Matplotlib

Learning Objectives

By the end of this lesson, you will be able to:
- Understand what Matplotlib is and why it's important
- Install and import Matplotlib
- Create basic plots (line, bar, scatter)
- Customize plots (colors, labels, titles, legends)
- Work with subplots
- Save plots to files
- Understand plot customization options
- Create publication-quality visualizations
- Handle different plot types
- Apply Matplotlib best practices
- Debug plotting issues

Lesson 23.1: Matplotlib

Learning Objectives

By the end of this lesson, you will be able to:

Understand what Matplotlib is and why it's important
Install and import Matplotlib
Create basic plots (line, bar, scatter)
Customize plots (colors, labels, titles, legends)
Work with subplots
Save plots to files
Understand plot customization options
Create publication-quality visualizations
Handle different plot types
Apply Matplotlib best practices
Debug plotting issues

Introduction to Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It's the foundation for many other visualization libraries.

Key Features:

Versatile: Line plots, bar charts, scatter plots, histograms, and more
Customizable: Extensive customization options
Publication-quality: Create publication-ready figures
Integration: Works with NumPy, Pandas, and other libraries
Multiple backends: Support for different output formats

Why Matplotlib?

Industry standard for Python visualization
Highly customizable
Works with scientific data
Foundation for other libraries (Seaborn, Plotly)
Extensive documentation and community support

Installation

Installing Matplotlib

# Install Matplotlib using pip
pip install matplotlib

# Install with conda (if using Anaconda)
conda install matplotlib

# Verify installation
python -c "import matplotlib; print(matplotlib.__version__)"

Importing Matplotlib

# Standard import
import matplotlib.pyplot as plt

# For inline plots in Jupyter
%matplotlib inline

# For interactive plots
%matplotlib notebook

Creating Basic Plots

Simple Line Plot

import matplotlib.pyplot as plt
import numpy as np

# Create data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
plt.plot(x, y)
plt.show()

Plot with Labels and Title

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Sine Wave')
plt.show()

Multiple Lines

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label='sin(x)')
plt.plot(x, y2, label='cos(x)')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Trigonometric Functions')
plt.legend()
plt.show()

Line Plots

Basic Line Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = x ** 2

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

Line Plot Customization

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

# Customize line
plt.plot(x, y, 
         color='red',           # Line color
         linestyle='--',        # Line style: '-', '--', '-.', ':'
         linewidth=2,           # Line width
         marker='o',            # Marker style
         markersize=5,         # Marker size
         label='sin(x)')
plt.legend()
plt.show()

Line Styles and Markers

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 20)

# Different line styles
plt.plot(x, x, 'r-', label='solid')      # Red solid line
plt.plot(x, x+1, 'g--', label='dashed')  # Green dashed line
plt.plot(x, x+2, 'b-.', label='dashdot')  # Blue dash-dot
plt.plot(x, x+3, 'm:', label='dotted')     # Magenta dotted

# Different markers
plt.plot(x, x+4, 'ro', label='circles')   # Red circles
plt.plot(x, x+5, 'gs', label='squares')   # Green squares
plt.plot(x, x+6, 'b^', label='triangles')  # Blue triangles

plt.legend()
plt.show()

Bar Plots

Basic Bar Plot

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
values = [10, 25, 15, 30]

plt.bar(categories, values)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Chart')
plt.show()

Horizontal Bar Plot

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
values = [10, 25, 15, 30]

plt.barh(categories, values)
plt.xlabel('Values')
plt.ylabel('Categories')
plt.title('Horizontal Bar Chart')
plt.show()

Grouped Bar Plot

import matplotlib.pyplot as plt
import numpy as np

categories = ['A', 'B', 'C', 'D']
values1 = [10, 25, 15, 30]
values2 = [15, 20, 25, 20]

x = np.arange(len(categories))
width = 0.35

plt.bar(x - width/2, values1, width, label='Group 1')
plt.bar(x + width/2, values2, width, label='Group 2')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Grouped Bar Chart')
plt.xticks(x, categories)
plt.legend()
plt.show()

Stacked Bar Plot

import matplotlib.pyplot as plt
import numpy as np

categories = ['A', 'B', 'C', 'D']
values1 = [10, 25, 15, 30]
values2 = [15, 20, 25, 20]

plt.bar(categories, values1, label='Group 1')
plt.bar(categories, values2, bottom=values1, label='Group 2')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Stacked Bar Chart')
plt.legend()
plt.show()

