{"id":1179137,"date":"2025-01-15T18:19:36","date_gmt":"2025-01-15T10:19:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1179137.html"},"modified":"2025-01-15T18:19:40","modified_gmt":"2025-01-15T10:19:40","slug":"python%e5%a6%82%e4%bd%95%e8%ae%be%e7%bd%ae%e5%90%88%e9%80%82%e7%9a%84%e7%94%bb%e5%b8%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1179137.html","title":{"rendered":"python\u5982\u4f55\u8bbe\u7f6e\u5408\u9002\u7684\u753b\u5e03"},"content":{"rendered":"

\"python\u5982\u4f55\u8bbe\u7f6e\u5408\u9002\u7684\u753b\u5e03\"<\/p>\n

Python\u4e2d\u8bbe\u7f6e\u5408\u9002\u7684\u753b\u5e03\u4e3b\u8981\u901a\u8fc7\u4f7f\u7528matplotlib\u5e93\u5b8c\u6210\u3002\u5408\u9002\u7684\u753b\u5e03\u8bbe\u7f6e\u5305\u62ec\u786e\u5b9a\u753b\u5e03\u5c3a\u5bf8\u3001\u5206\u8fa8\u7387\u3001\u80cc\u666f\u8272\u3001\u7f51\u683c\u5e03\u5c40\u3001\u5b50\u56fe\u5e03\u5c40\u7b49<\/strong>\u3002\u5176\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u662f\u901a\u8fc7figsize<\/code>\u53c2\u6570\u8bbe\u7f6e\u753b\u5e03\u5c3a\u5bf8\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u753b\u5e03\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387<\/h3>\n<\/p>\n

\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\uff0c\u753b\u5e03\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387\u662f\u6700\u57fa\u7840\u7684\u8bbe\u7f6e\u3002\u901a\u8fc7\u8c03\u6574\u8fd9\u4e24\u4e2a\u53c2\u6570\uff0c\u53ef\u4ee5\u786e\u4fdd\u56fe\u5f62\u5728\u4e0d\u540c\u7684\u663e\u793a\u8bbe\u5907\u548c\u6253\u5370\u8bbe\u5907\u4e0a\u90fd\u80fd\u4fdd\u6301\u826f\u597d\u7684\u663e\u793a\u6548\u679c\u3002<\/p>\n<\/p>\n

1. \u8bbe\u7f6e\u753b\u5e03\u5c3a\u5bf8<\/h4>\n<\/p>\n

\u4f7f\u7528matplotlib\u7684figure()<\/code>\u51fd\u6570\u53ef\u4ee5\u8bbe\u7f6e\u753b\u5e03\u5c3a\u5bf8\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
fig = plt.figure(figsize=(10, 6))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u91cc\u7684figsize<\/code>\u53c2\u6570\u662f\u4e00\u4e2a\u5143\u7ec4\uff0c\u8868\u793a\u753b\u5e03\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\uff0c\u5355\u4f4d\u662f\u82f1\u5bf8\u3002\u5408\u9002\u7684\u753b\u5e03\u5c3a\u5bf8\u5e94\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u5c55\u793a\u5e73\u53f0\u6765\u786e\u5b9a\u3002\u4f8b\u5982\uff0c\u5728\u7f51\u9875\u5c55\u793a\u4e2d\uff0c10\u82f1\u5bf8\u5bbd\u30016\u82f1\u5bf8\u9ad8\u7684\u753b\u5e03\u5c3a\u5bf8\u901a\u5e38\u6bd4\u8f83\u5408\u9002\u3002<\/p>\n<\/p>\n
2. \u8bbe\u7f6e\u5206\u8fa8\u7387<\/h4>\n<\/p>\n
\u5206\u8fa8\u7387\u7684\u8bbe\u7f6e\u901a\u8fc7dpi<\/code>\u53c2\u6570\u5b8c\u6210\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
fig = plt.figure(figsize=(10, 6), dpi=100)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u91cc\u7684dpi<\/code>\u8868\u793a\u6bcf\u82f1\u5bf8\u7684\u70b9\u6570\uff0c\u6570\u503c\u8d8a\u9ad8\uff0c\u56fe\u5f62\u7684\u5206\u8fa8\u7387\u8d8a\u9ad8\u3002\u9ad8\u5206\u8fa8\u7387\u7684\u56fe\u5f62\u5728\u6253\u5370\u65f6\u53ef\u4ee5\u4fdd\u6301\u66f4\u597d\u7684\u6e05\u6670\u5ea6\uff0c\u4f46\u5728\u7f51\u9875\u5c55\u793a\u65f6\u53ef\u80fd\u4f1a\u5f71\u54cd\u52a0\u8f7d\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u80cc\u666f\u8272<\/h3>\n<\/p>\n
\u80cc\u666f\u8272\u7684\u8bbe\u7f6e\u53ef\u4ee5\u901a\u8fc7facecolor<\/code>\u53c2\u6570\u5b8c\u6210\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
fig = plt.figure(figsize=(10, 6), facecolor='lightgrey')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bbe\u7f6e\u5408\u9002\u7684\u80cc\u666f\u8272\u53ef\u4ee5\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u5ea6\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u4e3b\u9898\u548c\u914d\u8272\u65b9\u6848\u9009\u62e9\u5408\u9002\u7684\u80cc\u666f\u8272\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7f51\u683c\u5e03\u5c40<\/h3>\n<\/p>\n
\u7f51\u683c\u5e03\u5c40\u7684\u8bbe\u7f6e\u53ef\u4ee5\u901a\u8fc7plt.grid()<\/code>\u51fd\u6570\u5b8c\u6210\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
plt.grid(True, which='both', linestyle='--', linewidth=0.5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
    \n
  • True<\/code>\u8868\u793a\u5f00\u542f\u7f51\u683c\uff1b<\/li>\n
  • which<\/code>\u53c2\u6570\u6307\u5b9a\u7f51\u683c\u7684\u663e\u793a\u65b9\u5f0f\uff0c\u53ef\u4ee5\u662f'major'<\/code>\uff08\u4e3b\u7f51\u683c\uff09\u3001'minor'<\/code>\uff08\u6b21\u7f51\u683c\uff09\u6216'both'<\/code>\uff08\u4e3b\u6b21\u7f51\u683c\uff09\uff1b<\/li>\n
  • linestyle<\/code>\u53c2\u6570\u8bbe\u7f6e\u7f51\u683c\u7ebf\u7684\u6837\u5f0f\uff1b<\/li>\n
  • linewidth<\/code>\u53c2\u6570\u8bbe\u7f6e\u7f51\u683c\u7ebf\u7684\u5bbd\u5ea6\u3002<\/li>\n<\/ul>\n
    \u56db\u3001\u5b50\u56fe\u5e03\u5c40<\/h3>\n<\/p>\n
    \u5b50\u56fe\u5e03\u5c40\u7684\u8bbe\u7f6e\u53ef\u4ee5\u901a\u8fc7plt.subplot()<\/code>\u6216plt.subplots()<\/code>\u51fd\u6570\u5b8c\u6210\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    fig, axs = plt.subplots(2, 2, figsize=(10, 6))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u91cc\u76842, 2<\/code>\u8868\u793a\u5728\u753b\u5e03\u4e0a\u521b\u5efa\u4e00\u4e2a2×2\u7684\u5b50\u56fe\u5e03\u5c40\u3002\u4f7f\u7528\u5b50\u56fe\u5e03\u5c40\u53ef\u4ee5\u5728\u540c\u4e00\u753b\u5e03\u4e0a\u5c55\u793a\u591a\u4e2a\u56fe\u5f62\uff0c\u4fbf\u4e8e\u5bf9\u6bd4\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
    1. \u5355\u4e2a\u5b50\u56fe<\/h4>\n<\/p>\n
    \u5728\u4e00\u4e2a\u5b50\u56fe\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u8bbe\u7f6e\u5177\u4f53\u7684\u56fe\u5f62\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    ax = plt.subplot(111)<\/p>\n
    ax.plot(x, y)<\/p>\n
    ax.set_title('Title')<\/p>\n
    ax.set_xlabel('X-axis')<\/p>\n
    ax.set_ylabel('Y-axis')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u591a\u4e2a\u5b50\u56fe<\/h4>\n<\/p>\n
