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Working with pandas

One of the most important features of xarray is the ability to convert to and from :py:mod:`pandas` objects to interact with the rest of the PyData ecosystem. For example, for plotting labeled data, we highly recommend using the visualization built in to pandas itself or provided by the pandas aware libraries such as Seaborn.

.. ipython:: python
   :suppress:

    import numpy as np
    import pandas as pd
    import xarray as xr
    np.random.seed(123456)

Hierarchical and tidy data

Tabular data is easiest to work with when it meets the criteria for tidy data:

  • Each column holds a different variable.
  • Each rows holds a different observation.

In this "tidy data" format, we can represent any :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` in terms of :py:class:`pandas.DataFrame` and :py:class:`pandas.Series`, respectively (and vice-versa). The representation works by flattening non-coordinates to 1D, and turning the tensor product of coordinate indexes into a :py:class:`pandas.MultiIndex`.

Dataset and DataFrame

To convert any dataset to a DataFrame in tidy form, use the :py:meth:`Dataset.to_dataframe() <xarray.Dataset.to_dataframe>` method:

.. ipython:: python

    ds = xr.Dataset({'foo': (('x', 'y'), np.random.randn(2, 3))},
                     coords={'x': [10, 20], 'y': ['a', 'b', 'c'],
                             'along_x': ('x', np.random.randn(2)),
                             'scalar': 123})
    ds
    df = ds.to_dataframe()
    df

We see that each variable and coordinate in the Dataset is now a column in the DataFrame, with the exception of indexes which are in the index. To convert the DataFrame to any other convenient representation, use DataFrame methods like :py:meth:`~pandas.DataFrame.reset_index`, :py:meth:`~pandas.DataFrame.stack` and :py:meth:`~pandas.DataFrame.unstack`.

To create a Dataset from a DataFrame, use the :py:meth:`~xarray.Dataset.from_dataframe` class method:

.. ipython:: python

    xr.Dataset.from_dataframe(df)

Notice that that dimensions of variables in the Dataset have now expanded after the round-trip conversion to a DataFrame. This is because every object in a DataFrame must have the same indices, so we need to broadcast the data of each array to the full size of the new MultiIndex.

Likewise, all the coordinates (other than indexes) ended up as variables, because pandas does not distinguish non-index coordinates.

DataArray and Series

DataArray objects have a complementary representation in terms of a :py:class:`pandas.Series`. Using a Series preserves the Dataset to DataArray relationship, because DataFrames are dict-like containers of Series. The methods are very similar to those for working with DataFrames:

.. ipython:: python

    s = ds['foo'].to_series()
    s

    xr.DataArray.from_series(s)

Both the from_series and from_dataframe methods use reindexing, so they work even if not the hierarchical index is not a full tensor product:

.. ipython:: python

    s[::2]
    xr.DataArray.from_series(s[::2])

Multi-dimensional data

:py:meth:`DataArray.to_pandas() <xarray.DataArray.to_pandas>` is a shortcut that lets you convert a DataArray directly into a pandas object with the same dimensionality (i.e., a 1D array is converted to a :py:class:`~pandas.Series`, 2D to :py:class:`~pandas.DataFrame` and 3D to :py:class:`~pandas.Panel`):

.. ipython:: python

    arr = xr.DataArray(np.random.randn(2, 3),
                       coords=[('x', [10, 20]), ('y', ['a', 'b', 'c'])])
    df = arr.to_pandas()
    df

To perform the inverse operation of converting any pandas objects into a data array with the same shape, simply use the DataArray constructor:

.. ipython:: python

    xr.DataArray(df)

xarray objects do not yet support hierarchical indexes, so if your data has a hierarchical index, you will either need to unstack it first or use the :py:meth:`~xarray.DataArray.from_series` or :py:meth:`~xarray.Dataset.from_dataframe` constructors described above.