matplotlib常见基础用法总结-白红宇

matplotlib常见基础用法总结

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发布时间：2019-03-25

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Matthew is not just about static graphs, it's also about dynamic interactive plots. Starting from the simplest usage:

import matplotlib.pyplot as pltplt.plot([1, 2, 3, 4], [3, 6, 7, 9], 'r--')plt.xlabel('X-Axis')plt.ylabel('Y-Axis')plt.title('Interactive Plot Example')plt.show()

This code draws a simple line graph with red dashed lines, labels for both axes, and a title. Below are some advanced examples toward greater customization:

Customizing Plot Styling

plt.rcParams['font.sans-serif'] = ['Arial']  # Set default fontplt.figure(figsize=(10, 6))  # Set figure sizeplt.plot([1, 2, 3, 4], [3, 6, 7, 9], color='red', linestyle='--', linewidth=2, marker='o')plt.xlabel('X Axis', fontsize=12)plt.ylabel('Y Axis', fontsize=12)plt.title('Interactive Plot Example', fontsize=14, color='black')plt.grid(True, which=1, color='black', linestyle='--', linewidth=1)plt.show()

Key elements in the above code:

-figure size: Adjusted the size of the plot

-color and style: Specified color, linestyle, and linewidth

grid: Added a grid with custom settings

** fonts**: Set default font to Arial for better readability

Combining Multiple Plots

# Create two subplots within one figureplt.figure(figsize=(10, 6))plt.subplot(2, 1, 1)plt.plot([1, 2, 3, 4], [3, 6, 7, 9], color='red', linestyle='-', linewidth=2)plt.xlabel('Time', fontsize=10)plt.ylabel('Temperature', fontsize=10)plt.title('Temperature vs Time', fontsize=12)plt.subplot(2, 1, 2)plt.plot([1, 2, 3, 4], [5, 4, 3, 2], color='green', linestyle='--', linewidth=1)plt.xlabel('Time', fontsize=10)plt.ylabel('Pressure', fontsize=10)plt.title('Pressure vs Time', fontsize=12)plt.show()

Key elements:

Figure and Subplot creation: Usingplt.figure() and subplot() for multiple graphs in one figure

Labels: Customized labels with specified font sizes

Titles: Added descriptive titles to each subplot

Customizing Axes Ranges and Limits

import numpy as npplt.figure(figsize=(8, 6))x = np.linspace(0, 10, 100)y = np.sin(x)plt.plot(x, y, color='blue', linestyle='-', linewidth=2)plt.xlabel('X (Degrees)', fontsize=10)plt.ylabel('Sine Wave', fontsize=10)plt.title('Sine WavePlot', fontsize=12)plt.xlim(0, 10)  # Set x-axis limitsplt.ylim(-1, 1)  # Set y-axis limitsplt.grid(True, which=1, color='black', linestyle='--', linewidth=0.5)plt.show()

Key elements:

Axes limits: Using xlim() and ylim() to set visible range of axes

Grid: Added minor grid lines

Custom titles and labels: Enhanced readability through labels and titles

Adding Annotations and Labels

# Example: Annotating points on the graphplt.figure(figsize=(8, 6))x = np.arange(0, 2, 0.1)y = np.random.rand(len(x))plt.plot(x, y, color='yellow', linestyle='-', linewidth=2, markersize=4, marker='o')plt.xlabel('X', fontsize=10)plt.ylabel('Random Values', fontsize=10)plt.title('Random Scatter Plot', fontsize=12)plt.grid(True, which=1, color='black', linestyle='--', linewidth=0.5)for i, (xi, yi) in enumerate(zip(x, y)):    plt.text(xi-0.05, yi+0.02, f'({xi:.2f}, {yi:.2f})', ha='right', va='bottom')plt.show()

Key elements:

Annotations: Added text annotations for each data point

Markers: Customized markers and sizes

Labels and title: Enhanced readability through proper annotation

subordinate curtain cleaning

# Create an interactive plot that includes annotations and multiple elementsimport numpy as npimport matplotlib.pyplot as pltplt.figure(figsize=(10, 6))x = np.arange(-5, 5, 0.5)y = np.arange(-5, 5, 0.5)plt.scatter(x, y, color='blue', s=5, marker='o')plt.xlabel('X', fontsize=10)plt.ylabel('Y', fontsize=10)plt.title('Scatter Plot with Annotations', fontsize=12)plt.grid(True, which=1, color='black', linestyle='--', linewidth=0.5)# Add an annotation to a specific pointplt.text(2, 3.5, 'Important Point', ha='center', va='bottom', color='white', backgroundcolor='black')plt.show()

Key elements: