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

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Merged
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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 -transform -transpose -values -xs"
-k"-_set_axis_name -_xs -describe -droplevel -groupby -interpolate -pct_change -pipe -reindex -reindex_axis -resample -sample -to_json -transform -transpose -values -xs"

if [ $? -ne "0" ]; then
RET=1
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68 changes: 35 additions & 33 deletions pandas/core/generic.py
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Expand Up @@ -2498,11 +2498,15 @@ def to_xarray(self):
a Dataset for a DataFrame
a DataArray for higher dims
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Do you mind replacing this to the standard format? Only the type in the first line, and a description in the next. For example:

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_csv : Write out to a csv file.
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See Also is with a capital A.

Personally I don't like "recommending" to_csv here, as besides not being a great format, it does not support multidimensional data. I think to_parquet and to_hdf seem more appropriate to me.


Examples
--------
>>> df = pd.DataFrame({'A' : [1, 1, 2],
'B' : ['foo', 'bar', 'foo'],
'C' : np.arange(4.,7)})
... 'B' : ['foo', 'bar', 'foo'],
... 'C' : np.arange(4.,7)})
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I think using arange makes the code more complicated for no reason. Using [4., 5., 6.] is simpler and clearer I think.

Also, it'd be good to have a meaningful example. The code in the example is difficult to follow as there is no way to know that the object column is B, more than looking at the example. If we get something few samples with animals with name, num_legs, speed, it's obvious which is the float, which the int, and which the str/object.

>>> df
A B C
0 1 foo 4.0
Expand All @@ -2520,9 +2524,9 @@ def to_xarray(self):
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'])
... 'B' : ['foo', 'bar', 'foo'],
... 'C' : np.arange(4.,7)}
... ).set_index(['B','A'])
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I don't see the need to repeat the previous DataFrame, we can just have df_multiindex = df.set_index(['B', 'A'])

>>> df
C
B A
Expand All @@ -2539,35 +2543,33 @@ def to_xarray(self):
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]]])
>>> index = pd.MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'],
... ['one', 'two']],
... labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
... names=['first', 'second'])

>>> s = pd.Series(np.arange(8), index=index)
>>> s
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I find this too complicated for what we need to show. To have a Series with a multiindex with a datetime level, we can have something like:

import pandas as pd

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

I haven't used much xarray myself, and not sure what makes sense to show here. May be:

  • Series.to_xarray()
  • DataFrame.to_xarray()
  • DataFrame(with multiindex including datetime).to_xarray()

If that makes sense, I think with the first example, we can have df.to_xarray() and df['max_speed'].to_xarray(), and then a example like the one I wrote.

@jreback does this make sense?

Sorry for requesting the changes @Moisan, but my I find like the current version gives the idea that we're trying to show something more complex than what we are actually showing.

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No problem, I'm happy to make the examples more relevant :).

first second
bar one 0
two 1
baz one 2
two 3
foo one 4
two 5
qux one 6
two 7
dtype: int64

>>> s.to_xarray()
<xarray.DataArray (first: 4, second: 2)>
array([[0, 1],
[2, 3],
[4, 5],
[6, 7]])
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'
* first (first) object 'bar' 'baz' 'foo' 'qux'
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would be better to have a datetime index for 1 level

* second (second) object 'one' 'two'

Notes
-----
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