Programs to demonstrate Dataset Operations using Matplotlib , Matplotlib is a Python 2D plotting library that produces high-quality charts and figures, which helps us visualize extensive data to understand better. Pandas is a handy and useful data-structure tool for analyzing large and complex data.
Exercise 1: Read Total profit of all months and show it using a line plot
#df = pd.read_excel(r'Path where the Excel file is stored\File name.xlsx')
#print(df)import pandas as pd
import matplotlib.pyplot as plt df = pd.read_csv("company_sales_data.csv")df| month_number | facecream | facewash | toothpaste | bathingsoap | shampoo | moisturizer | total_units | total_profit | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2500 | 1500 | 5200 | 9200 | 1200 | 1500 | 21100 | 211000 |
| 1 | 2 | 2630 | 1200 | 5100 | 6100 | 2100 | 1200 | 18330 | 183300 |
| 2 | 3 | 2140 | 1340 | 4550 | 9550 | 3550 | 1340 | 22470 | 224700 |
| 3 | 4 | 3400 | 1130 | 5870 | 8870 | 1870 | 1130 | 22270 | 222700 |
| 4 | 5 | 3600 | 1740 | 4560 | 7760 | 1560 | 1740 | 20960 | 209600 |
| 5 | 6 | 2760 | 1555 | 4890 | 7490 | 1890 | 1555 | 20140 | 201400 |
| 6 | 7 | 2980 | 1120 | 4780 | 8980 | 1780 | 1120 | 29550 | 295500 |
| 7 | 8 | 3700 | 1400 | 5860 | 9960 | 2860 | 1400 | 36140 | 361400 |
| 8 | 9 | 3540 | 1780 | 6100 | 8100 | 2100 | 1780 | 23400 | 234000 |
| 9 | 10 | 1990 | 1890 | 8300 | 10300 | 2300 | 1890 | 26670 | 266700 |
| 10 | 11 | 2340 | 2100 | 7300 | 13300 | 2400 | 2100 | 41280 | 412800 |
| 11 | 12 | 2900 | 1760 | 7400 | 14400 | 1800 | 1760 | 30020 | 300200 |
profitList = df ['total_profit'].tolist()
monthList = df ['month_number'].tolist()profitList[211000,
183300,
224700,
222700,
209600,
201400,
295500,
361400,
234000,
266700,
412800,
300200]monthList[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
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 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
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()
plt.plot(monthList, profitList, label = 'Profit data of last year')
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 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 ).
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 4: Read toothpaste sales data of each month and show it using a scatter plot
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 5: Read face cream and facewash product sales data and show it using the bar chart
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 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("company_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('sales_data_of_bathingsoap.png', dpi=150)
plt.show()
Exercise 8: Calculate total sale data for last year for each product and show it using a Pie chart
In Pie chart display Number of units sold per year for each product in percentage.
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 9: Read Bathing soap facewash of all months and display it using the Subplot
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
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()
