Gauusian Tests

gauss_test_1.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
#Converting the data from an array to a data frame
X = pd.DataFrame(load_iris()["data"]).copy()

gauss_test_2.py

X.hist(figsize=(10,10))

gauss_test_3.py

fig,ax = plt.subplots(2,2,figsize=(10,10))
row = col = 0
for n,c in enumerate(X.columns):
    if (n%2 == 0) & (n > 0):
        row += 1
        col = 0
    X[c].plot(kind="kde",ax=ax[row,col])
    ax[row,col].set_title(c)
    col += 1

gauss_test_4.py

from scipy.stats import probplot
for i in X.columns:
    probplot(x=X[i],dist='norm',plot=plt)
    plt.title(i)
    plt.show()

gauss_test_5.py

from scipy.stats import shapiro
for i in X.columns:
    print(f'{i}: {"Not Gaussian" if shapiro(X[i])[1]<0.05 else "Gaussian"}  {shapiro(X[i])}')

gauss_test_6.py

from scipy.stats import kstest
np.random.seed(11)
normal_dist = np.random.randn(1000)
pd.Series(normal_dist).plot(kind="kde")
print(f'{"Not Gaussian" if kstest(normal_dist,"norm")[1]<0.05 else "Gaussian"}  {kstest(normal_dist,"norm")}')

gauss_test_7.py

from scipy.stats import kstest
for i in X.columns:
    print(f'{i}: {"Not Gaussian" if kstest(X[i].values,"norm")[1]<0.05 else "Gaussian"}  {kstest(X[i].values,"norm")}')

gauss_test_8.py

from scipy.stats import normaltest
for i in X.columns:
    print(f'{i}: {"Not Gaussian" if normaltest(X[i].values,)[1]<0.05 else "Gaussian"}  {normaltest(X[i].values)}')