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# Pandas DataFrame | sem method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
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Pandas DataFrame.sem(~) method returns the unbiased standard error of the mean for each row or column of the source DataFrame.

Note that the unbiased standard error of the mean is defined as follows:

$$\sigma_\bar{X}=\frac{\sigma_X}{\sqrt{n}}$$

Where,

• $\sigma_\bar{X}$ is the unbiased standard error of the mean

• $\sigma_X$ is the unbiased sample standard deviation (normalized by $n-1$ by default).

• $n$ is the sample size (number of values in a column or row).

# Parameters

1. axislink | int or string | optional

Whether to compute the statistic row-wise or column-wise:

Axis

Description

Compute the SEM for each column.

0 or "index"

Compute the SEM for each row.

0 or "columns"

By default, axis=0.

2. skipna | boolean | optional

Whether or not to ignore NaN. By default, skipna=True.

3. level | int or string | optional

The level to target if the source DataFrame is a multi-index.

4. ddof | int | optional

The delta degree of freedom. This can be used to modify the denominator ddof of the sample variance:

$$\sigma_{X}^2=\frac{1}{N{\color{#6495ed}-ddof}}\sum_{i=0}^{N}\left(x_i-\bar{x}^2\right)$$

By default, ddof=1. Note that $\sigma_X$ is used to compute the SEM.

# Return Value

If the level parameter is specified, then a DataFrame will be returned. Otherwise, a Series will be returned.

# Examples

Consider the following DataFrame:

 df = pd.DataFrame({"A":[2,5,8], "B":[4,5,6]}, index=["a","b","c"])df    A  Ba  2  4b  5  5c  8  6 

## Computing SEM of each column

By default, axis=0, which means that we compute the SEM of each column:

 df.sem() A 1.732051B 0.577350dtype: float64 

## Computing SEM of each row

To compute the SEM of each row instead, pass in axis=1 like so:

 df.sem(axis=1) a 1.0b 0.0c 1.0dtype: float64