Documentation Index
Fetch the complete documentation index at: https://ai.tharung.in/llms.txt
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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
Importing Libraries
import matplotlib.pyplot as plt
import pandas as pd
Explanation
matplotlib.pyplot → plotting functionspandas → 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:
Explanation
plot() → creates line graphgrid() → adds background gridshow() → 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 colormarker='o' → circle markerslinestyle=':' → dotted linelinewidth=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 namesautopct → percentage displayexplode → separates sliceaxis('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:
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.
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
