Pandas DataFrame sem() Method
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 |
Optional, Which axis to check, default 0. |
| skip_na | True |
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 |
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.
