pandas.DataFrame.last_valid_index#

DataFrame.last_valid_index()[source] #

Return index for last 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 last 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.

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