`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 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.

DataFrame method	pandas Doc link	Implemented? (Y/N/P/D)	Notes for Current implementation
`T`	T	Y
`abs`	abs	Y
`add`	add	Y	Shuffles data in operations between DataFrames
`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
`apply`	apply	Y	See `agg`
`applymap`	applymap	Y
`as_blocks`	as_blocks	D	Becomes a non-parallel object
`as_matrix`	as_matrix	D	Becomes a non-parallel object
`asfreq`	asfreq	D
`asof`	asof	Y
`assign`	assign	Y
`astype`	astype	Y
`at`	at	Y
`at_time`	at_time	Y
`axes`	axes	Y
`between_time`	between_time	Y
`bfill`	bfill	Y
`blocks`	blocks	D
`bool`	bool	Y
`boxplot`	boxplot	D
`clip`	clip	Y
`clip_lower`	clip_lower	Y
`clip_upper`	clip_upper	Y
`combine`	combine	Y
`combine_first`	combine_first	Y
`compare`	compare	Y
`copy`	copy	Y
`corr`	corr	Y	Correlation floating point precision may slightly differ from pandas. For now pearson method is available only. For other methods defaults to pandas.
`corrwith`	corrwith	D
`count`	count	Y
`cov`	cov	Y	Covariance floating point precision may slightly differ from 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
`droplevel`	droplevel	Y
`drop_duplicates`	drop_duplicates	D
`dropna`	dropna	Y
`dtypes`	dtypes	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	D
`ffill`	ffill	Y
`fillna`	fillna	P	`value` parameter of type DataFrame defaults to pandas
`filter`	filter	Y
`first`	first	Y
`first_valid_index`	first_valid_index	Y
`floordiv`	floordiv	Y	See `add`
`from_dict`	from_dict	D
`from_items`	from_items	Y
`from_records`	from_records	D
`ftypes`	ftypes	Y
`ge`	ge	Y	See `add`
`get`	get	Y
`groupby`	groupby	Y	Not yet optimized for all operations
`gt`	gt	Y	See `add`
`head`	head	Y
`hist`	hist	D
`iat`	iat	Y
`idxmax`	idxmax	Y
`idxmin`	idxmin	Y
`iloc`	iloc	Y
`infer_objects`	infer_objects	D
`info`	info	Y
`insert`	insert	Y
`interpolate`	interpolate	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` it 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	Y	We do not support: boolean array, callable
`lookup`	lookup	D
`lt`	lt	Y	See `add`
`mad`	mad	Y
`mask`	mask	D
`max`	max	Y
`mean`	mean	Y
`median`	median	Y
`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.
`min`	min	Y
`mod`	mod	Y
`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
`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`
`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`
`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	Y
`rfloordiv`	rfloordiv	Y	See `add`
`rmod`	rmod	Y	See `add`
`rmul`	rmul	Y	See `add`
`rolling`	rolling	Y
`round`	round	Y
`rpow`	rpow	Y	See `add`
`rsub`	rsub	Y	See `add`
`rtruediv`	rtruediv	Y	See `add`
`sample`	sample	Y
`select_dtypes`	select_dtypes	Y
`sem`	sem	Y
`set_axis`	set_axis	Y
`set_index`	set_index	Y
`shape`	shape	Y
`shift`	shift	Y
`size`	size	Y
`skew`	skew	Y
`slice_shift`	slice_shift	Y
`sort_index`	sort_index	Y
`sort_values`	sort_values	Y	Shuffles data
`sparse`	sparse	N
`squeeze`	squeeze	Y
`stack`	stack	Y
`std`	std	Y
`style`	style	D
`sub`	sub	Y	See `add`
`subtract`	subtract	Y	See `add`
`sum`	sum	Y
`swapaxes`	swapaxes	Y
`swaplevel`	swaplevel	Y
`tail`	tail	Y
`take`	take	Y
`to_clipboard`	to_clipboard	D
`to_csv`	to_csv	Y
`to_dense`	to_dense	D
`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_msgpack`	to_msgpack	D
`to_parquet`	to_parquet	D
`to_period`	to_period	D
`to_pickle`	to_pickle	D
`to_records`	to_records	D
`to_sparse`	to_sparse	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	Y
`where`	where	Y

pd.DataFrame supported APIs¶

`pd.DataFrame` supported APIs¶