viz3_dist_plot.py

# Matplotlib version: 3.5.2 (My Local Jupyter Notebook)
# Seaborn version: 0.11.2 (My Local Jupyter Notebook)
# Python version: 3.7.4 (My Local Jupyter Notebook)

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(style="white",context="talk")

def dist_plot(data, bw_adjust, clip, bins, title='TITLE', xlabel = 'XLABEL', ylabel = 'Kernel Density', figsize=(14, 6)):
    """
    This dist_plot function visualizes the distribution of the data in histograms and KDE curve. 
    
    Input:
        data: pandas series. The variable.
        bw_adjust: float. KDE parameter. Factor that multiplicatively scales the value chosen using bw_method. Increasing will make the curve smoother.
        clip: tuple. KDE parameter. Do not evaluate the kernel density outside of these limits.
        bins: int. Number of bars.
        title: string. Title of the graph.
        xlabel: string. The title of the x-axis
        ylabel: string. The title of the y-axis. Note that y-axis stands for the Kernel Density, which corresponds to the histogram bin width and thus could take values greater than 1, rather than the actual probability.
        figsize: tuple. The width and height of the figure.
    Output:
        fig: figure object.
    """
    fig,ax = plt.subplots(nrows=1, ncols=1, figsize=figsize)
    sns.distplot(data,
                 label = 'XXX', 
                 ax = ax, 
                 color = 'royalblue',
                 bins = bins,  
                 kde = True,               
                 kde_kws = {                              
                     'bw_adjust': bw_adjust, # Important parameter!
                     # Factor, multiplied by the smoothing bandwidth, that determines how far the evaluation grid extends past the extreme datapoints. 
                     # When set to 0, truncate the curve at the data limits.
                     'cut': 0,  # Important parameter!
                     'clip': clip   # Important parameter!
                 },
                 hist_kws={
                     'histtype': 'bar',
                     'color': 'grey',   
                     'edgecolor': 'black',               
                     'linewidth': 1,    
                     'alpha': 0.2
                 }                                
    )
    ax.set_title(title, fontsize = 18, pad = 10)
    ax.set_xlabel(xlabel, fontsize = 16)
    ax.set_ylabel(ylabel, fontsize = 16)
    ax.grid(linestyle = "--", alpha=0.4)

    plt.show()   
    return fig
# ====================================================================================
# Illustration
# Load the data
tips = sns.load_dataset('tips')
tips['tip_perc']=tips.tip/tips.total_bill

fig = dist_plot(
    data = tips['tip_perc'],
    bw_adjust = 0.6, 
    clip = (0.0,1.0),
    bins = 20,
    title='Distribution of Tipping Percentage', 
    xlabel = 'Proportion', 
    ylabel = 'Kernel Density', 
    figsize=(14, 6)
)
fig.savefig(
    fname = "../dist_plot_tips.png", # path&filename for the output
    dsi = 300, # make it a high-resolution graph
    bbox_inches='tight' # sometimes default savefig method cuts off the x-axis and x-ticks. This param avoids that 
)