Python | Pandas dataframe.skew()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.skew() function return unbiased skew over requested axis Normalized by N-1. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. For more information on skewness, refer this link.

Pandas: DataFrame.skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) Parameters : axis : {index (0), columns (1)} 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 Series numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. Return : skew : Series or DataFrame (if level specified)

For link to the CSV file used in the code, click here Example #1: Use skew() function to find the skewness in data over the index axis. Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.read_csv("nba.csv")

# Print the dataframe
df

Let's use the dataframe.skew() function to find skewness Python3 1==

# skewness along the index axis
df.skew(axis = 0, skipna = True)

Output :   Example #2: Use skew() function to find the skewness of the data over the column axis. Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.read_csv("nba.csv")

# skip the na values
# find skewness in each row
df.skew(axis = 1, skipna = True)

Output :