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#
Groupby iterator.
Groupby iterator.
Dict {group name -> group labels}.
Dict {group name -> group labels}.
Dict {group name -> group 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.
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.
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.
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.
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.
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.
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.
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.
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.
Return unique values for each group.
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.
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.
Make plots of Series or DataFrame.
Make plots of Series or DataFrame.