<jupyter_start><jupyter_text>Activity 01<jupyter_code>import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv("wholesale_customers_data.csv")
data.isnull().sum()
outliers = {}
for i in range(data.shape[1]):
    min_t = data[data.columns[i]].mean() - (3 * data[data.columns[i]].std())
    max_t = data[data.columns[i]].mean() + (3 * data[data.columns[i]].std())
    count = 0
    for j in data[data.columns[i]]:
        if j < min_t or j > max_t:
            count += 1
    outliers[data.columns[i]] = [count,data.shape[0]-count]
print(outliers)
plt.hist(data["Fresh"])
plt.show()
plt.figure(figsize=(8,8))
plt.pie(outliers["Detergents_Paper"],autopct="%.2f")
plt.show()
data_standardized = (data - data.mean())/data.std()
data_standardized.head()<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup