset of a DataFrame by positions.
numpy.takeTake elements from an array along an axis.
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=['name', 'class', 'max_speed'], ... index=[0, 2, 3, 1]) >>> df name class max_speed 0 falcon bird 389.0 2 parrot bird 24.0 3 lion mammal 80.5 1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3]) name class max_speed 0 falcon bird 389.0 1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1) class max_speed 0 bird 389.0 2 bird 24.0 3 mammal 80.5 1 mammal NaN
We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2]) name class max_speed 1 monkey mammal NaN 3 lion mammal 80.5
Notes
See pandas API documentation for pandas.DataFrame.take for more.
- to_clipboard(excel=True, sep=None, **kwargs)
Copy object to the system clipboard.
Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.
- Parameters
excel (bool, default True) –
Produce output in a csv format for easy pasting into excel.
True, use the provided separator for csv pasting.
False, write a string representation of the object to the clipboard.
sep (str, default
'\t') – Field delimiter.**kwargs – These parameters will be passed to DataFrame.to_csv.
See also
DataFrame.to_csvWrite a DataFrame to a comma-separated values (csv) file.
read_clipboardRead text from clipboard and pass to read_table.
Notes
See pandas API documentation for pandas.DataFrame.to_clipboard for more. Requirements for your platform.
Linux : xclip, or xsel (with PyQt4 modules)
Windows : none
OS X : none
Examples
Copy the contents of a DataFrame to the clipboard.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',') ... # Wrote the following to the system clipboard: ... # ,A,B,C ... # 0,1,2,3 ... # 1,4,5,6
We can omit the index by passing the keyword index and setting it to false.
>>> df.to_clipboard(sep=',', index=False) ... # Wrote the following to the system clipboard: ... # A,B,C ... # 1,2,3 ... # 4,5,6
- to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors: str = 'strict', storage_options: Optional[Dict[str, Any]] = None)
Write object to a comma-separated values (csv) file.
- Parameters
path_or_buf (str or file handle, default None) –
File path or object, if None is provided the result is returned as a string. If a non-binary file object is passed, it should be opened with newline=’’, disabling universal newlines. If a binary file object is passed, mode might need to contain a ‘b’.
Changed in version 1.2.0: Support for binary file objects was introduced.
sep (str, default ',') – String of length 1. Field delimiter for the output file.
na_rep (str, default '') – Missing data representation.
float_format (str, default None) – Format string for floating point numbers.
columns (sequence, optional) – Columns to write.
header (bool or list of str, default True) – Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.
index (bool, default True) – Write row names (index).
index_label (str or sequence, or False, default None) – Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.
mode (str) – Python write mode, default ‘w’.
encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object.
compression (str or dict, default 'infer') –
If str, represents compression mode. If dict, value at ‘method’ is the compression mode. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is one of {‘zip’, ‘gzip’, ‘bz2’}, or inferred as one of the above, other entries passed as additional compression options.
Changed in version 1.0.0: May now be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.
Changed in version 1.1.0: Passing compression options as keys in dict is supported for compression modes ‘gzip’ and ‘bz2’ as well as ‘zip’.
Changed in version 1.2.0: Compression is supported for binary file objects.
Changed in version 1.2.0: Previous versions forwarded dict entries for ‘gzip’ to gzip.open instead of gzip.GzipFile which prevented setting mtime.
quoting (optional constant from csv module) – Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
quotechar (str, default '"') – String of length 1. Character used to quote fields.
line_terminator (str, optional) – The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (’\n’ for linux, ‘\r\n’ for Windows, i.e.).
chunksize (int or None) – Rows to write at a time.
date_format (str, default None) – Format string for datetime objects.
doublequote (bool, default True) – Control quoting of quotechar inside a field.
escapechar (str, default None) – String of length 1. Character used to escape sep and quotechar when appropriate.
decimal (str, default '.') – Character recognized as decimal separator. E.g. use ‘,’ for European data.
errors (str, default 'strict') –
Specifies how encoding and decoding errors are to be handled. See the errors argument for
open()for a full list of options.New in version 1.1.0.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllibas header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec. Please seefsspecandurllibfor more details.New in version 1.2.0.
- Returns
If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.
- Return type
None or str
See also
read_csvLoad a CSV file into a DataFrame.
to_excelWrite DataFrame to an Excel file.
Examples
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}) >>> df.to_csv(index=False) 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
Create ‘out.zip’ containing ‘out.csv’
>>> compression_opts = dict(method='zip', ... archive_name='out.csv') >>> df.to_csv('out.zip', index=False, ... compression=compression_opts)
Notes
See pandas API documentation for pandas.DataFrame.to_csv for more.
- to_dict(orient='dict', into=<class 'dict'>)
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters (see below).
- Parameters
orient (str {'dict', 'list', 'series', 'split', 'records', 'index'}) –
Determines the type of the values of the dictionary.
’dict’ (default) : dict like {column -> {index -> value}}
’list’ : dict like {column -> [values]}
’series’ : dict like {column -> Series(values)}
’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
’records’ : list like [{column -> value}, … , {column -> value}]
’index’ : dict like {index -> {column -> value}}
Abbreviations are allowed. s indicates series and sp indicates split.
into (class, default dict) – The collections.abc.Mapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
- Returns
Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.
