Base Query Compiler

Brief description

BaseQueryCompiler is an abstract class of query compiler, and sets a common interface that every other query compiler implementation in Modin must follow. The Base class contains a basic implementations for most of the interface methods, all of which default to pandas.

Subclassing BaseQueryCompiler

If you want to add new type of query compiler to Modin the new class needs to inherit from BaseQueryCompiler and implement the abstract methods:

(Please refer to the code documentation to see the full documentation for these functions).

This is a minimum set of operations to ensure a new query compiler will function in the Modin architecture, and the rest of the API can safely default to the pandas implementation via the base class implementation. To add a backend-specific implementation for some of the query compiler operations, just override the corresponding method in your query compiler class.

Example

As an exercise let’s define a new query compiler in Modin, just to see how easy it is. Usually, the query compiler routes formed queries to the underlying frame class, which submits operators to an execution engine. For the sake of simplicity and independence of this example, our execution engine will be the pandas itself.

We need to inherit a new class from BaseQueryCompiler and implement all of the abstract methods. In this case, with pandas as an execution engine, it’s trivial:

from modin.backends import BaseQueryCompiler

class DefaultToPandasQueryCompiler(BaseQueryCompiler):
    def __init__(self, pandas_df):
        self._pandas_df = pandas_df

    @classmethod
    def from_pandas(cls, df, *args, **kwargs):
        return cls(df)

    @classmethod
    def from_arrow(cls, at, *args, **kwargs):
        return cls(at.to_pandas())

    def to_pandas(self):
        return self._pandas_df.copy()

    def default_to_pandas(self, pandas_op, *args, **kwargs):
        return type(self)(pandas_op(self.to_pandas(), *args, **kwargs))

    def finalize(self):
        pass

    def free(self):
        pass

All done! Now you’ve got a fully functional query compiler, which is ready for extensions and already can be used in Modin DataFrame:

import pandas
pandas_df = pandas.DataFrame({"col1": [1, 2, 2, 1], "col2": [10, 2, 3, 40]})
# Building our query compiler from pandas object
qc = DefaultToPandasQueryCompiler.from_pandas(pandas_df)

import modin.pandas as pd
# Building Modin DataFrame from newly created query compiler
modin_df = pd.DataFrame(query_compiler=qc)

# Got fully functional Modin DataFrame
>>> print(modin_df.groupby("col1").sum().reset_index())
   col1  col2
0     1    50
1     2     5

To be able to select this query compiler as default via modin.config you also need to define the combination of your query compiler and pandas execution engine as a backend by adding the corresponding factory. To find more information about factories, visit corresponding section of the flow documentation.

Query Compiler API

class modin.backends.base.query_compiler.BaseQueryCompiler

Abstract class that handles the queries to Modin dataframes.

This class defines common query compilers API, most of the methods are already implemented and defaulting to pandas.

lazy_execution

Whether underlying execution engine is designed to be executed in a lazy mode only. If True, such QueryCompiler will be handled differently at the front-end in order to reduce execution triggering as much as possible.

Type

bool

Notes

See the Abstract Methods and Fields section immediately below this for a list of requirements for subclassing this object.

abs()

Get absolute numeric value of each element.

Returns

QueryCompiler with absolute numeric value of each element.

Return type

BaseQueryCompiler

add(other, **kwargs)

Perform element-wise addition (self + other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

add_prefix(prefix, axis=1)

Add string prefix to the index labels along specified axis.

Parameters
  • prefix (str) – The string to add before each label.

  • axis ({0, 1}, default: 1) – Axis to add prefix along. 0 is for index and 1 is for columns.

Returns

New query compiler with updated labels.

Return type

BaseQueryCompiler

add_suffix(suffix, axis=1)

Add string suffix to the index labels along specified axis.

Parameters
  • suffix (str) – The string to add after each label.

  • axis ({0, 1}, default: 1) – Axis to add suffix along. 0 is for index and 1 is for columns.

Returns

New query compiler with updated labels.

Return type

BaseQueryCompiler

all(**kwargs)

Return whether all the elements are true, potentially over an axis.

Parameters
  • axis ({0, 1}, optional) –

  • bool_only (bool, optional) –

  • skipna (bool) –

  • level (int or label) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

If axis was specified return one-column QueryCompiler with index labels of the specified axis, where each row contains boolean of whether all elements at the corresponding row or column are True. Otherwise return QueryCompiler with a single bool of whether all elements are True.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.all for more information about parameters and output format.

any(**kwargs)

Return whether any element is true, potentially over an axis.

Parameters
  • axis ({0, 1}, optional) –

  • bool_only (bool, optional) –

  • skipna (bool) –

  • level (int or label) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

If axis was specified return one-column QueryCompiler with index labels of the specified axis, where each row contains boolean of whether any element at the corresponding row or column is True. Otherwise return QueryCompiler with a single bool of whether any element is True.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.any for more information about parameters and output format.

apply(func, axis, *args, **kwargs)

Apply passed function across given axis.

Parameters
  • func (callable(pandas.Series) -> scalar, str, list or dict of such) – The function to apply to each column or row.

  • axis ({0, 1}) – Target axis to apply the function along. 0 is for index, 1 is for columns.

  • *args (iterable) – Positional arguments to pass to func.

  • **kwargs (dict) – Keyword arguments to pass to func.

Returns

QueryCompiler that contains the results of execution and is built by the following rules:

  • Labels of specified axis are the passed functions names.

  • Labels of the opposite axis are preserved.

  • Each element is the result of execution of func against corresponding row/column.

Return type

BaseQueryCompiler

applymap(func)

Apply passed function elementwise.

Parameters

func (callable(scalar) -> scalar) – Function to apply to each element of the QueryCompiler.

Returns

Transformed QueryCompiler.

Return type

BaseQueryCompiler

astype(col_dtypes, **kwargs)

Convert columns dtypes to given dtypes.

Parameters
  • col_dtypes (dict) – Map for column names and new dtypes.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with updated dtypes.

Return type

BaseQueryCompiler

cat_codes()

Convert underlying categories data into its codes.

Returns

New QueryCompiler containing the integer codes of the underlying categories.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.cat.codes for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

clip(lower, upper, **kwargs)

Trim values at input threshold.

Parameters
  • lower (float or list-like) –

  • upper (float or list-like) –

  • axis ({0, 1}) –

  • inplace ({False}) – This parameter serves the compatibility purpose. Always has to be False.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with values limited by the specified thresholds.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.clip for more information about parameters and output format.

columnarize()

Transpose this QueryCompiler if it has a single row but multiple columns.

This method should be called for QueryCompilers representing a Series object, i.e. self.is_series_like() should be True.

Returns

Transposed new QueryCompiler or self.

Return type

BaseQueryCompiler

combine(other, **kwargs)

Perform column-wise combine with another QueryCompiler with passed func.

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler) – Left operand of the binary operation.

  • func (callable(pandas.Series, pandas.Series) -> pandas.Series) – Function that takes two pandas.Series with aligned axes and returns one pandas.Series as resulting combination.

  • fill_value (float or None) – Value to fill missing values with after frame alignment occurred.

  • overwrite (bool) – If True, columns in self that do not exist in other will be overwritten with NaNs.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of combine.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.combine for more information about parameters and output format.

combine_first(other, **kwargs)

Fill null elements of self with value in the same location in other.

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler) – Provided frame to use to fill null values from.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.combine_first for more information about parameters and output format.

compare(other, align_axis, keep_shape, keep_equal)

Compare data of two QueryCompilers and highlight the difference.

Parameters
  • other (BaseQueryCompiler) – Query compiler to compare with. Have to be the same shape and the same labeling as self.

  • align_axis ({0, 1}) –

  • keep_shape (bool) –

  • keep_equal (bool) –

Returns

New QueryCompiler containing the differences between self and passed query compiler.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.compare for more information about parameters and output format.

concat(axis, other, **kwargs)

Concatenate self with passed query compilers along specified axis.

Parameters
  • axis ({0, 1}) – Axis to concatenate along. 0 is for index and 1 is for columns.

  • other (BaseQueryCompiler or list of such) – Objects to concatenate with self.

  • join ({'outer', 'inner', 'right', 'left'}, default: 'outer') – Type of join that will be used if indices on the other axis are different. (note: if specified, has to be passed as join=value).

  • ignore_index (bool, default: False) – If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, …, n - 1. (note: if specified, has to be passed as ignore_index=value).

  • sort (bool, default: False) – Whether or not to sort non-concatenation axis. (note: if specified, has to be passed as sort=value).

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Concatenated objects.

Return type

BaseQueryCompiler

conj(**kwargs)

Get the complex conjugate for every element of self.

