verticapy.vDataColumn.skewness#
- vDataColumn.skewness() bool | float | str | timedelta | datetime #
Utilizes the
skewness
aggregation method to analyze and aggregate the vDataColumn. Skewness, a measure of the asymmetry in the data’s distribution, helps us understand the data’s deviation from a perfectly symmetrical distribution. When we aggregate the vDataFrame using skewness, we gain insights into the data’s tendency to be skewed to the left or right, or if it follows a normal distribution.Warning
To compute skewness, VerticaPy needs to execute multiple queries. It necessitates, at a minimum, a query that includes a subquery to perform this type of aggregation. This complexity is the reason why calculating skewness is typically slower than some other types of aggregations.
Returns#
- PythonScalar
skewness
Examples#
For this example, let’s generate a dataset and calculate the skewness of a column:
import verticapy as vp data = vp.vDataFrame( { "x": [1, 2, 4, 9, 10, 15, 20, 22], "y": [1, 2, 1, 2, 1, 1, 2, 1], "z": [10, 12, 2, 1, 9, 8, 1, 3], } ) data["x"].skewness() Out[3]: 0.328467287554527
Note
All the calculations are pushed to the database.
Hint
For more precise control, please refer to the
aggregate
method.See also
vDataColumn.
kurtosis()
: Kurtosis for a specific column.vDataFrame.
skewness()
: Skewness for particular columns.vDataFrame.
std()
: Standard Deviation for particular columns.