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from sklearn.preprocessing import StandardScaler

import matplotlib.pyplot as plt

import pandas as pd

dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)

scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])

# Histograms
plot_gender = sns.distplot(df["gender"], label="gender",color="grey")
plot_age = sns.distplot(df["age"], label="age",color="blue")
plot_income = sns.distplot(df["income"], label="income",color="lightgreen")
plot_spend = sns.distplot(df["spending"], label="spend",color="orange")
plt.xlabel('')
plt.legend()
plt.show()

f, axes = plt.subplots(2, 2, figsize=(12, 6), sharex=True, sharey=True)
v1 = sns.violinplot(data=df, x='gender', color='gray', ax=axes[0, 0])
v2 = sns.violinplot(data=df, x='age', color='skyblue', ax=axes[0, 1])
v3 = sns.violinplot(data=df, x='income', color='lightgreen', ax=axes[1, 0])
v4 = sns.violinplot(data=df, x='spending', color='pink', ax=axes[1, 1])