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# GroupBy

`pandas.api.typing.DataFrameGroupBy` and `pandas.api.typing.SeriesGroupBy` instances are returned by groupby calls `pandas.DataFrame.groupby()` and `pandas.Series.groupby()` respectively.

## Indexing, iteration

`DataFrameGroupBy.__iter__`() | Groupby iterator.
`SeriesGroupBy.__iter__`() | Groupby iterator.
`DataFrameGroupBy.groups` | Dict {group name -> group labels}.
`SeriesGroupBy.groups` | Dict {group name -> group labels}.
`DataFrameGroupBy.indices` | Dict {group name -> group indices}.
`SeriesGroupBy.indices` | Dict {group name -> group indices}.
`DataFrameGroupBy.get_group`(name[, obj]) | Construct DataFrame from group with provided name.
`SeriesGroupBy.get_group`(name[, obj]) | Construct DataFrame from group with provided name.

`Grouper`(*args, **kwargs) | A Grouper allows the user to specify a groupby instruction for an object.

## Function application helper

`NamedAgg`(column, aggfunc) | Helper for column specific aggregation with control over output column names.

## Function application

`SeriesGroupBy.apply`(func, *args, **kwargs) | Apply function `func` group-wise and combine the results together.
`DataFrameGroupBy.apply`(func, *args[, ...]) | Apply function `func` group-wise and combine the results together.
`SeriesGroupBy.agg`([func, engine, engine_kwargs]) | Aggregate using one or more operations over the specified axis.
`DataFrameGroupBy.agg`([func, engine, ...]) | Aggregate using one or more operations over the specified axis.
`SeriesGroupBy.aggregate`([func, engine, ...]) | Aggregate using one or more operations over the specified axis.
`DataFrameGroupBy.aggregate`([func, engine, ...]) | Aggregate using one or more operations over the specified axis.
`SeriesGroupBy.transform`(func, *args[, ...]) | Call function producing a same-indexed Series on each group.
`DataFrameGroupBy.transform`(func, *args[, ...]) | Call function producing a same-indexed DataFrame on each group.
`SeriesGroupBy.pipe`(func, *args, **kwargs) | Apply a `func` with arguments to this GroupBy object and return its result.
`DataFrameGroupBy.pipe`(func, *args, **kwargs) | Apply a `func` with arguments to this GroupBy object and return its result.
`DataFrameGroupBy.filter`(func[, dropna]) | Filter elements from groups that don't satisfy a criterion.
`SeriesGroupBy.filter`(func[, dropna]) | Filter elements from groups that don't satisfy a criterion.

