Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.skew() function return unbiased skew over requested axis Normalized by N-1. Skewness is asymmetry in a statistical distribution, in which the curve appears distorted or skewed either to the left or to the right.
Syntax: Series.skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameter :
axis : Axis for the function to be applied on.
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
numeric_only : Include only float, int, boolean columns.
**kwargs : Additional keyword arguments to be passed to the function.Returns : skew : scalar or Series (if level specified)
Example #1 : Use Series.skew() function to find the skewness in the data of the given Series object.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([100, 25, 32, 118, 24, 65])   # Print the series print(sr) |
Output :
Now we will use Series.skew() function to find the skewness in the data.
# find skewness sr.skew() |
Output :
As we can see in the output, Series.skew() function has successfully calculated the skewness in the data of the given Series object.
Example #2 : Use Series.skew() function to find the skewness in the data of the given Series object. We have some missing values in our series object, so skip those missing values.
# importing pandas as pd import pandas as pd   # Creating the Series sr = pd.Series([19.5, 16.8, None, 22.78, None, 20.124, None, 18.1002, None])   # Print the series print(sr) |
Output :
Now we will use Series.skew() function to find the skewness in the data.
# find skewness sr.skew(skipna = True) |
Output :
As we can see in the output, Series.skew() function has successfully calculated the skewness in the data of the given Series object. Missing values has been skipped while calculating the skewness in the given data.
