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Python Matplotlib Exercise

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This Matplotlib exercise project is to help Python developer to learn and practice Data data visualization using Matplotlib by solving multiple questions and problems.

Python Matplotlib Exercise

This exercise is part of a Python Exercises with Solutions

Matplotlib is a Python 2D plotting library which produces high-quality charts and figures and which helps us visualize large data for better understanding. Pandas is a handy and useful data-structure tool for analyzing large and complex data.

For this exercise, we are using Pandas and Matplotlib to visualize Company Sales Data.

Matplotlib Exercise company sales data
Matplotlib Exercise company sales data

Use following CSV file for this exercise. Read this file using Pandas or numpy or using in-built matplotlib function.

 Download data in CSV format

What included in this Matplotlib Exercise?

This exercise contains ten questions. The solution provided for each issue. Each question includes a specific Matplotlib topic you need to learn, When you complete each issue you get more familiar with Data data visualization using matplotlib.

Let’s start the exercise.

Exercise 1: Read Total profit of all months and show it using a line plot

Total profit data provided for each month. Generated line plot must include the following properties: –

  • X label name = Month Number
  • Y label name = Total profit

The line plot graph should look like this.

Matplotlib Exercise 1: Read Total profit of all months and show it using a line plot

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
profitList = df ['total_profit'].tolist()
monthList  = df ['month_number'].tolist()
plt.plot(monthList, profitList, label = 'Month-wise Profit data of last year')
plt.xlabel('Month number')
plt.ylabel('Profit in dollar')
plt.xticks(monthList)
plt.title('Company profit per month')
plt.yticks([100000, 200000, 300000, 400000, 500000])
plt.show()

Exercise Question 2: Get Total profit of all months and show line plot with the following Style properties

Generated line plot must include following Style properties: –

  • Line Style dotted and Line-color should be red
  • Show legend at the lower right location.
  • X label name = Month Number
  • Y label name = Sold units number
  • Add a circle marker.
  • Line marker color as read
  • Line width should be 3

The line plot graph should look like this.

Matplotlib Exercise 2: Get Total profit of all months and show line plot with the following Style properties

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
profitList = df ['total_profit'].tolist()
monthList  = df ['month_number'].tolist()

plt.plot(monthList, profitList, label = 'Profit data of last year', 
      color='r', marker='o', markerfacecolor='k', 
      linestyle='--', linewidth=3)
      
plt.xlabel('Month Number')
plt.ylabel('Profit in dollar')
plt.legend(loc='lower right')
plt.title('Company Sales data of last year')
plt.xticks(monthList)
plt.yticks([100000, 200000, 300000, 400000, 500000])
plt.show()

Exercise Question 3: Read all product sales data and show it  using a multiline plot

Display the number of units sold per month for each product using multiline plots. (i.e., Separate Plotline for each product for each product).

The graph should look like this.

Matplotlib Exercise 3: Read all product sales data and show it  using a multiline plot

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()
faceCremSalesData   = df ['facecream'].tolist()
faceWashSalesData   = df ['facewash'].tolist()
toothPasteSalesData = df ['toothpaste'].tolist()
bathingsoapSalesData   = df ['bathingsoap'].tolist()
shampooSalesData   = df ['shampoo'].tolist()
moisturizerSalesData = df ['moisturizer'].tolist()

plt.plot(monthList, faceCremSalesData,   label = 'Face cream Sales Data', marker='o', linewidth=3)
plt.plot(monthList, faceWashSalesData,   label = 'Face Wash Sales Data',  marker='o', linewidth=3)
plt.plot(monthList, toothPasteSalesData, label = 'ToothPaste Sales Data', marker='o', linewidth=3)
plt.plot(monthList, bathingsoapSalesData, label = 'ToothPaste Sales Data', marker='o', linewidth=3)
plt.plot(monthList, shampooSalesData, label = 'ToothPaste Sales Data', marker='o', linewidth=3)
plt.plot(monthList, moisturizerSalesData, label = 'ToothPaste Sales Data', marker='o', linewidth=3)

plt.xlabel('Month Number')
plt.ylabel('Sales units in number')
plt.legend(loc='upper left')
plt.xticks(monthList)
plt.yticks([1000, 2000, 4000, 6000, 8000, 10000, 12000, 15000, 18000])
plt.title('Sales data')
plt.show()

Exercise Question 4: Read toothpaste sales data of each month and show it using a scatter plot

Also, add a grid in the plot. gridline style should “–“.

