Data Visualization with Matplotlib Pyplot

Data visualization is the graphical representation of information and data. In CBSE Class 12 Information Practices (Code 065), Matplotlib (specifically the matplotlib.pyplot module) is the standard charting library used to generate line charts, bar plots, and histograms.

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1. Introduction to matplotlib.pyplot

matplotlib.pyplot is a collection of command style functions that make Matplotlib work like MATLAB. Each pyplot function makes some change to a figure (e.g., creates a figure, creates a plotting area in a figure, plots lines or bars, decorates the plot with labels).

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

Essential Anatomy of a Pyplot Chart

• Figure: The whole window or page on which everything is drawn.
• Axes: The region of the figure containing the data space ($x$-axis and $y$-axis).
• Title (plt.title() / plt.suptitle()): Text heading for the chart.
• Labels (plt.xlabel(), plt.ylabel()): Descriptive labels for the coordinate axes.
• Legend (plt.legend()): A guide explaining the colors or markers corresponding to datasets.
• Grid (plt.grid()): Background reference lines.
• Show (plt.show()): Displays the chart on screen.
• Save (plt.savefig()): Exports the figure to an image file (e.g., png, pdf, jpg).

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2. Line Plots (plt.plot())

A line plot connects a series of data points with straight line segments. It is ideal for showing continuous data trends over time.

Syntax

plt.plot(x, y, color='...', linestyle='...', linewidth=..., marker='...', markersize=..., label='...')

Parameters Deep Dive

Parameter	Description	Accepted Values
color or c	Line and marker color	'r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', '#FF5733', 'purple'
linestyle or ls	Style of the line	'-' (solid), '--' (dashed), '-.' (dash-dot), ':' (dotted)
linewidth or lw	Thickness of the stroke	Numeric integer or float (e.g., 2, 2.5, 4)
marker	Data point indicator shape	'o' (circle), 's' (square), '^' (triangle), '*' (star), '+' (plus), 'D' (diamond)
markersize or ms	Size of the marker symbol	Numeric (e.g., 6, 8, 10)
markeredgecolor (mec)	Color of marker border	Color string
markerfacecolor (mfc)	Color of marker fill	Color string
label	Name of series for legend	String

Complete Line Plot Example

import matplotlib.pyplot as plt

# Monthly Sales Data (in Lakhs)
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales_2025 = [12, 18, 15, 24, 28, 35]
sales_2026 = [15, 22, 20, 31, 36, 42]

plt.figure(figsize=(8, 4.5))
plt.plot(months, sales_2025, color='royalblue', linestyle='--', marker='o', linewidth=2, label='FY 2024-25')
plt.plot(months, sales_2026, color='crimson', linestyle='-', marker='s', linewidth=2.5, label='FY 2025-26')

plt.title('Monthly Sales Growth Comparison', fontsize=14, fontweight='bold', color='navy')
plt.xlabel('Financial Month', fontsize=11)
plt.ylabel('Revenue (₹ in Lakhs)', fontsize=11)
plt.grid(True, linestyle=':', alpha=0.6)
plt.legend(loc='upper left')

plt.show()

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3. Bar Charts (plt.bar() & plt.barh())

Bar charts represent categorical data with rectangular bars where heights or lengths are proportional to the values they represent.

Vertical Bar Chart (plt.bar())

plt.bar(x, height, width=0.8, color='...', edgecolor='...', label='...')

import matplotlib.pyplot as plt

streams = ['Science', 'Commerce', 'Humanities', 'Vocational']
students = [450, 620, 380, 150]
colors = ['teal', 'orange', 'forestgreen', 'mediumpurple']

plt.bar(streams, students, width=0.5, color=colors, edgecolor='black', linewidth=1.2)
plt.title('Senior Secondary Stream Enrollment (2026)', fontsize=13)
plt.xlabel('Academic Stream')
plt.ylabel('Number of Enrolled Students')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

Multiple Bar Charts (Side-by-Side Comparison)

IMPORTANT

High-Yield CBSE Question: Creating multiple bar charts on the same $x$-axis requires using NumPy index arrays and offsetting the bar positions by width.

