{"id":1106843,"date":"2025-01-08T16:44:02","date_gmt":"2025-01-08T08:44:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1106843.html"},"modified":"2025-01-08T16:44:04","modified_gmt":"2025-01-08T08:44:04","slug":"python%e8%af%bb%e5%8f%96csv%e5%a6%82%e4%bd%95%e8%a1%a8%e7%a4%ba%e6%af%8f%e4%b8%80%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1106843.html","title":{"rendered":"python\u8bfb\u53d6csv\u5982\u4f55\u8868\u793a\u6bcf\u4e00\u5217"},"content":{"rendered":"

\"python\u8bfb\u53d6csv\u5982\u4f55\u8868\u793a\u6bcf\u4e00\u5217\"<\/p>\n

Python\u8bfb\u53d6CSV\u5e76\u8868\u793a\u6bcf\u4e00\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u5305\u62ec\u4f7f\u7528Pandas\u3001csv\u6a21\u5757\u7b49\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528Pandas\u5e93\uff1aPandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u5de5\u5177\uff0c\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u4fbf\u6377\u3002 <\/li>\n
  2. \u4f7f\u7528csv\u6a21\u5757\uff1aPython\u5185\u7f6e\u7684csv\u6a21\u5757\u4e5f\u53ef\u4ee5\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u4f46\u9700\u8981\u624b\u52a8\u5904\u7406\u6570\u636e\u3002 <\/li>\n
  3. \u4f7f\u7528numpy\u5e93\uff1anumpy\u5e93\u9002\u7528\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e\uff0c\u80fd\u65b9\u4fbf\u5730\u8fdb\u884c\u77e9\u9635\u548c\u6570\u7ec4\u64cd\u4f5c\u3002<\/li>\n<\/ol>\n

    \u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u8868\u793a\u6bcf\u4e00\u5217\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

    \u4e00\u3001\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n

    Pandas\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u8bfb\u53d6\u548c\u5904\u7406CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    1.1 \u5b89\u88c5Pandas<\/h4>\n<\/p>\n