- Return type
dict, list or collections.abc.Mapping
See also
DataFrame.from_dictCreate a DataFrame from a dictionary.
DataFrame.to_jsonConvert a DataFrame to JSON format.
Examples
>>> df = pd.DataFrame({'col1': [1, 2], ... 'col2': [0.5, 0.75]}, ... index=['row1', 'row2']) >>> df col1 col2 row1 1 0.50 row2 2 0.75 >>> df.to_dict() {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series') {'col1': row1 1 row2 2 Name: col1, dtype: int64, 'col2': row1 0.50 row2 0.75 Name: col2, dtype: float64}
>>> df.to_dict('split') {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'], 'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records') [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index') {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict >>> df.to_dict(into=OrderedDict) OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])), ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a defaultdict, you need to initialize it:
>>> dd = defaultdict(list) >>> df.to_dict('records', into=dd) [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}), defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
Notes
See pandas API documentation for pandas.DataFrame.to_dict for more.
- to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None, storage_options: Optional[Dict[str, Any]] = None)
Write object to an Excel sheet.
To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.
Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.
- Parameters
excel_writer (path-like, file-like, or ExcelWriter object) – File path or existing ExcelWriter.
sheet_name (str, default 'Sheet1') – Name of sheet which will contain DataFrame.
na_rep (str, default '') – Missing data representation.
float_format (str, optional) – Format string for floating point numbers. For example
float_format="%.2f"will format 0.1234 to 0.12.columns (sequence or list of str, optional) – Columns to write.
header (bool or list of str, default True) – Write out the column names. If a list of string is given it is assumed to be aliases for the column names.
index (bool, default True) – Write row names (index).
index_label (str or sequence, optional) – Column label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
startrow (int, default 0) – Upper left cell row to dump data frame.
startcol (int, default 0) – Upper left cell column to dump data frame.
engine (str, optional) –
Write engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options
io.excel.xlsx.writer,io.excel.xls.writer, andio.excel.xlsm.writer.Deprecated since version 1.2.0: As the xlwt package is no longer maintained, the
xlwtengine will be removed in a future version of pandas.merge_cells (bool, default True) – Write MultiIndex and Hierarchical Rows as merged cells.
encoding (str, optional) – Encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively.
inf_rep (str, default 'inf') – Representation for infinity (there is no native representation for infinity in Excel).
verbose (bool, default True) – Display more information in the error logs.
freeze_panes (tuple of int (length 2), optional) – Specifies the one-based bottommost row and rightmost column that is to be frozen.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllibas header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec. Please seefsspecandurllibfor more details.New in version 1.2.0.
See also
to_csvWrite DataFrame to a comma-separated values (csv) file.
ExcelWriterClass for writing DataFrame objects into excel sheets.
read_excelRead an Excel file into a pandas DataFrame.
read_csvRead a comma-separated values (csv) file into DataFrame.
Notes
See pandas API documentation for pandas.DataFrame.to_excel for more. For compatibility with
to_csv(), to_excel serializes lists and dicts to strings before writing.Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.
Examples
Create, write to and save a workbook:
>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df1.to_excel("output.xlsx")
To specify the sheet name:
>>> df1.to_excel("output.xlsx", ... sheet_name='Sheet_name_1')
If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object:
>>> df2 = df1.copy() >>> with pd.ExcelWriter('output.xlsx') as writer: ... df1.to_excel(writer, sheet_name='Sheet_name_1') ... df2.to_excel(writer, sheet_name='Sheet_name_2')
ExcelWriter can also be used to append to an existing Excel file:
>>> with pd.ExcelWriter('output.xlsx', ... mode='a') as writer: ... df.to_excel(writer, sheet_name='Sheet_name_3')
To set the library that is used to write the Excel file, you can pass the engine keyword (the default engine is automatically chosen depending on the file extension):
>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')
- to_hdf(path_or_buf, key, format='table', **kwargs)
Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.
In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.
Warning
One can store a subclass of
DataFrameorSeriesto HDF5, but the type of the subclass is lost upon storing.For more information see the user guide.
- Parameters
path_or_buf (str or pandas.HDFStore) – File path or HDFStore object.
key (str) – Identifier for the group in the store.
mode ({'a', 'w', 'r+'}, default 'a') –
Mode to open file:
’w’: write, a new file is created (an existing file with the same name would be deleted).
’a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.
’r+’: similar to ‘a’, but the file must already exist.
complevel ({0-9}, optional) – Specifies a compression level for data. A value of 0 disables compression.
complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') – Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.
append (bool, default False) – For Table formats, append the input data to the existing.
format ({'fixed', 'table', None}, default 'fixed') –
Possible values:
’fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable.
’table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”
errors (str, default 'strict') – Specifies how encoding and decoding errors are to be handled. See the errors argument for
open()for a full list of options.encoding (str, default "UTF-8") –
min_itemsize (dict or int, optional) – Map column names to minimum string sizes for columns.
nan_rep (Any, optional) – How to represent null values as str. Not allowed with append=True.
data_columns (list of columns or True, optional) – List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See io.hdf5-query-data-columns. Applicable only to format=’table’.
See also
read_hdfRead from HDF file.
