pandas.DataFrame.first_valid_index#

DataFrame.first_valid_index()[source]#

Return index for first non-NA value or None, if no non-NA value is found.

Returns:

type of index

Examples

For Series:

>>>s=pd.Series([None,3,4])>>>s.first_valid_index()1>>>s.last_valid_index()2

>>>s=pd.Series([None,None])>>>print(s.first_valid_index())None>>>print(s.last_valid_index())None

If all elements in Series are NA/null, returns None.

>>>s=pd.Series()>>>print(s.first_valid_index())None>>>print(s.last_valid_index())None

If Series is empty, returns None.

For DataFrame:

>>>df=pd.DataFrame({'A':[None,None,2],'B':[None,3,4]})>>>df     A      B0  NaN    NaN1  NaN    3.02  2.0    4.0>>>df.first_valid_index()1>>>df.last_valid_index()2

>>>df=pd.DataFrame({'A':[None,None,None],'B':[None,None,None]})>>>df     A      B0  None   None1  None   None2  None   None>>>print(df.first_valid_index())None>>>print(df.last_valid_index())None

If all elements in DataFrame are NA/null, returns None.

>>>df=pd.DataFrame()>>>dfEmpty DataFrameColumns: []Index: []>>>print(df.first_valid_index())None>>>print(df.last_valid_index())None

If DataFrame is empty, returns None.