pandas.api.typing.DataFrameGroupBy and pandas.api.typing.SeriesGroupBy instances are returned by groupby calls pandas.DataFrame.groupby() and pandas.Series.groupby() respectively · DataFrameGroupBy.__iter__()

Call function producing a same-indexed DataFrame on each group · Returns a DataFrame having the same indexes as the original object filled with the transformed values

On a DataFrame, we obtain a GroupBy object by calling groupby(). This method returns a pandas.api.typing.DataFrameGroupBy instance. We could naturally group by either the A or B columns, or both: In [7]: grouped = df.groupby("A") In [8]: grouped = df.groupby("B") In [9]: grouped = df.groupby(["A", ...

For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").shift(1) b c tuna NaN NaN salmon 2.0 3.0 catfish NaN NaN goldfish 5.0 8.0 ·

The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column. Pandas provides the pandas.NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are.

For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).median() a b dog 3.0 4.0 mouse 7.0 3.0 ·

Take the nth row from each group if n is an int, otherwise a subset of rows · Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices

The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column. Pandas provides the pandas.NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are.

For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").shift(1) b c tuna NaN NaN salmon 2.0 3.0 catfish NaN NaN goldfish 5.0 8.0 ·

For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").prod() b c a 1 16 10 2 30 72 ·

DataFrameGroupBy.ffill(limit=None)[source]# Forward fill the values. Parameters: limitint, optional · Limit of how many values to fill. Returns: Series or DataFrame · Object with missing values filled. See also · Series.ffill · Returns Series with minimum number of char in object.

DataFrameGroupBy.ohlc()[source]# Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex · Returns: DataFrame · Open, high, low and close values within each group. See also · DataFrame.agg ·

pandas.api.typing.DataFrameGroupBy · Returns a groupby object that contains information about the groups. See also · resample · Convenience method for frequency conversion and resampling of time series. Notes · See the user guide for more detailed usage and examples, including splitting ...

DataFrameGroupBy.first(numeric_only=False, min_count=-1, skipna=True)[source]# Compute the first entry of each column within each group. Defaults to skipping NA elements. Parameters: numeric_onlybool, default False · Include only float, int, boolean columns.

The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column. Pandas provides the pandas.NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are.

DataFrameGroupBy.nunique(dropna=True)[source]# Return DataFrame with counts of unique elements in each position. Parameters: dropnabool, default True · Don’t include NaN in the counts. Returns: nunique: DataFrame · Counts of unique elements in each position.

DataFrameGroupBy.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, times=None, method='single')[source]#

DataFrameGroupBy.size()[source]# Compute group sizes. Returns: DataFrame or Series · Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See also · Series.size · Apply function size to a Series. DataFrame.size ·

pandas.api.typing.ExpandingGroupby · An object that supports expanding transformations over each group. See also · Series.expanding · Expanding transformations for Series. DataFrame.expanding · Expanding transformations for DataFrames. Series.groupby · Apply a function groupby to a Series.

The reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used.