Scatter Plots

Basic Scatter Plot

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(100)
y = np.random.randn(100)

plt.scatter(x, y)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot')
plt.show()

Scatter Plot Customization

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

plt.scatter(x, y, 
            c=colors,        # Color array
            s=sizes,        # Size array
            alpha=0.5,      # Transparency
            cmap='viridis')  # Colormap
plt.colorbar()  # Add colorbar
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Customized Scatter Plot')
plt.show()

Scatter Plot with Different Markers

import matplotlib.pyplot as plt
import numpy as np

x1 = np.random.randn(50)
y1 = np.random.randn(50)
x2 = np.random.randn(50)
y2 = np.random.randn(50)

plt.scatter(x1, y1, marker='o', label='Group 1')
plt.scatter(x2, y2, marker='s', label='Group 2')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot with Groups')
plt.legend()
plt.show()

Customization

Colors

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

# Named colors
plt.plot(x, y, color='red')
plt.plot(x, y+1, color='blue')
plt.plot(x, y+2, color='green')

# RGB tuples (0-1 range)
plt.plot(x, y+3, color=(0.5, 0.3, 0.8))

# Hex colors
plt.plot(x, y+4, color='#FF5733')

plt.show()

Labels and Titles

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel('X Axis Label', fontsize=12, fontweight='bold')
plt.ylabel('Y Axis Label', fontsize=12)
plt.title('Plot Title', fontsize=14, fontweight='bold')
plt.show()

Legends

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label='sin(x)')
plt.plot(x, y2, label='cos(x)')
plt.legend(loc='upper right', fontsize=10, frameon=True, shadow=True)
plt.show()

Grid and Axes

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.grid(True, alpha=0.3, linestyle='--')
plt.xlim(0, 10)
plt.ylim(-1.5, 1.5)
plt.xticks([0, 2, 4, 6, 8, 10])
plt.yticks([-1, 0, 1])
plt.show()

Figure Size and DPI

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

# Set figure size
plt.figure(figsize=(10, 6))  # Width, height in inches
plt.plot(x, y)
plt.show()

# Set DPI for higher resolution
plt.figure(figsize=(10, 6), dpi=300)
plt.plot(x, y)
plt.show()

Style Sheets

import matplotlib.pyplot as plt
import numpy as np

# Available styles
print(plt.style.available)

# Use a style
plt.style.use('seaborn-v0_8-darkgrid')

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

# Reset to default
plt.style.use('default')

Subplots

Creating Subplots

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

# Create 2x2 subplots
fig, axes = plt.subplots(2, 2, figsize=(10, 8))

axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('sin(x)')

axes[0, 1].plot(x, np.cos(x))
axes[0, 1].set_title('cos(x)')

axes[1, 0].plot(x, np.tan(x))
axes[1, 0].set_title('tan(x)')

axes[1, 1].plot(x, np.exp(-x))
axes[1, 1].set_title('exp(-x)')

plt.tight_layout()
plt.show()

Subplot with Shared Axes

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)

axes[0].plot(x, np.sin(x))
axes[0].set_ylabel('sin(x)')
axes[0].set_title('Trigonometric Functions')

axes[1].plot(x, np.cos(x))
axes[1].set_xlabel('X')
axes[1].set_ylabel('cos(x)')

plt.tight_layout()
plt.show()

Other Plot Types

Histogram

import matplotlib.pyplot as plt
import numpy as np

data = np.random.normal(100, 15, 1000)

plt.hist(data, bins=30, edgecolor='black', alpha=0.7)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()

Pie Chart

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]
colors = ['red', 'blue', 'green', 'yellow']

plt.pie(sizes, labels=categories, colors=colors, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()

Box Plot

import matplotlib.pyplot as plt
import numpy as np

data = [np.random.normal(0, std, 100) for std in range(1, 4)]

plt.boxplot(data, labels=['Group 1', 'Group 2', 'Group 3'])
plt.ylabel('Value')
plt.title('Box Plot')
plt.show()

Saving Plots

Save to File

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Sine Wave')

# Save as PNG
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

# Save as PDF
plt.savefig('plot.pdf', bbox_inches='tight')

# Save as SVG
plt.savefig('plot.svg', bbox_inches='tight')

plt.show()

