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DOC: fix warnings in docstrings examples for deprecated functions #24642

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Jan 5, 2019
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DOC: fix warnings in docstrings examples for deprecated functions #24642

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16 changes: 8 additions & 8 deletions pandas/core/arrays/datetimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -766,8 +766,8 @@ def tz_convert(self, tz):
With the `tz` parameter, we can change the DatetimeIndex
to other time zones:

>>> dti = pd.DatetimeIndex(start='2014-08-01 09:00',
... freq='H', periods=3, tz='Europe/Berlin')
>>> dti = pd.date_range(start='2014-08-01 09:00',
... freq='H', periods=3, tz='Europe/Berlin')

>>> dti
DatetimeIndex(['2014-08-01 09:00:00+02:00',
Expand All @@ -784,8 +784,8 @@ def tz_convert(self, tz):
With the ``tz=None``, we can remove the timezone (after converting
to UTC if necessary):

>>> dti = pd.DatetimeIndex(start='2014-08-01 09:00',freq='H',
... periods=3, tz='Europe/Berlin')
>>> dti = pd.date_range(start='2014-08-01 09:00',freq='H',
... periods=3, tz='Europe/Berlin')

>>> dti
DatetimeIndex(['2014-08-01 09:00:00+02:00',
Expand Down Expand Up @@ -1037,8 +1037,8 @@ def normalize(self):

Examples
--------
>>> idx = pd.DatetimeIndex(start='2014-08-01 10:00', freq='H',
... periods=3, tz='Asia/Calcutta')
>>> idx = pd.date_range(start='2014-08-01 10:00', freq='H',
... periods=3, tz='Asia/Calcutta')
>>> idx
DatetimeIndex(['2014-08-01 10:00:00+05:30',
'2014-08-01 11:00:00+05:30',
Expand Down Expand Up @@ -1164,7 +1164,7 @@ def month_name(self, locale=None):

Examples
--------
>>> idx = pd.DatetimeIndex(start='2018-01', freq='M', periods=3)
>>> idx = pd.date_range(start='2018-01', freq='M', periods=3)
>>> idx
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
dtype='datetime64[ns]', freq='M')
Expand Down Expand Up @@ -1200,7 +1200,7 @@ def day_name(self, locale=None):

Examples
--------
>>> idx = pd.DatetimeIndex(start='2018-01-01', freq='D', periods=3)
>>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)
>>> idx
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
dtype='datetime64[ns]', freq='D')
Expand Down
24 changes: 12 additions & 12 deletions pandas/core/generic.py
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Expand Up @@ -4433,8 +4433,8 @@ def _reindex_multi(self, axes, copy, fill_value):
num_legs num_wings
dog 4 0
hawk 2 2
>>> df.reindex_axis(['num_wings', 'num_legs', 'num_heads'],
... axis='columns')
>>> df.reindex(['num_wings', 'num_legs', 'num_heads'],
... axis='columns')
num_wings num_legs num_heads
dog 0 4 NaN
hawk 2 2 NaN
Expand Down Expand Up @@ -7352,19 +7352,19 @@ def clip_upper(self, threshold, axis=None, inplace=False):
4 5
dtype: int64

>>> s.clip_upper(3)
>>> s.clip(upper=3)
0 1
1 2
2 3
3 3
4 3
dtype: int64

>>> t = [5, 4, 3, 2, 1]
>>> t
>>> elemwise_thresholds = [5, 4, 3, 2, 1]
>>> elemwise_thresholds
[5, 4, 3, 2, 1]

>>> s.clip_upper(t)
>>> s.clip(upper=elemwise_thresholds)
0 1
1 2
2 3
Expand Down Expand Up @@ -7428,7 +7428,7 @@ def clip_lower(self, threshold, axis=None, inplace=False):
Series single threshold clipping:

>>> s = pd.Series([5, 6, 7, 8, 9])
>>> s.clip_lower(8)
>>> s.clip(lower=8)
0 8
1 8
2 8
Expand All @@ -7440,7 +7440,7 @@ def clip_lower(self, threshold, axis=None, inplace=False):
should be the same length as the Series.

>>> elemwise_thresholds = [4, 8, 7, 2, 5]
>>> s.clip_lower(elemwise_thresholds)
>>> s.clip(lower=elemwise_thresholds)
0 5
1 8
2 7
Expand All @@ -7457,7 +7457,7 @@ def clip_lower(self, threshold, axis=None, inplace=False):
1 3 4
2 5 6

>>> df.clip_lower(3)
>>> df.clip(lower=3)
A B
0 3 3
1 3 4
Expand All @@ -7466,7 +7466,7 @@ def clip_lower(self, threshold, axis=None, inplace=False):
Or to an array of values. By default, `threshold` should be the same
shape as the DataFrame.

>>> df.clip_lower(np.array([[3, 4], [2, 2], [6, 2]]))
>>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))
A B
0 3 4
1 3 4
Expand All @@ -7476,13 +7476,13 @@ def clip_lower(self, threshold, axis=None, inplace=False):
`threshold` should be the same length as the axis specified by
`axis`.

>>> df.clip_lower([3, 3, 5], axis='index')
>>> df.clip(lower=[3, 3, 5], axis='index')
A B
0 3 3
1 3 4
2 5 6

>>> df.clip_lower([4, 5], axis='columns')
>>> df.clip(lower=[4, 5], axis='columns')
A B
0 4 5
1 4 5
Expand Down