Gurkirat has to fill in the blanks in the given Python program that generates a line plot as shown below. The

Question

Gurkirat has to fill in the blanks in the given Python program that generates a line plot as shown below. The given line plot represents the temperature (in degree Celcius) over five days as given in the table:

Days	Temperature
Day 1	30
Day 2	32
Day 3	31
Day 4	29
Day 5	28

import ______ as plt # Statement-1 days = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] temp = [30, 32, 31, 29, 28] plt.(days, temp) # Statement-2 plt.xlabel('') # Statement-3 plt.ylabel('Temperature') plt.title('______') # Statement-4 plt.show() Write the missing statements according to the given specifications:

Write the suitable code to import the required module in the blank space in the line marked as Statement-1.
Fill in the blank in Statement-2 with a suitable Python function name to create a line plot.
Refer to the graph shown and fill in the blank in Statement-3 to display the appropriate label for x-axis.
Refer to the graph shown and fill in the blank in Statement-4 to display the suitable chart title.

CBSECBSE Class XII Board 2025Subjective· 4mImportance★★★★★

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Concept understanding — Data Visualization

Data Visualization: Seeing the Story in Numbers

Imagine you are given a long table of numbers — monthly sales figures for a year, or marks scored by students in a class. Reading row after row of digits is tiring, and patterns are hard to spot. Now imagine instead a simple bar chart: one bar for each month, rising and falling with sales. In a single glance, you see which months were strong and which were weak. That shift — from a list of numbers to a picture — is the heart of data visualization.

What It Really Means

Data visualization is the technique of presenting numerical information in a visual form — graphs, charts, maps, or diagrams — so that patterns, trends, and comparisons become immediately visible. It is not decoration. It is a tool for thinking. The human brain processes images far faster than it processes tables of text. A well-made chart lets you grasp the big picture without getting lost in individual numbers.

The NCERT textbook for Class 11 Economics (Statistics for Economics) introduces this idea in the chapter on presentation of data. It explains that data can be presented in three ways: textual (in sentences), tabular (in rows and columns), and diagrammatic/graphic (as pictures). The third way — diagrammatic and graphic presentation — is what we call data visualization. The textbook emphasizes that visual presentation makes data more attractive, easier to understand, and quicker to compare.

Why It Matters for a Commerce/Humanities Student

You will encounter data everywhere — in business reports, government budgets, economic surveys, newspaper articles, and research papers. Being able to read a chart is as important as being able to read a sentence. And being able to choose the right chart for your data is a skill that sets you apart.

Here are the main reasons data visualization is essential:

Clarity: A complex set of numbers becomes simple when turned into a bar chart or a pie diagram. You can see the largest and smallest values at a glance.
Comparison: Two or more sets of data can be compared side by side — for example, sales in different years, or expenditure across different heads.
Trend identification: Line graphs show how something changes over time — rising, falling, or staying steady. This is crucial in economics and business studies.
Memory: People remember visual information longer than text or tables. A striking chart stays in the mind.
Communication: When you present your findings — in an exam answer, a project, or a business meeting — a chart speaks faster and more powerfully than a paragraph of numbers.

Note

In your NCERT syllabus, you will learn specific types of diagrams: bar diagrams (simple, multiple, and subdivided), pie diagrams (also called circle charts), and line graphs (for time series data). Each type has a purpose. For instance, a pie chart shows parts of a whole — like how a family spends its monthly income across food, rent, education, and savings. A bar chart is better for comparing separate categories — like the population of different states.

A Simple Everyday Example

Think of your monthly pocket money. Suppose you spend ₹500 on food, ₹300 on travel, ₹200 on entertainment, and ₹100 on savings. A table of these four numbers is fine, but a pie chart shows instantly that food takes up the biggest slice — half of your money. A bar chart lets you compare the amounts directly: the food bar is tallest, savings bar is shortest. You don't need to calculate percentages or differences; the picture does the work.

The Core Principle: Choose the Right Visual …

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To generate a line plot using Python's matplotlib library, specific functions are used for importing the module, plotting the data, and labeling the axes and title.

