Just remember it when you use it = = Too much time, and slowly forget it.
table of Contents
1. QQ map
Let's take a look at the standard normal distribution chart:
stats.probplot(df1['3#3temp'], dist="norm", plot=plt)
plt.show()
result:
2. Histogram
plt.hist(df1['3#3temp'])
3. Shapiro test
stats.shapiro(df1[str(a)])
The return value can be seen by the p value, and the smaller the value, the compliance.
Finally, let me talk about == What if the data does not conform to the normal distribution?
If the skewness is not serious, the data can be converted by taking the square root. If the skewness is severe, logarithmic transformation can be performed on the data.