The scatter plot should look like this.

Matplotlib Exercise 4: Read toothpaste sales data of each month and show it using a scatter plot

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()
toothPasteSalesData = df ['toothpaste'].tolist()
plt.scatter(monthList, toothPasteSalesData, label = 'Tooth paste Sales data')
plt.xlabel('Month Number')
plt.ylabel('Number of units Sold')
plt.legend(loc='upper left')
plt.title(' Tooth paste Sales data')
plt.xticks(monthList)
plt.grid(True, linewidth= 1, linestyle="--")
plt.show()

Exercise Question 5: Read face cream and facewash product sales data and show it using the bar chart

Bar chart should display the number of units sold per month for each product. Add a separate bar for each product in the same chart.

The bar chart should look like this.

Matplotlib Exercise 5: Read face cream and facewash product sales data and show it using the bar chart

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()
faceCremSalesData   = df ['facecream'].tolist()
faceWashSalesData   = df ['facewash'].tolist()

plt.bar([a-0.25 for a in monthList], faceCremSalesData, width= 0.25, label = 'Face Cream sales data', align='edge')
plt.bar([a+0.25 for a in monthList], faceWashSalesData, width= -0.25, label = 'Face Wash sales data', align='edge')
plt.xlabel('Month Number')
plt.ylabel('Sales units in number')
plt.legend(loc='upper left')
plt.title(' Sales data')

plt.xticks(monthList)
plt.grid(True, linewidth= 1, linestyle="--")
plt.title('Facewash and facecream sales data')
plt.show()

Exercise Question 6: Read sales data of bathing soap of all months and show it using a bar chart. Save this plot to your hard disk

The bar chart should look like this.

Matplotlib Exercise 6: Read sales data of bathing soap of all months and show it using a bar chart. Save this plot to your hard disk

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()
bathingsoapSalesData   = df ['bathingsoap'].tolist()
plt.bar(monthList, bathingsoapSalesData)
plt.xlabel('Month Number')
plt.ylabel('Sales units in number')
plt.title(' Sales data')
plt.xticks(monthList)
plt.grid(True, linewidth= 1, linestyle="--")
plt.title('bathingsoap sales data')
plt.savefig('D:\Python\Articles\matplotlib\sales_data_of_bathingsoap.png', dpi=150)
plt.show()

Exercise Question 7: Read the total profit of each month and show it using the histogram to see most common profit ranges

The histogram should look like this.

Matplotlib Exercise 7: Read the total profit of each month and show it using the histogram to see most common profit ranges

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
profitList = df ['total_profit'].tolist()
labels = ['low', 'average', 'Good', 'Best']
profit_range = [150000, 175000, 200000, 225000, 250000, 300000, 350000]
plt.hist(profitList, profit_range, label = 'Profit data')
plt.xlabel('profit range in dollar')
plt.ylabel('Actual Profit in dollar')
plt.legend(loc='upper left')
plt.xticks(profit_range)
plt.title('Profit data')
plt.show()

Exercise Question 8: Calculate total sale data for last year for each product and show it using a Pie chart

Note: In Pie chart display Number of units sold per year for each product in percentage.

The Pie chart should look like this.

