Goal

Learn how to create professional plots and visualizations using Matplotlib. Data visualization is crucial for understanding results and communicating findings.

Prerequisites

1. Introduction

Matplotlib is Python’s primary library for creating static plots and visualizations. It’s widely used in scientific research for publication-quality figures.

In this tutorial, you’ll learn to create:

Line plots
Scatter plots
Histograms
Bar plots
Subplots (multiple plots in one figure)
Customized plots with labels, legend, and styling

2. Installation

Matplotlib usually comes with Anaconda/Miniconda. Install explicitly with:

pip install matplotlib

3. Basic Plotting

3.1 Simple Line Plot

import matplotlib.pyplot as plt
 
# Data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
 
# Create plot
plt.plot(x, y)
plt.xlabel("X-axis Label")
plt.ylabel("Y-axis Label")
plt.title("Simple Line Plot")
plt.show()

3.2 Scatter Plot

import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 6]
 
plt.scatter(x, y)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Scatter Plot")
plt.show()

3.3 Histogram

import matplotlib.pyplot as plt
import numpy as np
 
data = np.random.normal(100, 15, 1000)  # 1000 values, mean=100, std=15
 
plt.hist(data, bins=30)
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.title("Distribution of Data")
plt.show()

3.4 Bar Plot

import matplotlib.pyplot as plt
 
categories = ["Physics", "Chemistry", "Biology"]
values = [85, 78, 92]
 
plt.bar(categories, values)
plt.ylabel("Score")
plt.title("Subject Scores")
plt.show()

4. Customizing Plots

4.1 Colors, Markers, and Line Styles

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
 
# Customize appearance
plt.plot(x, y, 
    color='red',           # or 'r', '#FF0000'
    marker='o',            # o, s, ^, *, +
    linestyle='--',        # '-', '--', '-.', ':'
    linewidth=2,
    markersize=8,
    label='y = x²'
)
 
plt.legend()               # Show label
plt.show()

4.2 Axis Limits and Grid

x = [1, 2, 3, 4]
y = [1, 4, 2, 3]
 
plt.plot(x, y)
plt.xlim(0, 5)             # Set x-axis limits
plt.ylim(0, 5)             # Set y-axis limits
plt.grid(True, alpha=0.3)  # Add grid
 
plt.show()

4.3 Labels and Text

plt.figure(figsize=(10, 6))  # Set figure size
 
plt.plot([1, 2, 3], [1, 4, 9])
plt.title("Quadratic Function", fontsize=16)
plt.xlabel("X", fontsize=12)
plt.ylabel("Y", fontsize=12)
 
# Add text at specific location
plt.text(2.5, 7, "Important Point", fontsize=10)
 
plt.show()

5. Multiple Plots (Subplots)

5.1 Create Subplots

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
 
# First subplot
ax1.plot([1, 2, 3], [1, 4, 9])
ax1.set_title("Plot 1")
ax1.set_xlabel("X")
ax1.set_ylabel("Y")
 
# Second subplot
ax2.scatter([1, 2, 3], [3, 2, 1])
ax2.set_title("Plot 2")
ax2.set_xlabel("X")
ax2.set_ylabel("Y")
 
plt.tight_layout()  # Adjust spacing
plt.show()

5.2 2x2 Subplots

fig, axes = plt.subplots(2, 2, figsize=(10, 8))
 
# Flatten to iterate easily
for ax in axes.flat:
    ax.plot([1, 2, 3], [1, 2, 3])
    ax.set_xlabel("X")
    ax.set_ylabel("Y")
 
plt.tight_layout()
plt.show()

6. Working with Data

With Pandas

import matplotlib.pyplot as plt
import pandas as pd
 
df = pd.DataFrame({
    "Month": ["Jan", "Feb", "Mar", "Apr"],
    "Sales": [100, 150, 120, 200]
})
 
plt.bar(df["Month"], df["Sales"])
plt.ylabel("Sales")
plt.title("Monthly Sales")
plt.show()

With NumPy

import numpy as np
import matplotlib.pyplot as plt
 
x = np.linspace(0, 2*np.pi, 100)  # 100 points from 0 to 2π
y = np.sin(x)
 
plt.plot(x, y)
plt.title("Sine Wave")
plt.show()

7. Saving Figures

import matplotlib.pyplot as plt
 
plt.plot([1, 2, 3], [1, 4, 9])
plt.title("My Plot")
 
# Save to file
plt.savefig("my_plot.png", dpi=300, bbox_inches='tight')
plt.show()
 
# Also save as PDF for publications
plt.savefig("my_plot.pdf")

8. Common Plot Types

Box Plot (for distributions)

data1 = [1, 2, 3, 4, 5]
data2 = [2, 4, 6, 8, 10]
 
plt.boxplot([data1, data2], labels=['Dataset 1', 'Dataset 2'])
plt.ylabel("Values")
plt.show()

Heatmap (requires seaborn or special handling)

import matplotlib.pyplot as plt
import numpy as np
 
data = np.random.random((5, 5))
 
plt.imshow(data, cmap='viridis')  # cmap = colormap
plt.colorbar()
plt.title("Heatmap")
plt.show()

9. Style and Aesthetics

import matplotlib.pyplot as plt
 
# Use a different style
plt.style.use('seaborn-v0_8-darkgrid')  # Other: 'ggplot', 'bmh', etc.
 
x = [1, 2, 3]
y = [1, 4, 9]
plt.plot(x, y, marker='o', linewidth=2)
plt.show()

10. Pro Tips

Always label axes - viewers need to understand what they’re looking at
Use appropriate plot types - bar plot for categories, scatter for correlations, histogram for distributions
Keep it simple - too many colors/elements make plots hard to read
Use high DPI when saving - dpi=300 is good for publications
Add units to labels - e.g., “Temperature (°C)” not just “Temperature”

Resources

Matplotlib Documentation
Matplotlib Gallery - examples of different plot types
Seaborn - higher-level plotting on top of Matplotlib

Next Steps

Now that you can visualize data, let’s learn about advanced statistical analysis: 13 SciPy and Fitting.

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12 Matplotlib and Data Visualization