pd.DataFrame supported APIs

The following table lists both implemented and not implemented methods. If you have need of an operation that is listed as not implemented, feel free to open an issue on the GitHub repository, or give a thumbs up to already created issues. Contributions are also welcome!

The following table is structured as follows: The first column contains the method name. The second column contains link to a description of corresponding pandas method. The third column is a flag for whether or not there is an implementation in Modin for the method in the left column. Y stands for yes, N stands for no, P stands for partial (meaning some parameters may not be supported yet), and D stands for default to pandas.

Note

Currently third column reflects implementation status for Ray and Dask engines. By default, support for a method in the HDK engine could be treated as D unless Notes column contains additional information. Similarly, by default Notes contains information about Ray and Dask engines unless Hdk is explicitly mentioned.

DataFrame method	pandas Doc link	Implemented? (Y/N/P/D)	Notes for Current implementation
T	T	Y
abs	abs	Y
add	add	Y	Ray and Dask: Shuffles data in operations between DataFrames. Hdk: P, support binary operations on scalars and projections of the same frame, otherwise D
add_prefix	add_prefix	Y
add_suffix	add_suffix	Y
agg / aggregate	agg / aggregate	P	Dictionary func parameter defaults to pandas Numpy operations default to pandas
align	align	D
all	all	Y
any	any	Y
append	append	Y	Hdk: Y but sort and ignore_index parameters ignored
apply	apply	Y	See agg
applymap	applymap	Y
asfreq	asfreq	D
asof	asof	Y
assign	assign	Y
astype	astype	Y	Hdk: P, int``<-> ``float supported
at	at	Y
at_time	at_time	Y
axes	axes	Y
between_time	between_time	Y
bfill	bfill	Y
bool	bool	Y
boxplot	boxplot	D
clip	clip	Y
combine	combine	Y
combine_first	combine_first	Y
compare	compare	Y
copy	copy	Y
corr	corr	P	Correlation floating point precision may slightly differ from pandas. For now pearson method is available only. For other methods and for numeric_only defaults to pandas.
corrwith	corrwith	D
count	count	Y	Hdk: P, only default params supported, otherwise D
cov	cov	P	Covariance floating point precision may slightly differ from pandas. For numeric_only defaults to pandas.
cummax	cummax	Y
cummin	cummin	Y
cumprod	cumprod	Y
cumsum	cumsum	Y
describe	describe	Y
diff	diff	Y
div	div	Y	See add
divide	divide	Y	See add
dot	dot	Y
drop	drop	Y	Hdk: P since row drop unsupported
droplevel	droplevel	Y
drop_duplicates	drop_duplicates	D
dropna	dropna	Y	Hdk: P since thresh and axis params unsupported
dtypes	dtypes	Y	Hdk: Y
duplicated	duplicated	Y
empty	empty	Y
eq	eq	Y	See add
equals	equals	Y	Requires shuffle, can be further optimized
eval	eval	Y
ewm	ewm	D
expanding	expanding	D
explode	explode	Y
ffill	ffill	Y
fillna	fillna	P	value parameter of type DataFrame defaults to pandas. Hdk: P, params limit, downcast and method unsupported. Also only axis = 0 supported for now
filter	filter	Y
first	first	Y
first_valid_index	first_valid_index	Y
floordiv	floordiv	Y	See add
from_dict	from_dict	D
from_records	from_records	D
ge	ge	Y	See add
get	get	Y
groupby	groupby	Y	Not yet optimized for all operations. Hdk: P. count, sum, size, mean, nunique, std, skew supported, otherwise D
gt	gt	Y	See add
head	head	Y
hist	hist	D
iat	iat	Y
idxmax	idxmax	Y
idxmin	idxmin	Y
iloc	iloc	Y	Hdk: P, read access fully supported, write access: no row and 2D assignments support
infer_objects	infer_objects	Y	Hdk: D
info	info	Y
insert	insert	Y
interpolate	interpolate	D
isetitem	isetitem	D
isin	isin	Y
isna	isna	Y
isnull	isnull	Y
items	items	Y
iteritems	iteritems	P	Modin does not parallelize iteration in Python
iterrows	iterrows	P	Modin does not parallelize iteration in Python
itertuples	itertuples	P	Modin does not parallelize iteration in Python
