Matplotlib Reference Guide

Complete reference for Matplotlib - the foundational plotting library in Python.

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Installation and Import

Installation

pip install matplotlib

Import

import matplotlib.pyplot as plt

Convention: Import pyplot as plt - this is the standard convention.

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Basic Plotting

plt.plot()

Description: Create a line plot

Syntax: plt.plot(x, y, format_string, **kwargs)

import matplotlib.pyplot as plt

# Simple line plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.show()

# With labels
plt.plot(x, y)
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('My Plot')
plt.show()

# Multiple lines
plt.plot(x, y, label='Line 1')
plt.plot(x, [1, 3, 5, 7, 9], label='Line 2')
plt.legend()
plt.show()

plt.scatter()

Description: Create a scatter plot

Syntax: plt.scatter(x, y, s=None, c=None, **kwargs)

# Basic scatter
plt.scatter(x, y)
plt.show()

# With size and color
plt.scatter(x, y, s=100, c='red', alpha=0.5)
plt.show()

# Color by values
colors = [1, 2, 3, 4, 5]
plt.scatter(x, y, c=colors, cmap='viridis')
plt.colorbar()
plt.show()

plt.bar()

Description: Create a bar chart

Syntax: plt.bar(x, height, width=0.8, **kwargs)

categories = ['A', 'B', 'C', 'D']
values = [25, 40, 30, 55]

plt.bar(categories, values)
plt.show()

# Horizontal bar
plt.barh(categories, values)
plt.show()

# With colors
plt.bar(categories, values, color=['red', 'blue', 'green', 'orange'])
plt.show()

plt.hist()

Description: Create a histogram

Syntax: plt.hist(x, bins=10, **kwargs)

data = [1, 2, 2, 3, 3, 3, 4, 4, 5]

plt.hist(data, bins=5)
plt.show()

# With customization
plt.hist(data, bins=10, color='skyblue', edgecolor='black', alpha=0.7)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

plt.pie()

Description: Create a pie chart

Syntax: plt.pie(x, labels=None, autopct=None, **kwargs)

sizes = [30, 25, 20, 25]
labels = ['A', 'B', 'C', 'D']

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

# Explode a slice
explode = (0.1, 0, 0, 0)
plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=explode)
plt.show()

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Customization

Line Styles and Colors

# Line styles
plt.plot(x, y, linestyle='-')   # Solid (default)
plt.plot(x, y, linestyle='--')  # Dashed
plt.plot(x, y, linestyle='-.')  # Dash-dot
plt.plot(x, y, linestyle=':')   # Dotted

# Short format
plt.plot(x, y, 'r--')  # Red dashed line
plt.plot(x, y, 'bo')   # Blue circles
plt.plot(x, y, 'g^')   # Green triangles

# Colors
plt.plot(x, y, color='red')
plt.plot(x, y, color='#FF5733')  # Hex color
plt.plot(x, y, color=(0.1, 0.2, 0.5))  # RGB tuple

# Line width
plt.plot(x, y, linewidth=3)

# Markers
plt.plot(x, y, marker='o', markersize=10, markerfacecolor='red')

Labels and Titles

plt.xlabel() / plt.ylabel()

Description: Set axis labels

Syntax: plt.xlabel(label, fontsize=None)

plt.xlabel('Time (seconds)', fontsize=14)
plt.ylabel('Temperature (°C)', fontsize=14)

plt.title()

Description: Set plot title

Syntax: plt.title(label, fontsize=None)

plt.title('Temperature Over Time', fontsize=16, fontweight='bold')

plt.legend()

Description: Add a legend

Syntax: plt.legend(loc='best')

plt.plot(x, y, label='Data 1')
plt.plot(x, y2, label='Data 2')
plt.legend()  # Auto position
plt.legend(loc='upper right')
plt.legend(loc='lower left')

Grid

plt.grid()

Description: Add grid lines

Syntax: plt.grid(visible=True, which='major', axis='both')

plt.grid(True)
plt.grid(True, linestyle='--', alpha=0.5)
plt.grid(True, axis='x')  # Only x-axis grid

Axis Limits

plt.xlim() / plt.ylim()

