pandas.core.groupby.DataFrameGroupBy.first — pandas 3.0.0.dev0+2098.g9c5b9ee823 documentation (original) (raw)

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.

min_countint, default -1

The required number of valid values to perform the operation. If fewer than min_count valid values are present the result will be NA.

skipnabool, default True

Exclude NA/null values. If an entire group is NA, the result will be NA.

Added in version 2.2.1.

Returns:

Series or DataFrame

First values within each group.

See also

DataFrame.groupby

Apply a function groupby to each row or column of a DataFrame.

core.groupby.DataFrameGroupBy.last

Compute the last non-null entry of each column.

core.groupby.DataFrameGroupBy.nth

Take the nth row from each group.

Examples

df = pd.DataFrame( ... dict( ... A=[1, 1, 3], ... B=[None, 5, 6], ... C=[1, 2, 3], ... D=["3/11/2000", "3/12/2000", "3/13/2000"], ... ) ... ) df["D"] = pd.to_datetime(df["D"]) df.groupby("A").first() B C D A 1 5.0 1 2000-03-11 3 6.0 3 2000-03-13 df.groupby("A").first(min_count=2) B C D A 1 NaN 1.0 2000-03-11 3 NaN NaN NaT df.groupby("A").first(numeric_only=True) B C A 1 5.0 1 3 6.0 3