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DeaMariaLeonDeaMariaLeon
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DOC: Fixing EX01 - Added examples (#53909)
* Examples Resampler.asfreq, count, nunique, max, mean, median * Example Resampler.min * Added Returns section to min and max * Changed M to MS and examples results accordingly
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‎ci/code_checks.sh

-7
Original file line numberDiff line numberDiff line change
@@ -118,13 +118,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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pandas.io.stata.StataReader.value_labels \
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pandas.io.stata.StataReader.variable_labels \
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pandas.io.stata.StataWriter.write_file \
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pandas.core.resample.Resampler.asfreq \
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pandas.core.resample.Resampler.count \
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pandas.core.resample.Resampler.nunique \
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pandas.core.resample.Resampler.max \
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pandas.core.resample.Resampler.mean \
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pandas.core.resample.Resampler.median \
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pandas.core.resample.Resampler.min \
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pandas.core.resample.Resampler.ohlc \
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pandas.core.resample.Resampler.prod \
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pandas.core.resample.Resampler.size \

‎pandas/core/groupby/generic.py

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Original file line numberDiff line numberDiff line change
@@ -633,6 +633,7 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
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Examples
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--------
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For SeriesGroupby:
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>>> lst = ['a', 'a', 'b', 'b']
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>>> ser = pd.Series([1, 2, 3, 3], index=lst)
@@ -646,6 +647,21 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
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a 2
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b 1
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dtype: int64
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For Resampler:
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>>> ser = pd.Series([1, 2, 3, 3], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 3
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dtype: int64
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>>> ser.resample('MS').nunique()
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2023-01-01 2
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2023-02-01 1
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Freq: MS, dtype: int64
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"""
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ids, _, _ = self.grouper.group_info
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‎pandas/core/groupby/groupby.py

+29
Original file line numberDiff line numberDiff line change
@@ -2167,6 +2167,21 @@ def count(self) -> NDFrameT:
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a
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1 0 2
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7 1 1
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For Resampler:
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>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 4
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dtype: int64
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>>> ser.resample('MS').count()
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2023-01-01 2
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2023-02-01 2
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Freq: MS, dtype: int64
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"""
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data = self._get_data_to_aggregate()
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ids, _, ngroups = self.grouper.group_info
@@ -2350,6 +2365,20 @@ def median(self, numeric_only: bool = False):
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a b
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dog 3.0 4.0
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mouse 7.0 3.0
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For Resampler:
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>>> ser = pd.Series([1, 2, 3, 3, 4, 5],
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... index=pd.DatetimeIndex(['2023-01-01',
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... '2023-01-10',
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... '2023-01-15',
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... '2023-02-01',
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... '2023-02-10',
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... '2023-02-15']))
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>>> ser.resample('MS').median()
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2023-01-01 2.0
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2023-02-01 4.0
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Freq: MS, dtype: float64
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"""
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result = self._cython_agg_general(
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"median",

‎pandas/core/resample.py

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Original file line numberDiff line numberDiff line change
@@ -1083,6 +1083,22 @@ def asfreq(self, fill_value=None):
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--------
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Series.asfreq: Convert TimeSeries to specified frequency.
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DataFrame.asfreq: Convert TimeSeries to specified frequency.
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Examples
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--------
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>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-31', '2023-02-01', '2023-02-28']))
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>>> ser
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2023-01-01 1
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2023-01-31 2
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2023-02-01 3
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2023-02-28 4
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dtype: int64
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>>> ser.resample('MS').asfreq()
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2023-01-01 1
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2023-02-01 3
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Freq: MS, dtype: int64
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"""
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return self._upsample("asfreq", fill_value=fill_value)
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@@ -1116,6 +1132,29 @@ def min(
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*args,
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**kwargs,
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):
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"""
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Compute min value of group.
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Returns
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-------
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Series or DataFrame
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Examples
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--------
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>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 4
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dtype: int64
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>>> ser.resample('MS').min()
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2023-01-01 1
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2023-02-01 3
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Freq: MS, dtype: int64
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"""
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maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
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nv.validate_resampler_func("min", args, kwargs)
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return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
@@ -1127,6 +1166,28 @@ def max(
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*args,
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**kwargs,
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):
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"""
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Compute max value of group.
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Returns
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-------
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Series or DataFrame
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Examples
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--------
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>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 4
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dtype: int64
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>>> ser.resample('MS').max()
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2023-01-01 2
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2023-02-01 4
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Freq: MS, dtype: int64
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"""
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maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
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nv.validate_resampler_func("max", args, kwargs)
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return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
@@ -1183,6 +1244,22 @@ def mean(
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-------
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DataFrame or Series
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Mean of values within each group.
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Examples
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--------
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>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
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... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
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>>> ser
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2023-01-01 1
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2023-01-15 2
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2023-02-01 3
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2023-02-15 4
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dtype: int64
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>>> ser.resample('MS').mean()
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2023-01-01 1.5
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2023-02-01 3.5
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Freq: MS, dtype: float64
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"""
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maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
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nv.validate_resampler_func("mean", args, kwargs)

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