Parameters

**kwargs (dict) –

Returns

QueryCompiler with conjugate applied element-wise.

Return type

BaseQueryCompiler

Notes

Please refer to numpy.conj for parameters description.

copy()

Make a copy of this object.

Returns

Copy of self.

Return type

BaseQueryCompiler

Notes

For copy, we don’t want a situation where we modify the metadata of the copies if we end up modifying something here. We copy all of the metadata to prevent that.

corr(**kwargs)

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
  • method ({'pearson', 'kendall', 'spearman'} or callable(pandas.Series, pandas.Series) -> pandas.Series) – Correlation method.

  • min_periods (int) – Minimum number of observations required per pair of columns to have a valid result. If fewer than min_periods non-NA values are present the result will be NA.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Correlation matrix.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.corr for more information about parameters and output format.

count(**kwargs)

Get the number of non-NaN values for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the number of non-NaN values for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.count for more information about parameters and output format.

cov(**kwargs)

Compute pairwise covariance of columns, excluding NA/null values.

Parameters
  • min_periods (int) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Covariance matrix.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.cov for more information about parameters and output format.

cummax(**kwargs)

Get cummulative maximum for every row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the maximum of all the previous values in this row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.cummax for more information about parameters and output format.

cummin(**kwargs)

Get cummulative minimum for every row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the minimum of all the previous values in this row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.cummin for more information about parameters and output format.

cumprod(**kwargs)

Get cummulative product for every row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the product of all the previous values in this row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.cumprod for more information about parameters and output format.

cumsum(**kwargs)

Get cummulative sum for every row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the sum of all the previous values in this row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.cumsum for more information about parameters and output format.

abstract default_to_pandas(pandas_op, *args, **kwargs)

Do fallback to pandas for the passed function.

Parameters
  • pandas_op (callable(pandas.DataFrame) -> object) – Function to apply to the casted to pandas frame.

  • *args (iterable) – Positional arguments to pass to pandas_op.

  • **kwargs (dict) – Key-value arguments to pass to pandas_op.

Returns

The result of the pandas_op, converted back to BaseQueryCompiler.

Return type

BaseQueryCompiler

delitem(key)

Drop key column.

Parameters

key (label) – Column name to drop.

Returns

New QueryCompiler without key column.

Return type

BaseQueryCompiler

describe(**kwargs)

Generate descriptive statistics.

Parameters
  • percentiles (list-like) –

  • include ("all" or list of dtypes, optional) –

  • exclude (list of dtypes, optional) –

  • datetime_is_numeric (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler object containing the descriptive statistics of the underlying data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.describe for more information about parameters and output format.

df_update(other, **kwargs)

Update values of self using non-NA values of other at the corresponding positions.

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler) – Frame to grab replacement values from.

  • join ({"left"}) – Specify type of join to align frames if axes are not equal (note: currently only one type of join is implemented).

  • overwrite (bool) – Whether to overwrite every corresponding value of self, or only if it’s NAN.

  • filter_func (callable(pandas.Series, pandas.Series) -> numpy.ndarray<bool>) – Function that takes column of the self and return bool mask for values, that should be overwriten in the self frame.

  • errors ({"raise", "ignore"}) – If “raise”, will raise a ValueError if self and other both contain non-NA data in the same place.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with updated values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.update for more information about parameters and output format.

diff(**kwargs)

First discrete difference of element.

Parameters
  • periods (int) –

  • axis ({0, 1}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the difference between the corresponding value and the previous value in this row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.diff for more information about parameters and output format.

dot(other, **kwargs)

Compute the matrix multiplication of self and other.

Parameters
  • other (BaseQueryCompiler or NumPy array) – The other query compiler or NumPy array to matrix multiply with self.

  • squeeze_self (boolean) – If self is a one-column query compiler, indicates whether it represents Series object.

  • squeeze_other (boolean) – If other is a one-column query compiler, indicates whether it represents Series object.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

A new query compiler that contains result of the matrix multiply.

Return type

BaseQueryCompiler

drop(index=None, columns=None)

Drop specified rows or columns.

Parameters
  • index (list of labels, optional) – Labels of rows to drop.

  • columns (list of labels, optional) – Labels of columns to drop.

Returns

New QueryCompiler with removed data.

Return type

BaseQueryCompiler

dropna(**kwargs)

Remove missing values.

Parameters
  • axis ({0, 1}) –

  • how ({"any", "all"}) –

  • thresh (int, optional) –

  • subset (list of labels) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with null values dropped along given axis.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.dropna for more information about parameters and output format.

dt_ceil(freq, ambiguous='raise', nonexistent='raise')

Perform ceil operation on the underlying time-series data to the specified freq.

Parameters
  • freq (str) –

  • ambiguous ({"raise", "infer", "NaT"} or bool mask, default: "raise") –

  • nonexistent ({"raise", "shift_forward", "shift_backward", "NaT"} or timedelta, default: "raise") –

Returns

New QueryCompiler with performed ceil operation on every element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.ceil for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_components()

Spread each date-time value into its components (days, hours, minutes…).

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.components for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_date()

Get the date without timezone information for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the date without timezone information for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.date for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_day()

Get day component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is day component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.day for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_day_name(locale=None)

Get day name for each datetime value.

Parameters

locale (str, optional) –

Returns

New QueryCompiler with the same shape as self, where each element is day name for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.day_name for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_dayofweek()

Get integer day of week for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is integer day of week for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.dayofweek for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_dayofyear()

Get day of year for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is day of year for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.dayofyear for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_days()

Get days for each interval value.

Returns

New QueryCompiler with the same shape as self, where each element is days for the corresponding interval value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.days for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_days_in_month()

Get number of days in month for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is number of days in month for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.days_in_month for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_daysinmonth()

Get number of days in month for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is number of days in month for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.daysinmonth for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_end_time()

Get the timestamp of end time for each period value.

Returns

New QueryCompiler with the same shape as self, where each element is the timestamp of end time for the corresponding period value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.end_time for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_floor(freq, ambiguous='raise', nonexistent='raise')

Perform floor operation on the underlying time-series data to the specified freq.

Parameters
  • freq (str) –

  • ambiguous ({"raise", "infer", "NaT"} or bool mask, default: "raise") –

  • nonexistent ({"raise", "shift_forward", "shift_backward", "NaT"} or timedelta, default: "raise") –

Returns

New QueryCompiler with performed floor operation on every element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.floor for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_freq()

Get the time frequency of the underlying time-series data.

Returns

QueryCompiler containing a single value, the frequency of the data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.freq for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_hour()

Get hour for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is hour for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.hour for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_leap_year()

Get the boolean of whether corresponding year is leap for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether corresponding year is leap for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_leap_year for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_month_end()

Get the boolean of whether the date is the last day of the month for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the last day of the month for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_month_end for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_month_start()

Get the boolean of whether the date is the first day of the month for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the first day of the month for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_month_start for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_quarter_end()

Get the boolean of whether the date is the last day of the quarter for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the last day of the quarter for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_quarter_end for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_quarter_start()

Get the boolean of whether the date is the first day of the quarter for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the first day of the quarter for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_quarter_start for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_year_end()

Get the boolean of whether the date is the last day of the year for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the last day of the year for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_year_end for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_is_year_start()

Get the boolean of whether the date is the first day of the year for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is the boolean of whether the date is the first day of the year for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.is_year_start for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_microsecond()

Get microseconds component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is microseconds component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.microsecond for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_microseconds()

Get microseconds component for each interval value.

Returns

New QueryCompiler with the same shape as self, where each element is microseconds component for the corresponding interval value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.microseconds for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_minute()

Get minute component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is minute component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.minute for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_month()

Get month component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is month component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.month for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_month_name(locale=None)

Get the month name for each datetime value.

Parameters

locale (str, optional) –

Returns

New QueryCompiler with the same shape as self, where each element is the month name for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.month name for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_nanosecond()

Get nanoseconds component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is nanoseconds component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.nanosecond for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_nanoseconds()

Get nanoseconds component for each interval value.

Returns

New QueryCompiler with the same shape as self, where each element is nanoseconds component for the corresponding interval value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.nanoseconds for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_normalize()

Set the time component of each date-time value to midnight.

Returns

New QueryCompiler containing date-time values with midnight time.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.normalize for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_quarter()

Get quarter component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is quarter component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.quarter for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_qyear()

Get the fiscal year for each period value.

Returns

New QueryCompiler with the same shape as self, where each element is the fiscal year for the corresponding period value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.qyear for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_round(freq, ambiguous='raise', nonexistent='raise')

Perform round operation on the underlying time-series data to the specified freq.

