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)	Construct DataFrame from group with provided name.
SeriesGroupBy.get_group(name)	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.
DataFrameGroupBy.agg([func, engine, ...])	Aggregate using one or more operations.
SeriesGroupBy.aggregate([func, engine, ...])	Aggregate using one or more operations.
DataFrameGroupBy.aggregate([func, engine, ...])	Aggregate using one or more operations.
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[, drop, ...])	(DEPRECATED) 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([numeric_only])	Cumulative max for each group.
DataFrameGroupBy.cummin([numeric_only])	Cumulative min for each group.
DataFrameGroupBy.cumprod([numeric_only])	Cumulative product for each group.
DataFrameGroupBy.cumsum([numeric_only])	Cumulative sum for each group.
DataFrameGroupBy.describe([percentiles, ...])	Generate descriptive statistics.
DataFrameGroupBy.diff([periods])	First discrete difference of element.
DataFrameGroupBy.ffill([limit])	Forward fill the values.
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([skipna, numeric_only])	Return index of first occurrence of maximum in each group.
DataFrameGroupBy.idxmin([skipna, numeric_only])	Return index of first occurrence of minimum in each group.
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(window[, ...])	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([skipna, numeric_only])	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, **kwargs)	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([numeric_only])	Cumulative max for each group.
SeriesGroupBy.cummin([numeric_only])	Cumulative min for each group.
SeriesGroupBy.cumprod([numeric_only])	Cumulative product for each group.
SeriesGroupBy.cumsum([numeric_only])	Cumulative sum for each group.
SeriesGroupBy.describe([percentiles, ...])	Generate descriptive statistics.
SeriesGroupBy.diff([periods])	First discrete difference of element.
SeriesGroupBy.ffill([limit])	Forward fill the values.
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([skipna])	Return the row label of the maximum value.
SeriesGroupBy.idxmin([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(window[, min_periods, ...])	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, ...])	Shift each group by periods observations.
SeriesGroupBy.size()	Compute group sizes.
SeriesGroupBy.skew([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, **kwargs)	Return the elements in the given positional indices in each group.
SeriesGroupBy.value_counts([normalize, ...])	Return a Series or DataFrame containing counts of unique rows.

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.