GroupBy.all([skipna])

Return True if all values in the group are truthful, else False.

GroupBy.any([skipna])

Return True if any value in the group is truthful, else False.

GroupBy.bfill([limit])

Backward fill the values.

GroupBy.backfill([limit])

(DEPRECATED) Backward fill the values.

GroupBy.count()

Compute count of group, excluding missing values.

GroupBy.cumcount([ascending])

Number each item in each group from 0 to the length of that group - 1.

GroupBy.cummax([axis,聽numeric_only])

Cumulative max for each group.

GroupBy.cummin([axis,聽numeric_only])

Cumulative min for each group.

GroupBy.cumprod([axis])

Cumulative product for each group.

GroupBy.cumsum([axis])

Cumulative sum for each group.

GroupBy.ffill([limit])

Forward fill the values.

GroupBy.first([numeric_only,聽min_count])

Compute the first non-null entry of each column.

GroupBy.head([n])

Return first n rows of each group.

GroupBy.last([numeric_only,聽min_count])

Compute the last non-null entry of each column.

GroupBy.max([numeric_only,聽min_count,聽...])

Compute max of group values.

GroupBy.mean([numeric_only,聽engine,聽...])

Compute mean of groups, excluding missing values.

GroupBy.median([numeric_only])

Compute median of groups, excluding missing values.

GroupBy.min([numeric_only,聽min_count,聽...])

Compute min of group values.

GroupBy.ngroup([ascending])

Number each group from 0 to the number of groups - 1.

GroupBy.nth

Take the nth row from each group if n is an int, otherwise a subset of rows.

GroupBy.ohlc()

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

GroupBy.pad([limit])

(DEPRECATED) Forward fill the values.

GroupBy.prod([numeric_only,聽min_count])

Compute prod of group values.

GroupBy.rank([method,聽ascending,聽na_option,聽...])

Provide the rank of values within each group.

GroupBy.pct_change([periods,聽fill_method,聽...])

Calculate pct_change of each value to previous entry in group.

GroupBy.size()

Compute group sizes.

GroupBy.sem([ddof,聽numeric_only])

Compute standard error of the mean of groups, excluding missing values.

GroupBy.std([ddof,聽engine,聽engine_kwargs,聽...])

Compute standard deviation of groups, excluding missing values.

GroupBy.sum([numeric_only,聽min_count,聽...])

Compute sum of group values.

GroupBy.var([ddof,聽engine,聽engine_kwargs,聽...])

Compute variance of groups, excluding missing values.

GroupBy.tail([n])

Return last n rows of each group.

The following methods are available in both SeriesGroupBy and DataFrameGroupBy objects, but may differ slightly, usually in that the DataFrameGroupBy version usually permits the specification of an axis argument, and often an argument indicating whether to restrict application to columns of a specific data type.

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

Compute pairwise correlation of columns, excluding NA/null values.

DataFrameGroupBy.count()

Compute count of group, excluding missing values.

DataFrameGroupBy.cov

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,聽numeric_only])

Cumulative max for each group.

DataFrameGroupBy.cummin([axis,聽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,聽axis])

First discrete difference of element.

DataFrameGroupBy.ffill([limit])

Forward fill the values.

DataFrameGroupBy.fillna

Fill NA/NaN values using the specified method.

DataFrameGroupBy.filter(func[,聽dropna])

Return a copy of a DataFrame excluding filtered elements.

DataFrameGroupBy.hist

Make a histogram of the DataFrame's columns.

DataFrameGroupBy.idxmax([axis,聽skipna,聽...])

Return index of first occurrence of maximum over requested axis.

DataFrameGroupBy.idxmin([axis,聽skipna,聽...])

Return index of first occurrence of minimum over requested axis.

DataFrameGroupBy.mad

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

DataFrameGroupBy.nunique([dropna])

Return DataFrame with counts of unique elements in each position.

DataFrameGroupBy.pad([limit])

(DEPRECATED) Forward fill the values.

DataFrameGroupBy.pct_change([periods,聽...])

Calculate pct_change of each value to previous entry in group.

DataFrameGroupBy.plot

Class implementing the .plot attribute for groupby objects.

DataFrameGroupBy.quantile([q,聽...])

Return group values at the given quantile, a la numpy.percentile.

DataFrameGroupBy.rank([method,聽ascending,聽...])

Provide the rank of values within each group.

DataFrameGroupBy.resample(rule,聽*args,聽**kwargs)

Provide resampling when using a TimeGrouper.

DataFrameGroupBy.sample([n,聽frac,聽replace,聽...])

Return a random sample of items from each group.

DataFrameGroupBy.shift([periods,聽freq,聽...])

Shift each group by periods observations.

DataFrameGroupBy.size()

Compute group sizes.

DataFrameGroupBy.skew

Return unbiased skew over requested axis.

DataFrameGroupBy.take

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

DataFrameGroupBy.tshift

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

DataFrameGroupBy.value_counts([subset,聽...])

Return a Series or DataFrame containing counts of unique rows.

The following methods are available only for SeriesGroupBy objects.

SeriesGroupBy.hist

Draw histogram of the input series using matplotlib.

SeriesGroupBy.nlargest([n,聽keep])

Return the largest n elements.

SeriesGroupBy.nsmallest([n,聽keep])

Return the smallest n elements.

SeriesGroupBy.unique

Return unique values of Series object.

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.

The following methods are available only for DataFrameGroupBy objects.

DataFrameGroupBy.corrwith

Compute pairwise correlation.

DataFrameGroupBy.boxplot([subplots,聽column,聽...])

Make box plots from DataFrameGroupBy data.