Parameters
  • freq (str) –

  • ambiguous ({"raise", "infer", "NaT"} or bool mask, default: "raise") –

  • nonexistent ({"raise", "shift_forward", "shift_backward", "NaT"} or timedelta, default: "raise") –

Returns

New QueryCompiler with performed round operation on every element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.round for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_second()

Get seconds component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is seconds component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.second for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_seconds()

Get seconds component for each interval value.

Returns

New QueryCompiler with the same shape as self, where each element is seconds component for the corresponding interval value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.seconds for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_start_time()

Get the timestamp of start time for each period value.

Returns

New QueryCompiler with the same shape as self, where each element is the timestamp of start time for the corresponding period value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.start_time for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_strftime(date_format)

Format underlying date-time data using specified format.

Parameters

date_format (str) –

Returns

New QueryCompiler containing formated date-time values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.strftime for more information about parameters and output format.

dt_time()

Get time component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is time component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.time for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_timetz()

Get time component with timezone information for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is time component with timezone information for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.timetz for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_to_period(freq=None)

Convert underlying data to the period at a particular frequency.

Parameters

freq (str, optional) –

Returns

New QueryCompiler containing period data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.to_period for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_to_pydatetime()

Convert underlying data to array of python native datetime.

Returns

New QueryCompiler containing 1D array of datetime objects.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.to_pydatetime for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_to_pytimedelta()

Convert underlying data to array of python native datetime.timedelta.

Returns

New QueryCompiler containing 1D array of datetime.timedelta.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.to_pytimedelta for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_to_timestamp()

Get the timestamp representation for each period value.

Returns

New QueryCompiler with the same shape as self, where each element is the timestamp representation for the corresponding period value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.to_timestamp for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_total_seconds()

Get duration in seconds for each interval value.

Returns

New QueryCompiler with the same shape as self, where each element is duration in seconds for the corresponding interval value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.total_seconds for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_tz()

Get the time-zone of the underlying time-series data.

Returns

QueryCompiler containing a single value, time-zone of the data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.tz for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_tz_convert(tz)

Convert time-series data to the specified time zone.

Parameters

tz (str, pytz.timezone) –

Returns

New QueryCompiler containing values with converted time zone.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.tz_convert for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_tz_localize(tz, ambiguous='raise', nonexistent='raise')

Localize tz-naive to tz-aware.

Parameters
  • tz (str, pytz.timezone, optional) –

  • ambiguous ({"raise", "inner", "NaT"} or bool mask, default: "raise") –

  • nonexistent ({"raise", "shift_forward", "shift_backward, "NaT"} or pandas.timedelta, default: "raise") –

Returns

New QueryCompiler containing values with localized time zone.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.tz_localize for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_week()

Get week component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is week component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.week for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_weekday()

Get integer day of week for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is integer day of week for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.weekday for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_weekofyear()

Get week of year for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is week of year for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.weekofyear for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

dt_year()

Get year component for each datetime value.

Returns

New QueryCompiler with the same shape as self, where each element is year component for the corresponding datetime value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.dt.year for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

property dtypes

Get columns dtypes.

Returns

Series with dtypes of each column.

Return type

pandas.Series

eq(other, **kwargs)

Perform element-wise equality comparison (self == other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

eval(expr, **kwargs)

Evaluate string expression on QueryCompiler columns.

Parameters
  • expr (str) –

  • **kwargs (dict) –

Returns

QueryCompiler containing the result of evaluation.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.eval for more information about parameters and output format.

fillna(**kwargs)

Replace NaN values using provided method.

Parameters
  • value (scalar or dict) –

  • method ({"backfill", "bfill", "pad", "ffill", None}) –

  • axis ({0, 1}) –

  • inplace ({False}) – This parameter serves the compatibility purpose. Always has to be False.

  • limit (int, optional) –

  • downcast (dict, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with all null values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.fillna for more information about parameters and output format.

abstract finalize()

Finalize constructing the dataframe calling all deferred functions which were used to build it.

first_valid_index()

Return index label of first non-NaN/NULL value.

Returns

Return type

scalar

floordiv(other, **kwargs)

Perform element-wise integer division (self // other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

abstract free()

Trigger a cleanup of this object.

abstract classmethod from_arrow(at, data_cls)

Build QueryCompiler from Arrow Table.

Parameters
  • at (Arrow Table) – The Arrow Table to convert from.

  • data_cls (type) – BasePandasFrame class (or its descendant) to convert to.

Returns

QueryCompiler containing data from the pandas DataFrame.

Return type

BaseQueryCompiler

abstract classmethod from_pandas(df, data_cls)

Build QueryCompiler from pandas DataFrame.

Parameters
  • df (pandas.DataFrame) – The pandas DataFrame to convert from.

  • data_cls (type) – BasePandasFrame class (or its descendant) to convert to.

Returns

QueryCompiler containing data from the pandas DataFrame.

Return type

BaseQueryCompiler

ge(other, **kwargs)

Perform element-wise greater than or equal comparison (self >= other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

get_axis(axis)

Return index labels of the specified axis.

Parameters

axis ({0, 1}) – Axis to return labels on. 0 is for index, when 1 is for columns.

Returns

Return type

pandas.Index

get_dummies(columns, **kwargs)

Convert categorical variables to dummy variables for certain columns.

Parameters
  • columns (label or list of such) – Columns to convert.

  • prefix (str or list of such) –

  • prefix_sep (str) –

  • dummy_na (bool) –

  • drop_first (bool) –

  • dtype (dtype) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with categorical variables converted to dummy.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.get_dummies for more information about parameters and output format.

get_index_name(axis=0)

Get index name of specified axis.

Parameters

axis ({0, 1}, default: 0) – Axis to get index name on.

Returns

Index name, None for MultiIndex.

Return type

hashable

get_index_names(axis=0)

Get index names of specified axis.

Parameters

axis ({0, 1}, default: 0) – Axis to get index names on.

Returns

Index names.

Return type

list

getitem_array(key)

Mask QueryCompiler with key.

Parameters

key (BaseQueryCompiler, np.ndarray or list of column labels) – Boolean mask represented by QueryCompiler or np.ndarray of the same shape as self, or enumerable of columns to pick.

Returns

New masked QueryCompiler.

Return type

BaseQueryCompiler

getitem_column_array(key, numeric=False)

Get column data for target labels.

Parameters
  • key (list-like) – Target labels by which to retrieve data.

  • numeric (bool, default: False) – Whether or not the key passed in represents the numeric index or the named index.

Returns

New QueryCompiler that contains specified columns.

Return type

BaseQueryCompiler

getitem_row_array(key)

Get row data for target indices.

Parameters

key (list-like) – Numeric indices of the rows to pick.

Returns

New QueryCompiler that contains specified rows.

Return type

BaseQueryCompiler

groupby_agg(by, is_multi_by, axis, agg_func, agg_args, agg_kwargs, groupby_kwargs, drop=False)

Group QueryCompiler data and apply passed aggregation function.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • is_multi_by (bool) – If by is a QueryCompiler or list of such indicates whether it’s grouping on multiple columns/rows.

  • axis ({0, 1}) – Axis to group and apply aggregation function along. 0 is for index, when 1 is for columns.

  • agg_func (dict or callable(DataFrameGroupBy) -> DataFrame) – Function to apply to the GroupBy object.

  • agg_args (dict) – Positional arguments to pass to the agg_func.

  • agg_kwargs (dict) – Key arguments to pass to the agg_func.

  • groupby_kwargs (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

QueryCompiler containing the result of groupby aggregation.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.GroupBy.aggregate for more information about parameters and output format.

groupby_all(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and check whether all elements are True for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the boolean of whether all elements are True for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.all for more information about parameters and output format.

groupby_any(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and check whether any element is True for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the boolean of whether there is any element which is True for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.any for more information about parameters and output format.

groupby_count(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and count non-null values for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the number of non-null values for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.count for more information about parameters and output format.

groupby_max(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and get the maximum value for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the maximum value for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.max for more information about parameters and output format.

groupby_min(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and get the minimum value for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the minimum value for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.min for more information about parameters and output format.

groupby_prod(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and compute product for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the product for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.prod for more information about parameters and output format.

groupby_size(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and get the number of elements for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the number of elements for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.size for more information about parameters and output format.

groupby_sum(by, axis, groupby_args, map_args, reduce_args=None, numeric_only=True, drop=False)

Group QueryCompiler data and compute sum for every group.

Parameters
  • by (BaseQueryCompiler, column or index label, Grouper or list of such) – Object that determine groups.

  • axis ({0, 1}) – Axis to group and apply reduction function along. 0 is for index, when 1 is for columns.