DataFrame.to_parquetWrite a DataFrame to the binary parquet format.
DataFrame.to_sqlWrite to a SQL table.
DataFrame.to_featherWrite out feather-format for DataFrames.
DataFrame.to_csvWrite out to a csv file.
Examples
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, ... index=['a', 'b', 'c']) >>> df.to_hdf('data.h5', key='df', mode='w')
We can add another object to the same file:
>>> s = pd.Series([1, 2, 3, 4]) >>> s.to_hdf('data.h5', key='s')
Reading from HDF file:
>>> pd.read_hdf('data.h5', 'df') A B a 1 4 b 2 5 c 3 6 >>> pd.read_hdf('data.h5', 's') 0 1 1 2 2 3 3 4 dtype: int64
Deleting file with data:
>>> import os >>> os.remove('data.h5')
Notes
See pandas API documentation for pandas.DataFrame.to_hdf for more.
- to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True, indent=None, storage_options: Optional[Dict[str, Any]] = None)
Convert the object to a JSON string.
Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.
- Parameters
path_or_buf (str or file handle, optional) – File path or object. If not specified, the result is returned as a string.
orient (str) –
Indication of expected JSON string format.
Series:
default is ‘index’
allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
DataFrame:
default is ‘columns’
allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
The format of the JSON string:
’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
’records’ : list like [{column -> value}, … , {column -> value}]
’index’ : dict like {index -> {column -> value}}
’columns’ : dict like {column -> {index -> value}}
’values’ : just the values array
’table’ : dict like {‘schema’: {schema}, ‘data’: {data}}
Describing the data, where data component is like
orient='records'.
date_format ({None, 'epoch', 'iso'}) – Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For
orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.double_precision (int, default 10) – The number of decimal places to use when encoding floating point values.
force_ascii (bool, default True) – Force encoded string to be ASCII.
date_unit (str, default 'ms' (milliseconds)) – The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.
default_handler (callable, default None) – Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
lines (bool, default False) – If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.
compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}) – A string representing the compression to use in the output file, only used when the first argument is a filename. By default, the compression is inferred from the filename.
index (bool, default True) – Whether to include the index values in the JSON string. Not including the index (
index=False) is only supported when orient is ‘split’ or ‘table’.indent (int, optional) –
Length of whitespace used to indent each record.
New in version 1.0.0.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllibas header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec. Please seefsspecandurllibfor more details.New in version 1.2.0.
- Returns
If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.
- Return type
None or str
See also
read_jsonConvert a JSON string to pandas object.
Notes
See pandas API documentation for pandas.DataFrame.to_json for more. The behavior of
indent=0varies from the stdlib, which does not indent the output but does insert newlines. Currently,indent=0and the defaultindent=Noneare equivalent in pandas, though this may change in a future release.orient='table'contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.Examples
>>> import json >>> df = pd.DataFrame( ... [["a", "b"], ["c", "d"]], ... index=["row 1", "row 2"], ... columns=["col 1", "col 2"], ... )
>>> result = df.to_json(orient="split") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) { "columns": [ "col 1", "col 2" ], "index": [ "row 1", "row 2" ], "data": [ [ "a", "b" ], [ "c", "d" ] ] }
Encoding/decoding a Dataframe using
'records'formatted JSON. Note that index labels are not preserved with this encoding.>>> result = df.to_json(orient="records") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) [ { "col 1": "a", "col 2": "b" }, { "col 1": "c", "col 2": "d" } ]
Encoding/decoding a Dataframe using
'index'formatted JSON:>>> result = df.to_json(orient="index") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) { "row 1": { "col 1": "a", "col 2": "b" }, "row 2": { "col 1": "c", "col 2": "d" } }
Encoding/decoding a Dataframe using
'columns'formatted JSON:>>> result = df.to_json(orient="columns") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) { "col 1": { "row 1": "a", "row 2": "c" }, "col 2": { "row 1": "b", "row 2": "d" } }
Encoding/decoding a Dataframe using
'values'formatted JSON:>>> result = df.to_json(orient="values") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) [ [ "a", "b" ], [ "c", "d" ] ]
Encoding with Table Schema:
>>> result = df.to_json(orient="table") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) { "schema": { "fields": [ { "name": "index", "type": "string" }, { "name": "col 1", "type": "string" }, { "name": "col 2", "type": "string" } ], "primaryKey": [ "index" ], "pandas_version": "0.20.0" }, "data": [ { "index": "row 1", "col 1": "a", "col 2": "b" }, { "index": "row 2", "col 1": "c", "col 2": "d" } ] }
- to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)
Render object to a LaTeX tabular, longtable, or nested table/tabular.
Requires
\usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with\input{table.tex}.Changed in version 1.0.0: Added caption and label arguments.
Changed in version 1.2.0: Added position argument, changed meaning of caption argument.
