pandas.core.resample.Resampler.first#
- Resampler.first(numeric_only=_NoDefault.no_default, min_count=0, *args, **kwargs)[source]#
Compute the first non-null entry of each column.
- 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
non-NA values are present the result will be NA.
- Returns
- Series or DataFrame
First non-null of values within each group.
See also
DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame.
DataFrame.core.groupby.GroupBy.last
Compute the last non-null entry of each column.
DataFrame.core.groupby.GroupBy.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