Let $N$ be the number of categories, $w$ be the width of each bar (e.g., $w=0.35$):

• Array of $x$-coordinates: $x=\text{np.arange}(N)=[0,1,2,\dots ,N-1]$
• Series 1 position: $x-w/2$ or $x$
• Series 2 position: $x+w/2$ or $x+w$

import matplotlib.pyplot as plt
import numpy as np

terms = ['Term 1', 'Term 2', 'Pre-Board']
section_A = [78, 85, 92]
section_B = [72, 88, 89]

x = np.arange(len(terms))  # [0, 1, 2]
width = 0.35

plt.bar(x - width/2, section_A, width=width, label='Section A', color='#1E88E5')
plt.bar(x + width/2, section_B, width=width, label='Section B', color='#FFC107')

plt.xticks(x, terms)  # Replaces [0, 1, 2] with ['Term 1', 'Term 2', 'Pre-Board']
plt.title('Average Subject Score by Section')
plt.xlabel('Examination')
plt.ylabel('Average Score (%)')
plt.ylim(0, 100)
plt.legend()
plt.show()

Horizontal Bar Chart (plt.barh())

plt.barh(y, width, height=0.8, color='...', edgecolor='...')

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4. Histograms (plt.hist())

A histogram represents the frequency distribution of continuous numerical data. The continuous data range is divided into a series of intervals called bins.

Syntax

plt.hist(x, bins=10, range=None, density=False, cumulative=False, color='...', edgecolor='...', orientation='vertical')

Key Parameters

• bins: Specifies the number of equal-width bins (e.g., bins=5) OR a sequence of bin edges (e.g., bins=[0, 20, 40, 60, 80, 100]).
• edgecolor: Highly recommended (edgecolor='black') to make bin boundaries distinctly visible.
• cumulative: If True, computes a cumulative frequency histogram (ogive).

Example: Score Frequency Distribution

import matplotlib.pyplot as plt

marks = [45, 55, 62, 71, 73, 75, 78, 82, 84, 85, 88, 90, 92, 95, 98, 58, 67, 74, 86, 91]
custom_bins = [40, 50, 60, 70, 80, 90, 100]

plt.hist(marks, bins=custom_bins, color='lightseagreen', edgecolor='black', linewidth=1.2)
plt.title('Score Distribution in Class 12 IP Pre-Board')
plt.xlabel('Marks Range (Bins)')
plt.ylabel('Number of Students (Frequency)')
plt.xticks(custom_bins)
plt.grid(axis='y', linestyle=':')
plt.show()

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5. Saving Figures (plt.savefig())

To save the plot to local storage rather than or in addition to displaying it:

plt.savefig('output_chart.png', dpi=300, bbox_inches='tight')

WARNING

Always call plt.savefig() BEFORE calling plt.show(). If you call plt.show() first, Matplotlib initializes a new blank canvas, and savefig() will export an empty white image!

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6. CBSE Exam Traps & Common Errors

Exam Trap	Incorrect Code	Correct Code	Explanation
Missing plt.show()	plt.plot(x, y) (script ends)	plt.plot(x, y) plt.show()	Without plt.show(), the window never renders in desktop Python environments.
savefig() after show()	plt.show() plt.savefig('f.png')	plt.savefig('f.png') plt.show()	plt.show() closes and resets the current active figure canvas.
plt.legend() without label	plt.plot(x, y) plt.legend()	plt.plot(x, y, label='Data') plt.legend()	If no label is defined in plot functions, plt.legend() shows an empty box or error.
Unequal coordinate lengths	x = [1, 2, 3] y = [10, 20] plt.plot(x, y)	x = [1, 2, 3] y = [10, 20, 30] plt.plot(x, y)	Raises ValueError: x and y must have same first dimension.

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7. Board Practice Drills

Drill 1: Code Writing (3 Marks)

Write a Python code snippet to draw a horizontal bar chart of 5 programming languages with their popularity scores ($[85,92,78,65,95]$). Set bar color to 'coral', title to 'Language Popularity', and add appropriate axis labels.

import matplotlib.pyplot as plt

languages = ['C++', 'Python', 'Java', 'Ruby', 'JavaScript']
popularity = [85, 92, 78, 65, 95]

plt.barh(languages, popularity, color='coral', edgecolor='black')
plt.title('Language Popularity')
plt.xlabel('Popularity Index')
plt.ylabel('Programming Language')
plt.show()

Drill 2: Output Identification (2 Marks)

Given the following code:

import matplotlib.pyplot as plt
import numpy as np

data = [12, 15, 18, 22, 25, 28, 32, 35, 40]
plt.hist(data, bins=[10, 20, 30, 40, 50], edgecolor='black')
plt.show()

Question: What are the frequencies for the bin intervals [10, 20), [20, 30), and [30, 40]?
Answer:

• Bin [10, 20) contains $\{12,15,18\}$ $\to$ Frequency = 3
• Bin [20, 30) contains $\{22,25,28\}$ $\to$ Frequency = 3
• Bin [30, 40] contains $\{32,35,40\}$ $\to$ Frequency = 3
• Bin (40, 50] contains $\{\}\to$ Frequency = 0