- Parameters
buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
columns (list of label, optional) – The subset of columns to write. Writes all columns by default.
col_space (int, optional) – The minimum width of each column.
header (bool or list of str, default True) – Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
index (bool, default True) – Write row names (index).
na_rep (str, default 'NaN') – Missing data representation.
formatters (list of functions or dict of {str: function}, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns.
float_format (one-parameter function or str, optional, default None) – Formatter for floating point numbers. For example
float_format="%.2f"andfloat_format="{:0.2f}".formatwill both result in 0.1234 being formatted as 0.12.sparsify (bool, optional) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module.
index_names (bool, default True) – Prints the names of the indexes.
bold_rows (bool, default False) – Make the row labels bold in the output.
column_format (str, optional) – The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’.
longtable (bool, optional) – By default, the value will be read from the pandas config module. Use a longtable environment instead of tabular. Requires adding a usepackage{longtable} to your LaTeX preamble.
escape (bool, optional) – By default, the value will be read from the pandas config module. When set to False prevents from escaping latex special characters in column names.
encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’.
decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.
multicolumn (bool, default True) – Use multicolumn to enhance MultiIndex columns. The default will be read from the config module.
multicolumn_format (str, default 'l') – The alignment for multicolumns, similar to column_format The default will be read from the config module.
multirow (bool, default False) – Use multirow to enhance MultiIndex rows. Requires adding a usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.
caption (str or tuple, optional) –
Tuple (full_caption, short_caption), which results in
\caption[short_caption]{full_caption}; if a single string is passed, no short caption will be set.New in version 1.0.0.
Changed in version 1.2.0: Optionally allow caption to be a tuple
(full_caption, short_caption).label (str, optional) –
The LaTeX label to be placed inside
\label{}in the output. This is used with\ref{}in the main.texfile.New in version 1.0.0.
position (str, optional) –
The LaTeX positional argument for tables, to be placed after
\begin{}in the output.New in version 1.2.0:
- str or None
If buf is None, returns the result as a string. Otherwise returns None.
See also
DataFrame.to_stringRender a DataFrame to a console-friendly tabular output.
DataFrame.to_htmlRender a DataFrame as an HTML table.
Examples
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'], ... mask=['red', 'purple'], ... weapon=['sai', 'bo staff'])) >>> print(df.to_latex(index=False)) \begin{tabular}{lll} \toprule name & mask & weapon \\ \midrule Raphael & red & sai \\ Donatello & purple & bo staff \\ \bottomrule \end{tabular}
Notes
See pandas API documentation for pandas.DataFrame.to_latex for more.
- to_markdown(buf=None, mode: str = 'wt', index: modin.pandas.base.BasePandasDataset.bool = True, storage_options: Optional[Dict[str, Any]] = None, **kwargs)
Print DataFrame in Markdown-friendly format.
New in version 1.0.0.
- Parameters
buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) –
Add index (row) labels.
New in version 1.1.0.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllibas header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec. Please seefsspecandurllibfor more details.New in version 1.2.0.
**kwargs – These parameters will be passed to tabulate.
- Returns
DataFrame in Markdown-friendly format.
- Return type
str
Notes
See pandas API documentation for pandas.DataFrame.to_markdown for more. Requires the tabulate package.
Examples
>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal") >>> print(s.to_markdown()) | | animal | |---:|:---------| | 0 | elk | | 1 | pig | | 2 | dog | | 3 | quetzal |
Output markdown with a tabulate option.
>>> print(s.to_markdown(tablefmt="grid")) +----+----------+ | | animal | +====+==========+ | 0 | elk | +----+----------+ | 1 | pig | +----+----------+ | 2 | dog | +----+----------+ | 3 | quetzal | +----+----------+
- to_numpy(dtype=None, copy=False, na_value=NoDefault.no_default)
Convert the DataFrame to a NumPy array.
By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are
float16andfloat32, the results dtype will befloat32. This may require copying data and coercing values, which may be expensive.- Parameters
dtype (str or numpy.dtype, optional) – The dtype to pass to
numpy.asarray().copy (bool, default False) – Whether to ensure that the returned value is not a view on another array. Note that
copy=Falsedoes not ensure thatto_numpy()is no-copy. Rather,copy=Trueensure that a copy is made, even if not strictly necessary.na_value (Any, optional) –
The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.
New in version 1.1.0.
- Returns
- Return type
numpy.ndarray
See also
Series.to_numpySimilar method for Series.
Examples
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy() array([[1, 3], [2, 4]])
With heterogeneous data, the lowest common type will have to be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]}) >>> df.to_numpy() array([[1. , 3. ], [2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2) >>> df.to_numpy() array([[1, 3.0, Timestamp('2000-01-01 00:00:00')], [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
Notes
See pandas API documentation for pandas.DataFrame.to_numpy for more.
- to_period(freq=None, axis=0, copy=True)
Convert DataFrame from DatetimeIndex to PeriodIndex.
Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).
- Parameters
freq (str, default) – Frequency of the PeriodIndex.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).
copy (bool, default True) – If False then underlying input data is not copied.
- Returns
- Return type
DataFrame with PeriodIndex
Notes
See pandas API documentation for pandas.DataFrame.to_period for more.
- to_pickle(path: Union[PathLike[str], str, IO, io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], compression: Optional[Union[str, Dict[str, Any]]] = 'infer', protocol: int = 4, storage_options: Optional[Dict[str, Any]] = None)
Pickle (serialize) object to file.