    \u5728\u591a\u4e2a\u5b50\u56fe\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u5206\u522b\u8bbe\u7f6e\u6bcf\u4e2a\u5b50\u56fe\u7684\u5185\u5bb9\uff0c\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
    fig, axs = plt.subplots(2, 2, figsize=(10, 6))<\/p>\n
    axs[0, 0].plot(x1, y1)<\/p>\n
    axs[0, 0].set_title('Title 1')<\/p>\n
    axs[0, 1].plot(x2, y2)<\/p>\n
    axs[0, 1].set_title('Title 2')<\/p>\n
    axs[1, 0].plot(x3, y3)<\/p>\n
    axs[1, 0].set_title('Title 3')<\/p>\n
    axs[1, 1].plot(x4, y4)<\/p>\n
    axs[1, 1].set_title('Title 4')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u5b9e\u9645\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
    1. \u8bbe\u7f6e\u5408\u9002\u7684\u753b\u5e03\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387<\/h4>\n<\/p>\n
    \u5728\u5c55\u793a\u67d0\u4e2a\u6570\u636e\u5206\u6790\u7ed3\u679c\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u753b\u5e03\u5c3a\u5bf8\u548c\u5206\u8fa8\u7387\u53ef\u4ee5\u786e\u4fdd\u56fe\u5f62\u7684\u6e05\u6670\u5ea6\u548c\u53ef\u8bfb\u6027\u3002\u4f8b\u5982\uff0c\u5728\u5c55\u793a\u4e00\u4e2a\u5927\u578b\u6570\u636e\u96c6\u7684\u6563\u70b9\u56fe\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u8f83\u5927\u7684\u753b\u5e03\u5c3a\u5bf8\u548c\u8f83\u9ad8\u7684\u5206\u8fa8\u7387\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    x = np.random.rand(1000)<\/p>\n
    y = np.random.rand(1000)<\/p>\n
    fig = plt.figure(figsize=(12, 8), dpi=120)<\/p>\n
    plt.scatter(x, y)<\/p>\n
    plt.title('Scatter Plot')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u8bbe\u7f6e\u80cc\u666f\u8272\u548c\u7f51\u683c\u5e03\u5c40<\/h4>\n<\/p>\n
    \u4e3a\u4e86\u63d0\u9ad8\u56fe\u5f62\u7684\u7f8e\u89c2\u5ea6\u548c\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u5408\u9002\u7684\u80cc\u666f\u8272\u548c\u7f51\u683c\u5e03\u5c40\u3002\u4f8b\u5982\uff0c\u5728\u5c55\u793a\u67d0\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u6d45\u8272\u80cc\u666f\u548c\u865a\u7ebf\u7f51\u683c\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    import matplotlib.dates as mdates<\/p>\n
    dates = pd.date_range('20230101', periods=100)<\/p>\n
    data = np.random.randn(100).cumsum()<\/p>\n
    fig = plt.figure(figsize=(10, 6), facecolor='lightgrey')<\/p>\n
    plt.plot(dates, data)<\/p>\n
    plt.title('Time Series Data')<\/p>\n
    plt.xlabel('Date')<\/p>\n
    plt.ylabel('Value')<\/p>\n
    plt.grid(True, which='both', linestyle='--', linewidth=0.5)<\/p>\n
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))<\/p>\n
    plt.xticks(rotation=45)<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. \u8bbe\u7f6e\u5b50\u56fe\u5e03\u5c40<\/h4>\n<\/p>\n
    \u5728\u5c55\u793a\u591a\u4e2a\u76f8\u5173\u56fe\u5f62\u65f6\uff0c\u8bbe\u7f6e\u5b50\u56fe\u5e03\u5c40\u53ef\u4ee5\u63d0\u9ad8\u5bf9\u6bd4\u5206\u6790\u7684\u6548\u679c\u3002\u4f8b\u5982\uff0c\u5728\u5c55\u793a\u67d0\u4e2a\u6570\u636e\u96c6\u7684\u4e0d\u540c\u7edf\u8ba1\u7279\u5f81\u65f6\uff0c\u53ef\u4ee5\u9009\u62e92×2\u7684\u5b50\u56fe\u5e03\u5c40\uff1a<\/p>\n<\/p>\n
