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Data Analysis · Lesson 36 of 56

Matplotlib

Source: 10-Data Analysis With Python/10.5-matplotlib.ipynb

Start here — no coding background needed

What you will learn

Draw charts from numbers — lines, bars, scatter.

In simple words

Charts help you see trends faster than reading hundreds of numbers.

Spreadsheet-style work with code — for data jobs. Beginners: read concepts, run small examples.

Easy example — try this first

Easy example — run this first. Change values and press Run again.

Python

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference notes (from full bootcamp)

Optional — deeper detail for when you are ready

Data Visualization With Matplotlib

Matplotlib is a powerful plotting library for Python that enables the creation of static, animated, and interactive visualizations. It is widely used for data visualization in data science and analytics. In this lesson, we will cover the basics of Matplotlib, including creating various types of plots and customizing them.

Example HCL
HCL
!pip install matplotlib

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Reference example
Python

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Example HCL
HCL
x=[1,2,3,4,5]
y=[1,4,9,16,25]

##create a line plot
plt.plot(x,y)
plt.xlabel('X axis')
plt.ylabel('Y Axis')
plt.title("Basic Line Plot")
plt.show()

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Reference example
Python

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference example
Python
Output
Expected (from notebook):
Text(0.5, 1.0, 'Plot 4')

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Example HCL
HCL
###Bar Plor
categories=['A','B','C','D','E']
values=[5,7,3,8,6]

##create a bar plot
plt.bar(categories,values,color='purple')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot')
plt.show()

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Histograms

Histograms are used to represent the distribution of a dataset. They divide the data into bins and count the number of data points in each bin.

Reference example
Python
Output
Expected (from notebook):
(array([1., 2., 3., 4., 5.]),
 array([1. , 1.8, 2.6, 3.4, 4.2, 5. ]),
 <BarContainer object of 5 artists>)

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Reference example
Python
Output
Expected (from notebook):
<matplotlib.collections.PathCollection at 0x25699a6b080>

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference example
Python
Output
Expected (from notebook):
([<matplotlib.patches.Wedge at 0x2569ea3ca10>,
  <matplotlib.patches.Wedge at 0x2569ea3c8f0>,
  <matplotlib.patches.Wedge at 0x2569ea3d4f0>,
  <matplotlib.patches.Wedge at 0x2569ea3dbb0>],
 [Text(0.764120788592483, 1.051722121304293, 'A'),
  Text(-0.8899187482945419, 0.6465637025335369, 'B'),
  Text(-0.3399185762739153, -1.046162206115244, 'C'),
  Text(1.0461622140716127, -0.3399185517867209, 'D')],
 [Text(0.47022817759537416, 0.6472136131103341, '30.0%'),
  Text(-0.4854102263424773, 0.3526711104728383, '20.0%'),
  Text(-0.1854101325130447, -0.5706339306083149, '40.0%'),
  Text(0.5706339349481523, -0.18541011915639322, '10.0%')])

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Example HCL
HCL
## Sales Data Visualization
import pandas as pd
sales_data_df=pd.read_csv('sales_data.csv')
sales_data_df.head(5)

Browser practice only — full example needs Python on your computer (files, Flask, threads, etc.).

Reference example
Python
Output
Expected (from notebook):
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 240 entries, 0 to 239
Data columns (total 9 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Transaction ID    240 non-null    int64  
 1   Date              240 non-null    object 
 2   Product Category  240 non-null    object 
 3   Product Name      240 non-null    object 
 4   Units Sold        240 non-null    int64  
 5   Unit Price        240 non-null    float64
 6   Total Revenue     240 non-null    float64
 7   Region            240 non-null    object 
 8   Payment Method    240 non-null    object 
dtypes: float64(2), int64(2), object(5)
memory usage: 17.0+ KB

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference example
Python
Output
Expected (from notebook):
Product Category
Beauty Products     2621.90
Books               1861.93
Clothing            8128.93
Electronics        34982.41
Home Appliances    18646.16
Sports             14326.52
Name: Total Revenue, dtype: float64

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference example
Python
Output
Expected (from notebook):
<Axes: xlabel='Product Category'>

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Reference example
Python
Output
Expected (from notebook):
[<matplotlib.lines.Line2D at 0x2569e9b46e0>]

Runs in your browser via Pyodide — no server. First run may take a few seconds.

Practice test — try yourself

Write code, press Check. Wrong answer shows the correct code to copy & run.

You learned "Matplotlib". Use print() to show: Done: Matplotlib

Hint: Use one print() with the exact text.

Python