pandas Utilities Supported

If you run import modin.pandas as pd, the following operations are available from pd.<op>, e.g. pd.concat. If you do not see an operation that pandas enables and would like to request it, feel free to open an issue_. Make sure you tell us your primary use-case so we can make it happen faster!

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.

+---------------------------+---------------------------------+----------------------------------------------------+ | Utility method | Modin Implementation? (Y/N/P/D) | Notes for Current implementation | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.concat_ | Y | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.eval_ | Y | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.unique_ | Y | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.value_counts | Y | The indices order of resulting object may differ | | | | from pandas. | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.cut_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.to_numeric_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.factorize_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.from_dummies_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.qcut_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.match | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.to_datetime_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.get_dummies_ | Y | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.date_range_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.bdate_range_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.to_timedelta_ | D | | +---------------------------+---------------------------------+----------------------------------------------------+ | pd.options | Y | | +---------------------------+---------------------------------+----------------------------------------------------+

Other objects & structures

This list is a list of objects not currently distributed by Modin. All of these objects are compatible with the distributed components of Modin. If you are interested in contributing a distributed version of any of these objects, feel free to open a pull request_.

• Panel
• Index
• MultiIndex
• CategoricalIndex
• DatetimeIndex
• Timedelta
• Timestamp
• NaT
• PeriodIndex
• Categorical
• Interval
• UInt8Dtype
• UInt16Dtype
• UInt32Dtype
• UInt64Dtype
• SparseDtype
• Int8Dtype
• Int16Dtype
• Int32Dtype
• Int64Dtype
• CategoricalDtype
• DatetimeTZDtype
• IntervalDtype
• PeriodDtype
• RangeIndex
• TimedeltaIndex
• IntervalIndex
• IndexSlice
• TimeGrouper
• Grouper
• array
• Period
• DateOffset
• ExcelWriter
• SparseArray

.. _open an issue: https://github.com/modin-project/modin/issues .. _pull request: https://github.com/modin-project/modin/pulls .. _pd.concat: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html#pandas.concat .. _pd.eval: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.eval.html#pandas.eval .. _pd.unique: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.unique.html#pandas.unique .. _pd.cut: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html#pandas.cut .. _pd.to_numeric: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html#pandas.to_numeric .. _pd.factorize: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html#pandas.factorize .. _pd.from_dummies: https://pandas.pydata.org/docs/reference/api/pandas.from_dummies.html#pandas-from-dummies .. _pd.qcut: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html#pandas.qcut .. _pd.to_datetime: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html#pandas.to_datetime .. _pd.get_dummies: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html#pandas.get_dummies .. _pd.date_range: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.date_range.html#pandas.date_range .. _pd.bdate_range: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.bdate_range.html#pandas.bdate_range .. _pd.to_timedelta: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_timedelta.html#pandas.to_timedelta