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# pandas.Series.var

Series.var(*, axis=None, skipna=True, ddof=1, numeric_only=False, **kwargs)

  Return unbiased variance over requested axis.

  Normalized by N-1 by default. This can be changed using the ddof argument.

  Parameters:
    axis{index (0)}

      For Series this parameter is unused and defaults to 0.

      Warning

      The behavior of DataFrame.var with `axis=None` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

    skipna bool, default True

      Exclude NA/null values. If an entire row/column is NA, the result will be NA.

    ddof int, default 1

      Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

    numeric_only bool, default False

      Include only float, int, boolean columns. Not implemented for Series.

    **kwargs

      Additional keywords passed.

  Returns:
    scalar or Series (if level specified)

      Unbiased variance over requested axis.

  See also

  `numpy.var`

    Equivalent function in NumPy.

  `Series.std`

    Returns the standard deviation of the Series.

  `DataFrame.var`

    Returns the variance of the DataFrame.

  `DataFrame.std`

    Return standard deviation of the values over the requested axis.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "person_id": [0, 1, 2, 3],
      ...         "age": [21, 25, 62, 43],
      ...         "height": [1.61, 1.87, 1.49, 2.01],
      ...     }
      ... ).set_index("person_id")
      >>> df
                 age  height
      person_id
      0           21    1.61
      1           25    1.87
      2           62    1.49
      3           43    2.01

      >>> df.var()
      age       352.916667
      height      0.056367
      dtype: float64

  Alternatively, `ddof=0` can be set to normalize by N instead of N-1:

      >>> df.var(ddof=0)
      age       264.687500
      height      0.042275
      dtype: float64