Description: Set axis limits

Syntax: plt.xlim(left, right)

plt.xlim(0, 10)
plt.ylim(-5, 5)

# Get current limits
x_min, x_max = plt.xlim()

Ticks

plt.xticks() / plt.yticks()

Description: Set tick locations and labels

Syntax: plt.xticks(ticks, labels=None)

plt.xticks([0, 2, 4, 6, 8, 10])
plt.xticks([0, 1, 2, 3], ['A', 'B', 'C', 'D'])
plt.xticks(rotation=45)  # Rotate labels

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Figure and Subplots

plt.figure()

Description: Create a new figure

Syntax: plt.figure(figsize=(width, height), dpi=100)

plt.figure(figsize=(10, 6))  # Width=10, Height=6 inches
plt.figure(figsize=(8, 8), dpi=150)  # Higher resolution

plt.subplots()

Description: Create figure and subplots

Syntax: plt.subplots(nrows=1, ncols=1, figsize=None)

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

# Access individual subplots
axes[0, 0].plot(x, y)
axes[0, 1].scatter(x, y)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data)

plt.tight_layout()  # Adjust spacing
plt.show()

# Single row
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
ax1.plot(x, y)
ax2.scatter(x, y)
ax3.bar(categories, values)

plt.subplot()

Description: Add subplot to current figure

Syntax: plt.subplot(nrows, ncols, index)

plt.subplot(2, 2, 1)  # 2x2 grid, position 1
plt.plot(x, y)

plt.subplot(2, 2, 2)  # Position 2
plt.scatter(x, y)

plt.subplot(2, 2, 3)  # Position 3
plt.bar(categories, values)

plt.subplot(2, 2, 4)  # Position 4
plt.hist(data)

plt.show()

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Saving Figures

plt.savefig()

Description: Save figure to file

Syntax: plt.savefig(filename, dpi=None, bbox_inches=None)

plt.plot(x, y)
plt.savefig('plot.png')
plt.savefig('plot.png', dpi=300)  # High resolution
plt.savefig('plot.png', bbox_inches='tight')  # Remove extra whitespace
plt.savefig('plot.pdf')  # Save as PDF
plt.savefig('plot.svg')  # Save as SVG

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Additional Plot Types

plt.fill_between()

Description: Fill area between two curves

Syntax: plt.fill_between(x, y1, y2, alpha=0.5)

plt.plot(x, y)
plt.fill_between(x, y, alpha=0.3)
plt.show()

plt.errorbar()

Description: Plot with error bars

Syntax: plt.errorbar(x, y, yerr=None, xerr=None)

errors = [0.5, 0.3, 0.4, 0.6, 0.2]
plt.errorbar(x, y, yerr=errors, fmt='o', capsize=5)
plt.show()

plt.boxplot()

Description: Create box plot

Syntax: plt.boxplot(data, labels=None)

data = [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]
plt.boxplot(data, labels=['A', 'B', 'C'])
plt.show()

plt.imshow()

Description: Display image or 2D array

Syntax: plt.imshow(X, cmap=None)

import numpy as np
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
plt.colorbar()
plt.show()

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Style and Themes

plt.style.use()

Description: Use predefined style

Syntax: plt.style.use(style_name)

plt.style.use('ggplot')
plt.style.use('seaborn')
plt.style.use('dark_background')
plt.style.use('bmh')

# See available styles
print(plt.style.available)

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Useful Functions

plt.show()

Description: Display all figures

Syntax: plt.show()

plt.plot(x, y)
plt.show()  # Display the plot

plt.close()

Description: Close figure

Syntax: plt.close(fig=None)

plt.close()  # Close current figure
plt.close('all')  # Close all figures

plt.clf()

Description: Clear current figure

Syntax: plt.clf()

plt.clf()  # Clear the figure

plt.text()

Description: Add text to plot

Syntax: plt.text(x, y, text, fontsize=None)

plt.plot(x, y)
plt.text(3, 6, 'Important Point', fontsize=12)
plt.show()

plt.annotate()

Description: Add annotation with arrow

Syntax: plt.annotate(text, xy, xytext, arrowprops=None)

plt.plot(x, y)
plt.annotate('Peak', xy=(3, 6), xytext=(4, 8),
             arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

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