{"id":1080267,"date":"2025-01-08T12:28:28","date_gmt":"2025-01-08T04:28:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1080267.html"},"modified":"2025-01-08T12:28:31","modified_gmt":"2025-01-08T04:28:31","slug":"python%e4%b8%adpandas%e5%a6%82%e4%bd%95%e7%94%bb%e6%8a%98%e7%ba%bf%e5%9b%be-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1080267.html","title":{"rendered":"python\u4e2dpandas\u5982\u4f55\u753b\u6298\u7ebf\u56fe"},"content":{"rendered":"

\"python\u4e2dpandas\u5982\u4f55\u753b\u6298\u7ebf\u56fe\"<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u6b65\u9aa4\u5982\u4e0b<\/strong>\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165Pandas\u548cMatplotlib\u5e93\u3002<\/li>\n
  2. \u521b\u5efa\u6216\u5bfc\u5165\u6570\u636e<\/strong>\uff1a\u63a5\u4e0b\u6765\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u67b6\uff08DataFrame\uff09\u6216\u8005\u5bfc\u5165\u4e00\u4e2a\u73b0\u6709\u7684\u6570\u636e\u96c6\u3002<\/li>\n
  3. \u4f7f\u7528DataFrame.plot()\u65b9\u6cd5<\/strong>\uff1a\u6700\u540e\u4f7f\u7528DataFrame\u7684plot()\u65b9\u6cd5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/li>\n<\/ol>\n

    \u5728Python\u4e2d\uff0cpandas\u548cmatplotlib\u662f\u7ed8\u5236\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\u7684\u4e24\u4e2a\u4e3b\u8981\u5e93<\/strong>\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u6298\u7ebf\u56fe\u3002\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u521b\u5efa\u6216\u5bfc\u5165\u6570\u636e\u3001\u4f7f\u7528DataFrame.plot()\u65b9\u6cd5<\/strong>\uff0c\u8fd9\u4e9b\u6b65\u9aa4\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5728pandas\u4e2d\u7ed8\u5236\u6298\u7ebf\u56fe\u3002\u4f8b\u5982\uff0c\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u662f\u5173\u952e\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u5b83\u4eec\u63d0\u4f9b\u4e86\u6240\u6709\u7684\u5de5\u5177\u548c\u51fd\u6570\u6765\u5904\u7406\u6570\u636e\u548c\u7ed8\u5236\u56fe\u8868\u3002<\/p>\n<\/p>\n

    \u4e0b\u9762\u662f\u8be6\u7ec6\u7684\u6307\u5357\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4e00\u6b65\u6b65\u5728Python\u4e2d\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n

    \u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

    \u5728\u5f00\u59cb\u7ed8\u5236\u6298\u7ebf\u56fe\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u5bfc\u5165Pandas\u548cMatplotlib\u5e93\u3002Pandas\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\uff0c\u800cMatplotlib\u5219\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u786e\u4fdd\u4f60\u5df2\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
    pip install pandas matplotlib<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u521b\u5efa\u6216\u5bfc\u5165\u6570\u636e<\/h3>\n<\/p>\n
    \u63a5\u4e0b\u6765\uff0c\u60a8\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u67b6\uff08DataFrame\uff09\u6216\u5bfc\u5165\u73b0\u6709\u7684\u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u662f\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u6570\u636e\u6846\u67b6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    data = {<\/p>\n
        'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],<\/p>\n
        'Sales': [150, 200, 300, 250, 400, 450]<\/p>\n
    }<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6216\u8005\uff0c\u60a8\u53ef\u4ee5\u4eceCSV\u6587\u4ef6\u4e2d\u5bfc\u5165\u6570\u636e\uff1a<\/p>\n<\/p>\n
    df = pd.read_csv('path_to_your_file.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528DataFrame.plot()\u65b9\u6cd5<\/h3>\n<\/p>\n
    \u6709\u4e86\u6570\u636e\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528DataFrame\u7684plot()\u65b9\u6cd5\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u6298\u7ebf\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    df.plot(x='Month', y='Sales', kind='line')<\/p>\n
    plt.title('Monthly Sales')<\/p>\n
    plt.xlabel('Month')<\/p>\n
    plt.ylabel('Sales')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u5c06\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff0cX\u8f74\u8868\u793a\u6708\u4efd\uff0cY\u8f74\u8868\u793a\u9500\u552e\u989d\u3002<\/p>\n<\/p>\n
    \u56db\u3001\u6298\u7ebf\u56fe\u7684\u9ad8\u7ea7\u8bbe\u7f6e<\/h3>\n<\/p>\n
    1\u3001\u8bbe\u7f6e\u6298\u7ebf\u56fe\u7684\u6837\u5f0f<\/h4>\n<\/p>\n
    \u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u81ea\u5b9a\u4e49\u6298\u7ebf\u56fe\u7684\u6837\u5f0f\uff0c\u4f8b\u5982\u66f4\u6539\u7ebf\u7684\u989c\u8272\u3001\u6837\u5f0f\u548c\u6807\u8bb0\u3002<\/p>\n<\/p>\n
    df.plot(x='Month', y='Sales', kind='line', color='green', linestyle='--', marker='o')<\/p>\n
    plt.title('Monthly Sales')<\/p>\n
    plt.xlabel('Month')<\/p>\n
    plt.ylabel('Sales')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u6dfb\u52a0\u591a\u4e2a\u6298\u7ebf<\/h4>\n<\/p>\n
    \u5982\u679c\u60a8\u7684\u6570\u636e\u6846\u67b6\u5305\u542b\u591a\u5217\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u5728\u540c\u4e00\u56fe\u4e2d\u7ed8\u5236\u591a\u6761\u6298\u7ebf\u3002<\/p>\n<\/p>\n
    data = {<\/p>\n
        'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],<\/p>\n
        'Sales': [150, 200, 300, 250, 400, 450],<\/p>\n
        'Expenses': [80, 100, 150, 120, 200, 220]<\/p>\n
    }<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    df.plot(x='Month', y=['Sales', 'Expenses'], kind='line')<\/p>\n
    plt.title('Monthly Sales and Expenses')<\/p>\n
    plt.xlabel('Month')<\/p>\n
    plt.ylabel('Amount')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h3>\n<\/p>\n
    Pandas\u5728\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\u975e\u5e38\u5f3a\u5927\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
    date_rng = pd.date_range(start='2023-01-01', end='2023-06-01', freq='M')<\/p>\n
    df = pd.DataFrame(date_rng, columns=['date'])<\/p>\n
    df['data'] = np.random.randint(0, 100, size=(len(date_rng)))<\/p>\n
    df.set_index('date', inplace=True)<\/p>\n
    df.plot()<\/p>\n
    plt.title('Random Time Series Data')<\/p>\n
    plt.xlabel('Date')<\/p>\n
    plt.ylabel('Value')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e<\/h3>\n<\/p>\n
    \u5728\u5b9e\u9645\u6570\u636e\u96c6\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u6570\u636e\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
    1\u3001\u586b\u5145\u7f3a\u5931\u6570\u636e<\/h4>\n<\/p>\n
    \u60a8\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u6570\u636e\uff0c\u4f8b\u5982\u524d\u5411\u586b\u5145\u3001\u540e\u5411\u586b\u5145\u6216\u4f7f\u7528\u7279\u5b9a\u503c\u3002<\/p>\n<\/p>\n
    df['data'].fillna(method='ffill', inplace=True)<\/p>\n
    df['data'].fillna(method='bfill', inplace=True)<\/p>\n
    df['data'].fillna(0, inplace=True)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u5220\u9664\u7f3a\u5931\u6570\u636e<\/h4>\n<\/p>\n
    \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u5220\u9664\u5305\u542b\u7f3a\u5931\u6570\u636e\u7684\u884c\u3002<\/p>\n<\/p>\n
    df.dropna(inplace=True)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e03\u3001\u7ed3\u5408\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177<\/h3>\n<\/p>\n
    \u9664\u4e86Matplotlib\uff0c\u60a8\u8fd8\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u4f8b\u5982Seaborn\u6216Plotly\uff0c\u4ee5\u521b\u5efa\u66f4\u9ad8\u7ea7\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
    1\u3001\u4f7f\u7528Seaborn<\/h4>\n<\/p>\n
    Seaborn\u57fa\u4e8eMatplotlib\u6784\u5efa\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u548c\u7f8e\u89c2\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
    import seaborn as sns<\/p>\n
    sns.lineplot(data=df, x='Month', y='Sales')<\/p>\n
    plt.title('Monthly Sales')<\/p>\n
    plt.xlabel('Month')<\/p>\n
    plt.ylabel('Sales')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u4f7f\u7528Plotly<\/h4>\n<\/p>\n
    Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u5408\u5728\u7f51\u9875\u4e2d\u5c55\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
    import plotly.express as px<\/p>\n
    fig = px.line(df, x='Month', y='Sales', title='Monthly Sales')<\/p>\n
    fig.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516b\u3001\u4fdd\u5b58\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
    \u7ed8\u5236\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\u3002Matplotlib\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\n
    df.plot(x='Month', y='Sales', kind='line')<\/p>\n
    plt.title('Monthly Sales')<\/p>\n
    plt.xlabel('Month')<\/p>\n
    plt.ylabel('Sales')<\/p>\n
    plt.savefig('monthly_sales.png')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u5728Python\u4e2d\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4efb\u52a1\u3002\u901a\u8fc7\u638c\u63e1\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u3001\u521b\u5efa\u6216\u5bfc\u5165\u6570\u636e\u3001\u4f7f\u7528DataFrame.plot()\u65b9\u6cd5\u7b49\u57fa\u672c\u6b65\u9aa4\uff0c\u4ee5\u53ca\u7406\u89e3\u5982\u4f55\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u7ed3\u5408\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177\u7b49\u9ad8\u7ea7\u8bbe\u7f6e\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u51fa\u7cbe\u7f8e\u7684\u6298\u7ebf\u56fe\u3002\u5e0c\u671b\u8fd9\u7bc7\u6307\u5357\u80fd\u591f\u5e2e\u52a9\u60a8\u5728\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u65b9\u9762\u66f4\u8fdb\u4e00\u6b65\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\uff1f<\/strong>
    \u8981\u5728Python\u4e2d\u4f7f\u7528Pandas\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5Pandas\u548cMatplotlib\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pandas.DataFrame.plot()<\/code>\u65b9\u6cd5\u6765\u521b\u5efa\u6298\u7ebf\u56fe\u3002\u53ea\u9700\u5c06\u6570\u636e\u6846\u7684\u5217\u4f20\u9012\u7ed9\u8be5\u65b9\u6cd5\uff0c\u5e76\u8bbe\u7f6e\u76f8\u5e94\u7684\u53c2\u6570\uff0c\u5982kind='line'<\/code>\u3002\u4f8b\u5982\uff0cdf.plot(kind='line')<\/code>\u53ef\u4ee5\u7ed8\u5236\u51fa\u6570\u636e\u6846df<\/code>\u4e2d\u7684\u6240\u6709\u6570\u503c\u5217\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n
    \u5728\u7ed8\u5236\u6298\u7ebf\u56fe\u65f6\uff0c\u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u548c\u6807\u7b7e\uff1f<\/strong>
    \u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u5b9a\u5236\u6298\u7ebf\u56fe\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528title<\/code>\u53c2\u6570\u8bbe\u7f6e\u56fe\u8868\u6807\u9898\uff0cxlabel<\/code>\u548cylabel<\/code>\u8bbe\u7f6e\u8f74\u6807\u7b7e\u3002\u6b64\u5916\uff0ccolor<\/code>\u548clinestyle<\/code>\u53c2\u6570\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u9009\u62e9\u6298\u7ebf\u7684\u989c\u8272\u548c\u6837\u5f0f\u3002\u4f7f\u7528Matplotlib\u7684plt<\/code>\u6a21\u5757\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u81ea\u5b9a\u4e49\u56fe\u8868\uff0c\u5982\u6dfb\u52a0\u56fe\u4f8b\u3001\u7f51\u683c\u7ebf\u7b49\u3002<\/p>\n
    \u5728Pandas\u4e2d\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u786e\u4fdd\u6298\u7ebf\u56fe\u7684\u51c6\u786e\u6027\uff1f<\/strong>
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