---
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entity_type: "chapter"
id: "66dfdec53f8b4e9e69bf7d04"
title: "Plotting Data using Matplotlib"
board: "CBSE"
curriculum: "CBSE"
class: "Class 12"
subject: "Informatics Practices"
book: "Informatics Practices"
chapter: "Plotting Data using Matplotlib"
chapter_slug: "plotting-data-using-matplotlib"
canonical_url: "https://www.edzy.ai/cbse-class-12-informatics-practices-plotting-data-using-matplotlib"
markdown_url: "https://www.edzy.ai/okf/chapter/cbse-class-12-informatics-practices-plotting-data-using-matplotlib.md"
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last_updated: "2026-06-20"
---

# Plotting Data using Matplotlib

In this chapter, we will learn how to visualise data using the Matplotlib library of Python by plotting various types of charts such as line, bar, and scatter. Data visualisation helps in making sense of results obtained after data analysis.

## Knowledge Snapshot

| Field | Details |
| :--- | :--- |
| Class | Class 12 |
| Subject | Informatics Practices |
| Book | Informatics Practices |
| Chapter | Plotting Data using Matplotlib |
| Pages | 105-136 |

## Chapter Summary

### Short Summary

The chapter discusses the importance of data visualisation and introduces the Matplotlib library in Python for creating different types of plots.

### Detailed Summary

In this chapter, we explore data visualisation, which provides graphical representations to better understand data results. The chapter covers how to install and import the Matplotlib library and use its functions to create various plots, customize them, and employ Pandas plotting functions. Each section introduces new plotting techniques and customisation options, enhancing our ability to present data effectively.

## Topic-Wise Explanation

### Introduction

This section introduces the concept of data visualisation and its significance in understanding data better.

### Plotting using Matplotlib

Matplotlib is used for creating static, animated, and interactive 2D plots in Python. The main module used is `Pyplot`. To create plots, we use the `plot()` function to form a figure and the `show()` function to display it. Different types of plots can be created, including line charts, bar charts, and scatter plots.

### Customisation of Plots

Pyplot offers numerous options to customise plots, including adding titles, labels, gridlines, and choosing marker styles and colors.

### The Pandas Plot Function (Pandas Visualisation)

Pandas DataFrames provide the `.plot()` method that acts as a wrapper for Matplotlib plots, allowing easy creation of various chart types directly from DataFrames.

## Core Ideas

| Idea | Explanation |
| :--- | :--- |
| Data Visualisation | Graphical representation of data to aid understanding and analysis. |

## Key Concepts

| Concept | Meaning |
| :--- | :--- |
| Matplotlib | A library in Python for data visualisation used to create plots. |
| Pyplot | A module in Matplotlib used for plotting functions. |
| DataFrame | A two-dimensional labeled data structure with columns of potentially different types. |

## Important Points for Revision

* Data visualisation aids in understanding data better.
* Matplotlib is the primary plotting library in Python.
* Use the `plot()` function to create visualisations and `show()` to display them.
* Various types of plots include line, bar, histogram, pie, and scatter plots.
* Customisation enhances the visual appeal and clarity of plots.
* Pandas DataFrames have an integrated `.plot()` method for easier graph creation.
* Understanding the type of data helps in choosing the appropriate plot type.

## Practice Questions

### Short Answer Questions

1. What is data visualisation?
2. Name a few types of plots available in Matplotlib.
3. What function is used to display plots created by Matplotlib?
4. What is the purpose of customisation in plots?
5. How does Pandas integrate with Matplotlib?

### Long Answer Questions

1. Explain the steps to create and display a line plot using Matplotlib with an example.
2. Discuss the significance of customising plots and provide examples of customisation options in Matplotlib.
3. Compare the plotting capabilities of Matplotlib versus Pandas visualisation methods.

## Source Attribution

| Field | Value |
| :--- | :--- |
| Source | Edzy |
| Reference Type | examSubjectBookChapter |
| Reference ID | 66dfdec53f8b4e9e69bf7d04 |
| Canonical URL | https://www.edzy.ai/cbse-class-12-informatics-practices-plotting-data-using-matplotlib |
| Markdown URL | https://www.edzy.ai/okf/chapter/cbse-class-12-informatics-practices-plotting-data-using-matplotlib.md |