EDS 217 Cheatsheet

matplotlib

Tip

For 🀯 inspiration + πŸ‘©β€πŸ’» code, check out the Python Graph Gallery

Basic Setup

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Set style for seaborn (optional)
sns.set_style("whitegrid")

Creating a Figure and Axes

# Create a new figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Create multiple subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

Common Plot Types

Line Plot

x = np.linspace(0, 10, 100)
y = np.sin(x)
ax.plot(x, y, label='sin(x)')

Scatter Plot

x = np.random.rand(50)
y = np.random.rand(50)
plt.scatter(x, y, alpha=0.5)

Bar Plot

categories = ['A', 'B', 'C', 'D']
values = [3, 7, 2, 5]
plt.bar(categories, values)

Histogram

data = np.random.randn(1000)
ax.hist(data, bins=30, edgecolor='black')

Customizing Plots

Labels and Title

ax.set_xlabel('X-axis label')
ax.set_ylabel('Y-axis label')
ax.set_title('Plot Title')

Legend

ax.legend()

Axis Limits

ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)

Grid

ax.grid(True, linestyle='--', alpha=0.7)

Ticks

ax.set_xticks([0, 2, 4, 6, 8, 10])
ax.set_yticks([-1, -0.5, 0, 0.5, 1])

Color and Style

Changing Colors

ax.plot(x, y, color='r')  # 'r' for red
ax.scatter(x, y, c='blue')

Line Styles

ax.plot(x, y, linestyle='--')  # dashed line
ax.plot(x, y, ls=':')  # dotted line

Marker Styles

ax.plot(x, y, marker='o')  # circles
ax.plot(x, y, marker='s')  # squares

Saving and Displaying

Saving the Figure

plt.savefig('my_plot.png', dpi=300, bbox_inches='tight')

Displaying the Plot

plt.show()

Useful Tips for Seaborn Users

  1. Use plt.subplots() to create custom layouts that Seaborn doesn’t provide.

  2. Access the underlying Matplotlib Axes object in Seaborn plots:

    g = sns.scatterplot(x='x', y='y', data=df)
g.set_xlabel('Custom X Label')

  3. Combine Seaborn and Matplotlib in the same figure:

    fig, ax = plt.subplots()
sns.scatterplot(x='x', y='y', data=df, ax=ax)
ax.plot(x, y, color='r', linestyle='--')

  4. Use Matplotlib’s plt.tight_layout() to automatically adjust subplot parameters for better spacing.

Remember, most Seaborn functions return a Matplotlib Axes object, allowing you to further customize your plots using Matplotlib functions.