How to explain skewness and kurtosis with Python

This article mainly introduces "how to explain skewness and kurtosis with Python". In daily operation, I believe many people have doubts about how to explain skewness and kurtosis with Python. I have consulted all kinds of materials and sorted out simple and easy operation methods. I hope to help you answer the doubts about "how to explain skewness and kurtosis with Python"! Next, please follow the small series to learn together!

Let me first introduce the concepts of skewness and kurtosis.

Figure 1. skewness and kurtosis formula

Skewness, also known as skewness or skewness coefficient, is a measure of the direction and degree of skewness of data distribution, which is a numerical characteristic measuring the asymmetry of data distribution. For a random variable X, whose skewness is the third-order normalized moment of the sample, the calculation formula is shown in Equation (1) in Figure 1.

Skewness is measured by the skewness of a normal distribution relative to a normal distribution of zero. Therefore, we say that if the data distribution is symmetric, the skewness is 0; if the skewness>0, the distribution can be considered to be right-skewed, also called positive skewness, that is, the distribution has a long tail on the right; if the skewness is 0, it is morphologically steeper or thicker than the normal distribution; and the kurtosis coefficient