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Explore structured advanced study materials through the CBSE Class 12 Informatics Practices Plotting Data using Matplotlib Case Study Questions Set 01. Tailored for Class 12 learners, utilizing these Informatics Practices resources ensures thorough preparation and strengthens foundational knowledge before final CBSE evaluations.
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View or download the dedicated CBSE Class 12 Informatics Practices Plotting Data using Matplotlib Case Study Questions Set 01 resource below. Engaging with these advanced study guides under focused conditions ensures continuous academic progress and mastery of the 2026-27 curriculum for Plotting Data using Matplotlib.
Case Study Based Questions
Case Study 1: Mr. Sharma works in the game development industry. He is comparing the ratings of some popular games on the Play Store using the bar chart given below.
He is writing the following code to plot the graph. Help him fill in the blanks to get the desired output.
import ____________________ #Statement 1
Games=["Subway Surfer","Temple Run","Candy Crush","Bottle Shot","Runner Best"]
Rating=[4.2,4.8,5.0,3.8,4.1]
plt.____________(Games,Rating) #Statement 2
plt.xlabel("Games")
plt.____________("Rating") #Statement 3
plt.____________ #Statement 4(a)
matplotlib as plt(b)
pyplot as plt(c)
matplotlib.pyplot as plt(d)
matplotlib.plt as pyplotShow Answer & Explanation
Answer: (c) matplotlib.pyplot as plt
Explanation:
1. The plotting functions bar(), xlabel(), ylabel() and show() belong to the pyplot module of the matplotlib library.
2. The code calls them through the name plt, so the module must be imported as plt.
3. So Statement 1 is import matplotlib.pyplot as plt.
Teacher's Note:
1. pyplot is a sub-module, so it is written as matplotlib.pyplot.
2. plt is only an alias; the code must use the same alias everywhere.
(a)
line()(b)
bar()(c)
hist()(d)
barh()Show Answer & Explanation
Answer: (b) bar()
Explanation:
1. The chart shows vertical bars, one for each game.
2. Vertical bars are drawn with plt.bar(x, height), so Statement 2 is plt.bar(Games,Rating).
3. barh() would draw horizontal bars, and hist() groups numbers into ranges.
Teacher's Note:
1. Use plot() for a line chart, bar() for a vertical bar chart and hist() for a histogram.
2. There is no line() function in pyplot.
(a)
title("Rating")(b)
ytitle("Rating")(c)
ylabel("Rating")(d)
yaxis("Rating")Show Answer & Explanation
Answer: (c) ylabel("Rating")
Explanation:
1. The word Rating appears along the y-axis of the chart.
2. The y-axis label is set with ylabel(), so Statement 3 is plt.ylabel("Rating").
Teacher's Note:
1. xlabel() and ylabel() label the axes; title() puts a heading above the chart.
2. ytitle() and yaxis() are not pyplot functions.
(a)
display()(b)
print()(c)
bar()(d)
show()Show Answer & Explanation
Answer: (d) show()
Explanation:
1. After the chart has been built, plt.show() opens the window and displays it.
2. So Statement 4 is plt.show().
Teacher's Note:
1. print() shows text output, not a chart.
2. To save the chart as an image file instead, use plt.savefig("name.png").
(a) Statement 1
(b) Statement 2
(c) Statement 3
(d) Statement 4
Show Answer & Explanation
Answer: (b) Statement 2
Explanation:
1. The type of chart is decided by the plotting function used.
2. That function is in Statement 2 (plt.bar()). Replacing it with, say, plt.plot() gives a line chart.
3. So he should change Statement 2.
Teacher's Note:
1. The labels and the show() call stay the same for every chart type.
2. Only the plotting call (plot, bar, hist) changes the kind of chart.
Case Study 2: Gaurav has written Python code to create the bar plot given below from the following data:
| City | Happiness_Index Male | Happiness_Index Female |
|---|---|---|
| Delhi | 60 | 30 |
| Beijing | 40 | 60 |
| Washington | 70 | 70 |
| Tokyo | 65 | 55 |
| Moscow | 85 | 75 |
import ________ as ______ #Statement 1
City=['Delhi','Beijing','Washington','Tokyo','Moscow']
Happiness_Index_Male=[60,40,70,65,85]
Happiness_Index_Female=[30,60,70,55,75]
plt.bar([0.25,1.25,2.25,3.25,4.25],Happiness_Index_Male,color='blue',label="Male",width=.5)
plt.______([.75,1.75,2.75,3.75,4.75],Happiness_Index_Female,color='green',width=.5,label="Female") #Statement 2
pos=range(len(City))
plt.xticks(pos,City,fontsize=10)
plt.xlabel('City',fontsize=16)
plt.ylabel('Happiness_Index',fontsize=16)
________ #Statement 3
________ #Statement 4
________ #Statement 5(a)
matplotlib as plt(b)
numpy as np(c)
pandas as pd(d)
matplotlib.pyplot as pltShow Answer & Explanation
Answer: (d) matplotlib.pyplot as plt
Explanation:
1. All the functions in the code (bar, xticks, xlabel, ylabel) are called as plt.something.
2. They belong to the pyplot module, so Statement 1 is import matplotlib.pyplot as plt.
Teacher's Note:
1. The data here are plain Python lists, so neither numpy nor pandas is needed.
2. Importing only matplotlib (option a) does not give direct access to bar().
(a)
hist()(b)
pie()(c)
bar()(d)
scatter()Show Answer & Explanation
Answer: (c) bar()
Explanation:
1. Statement 2 draws the second (green) set of bars for the Female values.
2. Bars are drawn with bar(), so the statement is plt.bar([.75,1.75,...],Happiness_Index_Female,...).
Teacher's Note:
1. Calling bar() twice with shifted x positions puts two bars side by side for each city.
2. The width=.5 and the 0.5 shift between the two lists stop the bars from overlapping.
(a)
plt.xtitle("Happiness Index across cities by gender",fontsize=18)(b)
plt.title("Happiness Index across cities by gender",fontsize=18)(c)
plt.ytitle("Happiness Index across cities by gender",fontsize=18)(d)
plt.show("Happiness Index across cities by gender",fontsize=18)Show Answer & Explanation
Answer: (b) plt.title("Happiness Index across cities by gender",fontsize=18)
Explanation:
1. The heading above a chart is set with plt.title().
2. The fontsize argument sets its size, so Statement 3 is plt.title("Happiness Index across cities by gender",fontsize=18).
Teacher's Note:
1. xtitle() and ytitle() do not exist; the axes use xlabel() and ylabel().
2. fontsize works the same way in title(), xlabel(), ylabel() and xticks().
(a)
plt.showlegend()(b)
plt.legend()(c)
plt.display()(d)
plt.show()Show Answer & Explanation
Answer: (b) plt.legend()
Explanation:
1. Both bar() calls have a label ("Male" and "Female").
2. plt.legend() collects these labels and draws the key box. So Statement 4 is plt.legend().
Teacher's Note:
1. legend() shows nothing unless the plotting calls were given a label.
2. The legend must be added before plt.show() is called.
(a)
plt.plot()(b)
plt.showplot()(c)
plt.display()(d)
plt.show()Show Answer & Explanation
Answer: (d) plt.show()
Explanation:
1. The finished chart is displayed with plt.show(), so Statement 5 is plt.show().
Teacher's Note:
1. plt.plot() draws a line chart; it does not display anything by itself.
2. plt.show() is normally the last statement of a plotting program.
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Free CBSE Study Guides: Class 12 Informatics Practices Plotting Data using Matplotlib
Comprehensive Study Resources for Plotting Data using Matplotlib
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