Series.sem()

Series.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source]

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:

Parameters:	axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar ddof : int, default 1 degrees of freedom numeric_only : boolean, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns:	sem : scalar or Series (if level specified)

axis : {index (0)}

skipna : boolean, default True

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

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar

ddof : int, default 1

degrees of freedom

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns:

sem : scalar or Series (if level specified)

Links:

doc_Pandas

2017-01-12 04:54:42

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