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DOC: Fixing EX01 - Added examples #53906

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Merged
merged 3 commits into from
Jun 28, 2023
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DOC: Fixing EX01 - Added examples #53906

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113522f
Examples Resampler.__iter__, groups, indices, get_group, ffill
DeaMariaLeon Jun 28, 2023
9070a75
Merge remote-tracking branch 'upstream/main' into Jun-28
DeaMariaLeon Jun 28, 2023
5a778de
Improved explanation for ffill
DeaMariaLeon Jun 28, 2023
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7 changes: 0 additions & 7 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -106,8 +106,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.test \
pandas.NaT \
pandas.io.formats.style.Styler.to_html \
pandas.HDFStore.groups \
pandas.HDFStore.walk \
pandas.read_feather \
pandas.DataFrame.to_feather \
pandas.read_parquet \
Expand All @@ -120,11 +118,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.io.stata.StataReader.value_labels \
pandas.io.stata.StataReader.variable_labels \
pandas.io.stata.StataWriter.write_file \
pandas.core.resample.Resampler.__iter__ \
pandas.core.resample.Resampler.groups \
pandas.core.resample.Resampler.indices \
pandas.core.resample.Resampler.get_group \
pandas.core.resample.Resampler.ffill \
pandas.core.resample.Resampler.asfreq \
pandas.core.resample.Resampler.count \
pandas.core.resample.Resampler.nunique \
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63 changes: 63 additions & 0 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -753,6 +753,19 @@ def groups(self) -> dict[Hashable, np.ndarray]:
2 7 8 9
>>> df.groupby(by=["a"]).groups
{1: [0, 1], 7: [2]}

For Resampler:

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('M').groups
{Timestamp('2023-01-31 00:00:00'): 2, Timestamp('2023-02-28 00:00:00'): 4}
"""
return self.grouper.groups

Expand Down Expand Up @@ -794,6 +807,20 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
eagle 7 8 9
>>> df.groupby(by=["a"]).indices
{1: array([0, 1]), 7: array([2])}

For Resampler:

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('M').indices
defaultdict(<class 'list'>, {Timestamp('2023-01-31 00:00:00'): [0, 1],
Timestamp('2023-02-28 00:00:00'): [2, 3]})
"""
return self.grouper.indices

Expand Down Expand Up @@ -965,6 +992,21 @@ def get_group(self, name, obj=None) -> DataFrame | Series:
a b c
owl 1 2 3
toucan 1 5 6

For Resampler:

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('M').get_group('2023-01-31')
2023-01-01 1
2023-01-15 2
dtype: int64
"""
inds = self._get_index(name)
if not len(inds):
Expand Down Expand Up @@ -1032,6 +1074,27 @@ def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]:
(7,)
a b c
2 7 8 9

For Resampler:

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> for x, y in ser.resample('M'):
... print(f'{x}\\n{y}\\n')
2023-01-31 00:00:00
2023-01-01 1
2023-01-15 2
dtype: int64
2023-02-28 00:00:00
2023-02-01 3
2023-02-15 4
dtype: int64
"""
keys = self.keys
level = self.level
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47 changes: 47 additions & 0 deletions pandas/core/resample.py
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Expand Up @@ -503,6 +503,53 @@ def ffill(self, limit: int | None = None):
--------
Series.fillna: Fill NA/NaN values using the specified method.
DataFrame.fillna: Fill NA/NaN values using the specified method.

Examples
--------
Here we only create a ``Series``.

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64

Example for ``ffill`` with downsampling (we have fewer dates after resampling):

>>> ser.resample('M').ffill()
2023-01-31 2
2023-02-28 4
Freq: M, dtype: int64

Example for ``ffill`` with upsampling (we have more dates after resampling):
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Suggested change
Example for ``ffill`` with upsampling (we have more dates after resampling):
Example for ``ffill`` with upsampling (fill the new dates with the previous value):

Might be a little more clear

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Yes, thank you.


>>> ser.resample('W').ffill()
2023-01-01 1
2023-01-08 1
2023-01-15 2
2023-01-22 2
2023-01-29 2
2023-02-05 3
2023-02-12 3
2023-02-19 4
Freq: W-SUN, dtype: int64

With upsampling and limiting (we have more dates after resampling but
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Then here the commentary can be (only fill the first new date with the previous value)

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Done.

we cannot fill them all because of the given limit):

>>> ser.resample('W').ffill(limit=1)
2023-01-01 1.0
2023-01-08 1.0
2023-01-15 2.0
2023-01-22 2.0
2023-01-29 NaN
2023-02-05 3.0
2023-02-12 NaN
2023-02-19 4.0
Freq: W-SUN, dtype: float64
"""
return self._upsample("ffill", limit=limit)

Expand Down