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DOC: Fixing EX01 - Added examples #53909

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Merged
merged 6 commits into from
Jun 29, 2023
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DOC: Fixing EX01 - Added examples #53909

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9a6794d
Examples Resampler.asfreq, count, nunique, max, mean, median
DeaMariaLeon Jun 28, 2023
beb759c
Example Resampler.min
DeaMariaLeon Jun 28, 2023
cf14374
Added Returns section to min and max
DeaMariaLeon Jun 28, 2023
e707b00
Added Returns section to min and max
DeaMariaLeon Jun 28, 2023
7f397a5
Changed M to MS and examples results accordingly
DeaMariaLeon Jun 29, 2023
4967ced
Merge remote-tracking branch 'upstream/main' into Jun28-2
DeaMariaLeon Jun 29, 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 @@ -118,13 +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.asfreq \
pandas.core.resample.Resampler.count \
pandas.core.resample.Resampler.nunique \
pandas.core.resample.Resampler.max \
pandas.core.resample.Resampler.mean \
pandas.core.resample.Resampler.median \
pandas.core.resample.Resampler.min \
pandas.core.resample.Resampler.ohlc \
pandas.core.resample.Resampler.prod \
pandas.core.resample.Resampler.size \
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16 changes: 16 additions & 0 deletions pandas/core/groupby/generic.py
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Expand Up @@ -633,6 +633,7 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:

Examples
--------
For SeriesGroupby:

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = pd.Series([1, 2, 3, 3], index=lst)
Expand All @@ -646,6 +647,21 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
a 2
b 1
dtype: int64

For Resampler:

>>> ser = pd.Series([1, 2, 3, 3], 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 3
dtype: int64
>>> ser.resample('MS').nunique()
2023-01-01 2
2023-02-01 1
Freq: MS, dtype: int64
"""
ids, _, _ = self.grouper.group_info

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29 changes: 29 additions & 0 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -2167,6 +2167,21 @@ def count(self) -> NDFrameT:
a
1 0 2
7 1 1

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('MS').count()
2023-01-01 2
2023-02-01 2
Freq: MS, dtype: int64
"""
data = self._get_data_to_aggregate()
ids, _, ngroups = self.grouper.group_info
Expand Down Expand Up @@ -2350,6 +2365,20 @@ def median(self, numeric_only: bool = False):
a b
dog 3.0 4.0
mouse 7.0 3.0

For Resampler:

>>> ser = pd.Series([1, 2, 3, 3, 4, 5],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').median()
2023-01-01 2.0
2023-02-01 4.0
Freq: MS, dtype: float64
"""
result = self._cython_agg_general(
"median",
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77 changes: 77 additions & 0 deletions pandas/core/resample.py
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Expand Up @@ -1083,6 +1083,22 @@ def asfreq(self, fill_value=None):
--------
Series.asfreq: Convert TimeSeries to specified frequency.
DataFrame.asfreq: Convert TimeSeries to specified frequency.

Examples
--------

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-31', '2023-02-01', '2023-02-28']))
>>> ser
2023-01-01 1
2023-01-31 2
2023-02-01 3
2023-02-28 4
dtype: int64
>>> ser.resample('MS').asfreq()
2023-01-01 1
2023-02-01 3
Freq: MS, dtype: int64
"""
return self._upsample("asfreq", fill_value=fill_value)

Expand Down Expand Up @@ -1116,6 +1132,29 @@ def min(
*args,
**kwargs,
):
"""
Compute min value of group.

Returns
-------
Series or DataFrame

Examples
--------
>>> 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('MS').min()
2023-01-01 1
2023-02-01 3
Freq: MS, dtype: int64
"""

maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
nv.validate_resampler_func("min", args, kwargs)
return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
Expand All @@ -1127,6 +1166,28 @@ def max(
*args,
**kwargs,
):
"""
Compute max value of group.

Returns
-------
Series or DataFrame

Examples
--------
>>> 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('MS').max()
2023-01-01 2
2023-02-01 4
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
nv.validate_resampler_func("max", args, kwargs)
return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
Expand Down Expand Up @@ -1183,6 +1244,22 @@ def mean(
-------
DataFrame or Series
Mean of values within each group.

Examples
--------

>>> 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('MS').mean()
2023-01-01 1.5
2023-02-01 3.5
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
nv.validate_resampler_func("mean", args, kwargs)
Expand Down