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Pandas DataFrame sem() Method

❮ DataFrame Reference


Example

Return the standard error of the mean for each column:

import pandas as pd

data = [[10, 18, 11], [13, 15, 8], [9, 20, 3]]

df = pd.DataFrame(data)

print(df.sem())
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Definition and Usage

The sem() method calculates the standard error of the mean for each column.

By specifying the column axis (axis='columns'), the sem() method searches column-wise and returns the standard error of the mean for each row.


Syntax

dataframe.sem(axis, skipna, level, ddof, numeric_only)

Parameters

The parameters are keyword arguments.

Parameter Value Description
axis 0
1
'index'
'columns'
Optional, Which axis to check, default 0.
skip_na True
False
Optional, default True. Set to False if the result should NOT skip NULL values
level Number
level name
Optional, default None. Specifies which level ( in a hierarchical multi index) to check along
ddof Number
Optional, default 1. Specifies the Delta Degrees of Freedom
numeric_only None
True
False
Optional. Specifies whether to only check numeric values. Default None

Return Value

A Series with the standard deviations.

If the level argument is specified, this method will return a DataFrame object.

This function does NOT make changes to the original DataFrame object.


❮ DataFrame Reference