  • groupby_args (dict) – GroupBy parameters as expected by modin.pandas.DataFrame.groupby signature.

  • map_args (dict) – Keyword arguments to pass to the reduction function. If GroupBy is implemented via MapReduce approach, this argument is passed at the map phase only.

  • reduce_args (dict, optional) – If GroupBy is implemented with MapReduce approach, specifies arguments to pass to the reduction function at the reduce phase, has no effect otherwise.

  • numeric_only (bool, default: True) – Whether or not to drop non-numeric columns before executing GroupBy.

  • drop (bool, default: False) – If by is a QueryCompiler indicates whether or not by-data came from the self.

Returns

  • BaseQueryCompiler – QueryCompiler containing the result of groupby reduction built by the following rules:

    • Labels on the opposit of specified axis are preserved.

    • If groupby_args[“as_index”] is True then labels on the specified axis are the group names, otherwise labels would be default: 0, 1 … n.

    • If groupby_args[“as_index”] is False, then first N columns/rows of the frame contain group names, where N is the columns/rows to group on.

    • Each element of QueryCompiler is the sum for the corresponding group and column/row.

  • .. warningmap_args and reduce_args parameters are deprecated. They’re leaked here from PandasQueryCompiler.groupby_*, pandas backend implements groupby via MapReduce approach, but for other backends these parameters make no sense, and so they’ll be removed in the future.

Notes

Please refer to modin.pandas.GroupBy.sum for more information about parameters and output format.

gt(other, **kwargs)

Perform element-wise greater than comparison (self > other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

has_multiindex(axis=0)

Check if specified axis is indexed by MultiIndex.

Parameters

axis ({0, 1}, default: 0) – The axis to check (0 - index, 1 - columns).

Returns

True if index at specified axis is MultiIndex and False otherwise.

Return type

bool

idxmax(**kwargs)

Get position of the first occurence of the maximum for each row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains position of the maximum element for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.idxmax for more information about parameters and output format.

idxmin(**kwargs)

Get position of the first occurence of the minimum for each row or column.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains position of the minimum element for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.idxmin for more information about parameters and output format.

insert(loc, column, value)

Insert new column.

Parameters
  • loc (int) – Insertion position.

  • column (label) – Label of the new column.

  • value (One-column BaseQueryCompiler, 1D array or scalar) – Data to fill new column with.

Returns

QueryCompiler with new column inserted.

Return type

BaseQueryCompiler

insert_item(axis, loc, value, how='inner', replace=False)

Insert rows/columns defined by value at the specified position.

If frames are not aligned along specified axis, perform frames alignment first.

Parameters
  • axis ({0, 1}) – Axis to insert along. 0 means insert rows, when 1 means insert columns.

  • loc (int) – Position to insert value.

  • value (BaseQueryCompiler) – Rows/columns to insert.

  • how ({"inner", "outer", "left", "right"}, default: "inner") – Type of join that will be used if frames are not aligned.

  • replace (bool, default: False) – Whether to insert item after column/row at loc-th position or to replace it by value.

Returns

New QueryCompiler with inserted values.

Return type

BaseQueryCompiler

invert()

Apply bitwise invertion for each element of the QueryCompiler.

Returns

New QueryCompiler containing bitwise invertion for each value.

Return type

BaseQueryCompiler

is_monotonic_decreasing()

Return boolean if values in the object are monotonicly decreasing.

Returns

Return type

bool

is_monotonic_increasing()

Return boolean if values in the object are monotonicly increasing.

Returns

Return type

bool

is_series_like()

Check whether this QueryCompiler can represent modin.pandas.Series object.

Returns

Return True if QueryCompiler has a single column or row, False otherwise.

Return type

bool

isin(**kwargs)

Check for each element of self whether it’s contained in passed values.

Parameters
  • values (list-like, modin.pandas.Series, modin.pandas.DataFrame or dict) – Values to check elements of self in.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Boolean mask for self of whether an element at the corresponding position is contained in values.

Return type

BaseQueryCompiler

isna()

Check for each element of self whether it’s NaN.

Returns

Boolean mask for self of whether an element at the corresponding position is NaN.

Return type

BaseQueryCompiler

join(right, **kwargs)

Join columns of another QueryCompiler.

Parameters
  • right (BaseQueryCompiler) – QueryCompiler of the right frame to join with.

  • on (label or list of such) –

  • how ({"left", "right", "outer", "inner"}) –

  • lsuffix (str) –

  • rsuffix (str) –

  • sort (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler that contains result of the join.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.join for more information about parameters and output format.

kurt(axis, level=None, numeric_only=None, skipna=True, **kwargs)

Get the unbiased kurtosis for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the unbiased kurtosis for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.kurt for more information about parameters and output format.

last_valid_index()

Return index label of last non-NaN/NULL value.

Returns

Return type

scalar

le(other, **kwargs)

Perform element-wise less than or equal comparison (self <= other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

lt(other, **kwargs)

Perform element-wise less than comparison (self < other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

mad(axis, skipna, level=None)

Get the mean absolute deviation for each column or row.

Parameters
  • axis ({0, 1}) –

  • skipna (bool) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the mean absolute deviation for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.mad for more information about parameters and output format.

max(**kwargs)

Get the maximum value for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the maximum value for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.max for more information about parameters and output format.

mean(**kwargs)

Get the mean value for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the mean value for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.mean for more information about parameters and output format.

median(**kwargs)

Get the median value for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the median value for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.median for more information about parameters and output format.

melt(*args, **kwargs)

Unpivot QueryCompiler data from wide to long format.

Parameters
  • id_vars (list of labels, optional) –

  • value_vars (list of labels, optional) –

  • var_name (label) –

  • value_name (label) –

  • col_level (int or label) –

  • ignore_index (bool) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with unpivoted data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.melt for more information about parameters and output format.

memory_usage(**kwargs)

Return the memory usage of each column in bytes.

Parameters
  • index (bool) –

  • deep (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of self, where each row contains the memory usage for the corresponding column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.memory_usage for more information about parameters and output format.

merge(right, **kwargs)

Merge QueryCompiler objects using a database-style join.

Parameters
  • right (BaseQueryCompiler) – QueryCompiler of the right frame to merge with.

  • how ({"left", "right", "outer", "inner", "cross"}) –

  • on (label or list of such) –

  • left_on (label or list of such) –

  • right_on (label or list of such) –

  • left_index (bool) –

  • right_index (bool) –

  • sort (bool) –

  • suffixes (list-like) –

  • copy (bool) –

  • indicator (bool or str) –

  • validate (str) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler that contains result of the merge.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.merge for more information about parameters and output format.

min(**kwargs)

Get the minimum value for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the minimum value for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.min for more information about parameters and output format.

mod(other, **kwargs)

Perform element-wise modulo (self % other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

mode(**kwargs)

Get the modes for every column or row.

Parameters
  • axis ({0, 1}) –

  • numeric_only (bool) –

  • dropna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with modes calculated alogn given axis.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.mode for more information about parameters and output format.

mul(other, **kwargs)

Perform element-wise multiplication (self * other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

ne(other, **kwargs)

Perform element-wise not equal comparison (self != other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

negative(**kwargs)

Change the sign for every value of self.

Parameters

**kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Return type

BaseQueryCompiler

Notes

Be aware, that all QueryCompiler values have to be numeric.

nlargest(n=5, columns=None, keep='first')

Return the first n rows ordered by columns in descending order.

Parameters
  • n (int, default: 5) –

  • columns (list of labels, optional) – Column labels to order by. (note: this parameter can be omitted only for a single-column query compilers representing Series object, otherwise columns has to be specified).

  • keep ({"first", "last", "all"}, default: "first") –

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.nlargest for more information about parameters and output format.

notna()

Check for each element of self whether it’s existing (non-missing) value.

Returns

Boolean mask for self of whether an element at the corresponding position is not NaN.

Return type

BaseQueryCompiler

nsmallest(n=5, columns=None, keep='first')

Return the first n rows ordered by columns in ascending order.

Parameters
  • n (int, default: 5) –

  • columns (list of labels, optional) – Column labels to order by. (note: this parameter can be omitted only for a single-column query compilers representing Series object, otherwise columns has to be specified).

  • keep ({"first", "last", "all"}, default: "first") –

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.nsmallest for more information about parameters and output format.

nunique(**kwargs)

Get the number of unique values for each column or row.

Parameters
  • axis ({0, 1}) –

  • dropna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the number of unique values for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.nunique for more information about parameters and output format.

pivot(index, columns, values)

Produce pivot table based on column values.

