## How to use seaborn to visualise a Pandas dataframe
def Snippet_111():
    print()
    print(format('How to use seaborn to visualise a Pandas dataframe','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    import pandas as pd
    import random
    import matplotlib.pyplot as plt
    import seaborn as sns

    df = pd.DataFrame()
    df['x'] = random.sample(range(1, 50), 27)
    df['y'] = random.sample(range(1, 100), 27)
    print(); print(df.head())
    print(); print(df.tail())

    # Scatterplot
    sns.lmplot('x', 'y', data=df, fit_reg=False)

    # Scatterplot with regression line
    sns.lmplot('x', 'y', data=df, fit_reg=True)

    # Density Plot
    sns.kdeplot(df.y); plt.show()
    sns.kdeplot(df.y, df.x); plt.show()
    sns.distplot(df.x); plt.show()

    # Histogram
    plt.hist(df.x, alpha=.3)
    sns.rugplot(df.x)
    plt.show()

    # Boxplot
    sns.boxplot([df.y, df.x]); plt.show()

    # Violin Plot
    sns.violinplot([df.y, df.x]); plt.show()

    # Heatmap
    sns.heatmap([df.y, df.x], annot=False, fmt="d"); plt.show()

    # Clustermap
    sns.clustermap(df); plt.show()

Snippet_111()