dask_expr._groupby.SeriesGroupBy.first
dask_expr._groupby.SeriesGroupBy.first¶
- SeriesGroupBy.first(numeric_only=False, sort=None, **kwargs)¶
Compute the first entry of each column within each group.
This docstring was copied from pandas.core.groupby.groupby.GroupBy.first.
Some inconsistencies with the Dask version may exist.
Defaults to skipping NA elements.
- Parameters
- numeric_onlybool, default False
Include only float, int, boolean columns.
- min_countint, default -1 (Not supported in Dask)
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 (Not supported in Dask)
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
New 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.
pandas.core.groupby.DataFrameGroupBy.last
Compute the last non-null entry of each column.
pandas.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