Parameters
  • index (label or list of such, pandas.Index, optional) –

  • columns (label or list of such) –

  • values (label or list of such, optional) –

Returns

New QueryCompiler containing pivot table.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.pivot for more information about parameters and output format.

pivot_table(index, values, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)

Create a spreadsheet-style pivot table from underlying data.

Parameters
  • index (label, pandas.Grouper, array or list of such) –

  • values (label, optional) –

  • columns (column, pandas.Grouper, array or list of such) –

  • aggfunc (callable(pandas.Series) -> scalar, dict of list of such) –

  • fill_value (scalar, optional) –

  • margins (bool) –

  • dropna (bool) –

  • margins_name (str) –

  • observed (bool) –

  • sort (bool) –

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.pivot_table for more information about parameters and output format.

pow(other, **kwargs)

Perform element-wise exponential power (self ** other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

prod(**kwargs)

Get the production for each column or row.

Parameters
  • axis ({0, 1}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the production for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.prod for more information about parameters and output format.

prod_min_count(**kwargs)

Get the production for each column or row.

Parameters
  • axis ({0, 1}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the production for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.prod for more information about parameters and output format.

quantile_for_list_of_values(**kwargs)

Get the value at the given quantile for each column or row.

Parameters
  • q (list-like) –

  • axis ({0, 1}) –

  • numeric_only (bool) –

  • interpolation ({"linear", "lower", "higher", "midpoint", "nearest"}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the value at the given quantile for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.quantile for more information about parameters and output format.

quantile_for_single_value(**kwargs)

Get the value at the given quantile for each column or row.

Parameters
  • q (float) –

  • axis ({0, 1}) –

  • numeric_only (bool) –

  • interpolation ({"linear", "lower", "higher", "midpoint", "nearest"}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the value at the given quantile for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.quantile for more information about parameters and output format.

query(expr, **kwargs)

Query columns of the QueryCompiler with a boolean expression.

Parameters
  • expr (str) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the rows where the boolean expression is satisfied.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.query for more information about parameters and output format.

rank(**kwargs)

Compute numerical rank along the specified axis.

By default, equal values are assigned a rank that is the average of the ranks of those values, this behaviour can be changed via method parameter.

Parameters
  • axis ({0, 1}) –

  • method ({"average", "min", "max", "first", "dense"}) –

  • numeric_only (bool) –

  • na_option ({"keep", "top", "bottom"}) –

  • ascending (bool) –

  • pct (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler of the same shape as self, where each element is the numerical rank of the corresponding value along row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.rank for more information about parameters and output format.

reindex(axis, labels, **kwargs)

Align QueryCompiler data with a new index along specified axis.

Parameters
  • axis ({0, 1}) – Axis to align labels along. 0 is for index, 1 is for columns.

  • labels (list-like) – Index-labels to align with.

  • method ({None, "backfill"/"bfill", "pad"/"ffill", "nearest"}) – Method to use for filling holes in reindexed frame.

  • fill_value (scalar) – Value to use for missing values in the resulted frame.

  • limit (int) –

  • tolerance (int) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with aligned axis.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.reindex for more information about parameters and output format.

repeat(repeats)

Repeat each element of one-column QueryCompiler given number of times.

Parameters

repeats (int or array of ints) – The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty QueryCompiler.

Returns

New QueryCompiler with repeated elements.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.repeat for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

replace(**kwargs)

Replace values given in to_replace by value.

Parameters
  • to_replace (scalar, list-like, regex, modin.pandas.Series, or None) –

  • value (scalar, list-like, regex or dict) –

  • inplace ({False}) – This parameter serves the compatibility purpose. Always has to be False.

  • limit (int or None) –

  • regex (bool or same types as to_replace) –

  • method ({"pad", "ffill", "bfill", None}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with all to_replace values replaced by value.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.replace for more information about parameters and output format.

resample_agg_df(resample_args, func, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and apply passed aggregation function for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • func (str, dict, callable(pandas.Series) -> scalar, or list of such) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.agg for more information about parameters and output format.

resample_agg_ser(resample_args, func, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and apply passed aggregation function in a one-column query compiler for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • func (str, dict, callable(pandas.Series) -> scalar, or list of such) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.agg for more information about parameters and output format.

Warning

This method duplicates logic of resample_agg_df and will be removed soon.

resample_app_df(resample_args, func, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and apply passed aggregation function for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • func (str, dict, callable(pandas.Series) -> scalar, or list of such) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.apply for more information about parameters and output format.

Warning

This method duplicates logic of resample_agg_df and will be removed soon.

resample_app_ser(resample_args, func, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and apply passed aggregation function in a one-column query compiler for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • func (str, dict, callable(pandas.Series) -> scalar, or list of such) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.apply for more information about parameters and output format.

Warning

This method duplicates logic of resample_agg_df and will be removed soon.

resample_asfreq(resample_args, fill_value)

Resample time-series data and get the values at the new frequency.

Group data into intervals by time-series row/column with a specified frequency and get values at the new frequency.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • fill_value (scalar) –

Returns

New QueryCompiler containing values at the specified frequency.

Return type

BaseQueryCompiler

resample_backfill(resample_args, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using back-fill method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.backfill for more information about parameters and output format.

resample_bfill(resample_args, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using back-fill method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.bfill for more information about parameters and output format.

resample_count(resample_args)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute number of non-NA values for each group.

Parameters

resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the number of non-NA values for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.count for more information about parameters and output format.

resample_ffill(resample_args, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using forward-fill method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.ffill for more information about parameters and output format.

resample_fillna(resample_args, method, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using specified method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • method (str) –

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.fillna for more information about parameters and output format.

resample_first(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute first element for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the first element for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.first for more information about parameters and output format.

resample_get_group(resample_args, name, obj)

Resample time-series data and get the specified group.

Group data into intervals by time-series row/column with a specified frequency and get the values of the specified group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • name (object) –

  • obj (modin.pandas.DataFrame, optional) –

Returns

New QueryCompiler containing the values from the specified group.

Return type

BaseQueryCompiler

resample_interpolate(resample_args, method, axis, limit, inplace, limit_direction, limit_area, downcast, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using specified interpolation method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • method (str) –

  • axis ({0, 1}) –

  • limit (int) –

  • inplace ({False}) – This parameter serves the compatibility purpose. Always has to be False.

  • limit_direction ({"forward", "backward", "both"}) –

  • limit_area ({None, "inside", "outside"}) –

  • downcast (str, optional) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.interpolate for more information about parameters and output format.

resample_last(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute last element for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the last element for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.last for more information about parameters and output format.

resample_max(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute maximum value for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the maximum value for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.max for more information about parameters and output format.

resample_mean(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute mean value for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the mean value for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.mean for more information about parameters and output format.

resample_median(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute median value for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the median value for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.median for more information about parameters and output format.

resample_min(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute minimum value for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the minimum value for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.min for more information about parameters and output format.

resample_nearest(resample_args, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using ‘nearest’ method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.nearest for more information about parameters and output format.

resample_nunique(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute number of unique values for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the number of unique values for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.nunique for more information about parameters and output format.

resample_ohlc_df(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute open, high, low and close values for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the labels of columns containing computed values.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.ohlc for more information about parameters and output format.

resample_ohlc_ser(resample_args, _method, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute open, high, low and close values for each group over the specified axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • *args (iterable) – Positional arguments to pass to the aggregation function.

  • **kwargs (dict) – Keyword arguments to pass to the aggregation function.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are a MultiIndex, where first level contains preserved labels of this axis and the second level is the labels of columns containing computed values.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.ohlc for more information about parameters and output format.

resample_pad(resample_args, limit)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and fill missing values in each group independently using ‘pad’ method.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • limit (int) –

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • QueryCompiler contains unsampled data with missing values filled.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.pad for more information about parameters and output format.

resample_pipe(resample_args, func, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency, build equivalent pandas.Resampler object and apply passed function to it.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • func (callable(pandas.Resampler) -> object or tuple(callable, str)) –

  • *args (iterable) – Positional arguments to pass to function.

  • **kwargs (dict) – Keyword arguments to pass to function.

Returns

New QueryCompiler containing the result of passed function.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Resampler.pipe for more information about parameters and output format.

resample_prod(resample_args, _method, min_count, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute product for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • min_count (int) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the product for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.prod for more information about parameters and output format.

resample_quantile(resample_args, q, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute quantile for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • q (float) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the quantile for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.quantile for more information about parameters and output format.

resample_sem(resample_args, ddof=1, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute standart error of the mean for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • ddof (int, default: 1) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the standart error of the mean for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.sem for more information about parameters and output format.

resample_size(resample_args, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute number of elements in a group for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the number of elements in a group for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.size for more information about parameters and output format.

resample_std(resample_args, ddof, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute standart deviation for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • ddof (int) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the standart deviation for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.std for more information about parameters and output format.

resample_sum(resample_args, _method, min_count, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute sum for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • _method (str) –

  • min_count (int) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the sum for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.sum for more information about parameters and output format.

resample_transform(resample_args, arg, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and call passed function on each group. In contrast to resample_app_df apply function to the whole group, instead of a single axis.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • arg (callable(pandas.DataFrame) -> pandas.Series) –

  • *args (iterable) – Positional arguments to pass to function.

