pandas.core.resample.Resampler.first#

finalResampler.first(numeric_only=False,min_count=0,skipna=True,*args,**kwargs)[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 fewerthanmin_count valid values are present the result will be NA.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the resultwill 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.

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          DA1  5.0  1 2000-03-113  6.0  3 2000-03-13>>>df.groupby("A").first(min_count=2)    B    C          DA1 NaN  1.0 2000-03-113 NaN  NaN        NaT>>>df.groupby("A").first(numeric_only=True)     B  CA1  5.0  13  6.0  3