## `DataFrameGroupBy` computations / descriptive stats

`DataFrameGroupBy.all`([skipna]) | Return True if all values in the group are truthful, else False.
`DataFrameGroupBy.any`([skipna]) | Return True if any value in the group is truthful, else False.
`DataFrameGroupBy.bfill`([limit]) | Backward fill the values.
`DataFrameGroupBy.corr`([method, min_periods, ...]) | Compute pairwise correlation of columns, excluding NA/null values.
`DataFrameGroupBy.corrwith`(other[, axis, ...]) | Compute pairwise correlation.
`DataFrameGroupBy.count`() | Compute count of group, excluding missing values.
`DataFrameGroupBy.cov`([min_periods, ddof, ...]) | Compute pairwise covariance of columns, excluding NA/null values.
`DataFrameGroupBy.cumcount`([ascending]) | Number each item in each group from 0 to the length of that group - 1.
`DataFrameGroupBy.cummax`([axis, numeric_only]) | Cumulative max for each group.
`DataFrameGroupBy.cummin`([axis, numeric_only]) | Cumulative min for each group.
`DataFrameGroupBy.cumprod`([axis]) | Cumulative product for each group.
`DataFrameGroupBy.cumsum`([axis]) | Cumulative sum for each group.
`DataFrameGroupBy.describe`([percentiles, ...]) | Generate descriptive statistics.
`DataFrameGroupBy.diff`([periods, axis]) | First discrete difference of element.
`DataFrameGroupBy.ffill`([limit]) | Forward fill the values.
`DataFrameGroupBy.fillna`([value, method, ...]) | (DEPRECATED) Fill NA/NaN values using the specified method within groups.
`DataFrameGroupBy.first`([numeric_only, ...]) | Compute the first entry of each column within each group.
`DataFrameGroupBy.head`([n]) | Return first n rows of each group.
`DataFrameGroupBy.idxmax`([axis, skipna, ...]) | Return index of first occurrence of maximum over requested axis.
`DataFrameGroupBy.idxmin`([axis, skipna, ...]) | Return index of first occurrence of minimum over requested axis.
`DataFrameGroupBy.last`([numeric_only, ...]) | Compute the last entry of each column within each group.
`DataFrameGroupBy.max`([numeric_only, ...]) | Compute max of group values.
`DataFrameGroupBy.mean`([numeric_only, ...]) | Compute mean of groups, excluding missing values.
`DataFrameGroupBy.median`([numeric_only]) | Compute median of groups, excluding missing values.
`DataFrameGroupBy.min`([numeric_only, ...]) | Compute min of group values.
`DataFrameGroupBy.ngroup`([ascending]) | Number each group from 0 to the number of groups - 1.
`DataFrameGroupBy.nth` | Take the nth row from each group if n is an int, otherwise a subset of rows.
`DataFrameGroupBy.nunique`([dropna]) | Return DataFrame with counts of unique elements in each position.
`DataFrameGroupBy.ohlc`() | Compute open, high, low and close values of a group, excluding missing values.
`DataFrameGroupBy.pct_change`([periods, ...]) | Calculate pct_change of each value to previous entry in group.
`DataFrameGroupBy.prod`([numeric_only, min_count]) | Compute prod of group values.
`DataFrameGroupBy.quantile`([q, ...]) | Return group values at the given quantile, a la numpy.percentile.
`DataFrameGroupBy.rank`([method, ascending, ...]) | Provide the rank of values within each group.
`DataFrameGroupBy.resample`(rule, *args[, ...]) | Provide resampling when using a TimeGrouper.
`DataFrameGroupBy.rolling`(*args, **kwargs) | Return a rolling grouper, providing rolling functionality per group.
`DataFrameGroupBy.sample`([n, frac, replace, ...]) | Return a random sample of items from each group.
`DataFrameGroupBy.sem`([ddof, numeric_only]) | Compute standard error of the mean of groups, excluding missing values.
`DataFrameGroupBy.shift`([periods, freq, ...]) | Shift each group by periods observations.
`DataFrameGroupBy.size`() | Compute group sizes.
`DataFrameGroupBy.skew`([axis, skipna, ...]) | Return unbiased skew within groups.
`DataFrameGroupBy.std`([ddof, engine, ...]) | Compute standard deviation of groups, excluding missing values.
`DataFrameGroupBy.sum`([numeric_only, ...]) | Compute sum of group values.
`DataFrameGroupBy.var`([ddof, engine, ...]) | Compute variance of groups, excluding missing values.
`DataFrameGroupBy.tail`([n]) | Return last n rows of each group.
`DataFrameGroupBy.take`(indices[, axis]) | Return the elements in the given positional indices in each group.
`DataFrameGroupBy.value_counts`([subset, ...]) | Return a Series or DataFrame containing counts of unique rows.

