# Working with pandas¶

One of the most important features of xarray is the ability to convert to and
from `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.

## 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 `Dataset`

and
`DataArray`

in terms of `pandas.DataFrame`

and
`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 `pandas.MultiIndex`

.

### Dataset and DataFrame¶

To convert any dataset to a `DataFrame`

in tidy form, use the
`Dataset.to_dataframe()`

method:

```
In [1]: 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})
...:
In [2]: ds
Out[2]:
<xarray.Dataset>
Dimensions: (x: 2, y: 3)
Coordinates:
* y (y) |S1 'a' 'b' 'c'
* x (x) int64 10 20
scalar int64 123
along_x (x) float64 0.1192 -1.044
Data variables:
foo (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732
In [3]: df = ds.to_dataframe()
In [4]: df
Out[4]:
foo scalar along_x
x y
10 a 0.469112 123 0.119209
b -0.282863 123 0.119209
c -1.509059 123 0.119209
20 a -1.135632 123 -1.044236
b 1.212112 123 -1.044236
c -0.173215 123 -1.044236
```

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 `reset_index()`

,
`stack()`

and `unstack()`

.

To create a `Dataset`

from a `DataFrame`

, use the
`from_dataframe()`

class method or the equivalent
`pandas.DataFrame.to_xarray`

method (pandas
v0.18 or later):

```
In [5]: xr.Dataset.from_dataframe(df)
Out[5]:
<xarray.Dataset>
Dimensions: (x: 2, y: 3)
Coordinates:
* x (x) int64 10 20
* y (y) object 'a' 'b' 'c'
Data variables:
foo (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732
scalar (x, y) int64 123 123 123 123 123 123
along_x (x, y) float64 0.1192 0.1192 0.1192 -1.044 -1.044 -1.044
```

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
`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:

```
In [6]: s = ds['foo'].to_series()
In [7]: s
Out[7]:
x y
10 a 0.469112
b -0.282863
c -1.509059
20 a -1.135632
b 1.212112
c -0.173215
Name: foo, dtype: float64
# or equivalently, with Series.to_xarray()
In [8]: xr.DataArray.from_series(s)
Out[8]:
<xarray.DataArray 'foo' (x: 2, y: 3)>
array([[ 0.469, -0.283, -1.509],
[-1.136, 1.212, -0.173]])
Coordinates:
* x (x) int64 10 20
* y (y) object 'a' 'b' 'c'
```

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:

```
In [9]: s[::2]
Out[9]:
x y
10 a 0.469112
c -1.509059
20 b 1.212112
Name: foo, dtype: float64
In [10]: s[::2].to_xarray()
Out[10]:
<xarray.DataArray 'foo' (x: 2, y: 3)>
array([[ 0.469, nan, -1.509],
[ nan, 1.212, nan]])
Coordinates:
* x (x) int64 10 20
* y (y) object 'a' 'b' 'c'
```

## Multi-dimensional data¶

Tidy data is great, but it sometimes you want to preserve dimensions instead of
automatically stacking them into a `MultiIndex`

.

`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 `Series`

,
2D to `DataFrame`

and 3D to `Panel`

):

```
In [11]: arr = xr.DataArray(np.random.randn(2, 3),
....: coords=[('x', [10, 20]), ('y', ['a', 'b', 'c'])])
....:
In [12]: df = arr.to_pandas()
In [13]: df
Out[13]:
y a b c
x
10 -0.861849 -2.104569 -0.494929
20 1.071804 0.721555 -0.706771
```

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

constructor:

```
In [14]: xr.DataArray(df)
Out[14]:
<xarray.DataArray (x: 2, y: 3)>
array([[-0.862, -2.105, -0.495],
[ 1.072, 0.722, -0.707]])
Coordinates:
* x (x) int64 10 20
* y (y) object 'a' 'b' 'c'
```

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:

```
In [15]: index = pd.MultiIndex.from_arrays([['a', 'a', 'b'], [0, 1, 2]],
....: names=['one', 'two'])
....:
In [16]: df = pd.DataFrame({'x': 1, 'y': 2}, index=index)
In [17]: ds = xr.Dataset(df)
In [18]: ds
Out[18]:
<xarray.Dataset>
Dimensions: (dim_0: 3)
Coordinates:
* dim_0 (dim_0) object ('a', 0) ('a', 1) ('b', 2)
Data variables:
x (dim_0) int64 1 1 1
y (dim_0) int64 2 2 2
```

However, you will need to set dimension names explicitly, either with the
`dims`

argument on in the `DataArray`

constructor or by calling
`rename`

on the new object.

## Transitioning from pandas.Panel to xarray¶

`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 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
`Dataset`

containing a number of 2-dimensional DataArray objects.

Let’s take a look:

```
In [19]: 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'))
....:
In [20]: panel
Out[20]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: a to b
Major_axis axis: m to o
Minor_axis axis: 2000-01-01 00:00:00 to 2000-01-04 00:00:00
```

As a DataArray:

```
# or equivalently, with Panel.to_xarray()
In [21]: xr.DataArray(panel)
Out[21]:
<xarray.DataArray (dim_0: 2, dim_1: 3, date: 4)>
array([[[ 0.595, 0.138, 0.853, 0.236],
[ 0.146, 0.59 , 0.574, 0.061],
[ 0.59 , 0.245, 0.34 , 0.985]],
[[ 0.92 , 0.038, 0.862, 0.754],
[ 0.405, 0.344, 0.171, 0.395],
[ 0.642, 0.275, 0.462, 0.871]]])
Coordinates:
* dim_0 (dim_0) object 'a' 'b'
* dim_1 (dim_1) object 'm' 'n' 'o'
* date (date) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
```

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:

```
In [22]: xr.Dataset(panel)
Out[22]:
<xarray.Dataset>
Dimensions: (date: 4, dim_0: 3)
Coordinates:
* dim_0 (dim_0) object 'm' 'n' 'o'
* date (date) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
Data variables:
a (dim_0, date) float64 0.5948 0.1376 0.8529 0.2355 0.1462 0.5899 ...
b (dim_0, date) float64 0.9195 0.03777 0.8615 0.7536 0.4052 ...
```

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
`DataFrame`

objects with multiple types will be converted to`dtype=object`

. A`Dataset`

of multiple`DataArray`

objects each with its own dtype will allow original types to be preserved. - 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
`Dataset`

objects 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.