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# pandas.DataFrame.mean

DataFrame.mean(*, axis=0, skipna=True, numeric_only=False, **kwargs)

  Return the mean of the values over the requested axis.

  This computes the arithmetic mean of the values in each column (or row when `axis=1`), skipping missing values by default.

  Parameters:
    axis{index (0), columns (1)}, default 0

      Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

      For DataFrames, specifying `axis=None` will apply the aggregation across both axes.

      Added in version 2.0.0.

    skipna bool, default True

      Exclude NA/null values when computing the result.

    numeric_only bool, default False

      Include only float, int, boolean columns.

    **kwargs

      Additional keyword arguments have no effect but might be accepted for compatibility with NumPy. See NumPy compatibility kwargs for more.

  Returns:
    Series or scalar

      Value containing the calculation referenced in the description.

  See also

  `Series.sum`

    Return the sum.

  `Series.min`

    Return the minimum.

  `Series.max`

    Return the maximum.

  `Series.idxmin`

    Return the index of the minimum.

  `Series.idxmax`

    Return the index of the maximum.

  `DataFrame.sum`

    Return the sum over the requested axis.

  `DataFrame.min`

    Return the minimum over the requested axis.

  `DataFrame.max`

    Return the maximum over the requested axis.

  `DataFrame.idxmin`

    Return the index of the minimum over the requested axis.

  `DataFrame.idxmax`

    Return the index of the maximum over the requested axis.

  Examples

      >>> s = pd.Series([1, 2, 3])
      >>> s.mean()
      2.0

  With a DataFrame

      >>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
      >>> df
             a   b
      tiger  1   2
      zebra  2   3
      >>> df.mean()
      a   1.5
      b   2.5
      dtype: float64

  Using axis=1

      >>> df.mean(axis=1)
      tiger   1.5
      zebra   2.5
      dtype: float64

  In this case, numeric_only should be set to True to avoid getting an error.

      >>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
      >>> df.mean(numeric_only=True)
      a   1.5
      dtype: float64