Here are the suitable codes for the missing statements:

(i) Statement-1: The matplotlib.pyplot module is the standard library for creating plots in Python, and it is conventionally imported with the alias plt. import matplotlib.pyplot as plt
(ii) Statement-2: The plot() function from matplotlib.pyplot is used to create a line plot, taking the x-axis and y-axis data as arguments. plt.plot(days, temp)
(iii) Statement-3: The xlabel() function sets the label for the x-axis. Based on the provided table, the x-axis represents the 'Days'. plt.xlabel('Days') …

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The Python program uses the matplotlib.pyplot module to create a line plot, with plt.plot() drawing the graph, plt.xlabel() labeling the x-axis as 'Days', and plt.title() setting the chart title to 'Daily Temperature'.

Data visualization is a powerful technique for understanding and communicating insights from data. Instead of looking at raw numbers in a table, a visual representation like a line plot allows us to quickly spot trends, patterns, and anomalies. For Gurkirat's task, we are using Python's matplotlib library, which is a fundamental tool for creating static, animated, and interactive visualizations in Python.

A line plot is particularly effective for showing how a variable changes over a continuous range, such as time. In this case, we are tracking temperature changes across five consecutive days. Each point on the line plot represents the temperature on a specific day, and these points are connected by lines to illustrate the progression.

Let's break down the Python code statement by statement to understand its purpose and fill in the blanks.

Statement-1: import ______ as plt

To use the plotting functionalities of Matplotlib, we need to import its pyplot module. It is a common convention to import pyplot and give it the alias plt, which makes the code shorter and easier to read. This module contains functions for creating various types of plots, including line plots, bar charts, and scatter plots.

Important

matplotlib.pyplot is the standard module for creating plots in Matplotlib, and plt is its widely accepted alias.
Statement-2: plt.______(days, temp)

After importing the module, the next step is to actually create the line plot. The plt object, which refers to matplotlib.pyplot, has a specific function for drawing line graphs. This function takes two main arguments: the data for the x-axis and the data for the y-axis. In Gurkirat's program, days will be plotted on the x-axis and temp on the y-axis.
Statement-3: plt.xlabel('______') …

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Same / Similar Concept — real previous-year questions on the same or a closely similar concept, not this exact question.

CBSE 2026Set 90/1/11 markMCQ
$Q. In which scenario is a bar chart preferred over a line chart ? (A) To visualise monthly temperature trends over a year. (B) To compare the number of students in different classes. (C) To show stock price movements over time. (D) To analyse trends in website traffic over days.$
›Reveal solutionSolution
A bar chart is best for comparing discrete categories, while a line chart excels at showing continuous trends over time. For comparing the number of students in different classes (distinct groups), a bar chart is the clear choice.

The core idea here is the nature of the data you are trying to display. Charts are not interchangeable; each has a specific strength.

A line chart connects data points with a continuous line. This is perfect for showing trends over time — how something changes from one moment to the next. Think of temperature, stock prices, or website traffic: you want to see the direction and rate of change.

A bar chart uses separate, non-connected bars. This is ideal for comparing discrete categories — distinct groups that have no natural, continuous order between them. You are not looking for a trend; you are comparing the size or quantity of each category against the others.

Let's apply this logic to each option.

Option (A): Monthly temperature trends over a year.

Months have a natural, continuous order (Jan → Feb → Mar …). You want to see how temperature flows from one month to the next. A line chart is the standard choice here. A bar chart would work, but it would hide the smooth trend.

Option (B): Number of students in different classes.

Classes (e.g., Class 6, Class 7, Class 8) are discrete categories. There is no continuous "trend" between them — you are simply comparing the height of each bar. A bar chart is the perfect tool for this. A line chart would be misleading because it would imply a connection between the classes that doesn't exist.

Option (C): Stock price movements over time.

Time is continuous, and the goal is to see the trend (up, down, volatile). This is a textbook case for a line chart.

Option (D): Website traffic over days. …
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