- Parameters
path (str) – File path where the pickled object will be stored.
compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') – A string representing the compression to use in the output file. By default, infers from the file extension in specified path. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries passed as additional compression options.
protocol (int) –
Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllibas header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec. Please seefsspecandurllibfor more details.New in version 1.2.0.
See also
read_pickleLoad pickled pandas object (or any object) from file.
DataFrame.to_hdfWrite DataFrame to an HDF5 file.
DataFrame.to_sqlWrite DataFrame to a SQL database.
DataFrame.to_parquetWrite a DataFrame to the binary parquet format.
Examples
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)}) >>> original_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl") >>> unpickled_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9
>>> import os >>> os.remove("./dummy.pkl")
Notes
See pandas API documentation for pandas.DataFrame.to_pickle for more.
- to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.
- Parameters
name (str) – Name of SQL table.
con (sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) – Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here.
schema (str, optional) – Specify the schema (if database flavor supports this). If None, use default schema.
if_exists ({'fail', 'replace', 'append'}, default 'fail') –
How to behave if the table already exists.
fail: Raise a ValueError.
replace: Drop the table before inserting new values.
append: Insert new values to the existing table.
index (bool, default True) – Write DataFrame index as a column. Uses index_label as the column name in the table.
index_label (str or sequence, default None) – Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
chunksize (int, optional) – Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once.
dtype (dict or scalar, optional) – Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.
method ({None, 'multi', callable}, optional) –
Controls the SQL insertion clause used:
None : Uses standard SQL
INSERTclause (one per row).’multi’: Pass multiple values in a single
INSERTclause.callable with signature
(pd_table, conn, keys, data_iter).
Details and a sample callable implementation can be found in the section insert method.
- Raises
ValueError – When the table already exists and if_exists is ‘fail’ (the default).
See also
read_sqlRead a DataFrame from a table.
Notes
See pandas API documentation for pandas.DataFrame.to_sql for more. Timezone aware datetime columns will be written as
Timestamp with timezonetype with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.References
Examples
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine >>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']}) >>> df name 0 User 1 1 User 2 2 User 3
>>> df.to_sql('users', con=engine) >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
An sqlalchemy.engine.Connection can also be passed to con:
>>> with engine.begin() as connection: ... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']}) ... df1.to_sql('users', con=connection, if_exists='append')
This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.
>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']}) >>> df2.to_sql('users', con=engine, if_exists='append') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'), (0, 'User 4'), (1, 'User 5'), (0, 'User 6'), (1, 'User 7')]
Overwrite the table with just
df2.>>> df2.to_sql('users', con=engine, if_exists='replace', ... index_label='id') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 6'), (1, 'User 7')]
Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]}) >>> df A 0 1.0 1 NaN 2 2.0
>>> from sqlalchemy.types import Integer >>> df.to_sql('integers', con=engine, index=False, ... dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall() [(1,), (None,), (2,)]
- to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, max_colwidth=None, encoding=None)
Render a DataFrame to a console-friendly tabular output.
- Parameters
buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
columns (sequence, optional, default None) – The subset of columns to write. Writes all columns by default.
col_space (int, list or dict of int, optional) – The minimum width of each column.
header (bool or sequence, optional) – Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
index (bool, optional, default True) – Whether to print index (row) labels.
na_rep (str, optional, default 'NaN') – String representation of
NaNto use.formatters (list, tuple or dict of one-param. functions, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
float_format (one-parameter function, optional, default None) –
Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-
NaNelements, withNaNbeing handled byna_rep.Changed in version 1.2.0.
sparsify (bool, optional, default True) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
index_names (bool, optional, default True) – Prints the names of the indexes.
justify (str, default None) –
How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are
left
right
center
justify
justify-all
start
end
inherit
match-parent
initial
unset.
max_rows (int, optional) – Maximum number of rows to display in the console.
min_rows (int, optional) – The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).
max_cols (int, optional) – Maximum number of columns to display in the console.
show_dimensions (bool, default False) – Display DataFrame dimensions (number of rows by number of columns).
decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.
line_width (int, optional) – Width to wrap a line in characters.
max_colwidth (int, optional) –
Max width to truncate each column in characters. By default, no limit.
New in version 1.0.0.
encoding (str, default "utf-8") –
Set character encoding.
New in version 1.0.
- Returns
If buf is None, returns the result as a string. Otherwise returns None.
- Return type
str or None
See also
to_htmlConvert DataFrame to HTML.
Examples
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]} >>> df = pd.DataFrame(d) >>> print(df.to_string()) col1 col2 0 1 4 1 2 5 2 3 6
Notes
See pandas API documentation for pandas.DataFrame.to_string for more.
- to_timestamp(freq=None, how='start', axis=0, copy=True)
Cast to DatetimeIndex of timestamps, at beginning of period.
- Parameters
freq (str, default frequency of PeriodIndex) – Desired frequency.
how ({'s', 'e', 'start', 'end'}) – Convention for converting period to timestamp; start of period vs. end.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).
copy (bool, default True) – If False then underlying input data is not copied.
- Returns
- Return type
DataFrame with DatetimeIndex
Notes
See pandas API documentation for pandas.DataFrame.to_timestamp for more.
- to_xarray()
Return an xarray object from the pandas object.
- Returns
Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.
