Python --pandas(貝葉斯sample).py

import pandas as pd

dataDic = {
    'Weather': [
        'Sunny',
        'Overcast',
        'Rainy',
        'Sunny',
        'Sunny',
        'Overcast',
        'Rainy',
        'Rainy',
        'Sunny',
        'Rainy',
        'Sunny',
        'Overcast',
        'Overcast',
        'Rainy',
    ],
    'Play': [
        'No',
        'Yes',
        'Yes',
        'Yes',
        'Yes',
        'Yes',
        'No',
        'No',
        'Yes',
        'Yes',
        'No',
        'Yes',
        'Yes',
        'No',
    ]
}

# ##################################################
# Bayes: p(Yes/Sunny) = P(Yes)*P(Sunny/Yes)/p(Sunny)
# ##################################################
df = pd.DataFrame(dataDic)

# allEvent
allEvent = len(df)
# P(Yes)
pYes = len(df[df['Play'] == 'Yes']) / allEvent
# p(Sunny)
pSunny = len(df[df['Weather'] == 'Sunny']) / allEvent
# P(Sunny/Yes)
pSunny_Yes = len(df[(df['Play'] == 'Yes') & (df['Weather'] == 'Sunny')]) / len(
    df[df['Play'] == 'Yes'])
outcome = (pYes * pSunny_Yes) / pSunny

print(outcome)