GroupBy — pandas 2.2.3 documentation (original) (raw)
pandas.api.typing.DataFrameGroupBy and pandas.api.typing.SeriesGroupByinstances are returned by groupby calls pandas.DataFrame.groupby() andpandas.Series.groupby() respectively.
Indexing, iteration#
| DataFrameGroupBy.__iter__() | Groupby iterator. |
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| 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. |
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Function application helper#
| NamedAgg(column, aggfunc) | Helper for column specific aggregation with control over output column names. |
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Function application#
| SeriesGroupBy.apply(func, *args, **kwargs) | Apply function func group-wise and combine the results together. |
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| 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. |
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| 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. |
| 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([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. |
| 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, ...]) | 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(*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. |
| DataFrameGroupBy.size() | 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. |
| DataFrameGroupBy.tail([n]) | 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. |
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| 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([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. |
| 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(*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. |
| SeriesGroupBy.size() | 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, ...]) |