    \u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install pandas<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.2 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
    \u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u4e00\u884c\u4ee3\u7801\u5373\u53ef\u5b8c\u6210\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    df = pd.read_csv('filename.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3 \u8868\u793a\u6bcf\u4e00\u5217<\/h4>\n<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6\u540e\uff0c\u6570\u636e\u4f1a\u88ab\u5b58\u50a8\u5728\u4e00\u4e2aDataFrame\u5bf9\u8c61\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5217\u540d\u6765\u8bbf\u95ee\u6bcf\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
    # \u663e\u793a\u6240\u6709\u5217\u540d<\/p>\n
    print(df.columns)<\/p>\n
    \u8bbf\u95ee\u7279\u5b9a\u5217<\/strong><\/h2>\n
    column_data = df['column_name']<\/p>\n
    print(column_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong>
    \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u540d\u4e3adata.csv<\/code>\u7684\u6587\u4ef6\uff0c\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    name,age,city<\/p>\n
    Alice,30,New York<\/p>\n
    Bob,25,Los Angeles<\/p>\n
    Charlie,35,Chicago<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6\u8be5\u6587\u4ef6\u5e76\u8bbf\u95ee\u6bcf\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    \u663e\u793a\u6240\u6709\u5217\u540d<\/strong><\/h2>\n
    print("Columns:", df.columns)<\/p>\n
    \u8bbf\u95ee'name'\u5217<\/strong><\/h2>\n
    name_column = df['name']<\/p>\n
    print("Name Column:\\n", name_column)<\/p>\n
    \u8bbf\u95ee'age'\u5217<\/strong><\/h2>\n
    age_column = df['age']<\/p>\n
    print("Age Column:\\n", age_column)<\/p>\n
    \u8bbf\u95ee'city'\u5217<\/strong><\/h2>\n
    city_column = df['city']<\/p>\n
    print("City Column:\\n", city_column)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e0a\u8ff0\u4ee3\u7801\u5c06\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    Columns: Index(['name', 'age', 'city'], dtype='object')<\/p>\n
    Name Column:<\/p>\n
     0      Alice<\/p>\n
    1        Bob<\/p>\n
    2    Charlie<\/p>\n
    Name: name, dtype: object<\/p>\n
    Age Column:<\/p>\n
     0    30<\/p>\n
    1    25<\/p>\n
    2    35<\/p>\n
    Name: age, dtype: int64<\/p>\n
    City Column:<\/p>\n
     0       New York<\/p>\n
    1    Los Angeles<\/p>\n
    2        Chicago<\/p>\n
    Name: city, dtype: object<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
    csv\u6a21\u5757\u662fPython\u5185\u7f6e\u7684\u5e93\uff0c\u7528\u4e8e\u5904\u7406CSV\u6587\u4ef6\u3002\u867d\u7136\u4e0d\u5982Pandas\u65b9\u4fbf\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4e5f\u5f88\u6709\u7528\u3002<\/p>\n<\/p>\n
    2.1 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
    \u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    import csv<\/p>\n
    \u6253\u5f00CSV\u6587\u4ef6<\/strong><\/h2>\n
    with open('data.csv', mode='r') as file:<\/p>\n
        csv_reader = csv.reader(file)<\/p>\n
        # \u83b7\u53d6\u5217\u540d<\/p>\n
        columns = next(csv_reader)<\/p>\n
        # \u521b\u5efa\u4e00\u4e2a\u5b57\u5178\u6765\u5b58\u50a8\u5217\u6570\u636e<\/p>\n
        data = {column: [] for column in columns}<\/p>\n
        # \u8bfb\u53d6\u6bcf\u4e00\u884c\u7684\u6570\u636e<\/p>\n
        for row in csv_reader:<\/p>\n
            for i, column in enumerate(columns):<\/p>\n
                data[column].append(row[i])<\/p>\n
    \u663e\u793a\u6240\u6709\u5217\u6570\u636e<\/strong><\/h2>\n
    for column in data:<\/p>\n
        print(f"{column} Column:\\n", data[column])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e0a\u8ff0\u4ee3\u7801\u5c06\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    name Column:<\/p>\n
     ['Alice', 'Bob', 'Charlie']<\/p>\n
    age Column:<\/p>\n
     ['30', '25', '35']<\/p>\n
    city Column:<\/p>\n
     ['New York', 'Los Angeles', 'Chicago']<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528numpy\u5e93\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
    numpy\u5e93\u9002\u7528\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e\uff0c\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8bfb\u53d6CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
    3.1 \u5b89\u88c5numpy<\/h4>\n<\/p>\n
    \u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86numpy\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
    pip install numpy<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