    data = np.random.randn(1000)<\/p>\n
    fig, axs = plt.subplots(2, 2, figsize=(10, 6))<\/p>\n
    axs[0, 0].hist(data, bins=30)<\/p>\n
    axs[0, 0].set_title('Histogram')<\/p>\n
    axs[0, 1].boxplot(data)<\/p>\n
    axs[0, 1].set_title('Boxplot')<\/p>\n
    axs[1, 0].plot(data)<\/p>\n
    axs[1, 0].set_title('Line Plot')<\/p>\n
    axs[1, 1].scatter(range(len(data)), data)<\/p>\n
    axs[1, 1].set_title('Scatter Plot')<\/p>\n
    plt.tight_layout()<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4ee5\u4e0a\u8bbe\u7f6e\u548c\u5b9e\u9645\u6848\u4f8b\u5206\u6790\uff0c\u53ef\u4ee5\u770b\u51fa\u5408\u9002\u7684\u753b\u5e03\u8bbe\u7f6e\u5bf9\u4e8e\u63d0\u9ad8\u56fe\u5f62\u7684\u5c55\u793a\u6548\u679c\u548c\u53ef\u8bfb\u6027\u5177\u6709\u91cd\u8981\u4f5c\u7528<\/strong>\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5e94\u6839\u636e\u5177\u4f53\u9700\u6c42\u7075\u6d3b\u8c03\u6574\u753b\u5e03\u5c3a\u5bf8\u3001\u5206\u8fa8\u7387\u3001\u80cc\u666f\u8272\u3001\u7f51\u683c\u5e03\u5c40\u548c\u5b50\u56fe\u5e03\u5c40\u7b49\u53c2\u6570\uff0c\u4ee5\u8fbe\u5230\u6700\u4f73\u7684\u5c55\u793a\u6548\u679c\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u753b\u5e03\u5927\u5c0f\u4ee5\u6ee1\u8db3\u6211\u7684\u9879\u76ee\u9700\u6c42\uff1f<\/strong>
    \u9009\u62e9\u753b\u5e03\u5927\u5c0f\u65f6\uff0c\u9996\u5148\u9700\u8981\u8003\u8651\u9879\u76ee\u7684\u6700\u7ec8\u5c55\u793a\u65b9\u5f0f\u3002\u5982\u679c\u662f\u7528\u4e8e\u7f51\u9875\uff0c\u5efa\u8bae\u4f7f\u7528\u6807\u51c6\u7684\u7f51\u9875\u5c3a\u5bf8\uff0c\u59821920×1080\u62161280×720\u3002\u5982\u679c\u662f\u6253\u5370\uff0c\u786e\u4fdd\u753b\u5e03\u7684\u5206\u8fa8\u7387\u80fd\u591f\u6ee1\u8db3\u6253\u5370\u8981\u6c42\uff0c\u901a\u5e38\u4e3a300 DPI\uff08\u6bcf\u82f1\u5bf8\u70b9\u6570\uff09\u3002\u8fd8\u8981\u8003\u8651\u5230\u5185\u5bb9\u7684\u5e03\u5c40\u548c\u89c6\u89c9\u6548\u679c\uff0c\u786e\u4fdd\u91cd\u8981\u5143\u7d20\u4e0d\u4f1a\u88ab\u88c1\u526a\u6216\u9690\u85cf\u3002<\/p>\n
    \u5728Python\u4e2d\u5982\u4f55\u52a8\u6001\u8c03\u6574\u753b\u5e03\u7684\u5c3a\u5bf8\uff1f<\/strong>
    \u4f7f\u7528Python\u7684\u56fe\u5f62\u5e93\uff08\u5982matplotlib\u6216tkinter\uff09\uff0c\u53ef\u4ee5\u6839\u636e\u5185\u5bb9\u7684\u9700\u6c42\u52a8\u6001\u8c03\u6574\u753b\u5e03\u7684\u5c3a\u5bf8\u3002\u4f8b\u5982\uff0c\u5728matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528figsize<\/code>\u53c2\u6570\u6765\u8bbe\u5b9a\u753b\u5e03\u7684\u5bbd\u9ad8\uff0c\u6216\u8005\u901a\u8fc7set_size_inches()<\/code>\u65b9\u6cd5\u5728\u8fd0\u884c\u65f6\u8c03\u6574\u753b\u5e03\u7684\u5927\u5c0f\u3002\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97\u7528\u6237\u80fd\u591f\u6839\u636e\u6570\u636e\u53d8\u5316\u6216\u7528\u6237\u4ea4\u4e92\u6765\u4f18\u5316\u5c55\u793a\u6548\u679c\u3002<\/p>\n
    \u5982\u4f55\u5728Python\u4e2d\u8bbe\u7f6e\u753b\u5e03\u80cc\u666f\u989c\u8272\uff1f<\/strong>
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