Working with Pandas Data

Plotting from DataFrame

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

# Create sample data
df = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y1': np.sin(np.linspace(0, 10, 100)),
    'y2': np.cos(np.linspace(0, 10, 100))
})

# Plot directly from DataFrame
df.plot(x='x', y='y1', label='sin(x)')
df.plot(x='x', y='y2', label='cos(x)')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Plot from DataFrame')
plt.legend()
plt.show()

Plotting Multiple Columns

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

df = pd.DataFrame({
    'A': np.random.randn(100),
    'B': np.random.randn(100),
    'C': np.random.randn(100)
})

df.plot(kind='line')
plt.title('Multiple Columns')
plt.show()

Practical Examples

Example 1: Time Series Plot

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

# Create time series data
dates = pd.date_range('2023-01-01', periods=100, freq='D')
values = np.random.randn(100).cumsum()

plt.figure(figsize=(12, 6))
plt.plot(dates, values)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Time Series Plot')
plt.xticks(rotation=45)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

Example 2: Comparison Plot

import matplotlib.pyplot as plt
import numpy as np

categories = ['Q1', 'Q2', 'Q3', 'Q4']
sales_2022 = [100, 120, 110, 130]
sales_2023 = [110, 125, 115, 140]

x = np.arange(len(categories))
width = 0.35

plt.bar(x - width/2, sales_2022, width, label='2022')
plt.bar(x + width/2, sales_2023, width, label='2023')
plt.xlabel('Quarter')
plt.ylabel('Sales')
plt.title('Sales Comparison')
plt.xticks(x, categories)
plt.legend()
plt.show()

Common Mistakes and Pitfalls

1. Forgetting plt.show()

# WRONG: Plot may not display
plt.plot([1, 2, 3, 4])
# Missing plt.show()

# CORRECT: Always call plt.show()
plt.plot([1, 2, 3, 4])
plt.show()

2. Not Clearing Figure

# WRONG: Plots accumulate
for i in range(5):
    plt.plot([i, i+1, i+2])

# CORRECT: Clear figure or create new one
for i in range(5):
    plt.figure()
    plt.plot([i, i+1, i+2])
    plt.show()

3. Incorrect Subplot Indexing

# WRONG: May cause IndexError
fig, axes = plt.subplots(2, 2)
axes[2, 0].plot([1, 2, 3])  # IndexError!

# CORRECT: Use correct indices
fig, axes = plt.subplots(2, 2)
axes[1, 0].plot([1, 2, 3])  # Correct

Best Practices

1. Use Figure Objects

# Good: Explicit figure management
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Title')
plt.show()

2. Consistent Style

# Define style at the beginning
plt.style.use('seaborn-v0_8')
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['font.size'] = 12

3. Save High-Quality Figures

# Save with high DPI for publications
plt.savefig('figure.png', dpi=300, bbox_inches='tight')

Practice Exercise

Exercise: Plotting

Objective: Create various types of plots with customization.

Instructions:

Create line plots with different styles
Create bar charts (vertical and horizontal)
Create scatter plots with customization
Create subplots
Customize colors, labels, legends
Save plots to files

Example Solution:

"""
Matplotlib Plotting Exercise
This program demonstrates various plotting techniques.
"""

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

print("=" * 50)
print("Matplotlib Plotting Exercise")
print("=" * 50)

# Set style
plt.style.use('default')
plt.rcParams['figure.figsize'] = (10, 6)

# 1. Line Plot
print("\n1. Creating Line Plot:")
print("-" * 50)

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y1, label='sin(x)', linewidth=2, color='blue')
plt.plot(x, y2, label='cos(x)', linewidth=2, color='red', linestyle='--')
plt.xlabel('X', fontsize=12)
plt.ylabel('Y', fontsize=12)
plt.title('Trigonometric Functions', fontsize=14, fontweight='bold')
plt.legend(fontsize=10)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('line_plot.png', dpi=300, bbox_inches='tight')
print("Line plot saved as 'line_plot.png'")
plt.show()

# 2. Bar Chart
print("\n2. Creating Bar Chart:")
print("-" * 50)

categories = ['Product A', 'Product B', 'Product C', 'Product D']
sales = [150, 200, 175, 225]

plt.figure(figsize=(10, 6))
bars = plt.bar(categories, sales, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A'])
plt.xlabel('Products', fontsize=12)
plt.ylabel('Sales', fontsize=12)
plt.title('Product Sales', fontsize=14, fontweight='bold')
plt.grid(True, alpha=0.3, axis='y')

