Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and is highly customizable.

Matplotlib has the great advantage of customization. Below is the customized Histogram with matploltlib

import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Polygon
from matplotlib.patches import ConnectionPatch

def customized_Histogram(df, j):
    """
    Generate Histogram

    Parameters:
    ****************

    df:
        pandas dataframe

    j(str):
        column name in str

    ****************
    Generates Advanced Histogram
    """

    # Colours for different percentiles
    perc_25_colour = 'gold'
    perc_50_colour = 'mediumaquamarine'
    perc_75_colour = 'deepskyblue'
    perc_95_colour = 'peachpuff'

    # Plot the Histogram from the random data
    fig, ax = plt.subplots(figsize=(14,8))

    # '''
    # counts  = numpy.ndarray of count of data ponts for each bin/column in the histogram
    # bins    = numpy.ndarray of bin edge/range values
    # patches = a list of Patch objects.
    #         each Patch object contains a Rectnagle object. 
    #         e.g. Rectangle(xy=(-2.51953, 0), width=0.501013, height=3, angle=0)
    # '''
    counts, bins, patches = ax.hist(df[j], facecolor=perc_50_colour, edgecolor='gray')


    # Set the ticks to be at the edges of the bins.
    ax.set_xticks(bins.round(2))
    plt.xticks(rotation=70)

    # Set the graph title and axes titles
    plt.title(f'Distribution of {j}', fontsize=20)
    plt.ylabel('Count', fontsize=15)
    plt.xlabel(j, fontsize=15)

    # Change the colors of bars at the edges
    twentyfifth, seventyfifth, ninetyfifth = np.percentile(df[j], [25, 75, 95])

    for patch, leftside, rightside in zip(patches, bins[:-1], bins[1:]):

        if rightside < twentyfifth:
            patch.set_facecolor(perc_25_colour)
        elif leftside > ninetyfifth:
            patch.set_facecolor(perc_95_colour)
        elif leftside > seventyfifth:
            patch.set_facecolor(perc_75_colour)

    # Calculate bar centre to display the count of data points and %
    bin_x_centers = 0.5 * np.diff(bins)   bins[:-1]
    bin_y_centers = ax.get_yticks()[1] * 0.25

    # Display the the count of data points and % for each bar in histogram
    for i in range(len(bins)-1):
        bin_label = "{0:,}".format(counts[i])   "  ({0:,.2f}%)".format((counts[i]/counts.sum())*100)
        plt.text(bin_x_centers[i],
                  bin_y_centers, 
                  bin_label, 
                  rotation=90, 
                  rotation_mode='anchor')

    # Annotation for bar values
    ax.annotate('Each bar shows count and percentage of total',
                xy=(.80,.30), 
                xycoords='figure fraction',
                horizontalalignment='center', 
                verticalalignment='bottom',
                fontsize=10, 
                bbox=dict(boxstyle="round", 
                          fc="white"),
                rotation=-90)

    #create legend
    handles = [Rectangle((0,0),1,1,color=c,ec="k") for c in [
                                                             perc_25_colour, 
                                                             perc_50_colour, 
                                                             perc_75_colour, 
                                                             perc_95_colour
                                                             ]
                ]
    labels= ["0-25 Percentile","25-50 Percentile", "50-75 Percentile", ">95 Percentile"]
    plt.legend(handles, labels, bbox_to_anchor=(0.5, 0., 0.80, 0.99))


    # fig.savefig("filename.jpg",dpi=150, bbox_inches='tight')
    plt.show()

import seaborn as sns
tips = sns.load_dataset("tips")

customized_Histogram(tips, "total_bill")

Customized Histogram plot

How to plot the above plot using Plotly?

CodePudding user response：

• plotly can be build customised figures in same way. Fundamentally it's understanding the API and features (as is case with matplotlib solution)
• matplotlib hist() and numpy histogram() are fundamentally the same. Both return counts and bins