Plotting changes in Nifty50's top-5 sectors after last three market crashes

In this post we draw Pie plots, Bar charts and Grouped Bar Chart to visualize how top-5 sectors of Nifty50 have changes after last three market crashes:

Drawing the Pie plots 

import pandas as pd
import matplotlib.pyplot as plt

pd_00 = pd.DataFrame({
    "Sector": ['CONSUMER GOODS', 'OIL & GAS', 'IT', 'FINANCIAL SERVICES', 'PHARMA'],
    "Weightage": [27.5, 24.2, 12.2, 10.1, 7.2]
})

pd_08 = pd.DataFrame({
    "Sector": ['OIL & GAS', 'TELECOM', 'FINANCIAL SERVICES', 'POWER', 'CONSTRUCTION'],
    "Weightage": [19.3, 9.7, 8.9, 5.5, 3.6]
})

pd_20 = pd.DataFrame({
    "Sector": ['FINANCIAL SERVICES', 'OIL & GAS', 'IT', 'CONSUMER GOODS', 'AUTOMOBILE'],
    "Weightage": [34.4, 14.8, 14.2, 13.5, 5.5]
})

def add_others_col(df):
    return pd.concat([df, pd.DataFrame({'Sector': ['OTHERS'], 'Weightage': [(100 - sum(df.Weightage.values))] })], axis = 0)
	

pd_00 = add_others_col(pd_00)
pd_08 = add_others_col(pd_08)
pd_20 = add_others_col(pd_20)

def plot_pie(labels, sizes, title = ""):
    colors = ['#f47961', '#f0c419', '#255c61', '#78909c', '#6ad4cf', '#17aee8', '#5c6bc0', '#444b6e', '#ef4c60', '#744593', 
              '#ee5691', '#9ccc65', '#708b75', '#d1cb65', '#0d8de1', '#a4554b', '#694f5d', '#45adb3', '#26a69a', '#bdc7cc', ]
    colors = colors[0:len(labels)]
    
    explode = (0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)  # explode 1st slice
    explode = explode[0:len(labels)]
    
    # Plot
    plt.figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
    plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)
    plt.title(title)
    plt.axis('equal')
    plt.show()

plot_pie(pd_00.Sector.values, pd_00.Weightage.values, '2000 (Dot Com Bubble)')
plot_pie(pd_08.Sector.values, pd_08.Weightage.values, '2008 (Lehman Crisis)')
plot_pie(pd_20.Sector.values, pd_20.Weightage.values, '2020 (In Second Half Post COVID-19 Pandemic)') 










Drawing the Bar Charts 

def plot_bar_graph(categories, values, title = "", xlabel = 'Sectors', ylabel = 'Weightage', figsize = (10, 8)):
    colors = ['#f47961', '#f0c419', '#255c61', '#78909c', '#6ad4cf', '#17aee8', '#5c6bc0', '#444b6e', '#ef4c60', '#744593', 
              '#ee5691', '#9ccc65', '#708b75', '#d1cb65', '#0d8de1', '#a4554b', '#694f5d', '#45adb3', '#26a69a', '#bdc7cc', ]
    colors = colors[0:len(categories)]
    
    fig = plt.figure(figsize = figsize)
    ax = fig.add_axes([0,0,1,1])
    
    barlist = ax.bar(categories, values)
    for i in range(len(barlist)):
        barlist[i].set_color(colors[i])
    
    plt.xticks(rotation=70)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
	
plot_bar_graph(pd_00.Sector.values, pd_00.Weightage.values, title = '2000 (Dot Com Bubble)', figsize = (7, 5))
plot_bar_graph(pd_08.Sector.values, pd_08.Weightage.values, title = '2008 (Lehman Crisis)', figsize = (7, 5))
plot_bar_graph(pd_20.Sector.values, pd_20.Weightage.values, title = '2020 (In Second Half Post COVID-19 Pandemic)', figsize = (7, 5)) 










Drawing the Grouped Bar Charts 

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

def autolabel(rects, ax):
    """Attach a text label above each bar in *rects*, displaying its height."""
    for rect in rects:
        height = rect.get_height()
        ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')

def plot_grouped_bar_graph(xlabels, values, group_lables):
    x = np.arange(len(values[0]))  # the label locations
    width = 0.2  # the width of the bars
    
    fig, ax = plt.subplots()
    
    rects = []
    for i in range(len(group_lables)):
        rects += [ax.bar(x + (i * width), values[i], width, label = group_lables[i])]

    # Add some text for labels, title and custom x-axis tick labels, etc.
    ax.set_ylabel('Weightage')
    ax.set_title('Nifty50 Composition as Industry Sectors Across Market Crashes')
    ax.set_xticks(x)
    ax.set_xticklabels(xlabels)
    ax.legend()
    
    for i in range(len(rects)):
        autolabel(rects[i], ax)

    fig.tight_layout()
    plt.show()
	
group_lables = ['OIL & GAS', 'FINANCIAL SERVICES']

plot_grouped_bar_graph(
    xlabels = ['2000', '2008', '2020'],
    values = [
        [pd_00[pd_00.Sector == 'OIL & GAS']['Weightage'].values[0], 
         pd_08[pd_08.Sector == 'OIL & GAS']['Weightage'].values[0],
         pd_20[pd_20.Sector == 'OIL & GAS']['Weightage'].values[0]],
        
        [pd_00[pd_00.Sector == 'FINANCIAL SERVICES']['Weightage'].values[0], 
         pd_08[pd_08.Sector == 'FINANCIAL SERVICES']['Weightage'].values[0],
         pd_20[pd_20.Sector == 'FINANCIAL SERVICES']['Weightage'].values[0]]
    ],
    group_lables = group_lables
) 

The two sectors that have continued to be in top 5 across these years are 'Oil and Gas' and 'Financial Services':