GroupBy#

GroupBy objects are returned by groupby calls: pandas.DataFrame.groupby(), pandas.Series.groupby(), etc.

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, **kwargs)

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.backfill([limit])

(DEPRECATED) Backward fill the values.

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(**kwargs)

Generate descriptive statistics.

DataFrameGroupBy.diff([periods, axis])

First discrete difference of element.

DataFrameGroupBy.ffill([limit])

Forward fill the values.

DataFrameGroupBy.fillna([value, method, ...])

Fill NA/NaN values using the specified method.

DataFrameGroupBy.first([numeric_only, min_count])

Compute the first non-null entry of each column.

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, min_count])

Compute the last non-null entry of each column.

DataFrameGroupBy.mad([axis, skipna, level])

(DEPRECATED) Return the mean absolute deviation of the values over the requested axis.

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(n[, dropna])

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.pad([limit])

(DEPRECATED) Forward fill the 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, **kwargs)

Provide resampling when using a TimeGrouper.

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, level, ...])

Return unbiased skew over requested axis.

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, is_copy])

Return the elements in the given positional indices along an axis.

DataFrameGroupBy.tshift([periods, freq, axis])

(DEPRECATED) Shift the time index, using the index's frequency if available.

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.backfill([limit])

(DEPRECATED) Backward fill the values.

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(**kwargs)

Generate descriptive statistics.

SeriesGroupBy.diff([periods, axis])

First discrete difference of element.

SeriesGroupBy.ffill([limit])

Forward fill the values.

SeriesGroupBy.fillna([value, method, axis, ...])

Fill NA/NaN values using the specified method.

SeriesGroupBy.first([numeric_only, min_count])

Compute the first non-null entry of each column.

SeriesGroupBy.head([n])

Return first n rows of each group.

SeriesGroupBy.last([numeric_only, min_count])

Compute the last non-null entry of each column.

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 boolean if values in the object are monotonically increasing.

SeriesGroupBy.is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

SeriesGroupBy.mad([axis, skipna, level])

(DEPRECATED) Return the mean absolute deviation of the values over the requested axis.

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(n[, dropna])

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 of Series object.

SeriesGroupBy.ohlc()

Compute open, high, low and close values of a group, excluding missing values.

SeriesGroupBy.pad([limit])

(DEPRECATED) Forward fill the 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, **kwargs)

Provide resampling when using a TimeGrouper.

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, level, ...])

Return unbiased skew over requested axis.

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, is_copy])

Return the elements in the given positional indices along an axis.

SeriesGroupBy.tshift([periods, freq])

(DEPRECATED) Shift the time index, using the index's frequency if available.

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.