General utilities supported APIs¶

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 Snowpark for the method in the left column.

Note

Y stands for yes, i.e., supports distributed implementation, N stands for no and API simply errors out, P stands for partial (meaning some parameters may not be supported yet), and D stands for defaults to single node pandas execution via UDF/Sproc.

Data manipulations

Method	Snowpark implemented? (Y/N/P/D)	Missing parameters	Notes for current implementation
`concat`	P	`levels` is not supported, `copy` is ignored
`crosstab`	P		`N` if `aggfunc` is not one of “count”, “mean”, “min”, “max”, or “sum”, or margins is True, normalize is “all” or True, and values is passed.
`cut`	P	`retbins`, `labels`	`N` if retbins=True``or ``labels!=False
`factorize`	N
`from_dummies`	N
`get_dummies`	P	`sparse` is ignored	`Y` if params `dummy_na`, `drop_first` and `dtype` are default, otherwise `N`
`lreshape`	N
`melt`	P	`col_level`, `ignore_index`	`N` if df.columns is a MultiIndex
`merge`	P	`validate`	`N` if param `validate` is given
`merge_asof`	P	`suffixes`, `tolerance`	`N` if param `direction` is `nearest`
`merge_ordered`	N
`pivot`	P		See `pivot_table`
`pivot_table`	P	`observed`, `margins`, `sort`	`N` if `index`, `columns`, or `values` is not str; or MultiIndex; or any `argfunc` is not “count”, “mean”, “min”, “max”, or “sum”
`qcut`	P		`N` if `labels!=False` or `retbins=True`.
`read_pickle`	Y		Uses native pandas for reading.
`read_csv`	P		Reads both local and staged file(s) into a Snowpark pandas DataFrame. Note, the order of rows in the may differ from the order of rows in the original file(s) if using staged csvs. Local files are parsed with native pandas and thus support most of the parameters supported by pandas itself. The `usecols` and `names` parameter are applied after creating a temp table in snowflake. Previously staged files will use the Snowflake `COPY FROM` parser and schema inference. If you need to use staged files often, it is recommended that you upload these as parquet files to improve performance. You can force the use of the Snowflake parser with `engine=snowflake`
`read_excel`	Y		Uses native pandas to read excel files, using the engine specified by the pandas. You will need to separately install a supported excel reader such as openpyxl. Please refer to the native pandas read excel documentation for more details.
`read_json`	P	`orient`, `typ`, `dtype`, `convert_axes`, `lines`, `convert_dates`, `date_unit`, `keep_default_dates`, `encoding_errors`, `nrows`, and `chunksize` will raise an error. `precise_float`, `engine`, `dtype_backend`, and `storage_options` are ignored.	`P`: - if ndjson files are passed - Supported parameters are `compression` and `encoding`
`read_html`	Y		Uses native pandas for reading.
`read_xml`	Y		Uses native pandas for reading.
`read_parquet`	P	`use_nullable_dtypes`, `filesystem`, and `filters` will raise an error if used. `engine`, `storage_options`, `dtype_backend`, and `**kwargs` are ignored.	Supported parameter(s) are: `columns`
`read_snowflake`	Y		Reading from tables as well as SELECT SQL Queries supported, but ordering is not guaranteed for SQL Queries that contain ORDER BY clauses. More complex queries, including CTEs and CTEs with anonymous stored procedures are also supported. Obtaining results from stored procedures is also supported via CALL queries.
`read_sas`	Y		Uses native pandas to read sas files.
`read_table`	N
`to_pandas`	Y
`to_snowflake`	Y	`**kwargs` are currently ignored
`to_snowpark`	Y		Convert the Snowpark pandas DataFrame or Series to a Snowpark DataFrame. Once converted to a Snowpark DataFrame, no ordering information will be preserved.
`unique`	Y
`wide_to_long`	N

Top-level dealing with missing data

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`isna`	Y
`isnull`	Y
`notna`	Y
`notnull`	Y

Top-level dealing with numeric data

Method	Snowpark implemented? (Y/N/P/D)	Missing parameters	Notes for current implementation
`to_numeric`	P	`downcast` is ignored	`N` if `error == "ignore"`

Top-level dealing with datetime-like data

Method	Snowpark implemented? (Y/N/P/D)	Missing parameters	Notes for current implementation
`bdate_range`	P		`N` for custom frequencies
`date_range`	P		`N` for custom frequencies
`infer_freq`	N
`period_range`	N
`timedelta_range`	N
`to_datetime`	P	`cache` is ignored	`N`: - if `format` is None or not supported in Snowflake - or if params `exact`, `infer_datetime_format` is given - or `origin == "julian"` - or `arg` is DataFrame and data type is not int - or `arg` is Series and data type is string
`to_timedelta`	P	`errors`	`N` if `errors` is given or converting from string type

Top-level dealing with Interval data

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`interval_range`	N

Top-level evaluation

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`eval`	N

Datetime formats

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`tseries.api.guess_datetime_format`	N

Hashing

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`util.hash_array`	N
`util.hash_pandas_object`	N

Importing from other DataFrame libraries

Method	Snowpark implemented? (Y/N/P/D)	Notes for current implementation
`api.interchange.from_dataframe`	N