Pandas DataFrame | var method
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Pandas DataFrame.var(~) method computes the variance of each row or column of the source DataFrame. The (unbiased) variance 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.
The var(~) method can also compute the population variance. We do this by setting ddof=0.
Parameters
1. axislink | int or string | optional
Whether to compute the variance column-wise or row-wise:
Axis | Description |
|---|---|
| Variance is computed for each column. |
| Variance 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 | string or 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 variance cannot be computed. |
| Attempt computation with all types, and ignore all rows/columns whose variance cannot be computed without raising an error. |
Note that the variance 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 variance
To compute the variance for each column:
df.var() # axis=0
A 4.0B 9.0dtype: float64
Row-wise variance
To compute the variance for each row:
df.var(axis=1)
0 0.51 0.02 0.5dtype: float64
Specifying numeric_only
Consider the following DataFrame:
df = pd.DataFrame({"A":[3,5], "B":[True,5], "C":["x",7]})df
A B C0 3 True x1 5 5 7
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.var() # numeric_only=None
A 2.0B 8.0dtype: float64
The reason why the variance is still computable for column B is that, True is internally represented as a 1 in Pandas. In contrast, the variance 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 variance is not computable:
df.var(numeric_only=False)
TypeError: could not convert string to float: 'x'
True
To compute the variance of numeric rows/columns only:
df.var(numeric_only=True)
A 2.0dtype: float64