    \u4f7f\u7528numpy\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    data = np.genfromtxt('data.csv', delimiter=',', dtype=None, names=True, encoding='utf-8')<\/p>\n
    \u663e\u793a\u6240\u6709\u5217\u540d<\/strong><\/h2>\n
    print("Columns:", data.dtype.names)<\/p>\n
    \u8bbf\u95ee\u7279\u5b9a\u5217<\/strong><\/h2>\n
    name_column = data['name']<\/p>\n
    print("Name Column:\\n", name_column)<\/p>\n
    age_column = data['age']<\/p>\n
    print("Age Column:\\n", age_column)<\/p>\n
    city_column = data['city']<\/p>\n
    print("City Column:\\n", city_column)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e0a\u8ff0\u4ee3\u7801\u5c06\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    Columns: ('name', 'age', 'city')<\/p>\n
    Name Column:<\/p>\n
     ['Alice' 'Bob' 'Charlie']<\/p>\n
    Age Column:<\/p>\n
     [30 25 35]<\/p>\n
    City Column:<\/p>\n
     ['New York' 'Los Angeles' 'Chicago']<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u603b\u7ed3\uff1a<\/strong>
    \u4f7f\u7528Pandas\u5e93\u8bfb\u53d6\u548c\u8868\u793aCSV\u6587\u4ef6\u7684\u6bcf\u4e00\u5217\u6570\u636e\u6700\u4e3a\u65b9\u4fbf\u3001\u529f\u80fd\u5f3a\u5927\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u573a\u666f\u3002
    csv\u6a21\u5757\u662fPython\u5185\u7f6e\u5e93\uff0c\u65e0\u9700\u5b89\u88c5\uff0c\u4f46\u529f\u80fd\u8f83\u4e3a\u57fa\u7840\uff0c\u9002\u7528\u4e8e\u7b80\u5355\u7684CSV\u6587\u4ef6\u64cd\u4f5c\u3002
    numpy\u5e93\u9002\u7528\u4e8e\u6570\u503c\u6570\u636e\u5904\u7406\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u8fdb\u884c\u77e9\u9635\u548c\u6570\u7ec4\u64cd\u4f5c\uff0c\u4f46\u5728\u5904\u7406\u590d\u6742\u6570\u636e\u65f6\u4e0d\u5982Pandas\u7075\u6d3b\u3002  <\/p>\n<\/p>\n
    \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u6765\u8bfb\u53d6\u548c\u8868\u793aCSV\u6587\u4ef6\u7684\u6bcf\u4e00\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u83b7\u53d6\u6bcf\u4e00\u5217\u7684\u6570\u636e\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u83b7\u53d6\u6bcf\u4e00\u5217\u7684\u6570\u636e\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5pandas<\/code>\u5e93\u3002\u4f7f\u7528pd.read_csv('\u6587\u4ef6\u8def\u5f84')<\/code>\u8bfb\u53d6CSV\u6587\u4ef6\u540e\uff0c\u6570\u636e\u5c06\u4ee5DataFrame\u7684\u5f62\u5f0f\u5b58\u50a8\u3002\u53ef\u4ee5\u901a\u8fc7\u5217\u540d\u8bbf\u95ee\u6bcf\u4e00\u5217\uff0c\u4f8b\u5982df['\u5217\u540d']<\/code>\uff0c\u4ece\u800c\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u6570\u636e\u3002<\/p>\n
    \u4f7f\u7528Python\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
    \u5728\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u7f3a\u5931\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u3002\u4f7f\u7528pandas<\/code>\u7684read_csv<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570na_values<\/code>\u6307\u5b9a\u54ea\u4e9b\u503c\u89c6\u4e3a\u7f3a\u5931\u503c\u3002\u8bfb\u53d6\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528df.fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u4f7f\u7528df.dropna()<\/code>\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n
    \u600e\u6837\u5728\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\u9009\u62e9\u7279\u5b9a\u7684\u5217\uff1f<\/strong>
    \u5728\u4f7f\u7528pandas<\/code>\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7usecols<\/code>\u53c2\u6570\u9009\u62e9\u9700\u8981\u52a0\u8f7d\u7684\u5217\u3002\u4f8b\u5982\uff0cpd.read_csv('\u6587\u4ef6\u8def\u5f84', usecols=['\u52171', '\u52172'])<\/code>\u5c06\u53ea\u8bfb\u53d6\u6307\u5b9a\u7684\u5217\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u8bfb\u53d6\u901f\u5ea6\uff0c\u8fd8\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8bfb\u53d6CSV\u5e76\u8868\u793a\u6bcf\u4e00\u5217\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u5305\u62ec\u4f7f\u7528Pandas\u3001csv\u6a21\u5757\u7b49\u65b9\u6cd5\u3002 \u4f7f\u7528Panda […]","protected":false},"author":3,"featured_media":1106851,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1106843"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1106843"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1106843\/revisions"}],"predecessor-version":[{"id":1106853,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1106843\/revisions\/1106853"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1106851"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1106843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1106843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1106843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}