GroupBy

DataFrameGroupBy and SeriesGroupBy instances are returned by groupby calls cudf.DataFrame.groupby() and cudf.Series.groupby() respectively.

Indexing, iteration

DataFrameGroupBy.__iter__()
SeriesGroupBy.__iter__()
DataFrameGroupBy.groups	Returns a dictionary mapping group keys to row labels.
SeriesGroupBy.groups	Returns a dictionary mapping group keys to row 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.

Function application

SeriesGroupBy.apply(func, *args, **kwargs)	Apply a python transformation function over the grouped chunk.
DataFrameGroupBy.apply(func, *args[, ...])	Apply a python transformation function over the grouped chunk.
SeriesGroupBy.agg(func, *args[, engine, ...])	Apply aggregation(s) to the groups.
DataFrameGroupBy.agg([func, engine, ...])	Apply aggregation(s) to the groups.
SeriesGroupBy.aggregate(func, *args[, ...])	Apply aggregation(s) to the groups.
DataFrameGroupBy.aggregate([func, engine, ...])	Apply aggregation(s) to the groups.
SeriesGroupBy.transform(func, *args[, ...])	Apply an aggregation, then broadcast the result to the group size.
DataFrameGroupBy.transform(func, *args[, ...])	Apply an aggregation, then broadcast the result to the group size.
SeriesGroupBy.pipe(func, *args, **kwargs)	Apply a function func with arguments to this GroupBy object and return the function's result.
DataFrameGroupBy.pipe(func, *args, **kwargs)	Apply a function func with arguments to this GroupBy object and return the function's 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 NA values.
DataFrameGroupBy.corr([method, min_periods, ...])	Compute pairwise correlation of columns, excluding NA/null values.
DataFrameGroupBy.count([dropna])	Compute the number of values in each column.
DataFrameGroupBy.cov([min_periods, ddof, ...])	Compute the pairwise covariance among the columns of a DataFrame, excluding NA/null values.
DataFrameGroupBy.cumcount([ascending])	Return the cumulative count of keys in each group.
DataFrameGroupBy.cummax(*args, **kwargs)	Cumulative max for each group.
DataFrameGroupBy.cummin(*args, **kwargs)	Cumulative min for each group.
DataFrameGroupBy.cumprod(*args, **kwargs)	Cumulative product for each group.
DataFrameGroupBy.cumsum(*args, **kwargs)	Cumulative sum for each group.
DataFrameGroupBy.describe([percentiles, ...])	Generate descriptive statistics that summarizes the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.
DataFrameGroupBy.diff([periods, axis])	Get the difference between the values in each group.
DataFrameGroupBy.ewm(*args, **kwargs)	Return an ewm grouper, providing ewm functionality per group.
DataFrameGroupBy.expanding(*args, **kwargs)	Return an expanding grouper, providing expanding functionality per group.
DataFrameGroupBy.ffill([limit])	Forward fill NA values.
DataFrameGroupBy.first([numeric_only, min_count])	Compute first of group values.
DataFrameGroupBy.head([n, preserve_order])	Return first n rows of each group
DataFrameGroupBy.idxmax([numeric_only, ...])	Compute idxmax of group values.
DataFrameGroupBy.idxmin([numeric_only, ...])	Compute idxmin of group values.
DataFrameGroupBy.last([numeric_only, min_count])	Compute last of group values.
DataFrameGroupBy.max([numeric_only, min_count])	Compute max of group values.
DataFrameGroupBy.mean([numeric_only, min_count])	Compute mean of group values.
DataFrameGroupBy.median([numeric_only, ...])	Compute median of group values.
DataFrameGroupBy.min([numeric_only, min_count])	Compute min of group values.
DataFrameGroupBy.ngroup([ascending])	Number each group from 0 to the number of groups - 1.
DataFrameGroupBy.nth(n[, dropna])	Return the nth row from each group.
DataFrameGroupBy.nunique([dropna])	Return number of unique elements in the group.
DataFrameGroupBy.ohlc()	Compute open, high, low and close values of a group, excluding missing values.
DataFrameGroupBy.pct_change([periods, ...])	Calculates the percent change between sequential elements in the group.
DataFrameGroupBy.prod([numeric_only, min_count])	Compute prod of group values.
DataFrameGroupBy.quantile([q, ...])	Compute the column-wise quantiles of the values in each group.
DataFrameGroupBy.rank([method, ascending, ...])	Return the rank of values within each group.
DataFrameGroupBy.resample(rule, *args[, ...])	Provide resampling when using a TimeGrouper.
DataFrameGroupBy.rolling(*args, **kwargs)	Returns a RollingGroupby object that enables rolling window calculations on the groups.
