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# pandas.Series.first_valid_index

Series.first_valid_index()

  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.