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# Pandas DataFrame | std 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.std(~) method computes the standard deviation of each row or column of the source DataFrame. The (unbiased) standard deviation is computed using the following formula:

$$\sqrt{\frac{1}{N-1}\sum_{i=0}^{N-1}\left(x_i-\bar{x}^2\right)}$$

Where,

$N$ is the size of the row or column

$x_i$ is the value of the $i$-th index in the row or column

$\bar{x}$ is the mean of the values in the row or column.

NOTE

std(~) can also compute the population standard deviation. We do this by setting ddof=0.

# Parameters

1. axislink | int or string | optional

Whether to compute the standard deviation column-wise or row-wise:

Axis

Description

Standard deviation is computed for each column.

"index" or 0

Standard deviation is computed for each row.

"columns" or 1

By default, axis=0.

2. skipna | boolean | optional

Whether or not to skip NaN. Skipped NaN would not count towards the total size ($N$). By default, skipna=True.

3. level | int | optional

The name or the integer index of the level to consider. This is needed only if your DataFrame is Multi-index.

4. ddof | int | optional

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

$$\sqrt{\frac{1}{N\color{#4fc3f7}{-ddof}}\sum_{i=0}^{N-1}\left(x_i-\bar{x}^2\right)}$$

By default, ddof=1.

5. numeric_onlylink | None or boolean | optional

The allowed values are as follows:

Value

Description

True

Only numeric rows/columns will be considered (e.g. float, int, boolean).

False

Attempt computation with all types (e.g. strings and dates), and throw an error whenever the standard deviation cannot be computed.

None

Attempt computation with all types, and ignore all rows/columns whose standard deviation cannot be computed without raising an error.

Note that the standard deviation can only be computed when the + operator is well-defined between the types.

By default, numeric_only=None.

# 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":[3,5,7], "B":[2,5,8]})df    A  B0  3  21  5  52  7  8 

## Column-wise standard deviation

To compute the standard deviation for each column:

 df.std()   # axis=0 A 2.0B 3.0dtype: float64 

## Row-wise standard deviation

To compute the standard deviation for each row:

 df.std(axis=1) 0 0.7071071 0.0000002 0.707107dtype: float64 

## Specifying numeric_only

Consider the following DataFrame:

 df = pd.DataFrame({"A":[3,5], "B":[True,5], "C":["x",6]})df    A  B C0  3  True x1  5  5 6 

Here, columns B and C are of mixed-type.

### None

By default, numeric_only=None, which means that rows/columns with mixed types will also be considered:

 df.std()   # numeric_only=None A 1.414214B 2.828427dtype: float64 

The reason why the standard deviation is still computable for column B is that, True is internally represented as a 1 in Pandas. In contrast, the standard deviation for column C cannot be computed since "x"+7 is undefined.

### False

numeric_only=False means that the rows/columns of mixed type will also be considered, but an error will be raised if the standard deviation cannot be computed:

 df.std(numeric_only=False) TypeError: could not convert string to float: 'x' 

### True

To compute the standard deviation of numeric rows/columns only:

 df.std(numeric_only=True) A 1.414214dtype: float64