  • **kwargs (dict) – Keyword arguments to pass to function.

Returns

New QueryCompiler containing the result of passed function.

Return type

BaseQueryCompiler

resample_var(resample_args, ddof, *args, **kwargs)

Resample time-series data and apply aggregation on it.

Group data into intervals by time-series row/column with a specified frequency and compute variance for each group.

Parameters
  • resample_args (list) – Resample parameters as expected by modin.pandas.DataFrame.resample signature.

  • ddof (int) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the result of resample aggregation built by the following rules:

  • Labels on the specified axis are the group names (time-stamps)

  • Labels on the opposit of specified axis are preserved.

  • Each element of QueryCompiler is the variance for the corresponding group and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.Resampler.var for more information about parameters and output format.

reset_index(**kwargs)

Reset the index, or a level of it.

Parameters
  • drop (bool) – Whether to drop the reset index or insert it at the beginning of the frame.

  • level (int or label, optional) – Level to remove from index. Removes all levels by default.

  • col_level (int or label) – If the columns have multiple levels, determines which level the labels are inserted into.

  • col_fill (label) – If the columns have multiple levels, determines how the other levels are named.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with reset index.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.reset_index for more information about parameters and output format.

rfloordiv(other, **kwargs)

Perform element-wise integer division (other // self).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

rmod(other, **kwargs)

Perform element-wise modulo (other % self).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

rolling_aggregate(rolling_args, func, *args, **kwargs)

Create rolling window and apply specified functions for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • func (str, dict, callable(pandas.Series) -> scalar, or list of such) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the result of passed functions for each window, built by the following rulles:

  • Labels on the specified axis are preserved.

  • Labels on the opposit of specified axis are MultiIndex, where first level contains preserved labels of this axis and the second level has the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding window and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.aggregate for more information about parameters and output format.

rolling_apply(rolling_args, func, raw=False, engine=None, engine_kwargs=None, args=None, kwargs=None)

Create rolling window and apply specified function for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • func (callable(pandas.Series) -> scalar) –

  • raw (bool, default: False) –

  • engine (None, default: None) – This parameters serves the compatibility purpose. Always has to be None.

  • engine_kwargs (None, default: None) – This parameters serves the compatibility purpose. Always has to be None.

  • args (tuple, optional) –

  • kwargs (dict, optional) –

Returns

New QueryCompiler containing the result of passed function for each window, built by the following rulles:

  • Labels on the specified axis are preserved.

  • Labels on the opposit of specified axis are MultiIndex, where first level contains preserved labels of this axis and the second level has the function names.

  • Each element of QueryCompiler is the result of corresponding function for the corresponding window and column/row.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.apply for more information about parameters and output format.

Warning

This method duplicates logic of rolling_aggregate and will be removed soon.

rolling_corr(rolling_args, other=None, pairwise=None, *args, **kwargs)

Create rolling window and compute correlation for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • other (modin.pandas.Series, modin.pandas.DataFrame, list-like, optional) –

  • pairwise (bool, optional) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing correlation for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the correlation for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.corr for more information about parameters and output format.

rolling_count(rolling_args)

Create rolling window and compute number of non-NA values for each window.

Parameters

rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

Returns

New QueryCompiler containing number of non-NA values for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the number of non-NA values for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.count for more information about parameters and output format.

rolling_cov(rolling_args, other=None, pairwise=None, ddof=1, **kwargs)

Create rolling window and compute covariance for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • other (modin.pandas.Series, modin.pandas.DataFrame, list-like, optional) –

  • pairwise (bool, optional) –

  • ddof (int, default: 1) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing covariance for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the covariance for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.cov for more information about parameters and output format.

rolling_kurt(rolling_args, **kwargs)

Create rolling window and compute unbiased kurtosis for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • **kwargs (dict) –

Returns

New QueryCompiler containing unbiased kurtosis for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the unbiased kurtosis for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.kurt for more information about parameters and output format.

rolling_max(rolling_args, *args, **kwargs)

Create rolling window and compute maximum value for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing maximum value for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the maximum value for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.max for more information about parameters and output format.

rolling_mean(rolling_args, *args, **kwargs)

Create rolling window and compute mean value for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing mean value for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the mean value for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.mean for more information about parameters and output format.

rolling_median(rolling_args, **kwargs)

Create rolling window and compute median value for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • **kwargs (dict) –

Returns

New QueryCompiler containing median value for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the median value for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.median for more information about parameters and output format.

rolling_min(rolling_args, *args, **kwargs)

Create rolling window and compute minimum value for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing minimum value for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the minimum value for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.min for more information about parameters and output format.

rolling_quantile(rolling_args, quantile, interpolation='linear', **kwargs)

Create rolling window and compute quantile for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • quantile (float) –

  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}, default: 'linear') –

  • **kwargs (dict) –

Returns

New QueryCompiler containing quantile for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the quantile for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.quantile for more information about parameters and output format.

rolling_skew(rolling_args, **kwargs)

Create rolling window and compute unbiased skewness for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • **kwargs (dict) –

Returns

New QueryCompiler containing unbiased skewness for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the unbiased skewness for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.skew for more information about parameters and output format.

rolling_std(rolling_args, ddof=1, *args, **kwargs)

Create rolling window and compute standart deviation for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • ddof (int, default: 1) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing standart deviation for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the standart deviation for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.std for more information about parameters and output format.

rolling_sum(rolling_args, *args, **kwargs)

Create rolling window and compute sum for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing sum for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the sum for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.sum for more information about parameters and output format.

rolling_var(rolling_args, ddof=1, *args, **kwargs)

Create rolling window and compute variance for each window.

Parameters
  • rolling_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • ddof (int, default: 1) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing variance for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the variance for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.var for more information about parameters and output format.

round(**kwargs)

Round every numeric value up to specified number of decimals.

Parameters
  • decimals (int or list-like) – Number of decimals to round each column to.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with rounded values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.round for more information about parameters and output format.

rpow(other, **kwargs)

Perform element-wise exponential power (other ** self).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

rsub(other, **kwargs)

Perform element-wise substraction (other - self).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

rtruediv(other, **kwargs)

Perform element-wise division (other / self).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

searchsorted(**kwargs)

Find positions in a sorted self where value should be inserted to maintain order.

Parameters
  • value (list-like) –

  • side ({"left", "right"}) –

  • sorter (list-like, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler which contains indices to insert.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.searchsorted for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

sem(**kwargs)

Get the standard deviation of the mean for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • ddof (int) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the standard deviation of the mean for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sem for more information about parameters and output format.

series_update(other, **kwargs)

Update values of self using values of other at the corresponding indices.

Parameters
  • other (BaseQueryCompiler) – One-column query compiler with updated values.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with updated values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.update for more information about parameters and output format.

series_view(**kwargs)

Reinterpret underlying data with new dtype.

Parameters
  • dtype (dtype) – Data type to reinterpret underlying data with.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler of the same data in memory, with reinterpreted values.

Return type

BaseQueryCompiler

Notes

  • Be aware, that if this method do fallback to pandas, then newly created QueryCompiler will be the copy of the original data.

  • Please refer to modin.pandas.Series.view for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

set_index_from_columns(keys: List[Hashable], drop: bool = True, append: bool = False)

Create new row labels from a list of columns.

Parameters
  • keys (list of hashable) – The list of column names that will become the new index.

  • drop (bool, default: True) – Whether or not to drop the columns provided in the keys argument.

  • append (bool, default: True) – Whether or not to add the columns in keys as new levels appended to the existing index.

Returns

A new QueryCompiler with updated index.

Return type

BaseQueryCompiler

set_index_name(name, axis=0)

Set index name for the specified axis.

Parameters
  • name (hashable) – New index name.

  • axis ({0, 1}, default: 0) – Axis to set name along.

set_index_names(names, axis=0)

Set index names for the specified axis.

Parameters
  • names (list) – New index names.

  • axis ({0, 1}, default: 0) – Axis to set names along.

setitem(axis, key, value)

Set the row/column defined by key to the value provided.

Parameters
  • axis ({0, 1}) – Axis to set value along. 0 means set row, 1 means set column.

