One of the most important features of xarray is the ability to convert to and from :pypandas 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.
python
import numpy as np import pandas as pd import xarray as xr np.random.seed(123456)
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~xarray.Dataset and :py~xarray.DataArray in terms of :pypandas.DataFrame and :pypandas.Series, respectively (and vice-versa). The representation works by flattening non-coordinates to 1D, and turning the tensor product of coordinate indexes into a :pypandas.MultiIndex.
To convert any dataset to a DataFrame in tidy form, use the :pyDataset.to_dataframe() <xarray.Dataset.to_dataframe> method:
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~pandas.DataFrame.reset_index, :py~pandas.DataFrame.stack and :py~pandas.DataFrame.unstack.
To create a Dataset from a DataFrame, use the :py~xarray.Dataset.from_dataframe class method or the equivalent :pypandas.DataFrame.to_xarray <DataFrame.to_xarray> method (pandas v0.18 or later):
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 objects have a complementary representation in terms of a :pypandas.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:
python
s = ds['foo'].to_series() s
# or equivalently, with Series.to_xarray() 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:
python
s[::2] s[::2].to_xarray()
Tidy data is great, but it sometimes you want to preserve dimensions instead of automatically stacking them into a MultiIndex.
:pyDataArray.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~pandas.Series, 2D to :py~pandas.DataFrame and 3D to :py~pandas.Panel):
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 :py~xarray.DataArray constructor:
python
xr.DataArray(df)
Both the DataArray and Dataset constructors directly convert pandas objects into xarray objects with the same shape. This means that they preserve all use of multi-indexes:
python
- index = pd.MultiIndex.from_arrays([['a', 'a', 'b'], [0, 1, 2]],
names=['one', 'two'])
df = pd.DataFrame({'x': 1, 'y': 2}, index=index) ds = xr.Dataset(df) ds
However, you will need to set dimension names explicitly, either with the dims argument on in the DataArray constructor or by calling :py~xarray.Dataset.rename on the new object.
:py~pandas.Panel, pandas's data structure for 3D arrays, has always been a second class data structure compared to the Series and DataFrame. To allow pandas developers to focus more on its core functionality built around the DataFrame, pandas plans to eventually deprecate Panel.
xarray has most of Panel's features, a more explicit API (particularly around indexing), and the ability to scale to >3 dimensions with the same interface.
As discussed elsewhere <data structures> in the docs, there are two primary data structures in xarray: DataArray and Dataset. You can imagine a DataArray as a n-dimensional pandas Series (i.e. a single typed array), and a Dataset as the DataFrame equivalent (i.e. a dict of aligned DataArray objects).
So you can represent a Panel, in two ways:
- As a 3-dimensional
DataArray, - Or as a
Datasetcontaining a number of 2-dimensional DataArray objects.
Let's take a look:
python
- panel = pd.Panel(np.random.rand(2, 3, 4), items=list('ab'), major_axis=list('mno'),
minor_axis=pd.date_range(start='2000', periods=4, name='date'))
panel
As a DataArray:
python
# or equivalently, with Panel.to_xarray() xr.DataArray(panel)
As you can see, there are three dimensions (each is also a coordinate). Two of the axes of the panel were unnamed, so have been assigned dim_0 and dim_1 respectively, while the third retains its name date.
As a Dataset:
python
xr.Dataset(panel)
Here, there are two data variables, each representing a DataFrame on panel's items axis, and labelled as such. Each variable is a 2D array of the respective values along the items dimension.
While the xarray docs are relatively complete, a few items stand out for Panel users:
- A DataArray's data is stored as a numpy array, and so can only contain a single type. As a result, a Panel that contains :py
~pandas.DataFrameobjects with multiple types will be converted todtype=object. ADatasetof multipleDataArrayobjects each with its own dtype will allow original types to be preserved. Indexing <indexing>is similar to pandas, but more explicit and leverages xarray's naming of dimensions.- Because of those features, making much higher dimensional data is very practical.
- Variables in
Datasetobjects can use a subset of its dimensions. For example, you can have one dataset with Person x Score x Time, and another with Person x Score. - You can use coordinates are used for both dimensions and for variables which _label the data variables, so you could have a coordinate Age, that labelled the Person dimension of a Dataset of Person x Score x Time.
While xarray may take some getting used to, it's worth it! If anything is unclear, please post an issue on GitHub or StackOverflow, and we'll endeavor to respond to the specific case or improve the general docs.