You are viewing the latest unreleased documentation v3.2.dev0. You may prefer a stable version.

iris.pandas

Provide conversion to and from Pandas data structures.

See also: http://pandas.pydata.org/

In this module:

iris.pandas.as_cube(pandas_array, copy=True, calendars=None)[source]

Convert a Pandas array into an Iris cube.

Parameters

DataFrame. (* pandas_array - A Pandas Series or) –

Kwargs:

  • copy - Whether to make a copy of the data.

    Defaults to True.

  • calendars - A dict mapping a dimension to a calendar.

    Required to convert datetime indices/columns.

Example usage:

as_cube(series, calendars={0: cf_units.CALENDAR_360_DAY})
as_cube(data_frame, calendars={1: cf_units.CALENDAR_GREGORIAN})

Note

This function will copy your data by default.

↑ top ↑

iris.pandas.as_data_frame(cube, copy=True)[source]

Convert a 2D cube to a Pandas DataFrame.

Parameters

DataFrame. (* cube - The cube to convert to a Pandas) –

Kwargs:

  • copy - Whether to make a copy of the data.

    Defaults to True. Must be True for masked data and some data types (see notes below).

Note

This function will copy your data by default. If you have a large array that cannot be copied, make sure it is not masked and use copy=False.

Note

Pandas will sometimes make a copy of the array, for example when creating from an int32 array. Iris will detect this and raise an exception if copy=False.

↑ top ↑

iris.pandas.as_series(cube, copy=True)[source]

Convert a 1D cube to a Pandas Series.

Parameters

Series. (* cube - The cube to convert to a Pandas) –

Kwargs:

  • copy - Whether to make a copy of the data.

    Defaults to True. Must be True for masked data.

Note

This function will copy your data by default. If you have a large array that cannot be copied, make sure it is not masked and use copy=False.