Data Visualization using Matplotlib
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Below is a complete guide covering various types of graphs with explanations, syntax, and example code.
Table of Contents
- Line Plot
- Bar Chart
- Histogram
- Scatter Plot
- Pie Chart
- Box Plot
- Violin Plot
- Heatmap
- Area Plot
- 3D Plotting
Line Plot
Used to visualize trends over time or ordered categories.
Syntax:
plt.plot(x, y, linestyle, color, marker, label)
Example:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(8, 4))
plt.plot(x, y, linestyle='-', color='blue', marker='.', label='sin(x)')
plt.title('Sine Wave')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.grid(True)
plt.show()
Bar Chart
Used to compare categorical data.
Syntax:
plt.bar(x, height, width, color, label)
Example:
categories = ['A', 'B', 'C', 'D']
values = [15, 20, 12, 25]
plt.figure(figsize=(6, 4))
plt.bar(categories, values, width=0.6, color=['red', 'green', 'blue', 'orange'])
plt.title('Bar Chart Example')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.show()
Histogram
Used to show the distribution of numerical data.
Syntax:
plt.hist(data, bins, color, edgecolor)
Example:
data = np.random.normal(0, 1, 1000)
plt.figure(figsize=(6, 4))
plt.hist(data, bins=30, color='skyblue', edgecolor='black')
plt.title('Histogram of Normal Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()
Scatter Plot
Used to show relationship between two numerical variables.
Syntax:
plt.scatter(x, y, color, marker, size)
Example:
x = np.random.rand(50)
y = x + np.random.normal(0, 0.1, 50)
plt.figure(figsize=(6, 4))
plt.scatter(x, y, color='green', marker='o', s=50)
plt.title('Scatter Plot Example')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.show()
Pie Chart
Used to show proportions of a whole.
Syntax:
plt.pie(data, labels, colors, explode, autopct)
Example:
sizes = [30, 25, 15, 10, 20]
labels = ['A', 'B', 'C', 'D', 'E']
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange']
plt.figure(figsize=(6, 6))
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
plt.title('Pie Chart Example')
plt.show()
Box Plot
Used to show distribution of data through quartiles.
Syntax:
plt.boxplot(data, vert, patch_artist)
Example:
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.figure(figsize=(6, 4))
plt.boxplot(data, vert=True, patch_artist=True)
plt.title('Box Plot Example')
plt.xlabel('Groups')
plt.ylabel('Values')
plt.show()
Violin Plot
Similar to box plot but shows the probability density.
Syntax:
plt.violinplot(data, showmeans, showmedians)
Example:
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.figure(figsize=(6, 4))
plt.violinplot(data, showmeans=True, showmedians=True)
plt.title('Violin Plot Example')
plt.xlabel('Groups')
plt.ylabel('Values')
plt.show()
Heatmap
Used to visualize matrix-like data with colors.
Syntax:
plt.imshow(data, cmap)
plt.colorbar()
Example:
data = np.random.rand(10, 10)
plt.figure(figsize=(6, 6))
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.title('Heatmap Example')
plt.show()
Area Plot
Used to show cumulative totals over time.
Syntax:
plt.stackplot(x, y1, y2, ..., colors, labels)
Example:
x = np.arange(0, 10, 0.1)
y1 = np.sin(x)
y2 = np.cos(x)
plt.figure(figsize=(8, 4))
plt.stackplot(x, y1, y2, colors=['skyblue', 'lightgreen'], labels=['sin(x)', 'cos(x)'])
plt.title('Area Plot Example')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.show()
3D Plotting
Used for three-dimensional visualizations.
Syntax:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap)
Example:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
ax.plot_surface(X, Y, Z, cmap='viridis')
ax.set_title('3D Surface Plot')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
plt.show()
Common Matplotlib Functions
| Function | Description |
|---|---|
plt.figure() |
Creates a new figure |
plt.title() |
Sets the title of the plot |
plt.xlabel()/plt.ylabel() |
Sets x/y-axis label |
plt.xlim()/plt.ylim() |
Sets x/y-axis limits |
plt.legend() |
Shows legend |
plt.grid() |
Adds grid lines |
plt.savefig() |
Saves the figure to a file |
plt.show() |
Displays the plot |
Remember to always include plt.show() at the end of your plotting commands to display the visualization. For Jupyter notebooks, you may want to use the magic command %matplotlib inline at the beginning of your notebook.