join	join	P	When on is set to right or outer or when validate is given defaults to pandas
keys	keys	Y
kurt	kurt	Y
kurtosis	kurtosis	Y
last	last	Y
last_valid_index	last_valid_index	Y
le	le	Y	See add
loc	loc	P	We do not support: boolean array, callable. Hdk: P, read access fully supported, write access: no row and 2D assignments support
lookup	lookup	D
lt	lt	Y	See add
mad	mad	Y
mask	mask	D
max	max	Y	Hdk: P, only default params supported, otherwise D
mean	mean	P	Modin defaults to pandas if given the level param. Hdk: P. D for level, axis, skipna and numeric_only params
median	median	P	Modin defaults to pandas if given the level param.
melt	melt	Y
memory_usage	memory_usage	Y
merge	merge	P	Implemented the following cases: left_index=True and right_index=True, how=left and how=inner for all values of parameters except left_index=True and right_index=False or left_index=False and right_index=True. Defaults to pandas otherwise. Hdk: P, only non-index joins for how=left and how=inner with explicit on are supported
min	min	Y	Hdk: P, only default params supported, otherwise D
mod	mod	Y	See add
mode	mode	Y
mul	mul	Y	See add
multiply	multiply	Y	See add
ndim	ndim	Y
ne	ne	Y	See add
nlargest	nlargest	Y
notna	notna	Y
notnull	notnull	Y
nsmallest	nsmallest	Y
nunique	nunique	Y	Hdk: P, no support for axis!=0 and dropna=False
pct_change	pct_change	D
pipe	pipe	Y
pivot	pivot	Y
pivot_table	pivot_table	Y
plot	plot	D
pop	pop	Y
pow	pow	Y	See add; Hdk: D
prod	prod	Y
product	product	Y
quantile	quantile	Y
query	query	P	Local variables not yet supported
radd	radd	Y	See add
rank	rank	Y
rdiv	rdiv	Y	See add; Hdk: D
reindex	reindex	Y	Shuffles data
reindex_like	reindex_like	D
rename	rename	Y
rename_axis	rename_axis	Y
reorder_levels	reorder_levels	Y
replace	replace	Y
resample	resample	Y
reset_index	reset_index	P	Hdk: P. D for level parameter Ray and Dask: D when names or allow_duplicates is non-default
rfloordiv	rfloordiv	Y	See add; Hdk: D
rmod	rmod	Y	See add; Hdk: D
rmul	rmul	Y	See add
rolling	rolling	Y
round	round	Y
rpow	rpow	Y	See add; Hdk: D
rsub	rsub	Y	See add; Hdk: D
rtruediv	rtruediv	Y	See add; Hdk: D
sample	sample	Y
select_dtypes	select_dtypes	Y
sem	sem	P	Modin defaults to pandas if given the level param.
set_axis	set_axis	Y
set_index	set_index	Y
shape	shape	Y	Hdk: Y
shift	shift	Y
size	size	Y
skew	skew	P	Modin defaults to pandas if given the level param
slice_shift	slice_shift	Y
sort_index	sort_index	Y
sort_values	sort_values	Y	Shuffles data. Order of indexes that have the same sort key is not guaranteed to be the same across sorts; Hdk: Y
sparse	sparse	N
squeeze	squeeze	Y
stack	stack	Y
std	std	P	Modin defaults to pandas if given the level param.
style	style	D
sub	sub	Y	See add
subtract	subtract	Y	See add; Hdk: D
sum	sum	Y	Hdk: P, only default params supported, otherwise D
swapaxes	swapaxes	Y
swaplevel	swaplevel	Y
tail	tail	Y
take	take	Y
to_clipboard	to_clipboard	D
to_csv	to_csv	Y
to_dict	to_dict	D
to_excel	to_excel	D
to_feather	to_feather	D
to_gbq	to_gbq	D
to_hdf	to_hdf	D
to_html	to_html	D
to_json	to_json	D
to_latex	to_latex	D
to_orc	to_orc	D
to_parquet	to_parquet	P	Ray/Dask/Unidist: Parallel implementation only if path parameter is a string; does not end with “.gz”, “.bz2”, “.zip”, or “.xz”; and compression parameter is not None or “snappy”. In these cases, the path parameter specifies a directory where one file is written per row partition of the Modin dataframe.
to_period	to_period	D
to_pickle	to_pickle	D	Experimental implementation: to_pickle_distributed
to_records	to_records	D
to_sql	to_sql	Y
to_stata	to_stata	D
to_string	to_string	D
to_timestamp	to_timestamp	D
to_xarray	to_xarray	D
transform	transform	Y
transpose	transpose	Y
truediv	truediv	Y	See add
truncate	truncate	Y
tshift	tshift	Y
tz_convert	tz_convert	Y
tz_localize	tz_localize	Y
unstack	unstack	Y
update	update	Y
values	values	Y
value_counts	value_counts	D
var	var	P	Modin defaults to pandas if given the level param.
where	where	Y