Return unbiased standard error of the mean over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
For Series this parameter is unused and defaults to 0.
Warning
The behavior of DataFrame.sem 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).
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
Include only float, int, boolean columns. Not implemented for Series.
Additional keywords have no effect but might be accepted for compatibility with NumPy.
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]) >>> s.sem().round(6) 0.57735
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.sem() a 0.5 b 0.5 dtype: float64
Using axis=1
>>> df.sem(axis=1) tiger 0.5 zebra 0.5 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.sem(numeric_only=True) a 0.5 dtype: float64
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