  • key (label) – Row/column label to set value in.

  • value (BaseQueryCompiler, list-like or scalar) – Define new row/column value.

Returns

New QueryCompiler with updated key value.

Return type

BaseQueryCompiler

skew(**kwargs)

Get the unbiased skew for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the unbiased skew for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.skew for more information about parameters and output format.

sort_columns_by_row_values(rows, ascending=True, **kwargs)

Reorder the columns based on the lexicographic order of the given rows.

Parameters
  • rows (label or list of labels) – The row or rows to sort by.

  • ascending (bool, default: True) – Sort in ascending order (True) or descending order (False).

  • kind ({"quicksort", "mergesort", "heapsort"}) –

  • na_position ({"first", "last"}) –

  • ignore_index (bool) –

  • key (callable(pandas.Index) -> pandas.Index, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler that contains result of the sort.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sort_values for more information about parameters and output format.

sort_index(**kwargs)

Sort data by index or column labels.

Parameters
  • axis ({0, 1}) –

  • level (int, label or list of such) –

  • ascending (bool) –

  • inplace (bool) –

  • kind ({"quicksort", "mergesort", "heapsort"}) –

  • na_position ({"first", "last"}) –

  • sort_remaining (bool) –

  • ignore_index (bool) –

  • key (callable(pandas.Index) -> pandas.Index, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler containing the data sorted by columns or indices.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sort_index for more information about parameters and output format.

sort_rows_by_column_values(columns, ascending=True, **kwargs)

Reorder the rows based on the lexicographic order of the given columns.

Parameters
  • columns (label or list of labels) – The column or columns to sort by.

  • ascending (bool, default: True) – Sort in ascending order (True) or descending order (False).

  • kind ({"quicksort", "mergesort", "heapsort"}) –

  • na_position ({"first", "last"}) –

  • ignore_index (bool) –

  • key (callable(pandas.Index) -> pandas.Index, optional) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler that contains result of the sort.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sort_values for more information about parameters and output format.

stack(level, dropna)

Stack the prescribed level(s) from columns to index.

Parameters
  • level (int or label) –

  • dropna (bool) –

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.stack for more information about parameters and output format.

std(**kwargs)

Get the standard deviation for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • ddof (int) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the standard deviation for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.std for more information about parameters and output format.

str_capitalize()

Apply “capitalize” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “capitalize” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.capitalize for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_center(width, fillchar=' ')

Apply “center” function to each string value in QueryCompiler.

Parameters
  • width (int) –

  • fillchar (str, default: ' ') –

Returns

New QueryCompiler containing the result of execution of the “center” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.center for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_contains(pat, case=True, flags=0, na=nan, regex=True)

Apply “contains” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • case (bool, default: True) –

  • flags (int, default: 0) –

  • na (object, default: np.NaN) –

  • regex (bool, default: True) –

Returns

New QueryCompiler containing the result of execution of the “contains” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.contains for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_count(pat, flags=0, **kwargs)

Apply “count” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • flags (int, default: 0) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the result of execution of the “count” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.count for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_endswith(pat, na=nan)

Apply “endswith” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • na (object, default: np.NaN) –

Returns

New QueryCompiler containing the result of execution of the “endswith” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.endswith for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_find(sub, start=0, end=None)

Apply “find” function to each string value in QueryCompiler.

Parameters
  • sub (str) –

  • start (int, default: 0) –

  • end (int, optional) –

Returns

New QueryCompiler containing the result of execution of the “find” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.find for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_findall(pat, flags=0, **kwargs)

Apply “findall” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • flags (int, default: 0) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the result of execution of the “findall” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.findall for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_get(i)

Apply “get” function to each string value in QueryCompiler.

Parameters

i (int) –

Returns

New QueryCompiler containing the result of execution of the “get” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.get for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_index(sub, start=0, end=None)

Apply “index” function to each string value in QueryCompiler.

Parameters
  • sub (str) –

  • start (int, default: 0) –

  • end (int, optional) –

Returns

New QueryCompiler containing the result of execution of the “index” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.index for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isalnum()

Apply “isalnum” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isalnum” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isalnum for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isalpha()

Apply “isalpha” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isalpha” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isalpha for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isdecimal()

Apply “isdecimal” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isdecimal” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isdecimal for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isdigit()

Apply “isdigit” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isdigit” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isdigit for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_islower()

Apply “islower” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “islower” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.islower for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isnumeric()

Apply “isnumeric” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isnumeric” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isnumeric for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isspace()

Apply “isspace” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isspace” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isspace for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_istitle()

Apply “istitle” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “istitle” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.istitle for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_isupper()

Apply “isupper” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “isupper” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.isupper for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_join(sep)

Apply “join” function to each string value in QueryCompiler.

Parameters

sep (str) –

Returns

New QueryCompiler containing the result of execution of the “join” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.join for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_len()

Apply “len” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “len” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.len for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_ljust(width, fillchar=' ')

Apply “ljust” function to each string value in QueryCompiler.

Parameters
  • width (int) –

  • fillchar (str, default: ' ') –

Returns

New QueryCompiler containing the result of execution of the “ljust” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.ljust for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_lower()

Apply “lower” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “lower” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.lower for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_lstrip(to_strip=None)

Apply “lstrip” function to each string value in QueryCompiler.

Parameters

to_strip (str, optional) –

Returns

New QueryCompiler containing the result of execution of the “lstrip” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.lstrip for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_match(pat, case=True, flags=0, na=nan)

Apply “match” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • case (bool, default: True) –

  • flags (int, default: 0) –

  • na (object, default: np.NaN) –

Returns

New QueryCompiler containing the result of execution of the “match” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.match for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_normalize(form)

Apply “normalize” function to each string value in QueryCompiler.

Parameters

form ({'NFC', 'NFKC', 'NFD', 'NFKD'}) –

Returns

New QueryCompiler containing the result of execution of the “normalize” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.normalize for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_pad(width, side='left', fillchar=' ')

Apply “pad” function to each string value in QueryCompiler.

Parameters
  • width (int) –

  • side ({'left', 'right', 'both'}, default: 'left') –

  • fillchar (str, default: ' ') –

Returns

New QueryCompiler containing the result of execution of the “pad” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.pad for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_partition(sep=' ', expand=True)

Apply “partition” function to each string value in QueryCompiler.

Parameters
  • sep (str, default: ' ') –

  • expand (bool, default: True) –

Returns

New QueryCompiler containing the result of execution of the “partition” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.partition for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_repeat(repeats)

Apply “repeat” function to each string value in QueryCompiler.

Parameters

repeats (int) –

Returns

New QueryCompiler containing the result of execution of the “repeat” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.repeat for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_replace(pat, repl, n=- 1, case=None, flags=0, regex=True)

Apply “replace” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • repl (str or callable) –

  • n (int, default: -1) –

  • case (bool, optional) –

  • flags (int, default: 0) –

  • regex (bool, default: True) –

Returns

New QueryCompiler containing the result of execution of the “replace” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.replace for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rfind(sub, start=0, end=None)

Apply “rfind” function to each string value in QueryCompiler.

Parameters
  • sub (str) –

  • start (int, default: 0) –

  • end (int, optional) –

Returns

New QueryCompiler containing the result of execution of the “rfind” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rfind for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rindex(sub, start=0, end=None)

Apply “rindex” function to each string value in QueryCompiler.

Parameters
  • sub (str) –

  • start (int, default: 0) –

  • end (int, optional) –

Returns

New QueryCompiler containing the result of execution of the “rindex” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rindex for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rjust(width, fillchar=' ')

Apply “rjust” function to each string value in QueryCompiler.

Parameters
  • width (int) –

  • fillchar (str, default: ' ') –

Returns

New QueryCompiler containing the result of execution of the “rjust” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rjust for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rpartition(sep=' ', expand=True)

Apply “rpartition” function to each string value in QueryCompiler.

Parameters
  • sep (str, default: ' ') –

  • expand (bool, default: True) –

Returns

New QueryCompiler containing the result of execution of the “rpartition” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rpartition for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rsplit(pat=None, n=- 1, expand=False)

Apply “rsplit” function to each string value in QueryCompiler.

Parameters
  • pat (str, optional) –

  • n (int, default: -1) –

  • expand (bool, default: False) –

Returns

New QueryCompiler containing the result of execution of the “rsplit” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rsplit for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_rstrip(to_strip=None)

Apply “rstrip” function to each string value in QueryCompiler.

Parameters

to_strip (str, optional) –

Returns

New QueryCompiler containing the result of execution of the “rstrip” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.rstrip for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_slice(start=None, stop=None, step=None)

Apply “slice” function to each string value in QueryCompiler.