DataFrameGroupBy.sample([n, frac, replace, ...])	Return a random sample of items in each group.
DataFrameGroupBy.shift([periods, freq, ...])	Shift each group by periods positions.
DataFrameGroupBy.size()	Return the size of each group.
DataFrameGroupBy.std([ddof, engine, ...])	Compute the column-wise std of the values in each group.
DataFrameGroupBy.sum([numeric_only, min_count])	Compute sum of group values.
DataFrameGroupBy.var([ddof, engine, ...])	Compute the column-wise variance of the values in each group.
DataFrameGroupBy.tail([n, preserve_order])	Return last n rows of each group
DataFrameGroupBy.take(indices)	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 NA values.
SeriesGroupBy.corr(other[, method, min_periods])
SeriesGroupBy.count([dropna])	Compute the number of values in each column.
SeriesGroupBy.cov([min_periods, ddof, ...])	Compute the pairwise covariance among the columns of a DataFrame, excluding NA/null values.
SeriesGroupBy.cumcount([ascending])	Return the cumulative count of keys in each group.
SeriesGroupBy.cummax(*args, **kwargs)	Cumulative max for each group.
SeriesGroupBy.cummin(*args, **kwargs)	Cumulative min for each group.
SeriesGroupBy.cumprod(*args, **kwargs)	Cumulative product for each group.
SeriesGroupBy.cumsum(*args, **kwargs)	Cumulative sum for each group.
SeriesGroupBy.describe([percentiles, ...])	Generate descriptive statistics that summarizes the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.
SeriesGroupBy.diff([periods, axis])	Get the difference between the values in each group.
SeriesGroupBy.ewm(*args, **kwargs)	Return an ewm grouper, providing ewm functionality per group.
SeriesGroupBy.expanding(*args, **kwargs)	Return an expanding grouper, providing expanding functionality per group.
SeriesGroupBy.ffill([limit])	Forward fill NA values.
SeriesGroupBy.first([numeric_only, min_count])	Compute first of group values.
SeriesGroupBy.head([n, preserve_order])	Return first n rows of each group
SeriesGroupBy.last([numeric_only, min_count])	Compute last of group values.
SeriesGroupBy.idxmax([numeric_only, min_count])	Compute idxmax of group values.
SeriesGroupBy.idxmin([numeric_only, min_count])	Compute idxmin of group values.
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, min_count])	Compute mean of group values.
SeriesGroupBy.median([numeric_only, min_count])	Compute median of group 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])	Return the nth row from each group.
SeriesGroupBy.nunique([dropna])	Return number of unique elements in the group.
SeriesGroupBy.unique()	Get a list of the unique values for each column in each group.
SeriesGroupBy.ohlc()	Compute open, high, low and close values of a group, excluding missing values.
SeriesGroupBy.pct_change([periods, ...])	Calculates the percent change between sequential elements in the group.
SeriesGroupBy.prod([numeric_only, min_count])	Compute prod of group values.
SeriesGroupBy.quantile([q, interpolation, ...])	Compute the column-wise quantiles of the values in each group.
SeriesGroupBy.rank([method, ascending, ...])	Return the rank of values within each group.
SeriesGroupBy.resample(rule, *args[, ...])	Provide resampling when using a TimeGrouper.
SeriesGroupBy.rolling(*args, **kwargs)	Returns a RollingGroupby object that enables rolling window calculations on the groups.
SeriesGroupBy.sample([n, frac, replace, ...])	Return a random sample of items in each group.
SeriesGroupBy.shift([periods, freq, axis, ...])	Shift each group by periods positions.
SeriesGroupBy.size()	Return the size of each group.
SeriesGroupBy.std([ddof, engine, ...])	Compute the column-wise std of the values in each group.
SeriesGroupBy.sum([numeric_only, min_count])	Compute sum of group values.
SeriesGroupBy.var([ddof, engine, ...])	Compute the column-wise variance of the values in each group.
SeriesGroupBy.tail([n, preserve_order])	Return last n rows of each group
SeriesGroupBy.take(indices)	Return the elements in the given positional indices in each group.
SeriesGroupBy.value_counts([normalize, ...])

Plotting and visualization

DataFrameGroupBy.boxplot([subplots, column, ...])
DataFrameGroupBy.hist([column, by, grid, ...])
SeriesGroupBy.hist([by, ax, grid, ...])
DataFrameGroupBy.plot	Make plots of a grouped Series or DataFrame.
SeriesGroupBy.plot	Make plots of a grouped Series or DataFrame.