GroupBy¶

Indexing, iteration

`DataFrameGroupBy.__iter__`()	GroupBy iterator.
`SeriesGroupBy.__iter__`()	GroupBy iterator.
`DataFrameGroupBy.get_group`(name[, obj])
`DataFrameGroupBy.groups`	Get a dictionary mapping group key to row labels.
`SeriesGroupBy.groups`	Get a dictionary mapping group key to row labels.
`DataFrameGroupBy.indices`	Get a dictionary mapping group key to row positions.
`SeriesGroupBy.indices`	Get a dictionary mapping group key to row positions.

Function application

`DataFrameGroupBy.apply`(func, *args[, ...])	Apply function `func` group-wise and combine the results together.
`DataFrameGroupBy.agg`([func, engine, ...])	Aggregate using one or more operations over the specified axis.
`SeriesGroupBy.agg`([func, engine, engine_kwargs])	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.aggregate`([func, engine, ...])	Aggregate using one or more operations over the specified axis.
`DataFrameGroupBy.transform`(func, *args[, ...])	Call function producing a same-indexed `DataFrame` on each group.

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.count`()	Compute count of group, excluding missing 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.cumsum`([axis])	Cumulative sum for each group.
`DataFrameGroupBy.first`([numeric_only, ...])
`DataFrameGroupBy.head`([n])	Return first n rows of each group.
`DataFrameGroupBy.idxmax`([axis, skipna, ...])	Return the index of the first occurrence of maximum over requested axis.
`DataFrameGroupBy.idxmin`([axis, skipna, ...])	Return the index of the first occurrence of minimum over requested axis.
`DataFrameGroupBy.last`([numeric_only, ...])
`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.nunique`([dropna])	Return `DataFrame` with counts of unique elements in each position.
`DataFrameGroupBy.pct_change`([periods, ...])	Calculate pct_change of each value to previous entry in group.
`DataFrameGroupBy.quantile`([q, interpolation])	Return group values at the given quantile, like `numpy.percentile`.
`DataFrameGroupBy.rank`([method, ascending, ...])	Provide the rank of values within each group.
`DataFrameGroupBy.shift`([periods, freq, ...])	Shift each group by periods observations.
`DataFrameGroupBy.size`()	Compute group sizes.
`DataFrameGroupBy.std`([ddof, engine, ...])	Compute standard deviation of groups, excluding missing values.
`DataFrameGroupBy.sum`([numeric_only, ...])	Compute sum of group values.
`DataFrameGroupBy.tail`([n])	Return last n rows of each group.
`DataFrameGroupBy.value_counts`([subset, ...])	Return a Series or `DataFrame` containing counts of unique rows.
`DataFrameGroupBy.var`([ddof, engine, ...])	Compute variance of groups, excluding missing values.

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.count`()	Compute count of group, 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.cumsum`([axis])	Cumulative sum for each group.
`SeriesGroupBy.first`([numeric_only, ...])
`SeriesGroupBy.head`([n])	Return first n rows of each group.
`SeriesGroupBy.idxmax`([axis, skipna, ...])	Return the index of the first occurrence of maximum over requested axis.
`SeriesGroupBy.idxmin`([axis, skipna, ...])	Return the index of the first occurrence of minimum over requested axis.
`SeriesGroupBy.last`([numeric_only, ...])
`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.nunique`([dropna])	Return `DataFrame` with counts of unique elements in each position.
`SeriesGroupBy.pct_change`([periods, ...])	Calculate pct_change of each value to previous entry in group.
`SeriesGroupBy.quantile`([q, interpolation])	Return group values at the given quantile, like `numpy.percentile`.
`SeriesGroupBy.rank`([method, ascending, ...])	Provide the rank of values within each group.
`SeriesGroupBy.shift`([periods, freq, axis, ...])	Shift each group by periods observations.
`SeriesGroupBy.size`()	Compute group sizes.
`SeriesGroupBy.std`([ddof, engine, ...])	Compute standard deviation of groups, excluding missing values.
`SeriesGroupBy.sum`([numeric_only, min_count, ...])	Compute sum of group values.
`SeriesGroupBy.tail`([n])	Return last n rows of each group.
`SeriesGroupBy.value_counts`([subset, ...])	Return a Series or `DataFrame` containing counts of unique rows.
`SeriesGroupBy.var`([ddof, engine, ...])	Compute variance of groups, excluding missing values.