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.