Pandas DataFrame | std method
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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:
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.
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. |
| Standard deviation is computed for each row. |
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:
By default, ddof=1.
5. numeric_onlylink | None or boolean | optional
The allowed values are as follows:
Value | Description |
|---|---|
| Only numeric rows/columns will be considered (e.g. |
| Attempt computation with all types (e.g. strings and dates), and throw an error whenever the standard deviation cannot be computed. |
| 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
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
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