Matplotlib Exercise 8: Calculate total sale data for last year for each product and show it using a Pie chart

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()

labels = ['FaceCream', 'FaseWash', 'ToothPaste', 'Bathing soap', 'Shampoo', 'Moisturizer']
salesData   = [df ['facecream'].sum(), df ['facewash'].sum(), df ['toothpaste'].sum(), 
         df ['bathingsoap'].sum(), df ['shampoo'].sum(), df ['moisturizer'].sum()]
plt.axis("equal")
plt.pie(salesData, labels=labels, autopct='%1.1f%%')
plt.legend(loc='lower right')
plt.title('Sales data')
plt.show()

Exercise Question 9: Read Bathing soap facewash of all months and display it using the Subplot

The Subplot should look like this.

Matplotlib Exercise 9: Read Bathing soap facewash of all months and display it using the Subplot

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()
bathingsoap   = df ['bathingsoap'].tolist()
faceWashSalesData   = df ['facewash'].tolist()

f, axarr = plt.subplots(2, sharex=True)
axarr[0].plot(monthList, bathingsoap, label = 'Bathingsoap Sales Data', color='k', marker='o', linewidth=3)
axarr[0].set_title('Sales data of  a Bathingsoap')
axarr[1].plot(monthList, faceWashSalesData, label = 'Face Wash Sales Data', color='r', marker='o', linewidth=3)
axarr[1].set_title('Sales data of  a facewash')

plt.xticks(monthList)
plt.xlabel('Month Number')
plt.ylabel('Sales units in number')
plt.show()

Exercise Question 10: Read all product sales data and show it using the stack plot

The Stack plot should look like this.

Matplotlib Exercise 10: Read all product sales data and show it using the stack plot

import pandas as pd
import matplotlib.pyplot as plt  

df = pd.read_csv("D:\\Python\\Articles\\matplotlib\\sales_data.csv")
monthList  = df ['month_number'].tolist()

faceCremSalesData   = df ['facecream'].tolist()
faceWashSalesData   = df ['facewash'].tolist()
toothPasteSalesData = df ['toothpaste'].tolist()
bathingsoapSalesData   = df ['bathingsoap'].tolist()
shampooSalesData   = df ['shampoo'].tolist()
moisturizerSalesData = df ['moisturizer'].tolist()

plt.plot([],[],color='m', label='face Cream', linewidth=5)
plt.plot([],[],color='c', label='Face wash', linewidth=5)
plt.plot([],[],color='r', label='Tooth paste', linewidth=5)
plt.plot([],[],color='k', label='Bathing soap', linewidth=5)
plt.plot([],[],color='g', label='Shampoo', linewidth=5)
plt.plot([],[],color='y', label='Moisturizer', linewidth=5)

plt.stackplot(monthList, faceCremSalesData, faceWashSalesData, toothPasteSalesData, 
              bathingsoapSalesData, shampooSalesData, moisturizerSalesData, 
              colors=['m','c','r','k','g','y'])

plt.xlabel('Month Number')
plt.ylabel('Sales unints in Number')
plt.title('Alll product sales data using stack plot')
plt.legend(loc='upper left')
plt.show()

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Comments

  2. HI Vishal,

    I just wanted to thank you for this great lessons. Keep it up. I love the way u r coding.

    Just 1 request.
    Could you please add some comments to the difficult point in the solutions?
    it would be so much helpful to the beginners like me.
    Thanks

    Reply

    • Hi Lakshmi Yapa, autopct enables you to display the percent value using Python string formatting. For example, if autopct=’%.2f’, then for each pie wedge, the format string is ‘%.2f’ and the numerical percent value for that wedge is pct, so the wedge label is set to the string ‘%.2f’%pct.

      To add percentages to each of the constitutents of the pie chart, we add in the line, autopct=’%1.1f%%’, to the plt.pie() function.

      This formats the percentage to the tenth place.

      If you want to format the percentage to the hundredths place, you would use the statement, autopct=’%1.2f%%’

      If you want to format the percentage to the thousandths place, you would use the statement, autopct=’%1.3f%%’

      Reply

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