- Return type
xarray.DataArray or xarray.Dataset
See also
DataFrame.to_hdfWrite DataFrame to an HDF5 file.
DataFrame.to_parquetWrite a DataFrame to the binary parquet format.
Notes
See pandas API documentation for pandas.DataFrame.to_xarray for more. See the xarray docs
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2), ... ('parrot', 'bird', 24.0, 2), ... ('lion', 'mammal', 80.5, 4), ... ('monkey', 'mammal', np.nan, 4)], ... columns=['name', 'class', 'max_speed', ... 'num_legs']) >>> df name class max_speed num_legs 0 falcon bird 389.0 2 1 parrot bird 24.0 2 2 lion mammal 80.5 4 3 monkey mammal NaN 4
>>> df.to_xarray() <xarray.Dataset> Dimensions: (index: 4) Coordinates: * index (index) int64 0 1 2 3 Data variables: name (index) object 'falcon' 'parrot' 'lion' 'monkey' class (index) object 'bird' 'bird' 'mammal' 'mammal' max_speed (index) float64 389.0 24.0 80.5 nan num_legs (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray() <xarray.DataArray 'max_speed' (index: 4)> array([389. , 24. , 80.5, nan]) Coordinates: * index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01', ... '2018-01-02', '2018-01-02']) >>> df_multiindex = pd.DataFrame({'date': dates, ... 'animal': ['falcon', 'parrot', ... 'falcon', 'parrot'], ... 'speed': [350, 18, 361, 15]}) >>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex speed date animal 2018-01-01 falcon 350 parrot 18 2018-01-02 falcon 361 parrot 15
>>> df_multiindex.to_xarray() <xarray.Dataset> Dimensions: (animal: 2, date: 2) Coordinates: * date (date) datetime64[ns] 2018-01-01 2018-01-02 * animal (animal) object 'falcon' 'parrot' Data variables: speed (date, animal) int64 350 18 361 15
- transform(func, axis=0, *args, **kwargs)
Call
funcon self producing a DataFrame with transformed values.Produced DataFrame will have same axis length as self.
- Parameters
func (function, str, list-like or dict-like) –
Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.
Accepted combinations are:
function
string function name
list-like of functions and/or function names, e.g.
[np.exp, 'sqrt']dict-like of axis labels -> functions, function names or list-like of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.
- Returns
A DataFrame that must have the same length as self.
- Return type
:raises ValueError : If the returned DataFrame has a different length than self.:
See also
DataFrame.aggOnly perform aggregating type operations.
DataFrame.applyInvoke function on a DataFrame.
Notes
See pandas API documentation for pandas.DataFrame.transform for more. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.
Examples
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)}) >>> df A B 0 0 1 1 1 2 2 2 3 >>> df.transform(lambda x: x + 1) A B 0 1 2 1 2 3 2 3 4
Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:
>>> s = pd.Series(range(3)) >>> s 0 0 1 1 2 2 dtype: int64 >>> s.transform([np.sqrt, np.exp]) sqrt exp 0 0.000000 1.000000 1 1.000000 2.718282 2 1.414214 7.389056
You can call transform on a GroupBy object:
>>> df = pd.DataFrame({ ... "Date": [ ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05", ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"], ... "Data": [5, 8, 6, 1, 50, 100, 60, 120], ... }) >>> df Date Data 0 2015-05-08 5 1 2015-05-07 8 2 2015-05-06 6 3 2015-05-05 1 4 2015-05-08 50 5 2015-05-07 100 6 2015-05-06 60 7 2015-05-05 120 >>> df.groupby('Date')['Data'].transform('sum') 0 55 1 108 2 66 3 121 4 55 5 108 6 66 7 121 Name: Data, dtype: int64
>>> df = pd.DataFrame({ ... "c": [1, 1, 1, 2, 2, 2, 2], ... "type": ["m", "n", "o", "m", "m", "n", "n"] ... }) >>> df c type 0 1 m 1 1 n 2 1 o 3 2 m 4 2 m 5 2 n 6 2 n >>> df['size'] = df.groupby('c')['type'].transform(len) >>> df c type size 0 1 m 3 1 1 n 3 2 1 o 3 3 2 m 4 4 2 m 4 5 2 n 4 6 2 n 4
- truediv(other, axis='columns', level=None, fill_value=None)
Get Floating division of dataframe and other, element-wise (binary operator truediv).
Equivalent to
dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.
- Parameters
other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
- Returns
Result of the arithmetic operation.
- Return type
See also
DataFrame.addAdd DataFrames.
DataFrame.subSubtract DataFrames.
DataFrame.mulMultiply DataFrames.
DataFrame.divDivide DataFrames (float division).
DataFrame.truedivDivide DataFrames (float division).
DataFrame.floordivDivide DataFrames (integer division).
DataFrame.modCalculate modulo (remainder after division).
DataFrame.powCalculate exponential power.
Notes
See pandas API documentation for pandas.DataFrame.truediv for more. Mismatched indices will be unioned together.
Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4], ... 'degrees': [360, 180, 360]}, ... index=['circle', 'triangle', 'rectangle']) >>> df angles degrees circle 0 360 triangle 3 180 rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1 angles degrees circle 1 361 triangle 4 181 rectangle 5 361
>>> df.add(1) angles degrees circle 1 361 triangle 4 181 rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10) angles degrees circle 0.0 36.0 triangle 0.3 18.0 rectangle 0.4 36.0
>>> df.rdiv(10) angles degrees circle inf 0.027778 triangle 3.333333 0.055556 rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2] angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub([1, 2], axis='columns') angles degrees circle -1 358 triangle 2 178 rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), ... axis='index') angles degrees circle -1 359 triangle 2 179 rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]}, ... index=['circle', 'triangle', 'rectangle']) >>> other angles circle 0 triangle 3 rectangle 4
>>> df * other angles degrees circle 0 NaN triangle 9 NaN rectangle 16 NaN
>>> df.mul(other, fill_value=0) angles degrees circle 0 0.0 triangle 9 0.0 rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6], ... 'degrees': [360, 180, 360, 360, 540, 720]}, ... index=[['A', 'A', 'A', 'B', 'B', 'B'], ... ['circle', 'triangle', 'rectangle', ... 'square', 'pentagon', 'hexagon']]) >>> df_multindex angles degrees A circle 0 360 triangle 3 180 rectangle 4 360 B square 4 360 pentagon 5 540 hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0) angles degrees A circle NaN 1.0 triangle 1.0 1.0 rectangle 1.0 1.0 B square 0.0 0.0 pentagon 0.0 0.0 hexagon 0.0 0.0
- truncate(before=None, after=None, axis=None, copy=True)
Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.
- Parameters
before (date, str, int) – Truncate all rows before this index value.
after (date, str, int) – Truncate all rows after this index value.
axis ({0 or 'index', 1 or 'columns'}, optional) – Axis to truncate. Truncates the index (rows) by default.
copy (bool, default is True,) – Return a copy of the truncated section.
- Returns
The truncated Series or DataFrame.
- Return type
type of caller
See also
DataFrame.locSelect a subset of a DataFrame by label.
DataFrame.ilocSelect a subset of a DataFrame by position.
Notes
See pandas API documentation for pandas.DataFrame.truncate for more. If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.
Examples
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'], ... 'B': ['f', 'g', 'h', 'i', 'j'], ... 'C': ['k', 'l', 'm', 'n', 'o']}, ... index=[1, 2, 3, 4, 5]) >>> df A B C 1 a f k 2 b g l 3 c h m 4 d i n 5 e j o
>>> df.truncate(before=2, after=4) A B C 2 b g l 3 c h m 4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns") A B 1 a f 2 b g 3 c h 4 d i 5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4) 2 b 3 c 4 d Name: A, dtype: object
The index values in
truncatecan be datetimes or string dates.>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s') >>> df = pd.DataFrame(index=dates, data={'A': 1}) >>> df.tail() A 2016-01-31 23:59:56 1 2016-01-31 23:59:57 1 2016-01-31 23:59:58 1 2016-01-31 23:59:59 1 2016-02-01 00:00:00 1
>>> df.truncate(before=pd.Timestamp('2016-01-05'), ... after=pd.Timestamp('2016-01-10')).tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1
Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.
>>> df.truncate('2016-01-05', '2016-01-10').tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1
Note that
truncateassumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.>>> df.loc['2016-01-05':'2016-01-10', :].tail() A 2016-01-10 23:59:55 1 2016-01-10 23:59:56 1 2016-01-10 23:59:57 1 2016-01-10 23:59:58 1 2016-01-10 23:59:59 1
- tshift(periods=1, freq=None, axis=0)
Shift the time index, using the index’s frequency if available.
Deprecated since version 1.1.0: Use shift instead.
- Parameters
periods (int) – Number of periods to move, can be positive or negative.
freq (DateOffset, timedelta, or str, default None) – Increment to use from the tseries module or time rule expressed as a string (e.g. ‘EOM’).
axis ({0 or ‘index’, 1 or ‘columns’, None}, default 0) – Corresponds to the axis that contains the Index.
- Returns
shifted
- Return type
Series/DataFrame
Notes
See pandas API documentation for pandas.DataFrame.tshift for more. If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown
- tz_convert(tz, axis=0, level=None, copy=True)
Convert tz-aware axis to target time zone.
- Parameters
tz (str or tzinfo object) –
axis (the axis to convert) –
level (int, str, default None) – If axis is a MultiIndex, convert a specific level. Otherwise must be None.
copy (bool, default True) – Also make a copy of the underlying data.
- Returns
Object with time zone converted axis.
- Return type
{klass}
- Raises
TypeError – If the axis is tz-naive.
Notes
See pandas API documentation for pandas.DataFrame.tz_convert for more.
- tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise')
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a timezone-naive Series, use
Series.dt.tz_localize().- Parameters
tz (str or tzinfo) –
axis (the axis to localize) –
level (int, str, default None) – If axis ia a MultiIndex, localize a specific level. Otherwise must be None.
copy (bool, default True) – Also make a copy of the underlying data.
ambiguous ('infer', bool-ndarray, 'NaT', default 'raise') –
When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.