Parameters
  • start (int, optional) –

  • stop (int, optional) –

  • step (int, optional) –

Returns

New QueryCompiler containing the result of execution of the “slice” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.slice for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_slice_replace(start=None, stop=None, repl=None)

Apply “slice_replace” function to each string value in QueryCompiler.

Parameters
  • start (int, optional) –

  • stop (int, optional) –

  • repl (str or callable, optional) –

Returns

New QueryCompiler containing the result of execution of the “slice_replace” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.slice_replace for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_split(pat=None, n=- 1, expand=False)

Apply “split” function to each string value in QueryCompiler.

Parameters
  • pat (str, optional) –

  • n (int, default: -1) –

  • expand (bool, default: False) –

Returns

New QueryCompiler containing the result of execution of the “split” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.split for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_startswith(pat, na=nan)

Apply “startswith” function to each string value in QueryCompiler.

Parameters
  • pat (str) –

  • na (object, default: np.NaN) –

Returns

New QueryCompiler containing the result of execution of the “startswith” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.startswith for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_strip(to_strip=None)

Apply “strip” function to each string value in QueryCompiler.

Parameters

to_strip (str, optional) –

Returns

New QueryCompiler containing the result of execution of the “strip” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.strip for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_swapcase()

Apply “swapcase” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “swapcase” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.swapcase for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_title()

Apply “title” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “title” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.title for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_translate(table)

Apply “translate” function to each string value in QueryCompiler.

Parameters

table (dict) –

Returns

New QueryCompiler containing the result of execution of the “translate” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.translate for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_upper()

Apply “upper” function to each string value in QueryCompiler.

Returns

New QueryCompiler containing the result of execution of the “upper” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.upper for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_wrap(width, **kwargs)

Apply “wrap” function to each string value in QueryCompiler.

Parameters
  • width (int) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing the result of execution of the “wrap” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.wrap for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

str_zfill(width)

Apply “zfill” function to each string value in QueryCompiler.

Parameters

width (int) –

Returns

New QueryCompiler containing the result of execution of the “zfill” function against each string element.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.str.zfill for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

sub(other, **kwargs)

Perform element-wise substraction (self - other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

sum(**kwargs)

Get the sum for each column or row.

Parameters
  • axis ({0, 1}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the sum for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sum for more information about parameters and output format.

sum_min_count(**kwargs)

Get the sum for each column or row.

Parameters
  • axis ({0, 1}) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the sum for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.sum for more information about parameters and output format.

to_datetime(*args, **kwargs)

Convert columns of the QueryCompiler to the datetime dtype.

Parameters
  • *args (iterable) –

  • **kwargs (dict) –

Returns

QueryCompiler with all columns converted to datetime dtype.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.to_datetime for more information about parameters and output format.

to_numeric(*args, **kwargs)

Convert underlying data to numeric dtype.

Parameters
  • errors ({"ignore", "raise", "coerce"}) –

  • downcast ({"integer", "signed", "unsigned", "float", None}) –

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with converted to numeric values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.to_numeric for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

to_numpy(**kwargs)

Convert underlying query compilers data to NumPy array.

Parameters
  • dtype (dtype) – The dtype of the resulted array.

  • copy (bool) – Whether to ensure that the returned value is not a view on another array.

  • na_value (object) – The value to replace missing values with.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

The QueryCompiler converted to NumPy array.

Return type

np.ndarray

abstract to_pandas()

Convert underlying query compilers data to pandas.DataFrame.

Returns

The QueryCompiler converted to pandas.

Return type

pandas.DataFrame

transpose(*args, **kwargs)

Transpose this QueryCompiler.

Parameters
  • copy (bool) – Whether to copy the data after transposing.

  • *args (iterable) – Serves the compatibility purpose. Does not affect the result.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Transposed new QueryCompiler.

Return type

BaseQueryCompiler

truediv(other, **kwargs)

Perform element-wise division (self / other).

If axes are not equal, perform frames alignment first.

Parameters
  • other (BaseQueryCompiler, scalar or array-like) – Other operand of the binary operation.

  • broadcast (bool, default: False) – If other is a one-column query compiler, indicates whether it is a Series or not. Frames and Series have to be processed differently, however we can’t distinguish them at the query compiler level, so this parameter is a hint that is passed from a high-level API.

  • level (int or label) – In case of MultiIndex match index values on the passed level.

  • axis ({{0, 1}}) – Axis to match indices along for 1D other (list or QueryCompiler that represents Series). 0 is for index, when 1 is for columns.

  • fill_value (float or None) – Value to fill missing elements during frame alignment.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

Result of binary operation.

Return type

BaseQueryCompiler

unique(**kwargs)

Get unique values of self.

Parameters

**kwargs (dict) – Serves compatibility purpose. Does not affect the result.

Returns

New QueryCompiler with unique values.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.unique for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

unstack(level, fill_value)

Pivot a level of the (necessarily hierarchical) index labels.

Parameters
  • level (int or label) –

  • fill_value (scalar or dict) –

Returns

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.unstack for more information about parameters and output format.

value_counts(**kwargs)

Count unique values of one-column self.

Parameters
  • normalize (bool) –

  • sort (bool) –

  • ascending (bool) –

  • bins (int, optional) –

  • dropna (bool) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler which index labels is a unique elements of self and each row contains the number of times corresponding value was met in the self.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Series.value_counts for more information about parameters and output format.

Warning

This method is supported only by one-column query compilers.

var(**kwargs)

Get the variance for each column or row.

Parameters
  • axis ({{0, 1}}) –

  • level (None, default: None) – Serves the compatibility purpose. Always has to be None.

  • numeric_only (bool, optional) –

  • skipna (bool, default: True) –

  • ddof (int) –

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

One-column QueryCompiler with index labels of the specified axis, where each row contains the variance for the corresponding row or column.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.var for more information about parameters and output format.

view(index=None, columns=None)

Mask QueryCompiler with passed keys.

Parameters
  • index (list of ints, optional) – Positional indices of rows to grab.

  • columns (list of ints, optional) – Positional indices of columns to grab.

Returns

New masked QueryCompiler.

Return type

BaseQueryCompiler

where(cond, other, **kwargs)

Update values of self using values from other at positions where cond is False.

Parameters
  • cond (BaseQueryCompiler) – Boolean mask. True - keep the self value, False - replace by other value.

  • other (BaseQueryCompiler or pandas.Series) – Object to grab replacement values from.

  • axis ({0, 1}) – Axis to align frames along if axes of self, cond and other are not equal. 0 is for index, when 1 is for columns.

  • level (int or label, optional) – Level of MultiIndex to align frames along if axes of self, cond and other are not equal. Currently level parameter is not implemented, so only None value is acceptable.

  • **kwargs (dict) – Serves the compatibility purpose. Does not affect the result.

Returns

QueryCompiler with updated data.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.DataFrame.where for more information about parameters and output format.

window_mean(window_args, *args, **kwargs)

Create window of the specified type and compute mean for each window.

Parameters
  • window_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing mean for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the mean for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.mean for more information about parameters and output format.

window_std(window_args, ddof=1, *args, **kwargs)

Create window of the specified type and compute standart deviation for each window.

Parameters
  • window_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • ddof (int, default: 1) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing standart deviation for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the standart deviation for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.std for more information about parameters and output format.

window_sum(window_args, *args, **kwargs)

Create window of the specified type and compute sum for each window.

Parameters
  • window_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing sum for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the sum for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.sum for more information about parameters and output format.

window_var(window_args, ddof=1, *args, **kwargs)

Create window of the specified type and compute variance for each window.

Parameters
  • window_args (list) – Rolling windows arguments with the same signature as modin.pandas.DataFrame.rolling.

  • ddof (int, default: 1) –

  • *args (iterable) –

  • **kwargs (dict) –

Returns

New QueryCompiler containing variance for each window, built by the following rulles:

  • Output QueryCompiler has the same shape and axes labels as the source.

  • Each element is the variance for the corresponding window.

Return type

BaseQueryCompiler

Notes

Please refer to modin.pandas.Rolling.var for more information about parameters and output format.

write_items(row_numeric_index, col_numeric_index, broadcasted_items)

Update QueryCompiler elements at the specified positions by passed values.

In contrast to setitem this method allows to do 2D assignments.

Parameters
  • row_numeric_index (list of ints) – Row positions to write value.

  • col_numeric_index (list of ints) – Column positions to write value.

  • broadcasted_items (2D-array) – Values to write. Have to be same size as defined by row_numeric_index and col_numeric_index.

Returns

New QueryCompiler with updated values.

Return type

BaseQueryCompiler