pandas.Series.first_valid_index#

Series.first_valid_index()[source]#

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

See the User Guide for more information on which values are considered missing.

Returns:

type of index: Index of first non-missing value.

See also

DataFrame.last_valid_index: Return index for last non-NA value or None, if no non-NA value is found.
Series.last_valid_index: Return index for last non-NA value or None, if no non-NA value is found.
DataFrame.isna: Detect missing values.

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      B
0  NaN    NaN
1  NaN    3.0
2  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      B
0  None   None
1  None   None
2  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()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.