Introduction to Matplotlib

What is Data Visualization?

Data Visualization is the process of converting raw data into charts, graphs, and plots to:

Understand patterns
Detect trends
Find outliers
Analyze relationships

What is Matplotlib?

Matplotlib is a Python library used for creating:

Line charts
Histograms
Boxplots
Pie charts
Scatter plots
Bar charts
3D plots

Widely used in:

Data Science
Machine Learning
Analytics

Installing Matplotlib

Installation

pip install matplotlib

Importing Libraries

import matplotlib.pyplot as plt
import pandas as pd

Explanation

matplotlib.pyplot → plotting functions
pandas → data handling

Basic Plot Function

`plt.plot()`

Creates a line plot.

x = [1,2,3]
y = [4,5,6]

plt.plot(x,y)
plt.grid()
plt.show()

Output

A line graph connecting points:

(1,4)
(2,5)
(3,6)

Explanation

plot() → creates line graph
grid() → adds background grid
show() → displays graph

Components of a Figure

Component	Purpose
Figure	Entire canvas
Axes	X and Y plotting area
Title	Graph heading
Labels	Axis descriptions
Legend	Explains colors/lines
Grid	Improves readability
Ticks	Scale markings

Univariate Analysis

Analyzing one variable.

Line Plot

Shows trends or changes in numerical data.

plt.plot(df['Salary'],
         color='red',
         marker='o',
         linestyle=':',
         linewidth=2)

plt.grid()
plt.show()

Explanation

color='red' → line color
marker='o' → circle markers
linestyle=':' → dotted line
linewidth=2 → line thickness

Output

Salary trend line graph.

Histogram

`plt.hist()`

Shows frequency distribution.

plt.hist(df['Salary'], bins=5, color='green')
plt.show()

Explanation

bins=5 → divides data into 5 ranges
Shows how many values fall into each range

Output

Bar-like histogram distribution.

Box Plot

`plt.boxplot()`

Used for:

detecting outliers
understanding spread
quartile analysis

plt.boxplot(df['Salary'])
plt.show()

Output

Boxplot showing median and quartiles.

Outlier Detection

df.loc[15] = [0]

plt.boxplot(df['Salary'])
plt.show()

Explanation

Adding 0 creates an outlier visible in boxplot.

Categorical Analysis

Pie Chart

Shows percentage contribution.

count = df['dept'].value_counts()

plt.pie(count,
        labels=count.index,
        autopct='%1.2f%%',
        explode=[0,0.1,0])

plt.axis('equal')
plt.show()

Explanation

labels → category names
autopct → percentage display
explode → separates slice
axis('equal') → perfect circle

Output

Department percentage pie chart.

Count Plot / Bar Chart

`plt.bar()`

Shows category frequencies.

plt.bar(count.index, count)
plt.show()

Output

Bar chart of department counts.

Bivariate Analysis

Analyzing relationship between two variables.

Scatter Plot

`plt.scatter()`

Shows relationship between two numerical variables.

plt.scatter(df['Salary'], df['age'])
plt.show()

Explanation

Each dot represents:

X → Salary
Y → Age

Output

Scatter plot showing salary vs age.

Sorted Line Plot

sort_salary = df.sort_values('Salary')

plt.plot(sort_salary['Salary'],
         df['age'],
         color='red',
         marker='o',
         linestyle=':')
plt.grid()
plt.show()

Explanation

Sorting improves line continuity.

Bar Chart

plt.bar(df['age'], df['Salary'], color='green')
plt.show()

Explanation

Compares salary for each age.

Numerical vs Categorical Analysis

Multiple Boxplots

plt.boxplot([hr_sal, it_sal, finance_sal],
            labels=['HR', 'IT', 'Finance'])

plt.show()

Explanation

Compares salary distributions across departments.

Output

Three side-by-side boxplots.

Department Salary Pie Chart

salary_by_dept = df.groupby('dept')['Salary'].sum()

plt.pie(salary_by_dept,
        labels=salary_by_dept.index,
        autopct='%1.2f%%',
        explode=[0,0.1,0],
        shadow=True)

plt.axis('equal')
plt.show()

Explanation

Shows total salary contribution by department.

Mean Salary Bar Chart

plt.bar(['HR','IT','Finance'],
        [hr_mean,it_mean,finance_mean])

plt.show()

Explanation

Displays average salary per department.

Multivariate Analysis

Analyzing 3 or more variables.

Bubble Plot

plt.scatter(df['Salary'],
            df['age'],
            s=df['experience']*100)

plt.title('Salary vs Age vs Experience')
plt.xlabel('Salary')
plt.ylabel('Age')

plt.show()

Explanation

X → Salary
Y → Age
Bubble Size → Experience

Output

Bubble plot with varying circle sizes.

Color-Based Scatter Plot

plt.scatter(df['Salary'],
            df['age'],
            c=df['dept'].map({
                'HR':'red',
                'IT':'green',
                'Finance':'blue'
            }))

plt.show()

Explanation

Different colors represent departments.

Scatter Plot with Legend

color = {'HR':'red',
         'IT':'green',
         'Finance':'blue'}

for dept in color:
    dept_data = df[df['dept'] == dept]

    plt.scatter(dept_data['Salary'],
                dept_data['age'],
                label=dept,
                color=color[dept])

plt.legend()
plt.show()

Explanation

Adds department-wise legend.

Object Oriented API

Provides more control over plots.

