dask.dataframe.Index.sem
dask.dataframe.Index.sem¶
- Index.sem(axis=None, skipna=True, ddof=1, split_every=False, numeric_only=None)¶
Return unbiased standard error of the mean over requested axis.
This docstring was copied from pandas.core.frame.DataFrame.sem.
Some inconsistencies with the Dask version may exist.
Normalized by N-1 by default. This can be changed using the ddof argument
- Parameters
- axis{index (0), columns (1)}
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).- skipnabool, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
- ddofint, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
- numeric_onlybool, default False
Include only float, int, boolean columns. Not implemented for Series.
- Returns
- Series or DataFrame (if level specified)
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