# Add value labels on bars
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{int(height)}',
             ha='center', va='bottom')

plt.tight_layout()
plt.savefig('bar_chart.png', dpi=300, bbox_inches='tight')
print("Bar chart saved as 'bar_chart.png'")
plt.show()

# 3. Scatter Plot
print("\n3. Creating Scatter Plot:")
print("-" * 50)

np.random.seed(42)
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

plt.figure(figsize=(10, 6))
plt.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')
plt.colorbar(label='Color Scale')
plt.xlabel('X', fontsize=12)
plt.ylabel('Y', fontsize=12)
plt.title('Scatter Plot with Color and Size', fontsize=14, fontweight='bold')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight')
print("Scatter plot saved as 'scatter_plot.png'")
plt.show()

# 4. Subplots
print("\n4. Creating Subplots:")
print("-" * 50)

x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Subplot 1: Line plot
axes[0, 0].plot(x, np.sin(x), color='blue')
axes[0, 0].set_title('sin(x)', fontweight='bold')
axes[0, 0].set_xlabel('X')
axes[0, 0].set_ylabel('Y')
axes[0, 0].grid(True, alpha=0.3)

# Subplot 2: Line plot
axes[0, 1].plot(x, np.cos(x), color='red')
axes[0, 1].set_title('cos(x)', fontweight='bold')
axes[0, 1].set_xlabel('X')
axes[0, 1].set_ylabel('Y')
axes[0, 1].grid(True, alpha=0.3)

# Subplot 3: Bar chart
categories = ['A', 'B', 'C']
values = [10, 20, 15]
axes[1, 0].bar(categories, values, color=['#FF6B6B', '#4ECDC4', '#45B7D1'])
axes[1, 0].set_title('Bar Chart', fontweight='bold')
axes[1, 0].set_xlabel('Category')
axes[1, 0].set_ylabel('Value')
axes[1, 0].grid(True, alpha=0.3, axis='y')

# Subplot 4: Histogram
data = np.random.normal(100, 15, 1000)
axes[1, 1].hist(data, bins=30, edgecolor='black', alpha=0.7, color='green')
axes[1, 1].set_title('Histogram', fontweight='bold')
axes[1, 1].set_xlabel('Value')
axes[1, 1].set_ylabel('Frequency')
axes[1, 1].grid(True, alpha=0.3, axis='y')

plt.suptitle('Multiple Subplots', fontsize=16, fontweight='bold', y=0.995)
plt.tight_layout()
plt.savefig('subplots.png', dpi=300, bbox_inches='tight')
print("Subplots saved as 'subplots.png'")
plt.show()

# 5. Grouped Bar Chart
print("\n5. Creating Grouped Bar Chart:")
print("-" * 50)

categories = ['Q1', 'Q2', 'Q3', 'Q4']
sales_2022 = [100, 120, 110, 130]
sales_2023 = [110, 125, 115, 140]

x = np.arange(len(categories))
width = 0.35

plt.figure(figsize=(10, 6))
bars1 = plt.bar(x - width/2, sales_2022, width, label='2022', color='#FF6B6B')
bars2 = plt.bar(x + width/2, sales_2023, width, label='2023', color='#4ECDC4')

plt.xlabel('Quarter', fontsize=12)
plt.ylabel('Sales', fontsize=12)
plt.title('Sales Comparison by Quarter', fontsize=14, fontweight='bold')
plt.xticks(x, categories)
plt.legend(fontsize=10)
plt.grid(True, alpha=0.3, axis='y')

# Add value labels
for bars in [bars1, bars2]:
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height,
                 f'{int(height)}',
                 ha='center', va='bottom', fontsize=9)

plt.tight_layout()
plt.savefig('grouped_bar.png', dpi=300, bbox_inches='tight')
print("Grouped bar chart saved as 'grouped_bar.png'")
plt.show()

print("\n" + "=" * 50)
print("Exercise completed!")
print("=" * 50)

Expected Output: Creates and saves various types of plots with customization.