’infer’ will attempt to infer fall dst-transition hours based on order
bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
’NaT’ will return NaT where there are ambiguous times
’raise’ will raise an AmbiguousTimeError if there are ambiguous times.
nonexistent (str, default 'raise') –
A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:
’shift_forward’ will shift the nonexistent time forward to the closest existing time
’shift_backward’ will shift the nonexistent time backward to the closest existing time
’NaT’ will return NaT where there are nonexistent times
timedelta objects will shift nonexistent times by the timedelta
’raise’ will raise an NonExistentTimeError if there are nonexistent times.
- Returns
Same type as the input.
- Return type
- Raises
TypeError – If the TimeSeries is tz-aware and tz is not None.
Examples
Localize local times:
>>> s = pd.Series([1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00'])) >>> s.tz_localize('CET') 2018-09-15 01:30:00+02:00 1 dtype: int64
Be careful with DST changes. When there is sequential data, pandas can infer the DST time:
>>> s = pd.Series(range(7), ... index=pd.DatetimeIndex(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.tz_localize('CET', ambiguous='infer') 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64
In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly
>>> s = pd.Series(range(3), ... index=pd.DatetimeIndex(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.tz_localize('CET', ambiguous=np.array([True, True, False])) 2018-10-28 01:20:00+02:00 0 2018-10-28 02:36:00+02:00 1 2018-10-28 03:46:00+01:00 2 dtype: int64
If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’ or ‘shift_backward’.
>>> s = pd.Series(range(2), ... index=pd.DatetimeIndex(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 2015-03-29 03:00:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 2015-03-29 01:59:59.999999999+01:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H')) 2015-03-29 03:30:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64
Notes
See pandas API documentation for pandas.DataFrame.tz_localize for more.
- value_counts(subset: Optional[Sequence[Hashable]] = None, normalize: modin.pandas.base.BasePandasDataset.bool = False, sort: modin.pandas.base.BasePandasDataset.bool = True, ascending: modin.pandas.base.BasePandasDataset.bool = False, dropna: modin.pandas.base.BasePandasDataset.bool = True)
Return a Series containing counts of unique rows in the DataFrame.
New in version 1.1.0.
- Parameters
subset (list-like, optional) – Columns to use when counting unique combinations.
normalize (bool, default False) – Return proportions rather than frequencies.
sort (bool, default True) – Sort by frequencies.
ascending (bool, default False) – Sort in ascending order.
dropna (bool, default True) –
Don’t include counts of rows that contain NA values.
New in version 1.3.0.
- Returns
- Return type
See also
Series.value_countsEquivalent method on Series.
Notes
See pandas API documentation for pandas.DataFrame.value_counts for more. The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.
Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6], ... 'num_wings': [2, 0, 0, 0]}, ... index=['falcon', 'dog', 'cat', 'ant']) >>> df num_legs num_wings falcon 2 2 dog 4 0 cat 4 0 ant 6 0
>>> df.value_counts() num_legs num_wings 4 0 2 2 2 1 6 0 1 dtype: int64
>>> df.value_counts(sort=False) num_legs num_wings 2 2 1 4 0 2 6 0 1 dtype: int64
>>> df.value_counts(ascending=True) num_legs num_wings 2 2 1 6 0 1 4 0 2 dtype: int64
>>> df.value_counts(normalize=True) num_legs num_wings 4 0 0.50 2 2 0.25 6 0 0.25 dtype: float64
With dropna set to False we can also count rows with NA values.
>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'], ... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']}) >>> df first_name middle_name 0 John Smith 1 Anne <NA> 2 John <NA> 3 Beth Louise
>>> df.value_counts() first_name middle_name Beth Louise 1 John Smith 1 dtype: int64
>>> df.value_counts(dropna=False) first_name middle_name Anne NaN 1 Beth Louise 1 John Smith 1 NaN 1 dtype: int64
- property values
Return a Numpy representation of the DataFrame.
Warning
We recommend using
DataFrame.to_numpy()instead.Only the values in the DataFrame will be returned, the axes labels will be removed.
- Returns
The values of the DataFrame.
- Return type
numpy.ndarray
See also
DataFrame.to_numpyRecommended alternative to this method.
DataFrame.indexRetrieve the index labels.
DataFrame.columnsRetrieving the column names.
Notes
See pandas API documentation for pandas.DataFrame.values for more. The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By
numpy.find_common_type()convention, mixing int64 and uint64 will result in a float64 dtype.Examples
A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type.
>>> df = pd.DataFrame({'age': [ 3, 29], ... 'height': [94, 170], ... 'weight': [31, 115]}) >>> df age height weight 0 3 94 31 1 29 170 115 >>> df.dtypes age int64 height int64 weight int64 dtype: object >>> df.values array([[ 3, 94, 31], [ 29, 170, 115]])
A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object).
>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'), ... ('lion', 80.5, 1), ... ('monkey', np.nan, None)], ... columns=('name', 'max_speed', 'rank')) >>> df2.dtypes name object max_speed float64 rank object dtype: object >>> df2.values array([['parrot', 24.0, 'second'], ['lion', 80.5, 1], ['monkey', nan, None]], dtype=object)
- var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)
Return unbiased variance over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
- Parameters
axis ({index (0), columns (1)}) –
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
level (int or level name, default None) – If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default None) – Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
- Returns
- Return type
Notes
See pandas API documentation for pandas.DataFrame.var for more. To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)