`plt.subplots()`

Creates multiple plots.

fig, axs = plt.subplots(2,2, figsize=(10,10))

Explanation

2 rows
2 columns
Figure size = 10x10

Multiple Plots

Line Plot

axs[0,0].plot(df['Salary'],
              color='red',
              marker='o',
              linestyle=':')

axs[0,0].grid()

Histogram

axs[0,1].hist(df['Salary'],
              bins=5,
              color='green')

Boxplot

axs[1,0].boxplot(df['Salary'])

Saving Figures

`savefig()`

Saves plot locally.

plt.savefig('plot.png')

Explanation

Saves graph as PNG image.

Multiple Line Plots

plt.plot(df2['Year'], df2['Sales'], label='Sales')

plt.plot(df2['Year'], df2['Profit'], label='Profit')

plt.plot(df2['Year'], df2['Expenses'], label='Expenses')

plt.legend()
plt.show()

Explanation

Displays multiple lines in same graph.

Output

Sales, Profit, and Expenses comparison graph.

3D Plot

ax = plt.figure().add_subplot(projection='3d')

ax.scatter(df2['Year'],
           df2['Sales'],
           df2['Profit'])

plt.show()

Explanation

Creates 3D scatter plot. Axes:

X → Year
Y → Sales
Z → Profit

Plotly 3D Plot

import plotly.express as px

fig = px.scatter_3d(df2,
                    x='Year',
                    y='Sales',
                    z='Profit')

fig.show()

Explanation

Interactive 3D visualization using Plotly.

Important Plot Types Summary

Plot Type	Used For
Line Plot	Trends over time
Histogram	Frequency distribution
Boxplot	Outlier detection
Pie Chart	Percentage distribution
Bar Chart	Category comparison
Scatter Plot	Relationship between variables
Bubble Plot	3-variable analysis
3D Plot	Three-dimensional analysis

Important Matplotlib Functions

Function	Purpose
`plot()`	Line graph
`hist()`	Histogram
`boxplot()`	Boxplot
`pie()`	Pie chart
`bar()`	Bar chart
`scatter()`	Scatter plot
`legend()`	Show legend
`title()`	Graph title
`xlabel()`	X-axis label
`ylabel()`	Y-axis label
`grid()`	Show grid
`show()`	Display graph
`savefig()`	Save figure

Matplotlib Usage

Matplotlib helps to:

Visualize datasets
Understand trends
Detect outliers
Compare categories
Analyze relationships
Create professional graphs

​Introduction to Matplotlib

​What is Data Visualization?

​What is Matplotlib?

​Installing Matplotlib

​Installation

​Importing Libraries

​Explanation

​Basic Plot Function

​plt.plot()

​Output

​Explanation

​Components of a Figure

​Univariate Analysis

​Line Plot

​Explanation

​Output

​Histogram

​plt.hist()

​Explanation

​Output

​Box Plot

​plt.boxplot()

​Output

​Outlier Detection

​Explanation

​Categorical Analysis

​Pie Chart

​Explanation

​Output

​Count Plot / Bar Chart

​plt.bar()

​Output

​Bivariate Analysis

​Scatter Plot

​plt.scatter()

​Explanation

​Output

​Sorted Line Plot

​Explanation

​Bar Chart

​Explanation

​Numerical vs Categorical Analysis

​Multiple Boxplots

​Explanation

​Output

​Department Salary Pie Chart

​Explanation

​Mean Salary Bar Chart

​Explanation

​Multivariate Analysis

​Bubble Plot

​Explanation

​Output

​Color-Based Scatter Plot

​Explanation

​Scatter Plot with Legend

​Explanation

​Object Oriented API

​plt.subplots()

​Explanation

​Multiple Plots

​Line Plot

​Histogram

​Boxplot

​Saving Figures

​savefig()

​Explanation

​Multiple Line Plots

​Explanation

​Output

​3D Plot

​Explanation

​Plotly 3D Plot

​Explanation

​Important Plot Types Summary

​Important Matplotlib Functions

​Matplotlib Usage

Introduction to Matplotlib

What is Data Visualization?

What is Matplotlib?

Installing Matplotlib

Installation

Importing Libraries

Explanation

Basic Plot Function

`plt.plot()`

Output

Explanation

Components of a Figure

Univariate Analysis

Line Plot

Explanation

Output

Histogram

`plt.hist()`

Explanation

Output

Box Plot

`plt.boxplot()`

Output

Outlier Detection

Explanation

Categorical Analysis

Pie Chart

Explanation

Output

Count Plot / Bar Chart

`plt.bar()`

Output

Bivariate Analysis

Scatter Plot

`plt.scatter()`

Explanation

Output

Sorted Line Plot

Explanation

Bar Chart

Explanation

Numerical vs Categorical Analysis

Multiple Boxplots

Explanation

Output

Department Salary Pie Chart

Explanation

Mean Salary Bar Chart

Explanation

Multivariate Analysis

Bubble Plot

Explanation

Output

Color-Based Scatter Plot

Explanation

Scatter Plot with Legend

Explanation

Object Oriented API

`plt.subplots()`

Explanation

Multiple Plots

Line Plot

Histogram

Boxplot

Saving Figures

`savefig()`

Explanation

Multiple Line Plots

Explanation

Output

3D Plot

Explanation

Plotly 3D Plot

Explanation

Important Plot Types Summary

Important Matplotlib Functions

Matplotlib Usage