Challenge (Optional):

Create interactive plots
Add annotations to plots
Create 3D plots
Create animated plots
Integrate with real data

Key Takeaways

Matplotlib - Powerful plotting library for Python
Basic plots - Line, bar, scatter plots
Customization - Colors, labels, titles, legends, grids
Subplots - Multiple plots in one figure
Line plots - For continuous data
Bar charts - For categorical data
Scatter plots - For relationships between variables
Saving plots - Save to PNG, PDF, SVG
Pandas integration - Plot directly from DataFrames
Style sheets - Use predefined styles
Figure management - Use figure objects for better control
Best practices - Consistent style, high DPI for publications
Common mistakes - Forgetting plt.show(), not clearing figures
Customization options - Extensive customization available
Publication quality - Create professional visualizations

Quiz: Matplotlib

Test your understanding with these questions:

What is the standard import for Matplotlib?
- A) import matplotlib
- B) import matplotlib.pyplot as plt
- C) from matplotlib import *
- D) import plt
What creates a line plot?
- A) plt.line()
- B) plt.plot()
- C) plt.draw()
- D) plt.show()
What adds a legend?
- A) plt.legend()
- B) plt.label()
- C) plt.key()
- D) plt.notes()
What creates subplots?
- A) plt.subplot()
- B) plt.subplots()
- C) plt.multiplot()
- D) plt.split()
What saves a plot?
- A) plt.save()
- B) plt.savefig()
- C) plt.export()
- D) plt.write()
What sets figure size?
- A) plt.size()
- B) plt.figsize()
- C) plt.figure(figsize=())
- D) plt.set_size()
What creates a bar chart?
- A) plt.bar()
- B) plt.barh()
- C) plt.barchart()
- D) Both A and B
What creates a scatter plot?
- A) plt.scatter()
- B) plt.points()
- C) plt.dot()
- D) plt.plot(kind='scatter')
What displays the plot?
- A) plt.display()
- B) plt.show()
- C) plt.render()
- D) plt.draw()
What adds grid to plot?
- A) plt.grid()
- B) plt.show_grid()
- C) plt.add_grid()
- D) plt.set_grid()

Answers:

B) import matplotlib.pyplot as plt (standard import)
B) plt.plot() (line plot function)
A) plt.legend() (add legend)
B) plt.subplots() (create subplots)
B) plt.savefig() (save figure)
C) plt.figure(figsize=()) (set figure size)
D) Both A and B (bar and barh)
A) plt.scatter() (scatter plot)
B) plt.show() (display plot)
A) plt.grid() (add grid)

Next Steps

Excellent work! You've mastered Matplotlib basics. You now understand:

Creating plots
Line, bar, scatter plots
Customization
How to visualize data

What's Next?

Lesson 23.2: Seaborn
Learn statistical visualization
Advanced plotting techniques
More visualization options

Additional Resources

Matplotlib Documentation: matplotlib.org/
Matplotlib Tutorials: matplotlib.org/stable/tutorials/index.html
Matplotlib Gallery: matplotlib.org/stable/gallery/index.html
Pyplot Tutorial: matplotlib.org/stable/tutorials/introductory/pyplot.html

Lesson completed! You're ready to move on to the next lesson.

Previous: Data Cleaning Next: Seaborn

Matplotlib

Learning Objectives

Lesson 23.1: Matplotlib

Learning Objectives

Introduction to Matplotlib

Installation

Installing Matplotlib

Importing Matplotlib

Creating Basic Plots

Simple Line Plot

Plot with Labels and Title

Multiple Lines

Line Plots

Basic Line Plot

Line Plot Customization

Line Styles and Markers

Bar Plots

Basic Bar Plot

Horizontal Bar Plot

Grouped Bar Plot

Stacked Bar Plot

Scatter Plots

Basic Scatter Plot

Scatter Plot Customization

Scatter Plot with Different Markers

Customization

Colors

Labels and Titles

Legends

Grid and Axes

Figure Size and DPI

Style Sheets

Subplots

Creating Subplots

Subplot with Shared Axes

Other Plot Types

Histogram

Pie Chart

Box Plot

Saving Plots

Save to File

Working with Pandas Data

Plotting from DataFrame

Plotting Multiple Columns

Practical Examples

Example 1: Time Series Plot

Example 2: Comparison Plot

Common Mistakes and Pitfalls

1. Forgetting plt.show()

2. Not Clearing Figure

3. Incorrect Subplot Indexing

Best Practices

1. Use Figure Objects

2. Consistent Style

3. Save High-Quality Figures

Practice Exercise

Exercise: Plotting

Key Takeaways

Quiz: Matplotlib

Next Steps

Additional Resources

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