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# marnunez/Normality.py

Last active Feb 14, 2020
Normality assessment tools
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 from scipy.stats import shapiro, normaltest, anderson, norm from statsmodels.graphics.gofplots import qqplot import seaborn as sns from colorama import Fore import matplotlib.pyplot as plt import numpy as np sns.set(color_codes=True) def check_shapiro(data, alpha=0.05): """ Check Shapiro-Wilk test. If p > alpha, reject H0 => it's Gaussian """ stat, p = shapiro(data) return True if p > alpha else False def check_dagostino(data, alpha=0.05): """ Check D’Agostino K^2 test. If p > alpha, reject H0 => it's Gaussian """ stat, p = normaltest(data) return True if p > alpha else False def check_anderson(data): """ Check Anderson-Darling test for normal distribution. Returns True if it can't reject H0 to 15%, 10% or 5%. => it's Gaussian Returns False otherwise """ result = anderson(data, dist="norm") return next( ( True for sig_value, crit_value in zip( result.significance_level[0:3], result.critical_values[0:3] ) if result.statistic < crit_value ), False, ) def is_normal(data): """ Check for normality. Returns True if 2 out of 3 statistical tests result in normality Returns False otherwise """ return ( True if [check_shapiro(data), check_dagostino(data), check_anderson(data)].count(True) >= 2 else False ) def analyze_normality(df): """ Visual aid for normality check. For every numeric column in df: - distribution plot with kde and fit to normal - qq plot - results of all three statistical tests """ si = Fore.GREEN + "NORMAL" + Fore.RESET no = Fore.RED + "NO NORMAL" + Fore.RESET for column in [i for i in df.columns if df[i].dtype.kind in 'biufc']: data = df[column].dropna() print(f"{column}: {len(data)} puntos") fig, ax = plt.subplots(1, 2, figsize=(7, 5)) sns.distplot(data, ax=ax[0], fit=norm) qqplot(data, line="s", ax=ax[1]) plt.show() print(f"\tSHAPIRO:\t{si if check_shapiro(data,0.05) else no}") print(f"\tD'AGOSTINO:\t{si if check_dagostino(data,0.05) else no}") print(f"\tANDERSON:\t{si if check_anderson(data) else no}") print("\n")