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Merged
merged 8 commits into from
Jun 30, 2023
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DOC: Fixing EX01 - Added examples #53930

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823aacd
Examples Resampler.ohlc, prod, size, sem, std, sum, var
DeaMariaLeon Jun 29, 2023
77dfce4
Merge remote-tracking branch 'upstream/main' into Jun29
DeaMariaLeon Jun 29, 2023
31808a3
Updated code_checks.sh
DeaMariaLeon Jun 29, 2023
7923950
Corrected sum docstring
DeaMariaLeon Jun 29, 2023
a497b17
Merge branch 'main' into Jun29
mroeschke Jun 29, 2023
b4aa7cb
corrected default no, removed print
DeaMariaLeon Jun 30, 2023
eb884fe
Merge remote-tracking branch 'upstream/main' into Jun29
DeaMariaLeon Jun 30, 2023
c547949
Merge remote-tracking branch 'origin/Jun29' into Jun29
DeaMariaLeon Jun 30, 2023
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8 changes: 0 additions & 8 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -118,14 +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.ohlc \
pandas.core.resample.Resampler.prod \
pandas.core.resample.Resampler.size \
pandas.core.resample.Resampler.sem \
pandas.core.resample.Resampler.std \
pandas.core.resample.Resampler.sum \
pandas.core.resample.Resampler.var \
pandas.core.resample.Resampler.quantile \
pandas.describe_option \
pandas.reset_option \
pandas.get_option \
Expand Down
42 changes: 42 additions & 0 deletions pandas/core/groupby/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2795,6 +2795,20 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
a
1 1.5 4.5
2 0.5 2.0

For Resampler:

>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').sem()
2023-01-01 0.577350
2023-02-01 1.527525
Freq: MS, dtype: float64
"""
if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype):
raise TypeError(
Expand Down Expand Up @@ -2851,6 +2865,20 @@ def size(self) -> DataFrame | Series:
1 2
7 1
dtype: int64

For Resampler:

>>> ser = pd.Series([1, 2, 3], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
dtype: int64
>>> ser.resample('MS').size()
2023-01-01 2
2023-02-01 1
Freq: MS, dtype: int64
"""
result = self.grouper.size()

Expand Down Expand Up @@ -3303,6 +3331,20 @@ def ohlc(self) -> DataFrame:
open high low close open high low close
CAC 2.3 4.5 1.0 1.0 9.0 9.4 1.0 1.0
SPX 1.2 8.9 1.2 2.0 3.4 8.8 3.4 8.2

For Resampler:

>>> ser = pd.Series([1, 3, 2, 4, 3, 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').ohlc()
open high low close
2023-01-01 1 3 1 2
2023-02-01 4 5 3 5
"""
if self.obj.ndim == 1:
obj = self._selected_obj
Expand Down
128 changes: 127 additions & 1 deletion pandas/core/resample.py
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Expand Up @@ -1109,18 +1109,89 @@ def sum(
*args,
**kwargs,
):
"""
Compute sum of group values.

Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.

.. versionchanged:: 2.0.0

numeric_only no longer accepts ``None``.

min_count : int, default 0
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.

Returns
-------
Series or DataFrame
Computed sum 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').sum()
2023-01-01 3
2023-02-01 7
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
nv.validate_resampler_func("sum", args, kwargs)
return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)

@doc(GroupBy.prod)
def prod(
self,
numeric_only: bool = False,
min_count: int = 0,
*args,
**kwargs,
):
"""
Compute prod of group values.

Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.

.. versionchanged:: 2.0.0

numeric_only no longer accepts ``None``.

min_count : int, default 0
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.

Returns
-------
Series or DataFrame
Computed prod 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').prod()
2023-01-01 2
2023-02-01 12
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
nv.validate_resampler_func("prod", args, kwargs)
return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
Expand Down Expand Up @@ -1292,6 +1363,21 @@ def std(
-------
DataFrame or Series
Standard deviation of values within each group.

Examples
--------

>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').std()
2023-01-01 1.000000
2023-02-01 2.645751
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
nv.validate_resampler_func("std", args, kwargs)
Expand Down Expand Up @@ -1325,6 +1411,26 @@ def var(
-------
DataFrame or Series
Variance of values within each group.

Examples
--------

>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').var()
2023-01-01 1.0
2023-02-01 7.0
Freq: MS, dtype: float64

>>> ser.resample('MS').var(ddof=0)
2023-01-01 0.666667
2023-02-01 4.666667
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
nv.validate_resampler_func("var", args, kwargs)
Expand Down Expand Up @@ -1421,6 +1527,26 @@ def quantile(self, q: float | AnyArrayLike = 0.5, **kwargs):
DataFrameGroupBy.quantile
Return a DataFrame, where the columns are groupby columns,
and the values are its quantiles.

Examples
--------

>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').quantile()
2023-01-01 2.0
2023-02-01 4.0
Freq: MS, dtype: float64

>>> ser.resample('MS').quantile(.25)
2023-01-01 1.5
2023-02-01 3.5
Freq: MS, dtype: float64
"""
return self._downsample("quantile", q=q, **kwargs)

Expand Down