Skip to main content
`Ctrl`+`K`

# pandas.Series.sem

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

  Return unbiased standard error of the mean over requested axis.

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

  Parameters:
    axis{index (0)}

      This parameter is unused and defaults to 0.

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

  Returns:
    scalar

      Unbiased standard error of the mean over requested axis.

  See also

  `scipy.stats.sem`

    Compute standard error of the mean.

  `Series.std`

    Return sample standard deviation over requested axis.

  `Series.var`

    Return unbiased variance over requested axis.

  `Series.mean`

    Return the mean of the values over the requested axis.

  `Series.median`

    Return the median of the values over the requested axis.

  `Series.mode`

    Return the mode(s) of the Series.

  Examples

      >>> s = pd.Series([1, 2, 3])
      >>> round(s.sem(), 6)
      0.57735