## `SeriesGroupBy` computations / descriptive stats

`SeriesGroupBy.all`([skipna]) | Return True if all values in the group are truthful, else False.
`SeriesGroupBy.any`([skipna]) | Return True if any value in the group is truthful, else False.
`SeriesGroupBy.bfill`([limit]) | Backward fill the values.
`SeriesGroupBy.corr`(other[, method, min_periods]) | Compute correlation with other Series, excluding missing values.
`SeriesGroupBy.count`() | Compute count of group, excluding missing values.
`SeriesGroupBy.cov`(other[, min_periods, ddof]) | Compute covariance with Series, excluding missing values.
`SeriesGroupBy.cumcount`([ascending]) | Number each item in each group from 0 to the length of that group - 1.
`SeriesGroupBy.cummax`([axis, numeric_only]) | Cumulative max for each group.
`SeriesGroupBy.cummin`([axis, numeric_only]) | Cumulative min for each group.
`SeriesGroupBy.cumprod`([axis]) | Cumulative product for each group.
`SeriesGroupBy.cumsum`([axis]) | Cumulative sum for each group.
`SeriesGroupBy.describe`([percentiles, ...]) | Generate descriptive statistics.
`SeriesGroupBy.diff`([periods, axis]) | First discrete difference of element.
`SeriesGroupBy.ffill`([limit]) | Forward fill the values.
`SeriesGroupBy.fillna`([value, method, axis, ...]) | (DEPRECATED) Fill NA/NaN values using the specified method within groups.
`SeriesGroupBy.first`([numeric_only, ...]) | Compute the first entry of each column within each group.
`SeriesGroupBy.head`([n]) | Return first n rows of each group.
`SeriesGroupBy.last`([numeric_only, ...]) | Compute the last entry of each column within each group.
`SeriesGroupBy.idxmax`([axis, skipna]) | Return the row label of the maximum value.
`SeriesGroupBy.idxmin`([axis, skipna]) | Return the row label of the minimum value.
`SeriesGroupBy.is_monotonic_increasing` | Return whether each group's values are monotonically increasing.
`SeriesGroupBy.is_monotonic_decreasing` | Return whether each group's values are monotonically decreasing.
`SeriesGroupBy.max`([numeric_only, min_count, ...]) | Compute max of group values.
`SeriesGroupBy.mean`([numeric_only, engine, ...]) | Compute mean of groups, excluding missing values.
`SeriesGroupBy.median`([numeric_only]) | Compute median of groups, excluding missing values.
`SeriesGroupBy.min`([numeric_only, min_count, ...]) | Compute min of group values.
`SeriesGroupBy.ngroup`([ascending]) | Number each group from 0 to the number of groups - 1.
`SeriesGroupBy.nlargest`([n, keep]) | Return the largest n elements.
`SeriesGroupBy.nsmallest`([n, keep]) | Return the smallest n elements.
`SeriesGroupBy.nth` | Take the nth row from each group if n is an int, otherwise a subset of rows.
`SeriesGroupBy.nunique`([dropna]) | Return number of unique elements in the group.
`SeriesGroupBy.unique`() | Return unique values for each group.
`SeriesGroupBy.ohlc`() | Compute open, high, low and close values of a group, excluding missing values.
`SeriesGroupBy.pct_change`([periods, ...]) | Calculate pct_change of each value to previous entry in group.
`SeriesGroupBy.prod`([numeric_only, min_count]) | Compute prod of group values.
`SeriesGroupBy.quantile`([q, interpolation, ...]) | Return group values at the given quantile, a la numpy.percentile.
`SeriesGroupBy.rank`([method, ascending, ...]) | Provide the rank of values within each group.
`SeriesGroupBy.resample`(rule, *args[, ...]) | Provide resampling when using a TimeGrouper.
`SeriesGroupBy.rolling`(*args, **kwargs) | Return a rolling grouper, providing rolling functionality per group.
`SeriesGroupBy.sample`([n, frac, replace, ...]) | Return a random sample of items from each group.
`SeriesGroupBy.sem`([ddof, numeric_only]) | Compute standard error of the mean of groups, excluding missing values.
`SeriesGroupBy.shift`([periods, freq, axis, ...]) | Shift each group by periods observations.
`SeriesGroupBy.size`() | Compute group sizes.
`SeriesGroupBy.skew`([axis, skipna, numeric_only]) | Return unbiased skew within groups.
`SeriesGroupBy.std`([ddof, engine, ...]) | Compute standard deviation of groups, excluding missing values.
`SeriesGroupBy.sum`([numeric_only, min_count, ...]) | Compute sum of group values.
`SeriesGroupBy.var`([ddof, engine, ...]) | Compute variance of groups, excluding missing values.
`SeriesGroupBy.tail`([n]) | Return last n rows of each group.
`SeriesGroupBy.take`(indices[, axis]) | Return the elements in the given positional indices in each group.
`SeriesGroupBy.value_counts`([normalize, ...]) |

## Plotting and visualization

`DataFrameGroupBy.boxplot`([subplots, column, ...]) | Make box plots from DataFrameGroupBy data.
`DataFrameGroupBy.hist`([column, by, grid, ...]) | Make a histogram of the DataFrame's columns.
`SeriesGroupBy.hist`([by, ax, grid, ...]) | Draw histogram of the input series using matplotlib.
`DataFrameGroupBy.plot` | Make plots of Series or DataFrame.
`SeriesGroupBy.plot` | Make plots of Series or DataFrame.