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DOC: Fix DataFrame.to_xarray doctests and allow the CI to run it. #22673

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Sep 19, 2018
Merged

DOC: Fix DataFrame.to_xarray doctests and allow the CI to run it. #22673

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670e768
DOC: Fix DataFrame.to_xarray doctests and allow the CI to run it.
Sep 12, 2018
a7ecbb2
Remove extended summary from to_xarray docstring
Sep 12, 2018
ce5098a
Refactor the examples and the See Also section of to_xarray docstring
Sep 13, 2018
08561d2
Simplify to_xarray doctests
Sep 14, 2018
93edaca
Merge branch 'master' into docstring_to_xarray
Moisan Sep 18, 2018
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2 changes: 1 addition & 1 deletion ci/doctests.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ if [ "$DOCTEST" ]; then
fi

pytest --doctest-modules -v pandas/core/generic.py \
-k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -resample -sample -to_json -to_xarray -transpose -values -xs"
-k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -resample -sample -to_json -transpose -values -xs"

if [ $? -ne "0" ]; then
RET=1
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114 changes: 52 additions & 62 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2500,80 +2500,70 @@ def to_xarray(self):

Returns
-------
a DataArray for a Series
a Dataset for a DataFrame
a DataArray for higher dims
xarray.DataArray or xarray.Dataset
Data in the pandas structure converted to Dataset if the object is
a DataFrame, or a DataArray if the object is a Series.

See Also
--------
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Examples
--------
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)})
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
A B C
0 1 foo 4.0
1 1 bar 5.0
2 2 foo 6.0
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4

>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 3)
Dimensions: (index: 4)
Coordinates:
* index (index) int64 0 1 2
* index (index) int64 0 1 2 3
Data variables:
A (index) int64 1 1 2
B (index) object 'foo' 'bar' 'foo'
C (index) float64 4.0 5.0 6.0

>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)}
).set_index(['B','A'])
>>> df
C
B A
foo 1 4.0
bar 1 5.0
foo 2 6.0

>>> df.to_xarray()
name (index) object 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 389.0 24.0 80.5 nan
num_legs (index) int64 2 2 4 4

>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. , 24. , 80.5, nan])
Coordinates:
* index (index) int64 0 1 2 3

>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot', 'falcon',
... 'parrot'],
... 'speed': [350, 18, 361, 15]}).set_index(['date',
... 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15

>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions: (A: 2, B: 2)
Dimensions: (animal: 2, date: 2)
Coordinates:
* B (B) object 'bar' 'foo'
* A (A) int64 1 2
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
C (B, A) float64 5.0 nan 4.0 6.0

>>> p = pd.Panel(np.arange(24).reshape(4,3,2),
items=list('ABCD'),
major_axis=pd.date_range('20130101', periods=3),
minor_axis=['first', 'second'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second

>>> p.to_xarray()
<xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)>
array([[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]],
[[18, 19],
[20, 21],
[22, 23]]])
Coordinates:
* items (items) object 'A' 'B' 'C' 'D'
* major_axis (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 # noqa
* minor_axis (minor_axis) object 'first' 'second'
speed (date, animal) int64 350 18 361 15

Notes
-----
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