# Skew() Function In Pandas

## Overview:

• Skewness is a measure of asymmetry of a distribution. Another measure that describes the shape of a distribution is kurtosis.
• In a normal distribution, the mean divides the curve symmetrically into two equal parts at the median and the value of skewness is zero.
• When a distribution is asymmetrical the tail of the distribution is skewed to one side-to the right or to the left.
• When the value of the skewness is negative, the tail of the distribution is longer towards the left hand side of the curve.
• When the value of the skewness is positive, the tail of the distribution is longer towards the right hand side of the curve.

## skewness()  function in pandas:

• The DataFrame class of pandas has a method skew() that computes the skewness of the data present in a given axis of the DataFrame object.
• Skewness is computed for each row or each column of the data present in the DataFrame object.

## Example:

 import pandas as pd   dataVal = [(10,20,30,40,50,60,70),            (10,10,40,40,50,60,70),            (10,20,30,50,50,60,80)] dataFrame = pd.DataFrame(data=dataVal); skewValue = dataFrame.skew(axis=1)   print("DataFrame:") print(dataFrame)   print("Skew:") print(skewValue)

## Output:

 DataFrame:     0   1   2   3   4   5   6 0  10  20  30  40  50  60  70 1  10  10  40  40  50  60  70 2  10  20  30  50  50  60  80 Skew: 0    0.000000 1   -0.340998 2    0.121467 dtype: float64
• A skewness value of 0 in the output denotes a symmetrical distribution of values in row 1.
• A negative skewness value in the output indicates an asymmetry in the distribution corresponding to row 2 and the tail is larger towards the left hand side of the distribution.
• A positive skewness value in the output indicates an asymmetry in the distribution corresponding to row 3 and the tail is larger towards the right hand side of the distribution.