{"id":39,"date":"2024-06-25T10:38:16","date_gmt":"2024-06-25T02:38:16","guid":{"rendered":"https:\/\/www.nickchan.cn\/?p=39"},"modified":"2024-07-11T09:55:32","modified_gmt":"2024-07-11T01:55:32","slug":"%e4%b8%ba%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e8%80%8c%e6%b8%85%e6%b4%97%e6%95%b0%e6%8d%ae-python%e7%9a%8421%e4%b8%aa%e6%a1%88%e4%be%8b%e5%92%8c%e4%bb%a3%e7%a0%81%ef%bc%88%e4%b8%8a","status":"publish","type":"post","link":"https:\/\/www.nickchan.cn\/index.php\/2024\/06\/25\/%e4%b8%ba%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e8%80%8c%e6%b8%85%e6%b4%97%e6%95%b0%e6%8d%ae-python%e7%9a%8421%e4%b8%aa%e6%a1%88%e4%be%8b%e5%92%8c%e4%bb%a3%e7%a0%81%ef%bc%88%e4%b8%8a\/","title":{"rendered":"\u4e3a\u6570\u636e\u5206\u6790\u800c\u6e05\u6d17\u6570\u636e\u2014\u2014Python\u768421\u4e2a\u6848\u4f8b\u548c\u4ee3\u7801\uff08\u4e0a\uff09"},"content":{"rendered":"
\u672c\u6587\u7ea65500\u5b57\uff0c\u5efa\u8bae\u9605\u8bfb15\u5206\u949f\n\u672c\u6587\u4e3a\u4e0a\u7bc7\uff0c\u4ecb\u7ecd\u4e86\u4e3a\u6570\u636e\u5206\u6790\u800c\u51c6\u5907\u7684\u6570\u636e\u6e05\u6d17\u768410\u4e2aPython\u6848\u4f8b\u53ca\u4ee3\u7801\uff0c\u4e0b\u7bc7\u5c06\u7ee7\u7eed\u4ecb\u7ecd\u53e6\u591611\u4e2aPython\u6848\u4f8b\u53ca\u4ee3\u7801\u3002<\/code><\/pre>\n
\u6570\u636e\u6e05\u6d17\u662f\u8bc6\u522b\u548c\u7ea0\u6b63\u9519\u8bef\u4ee5\u53ca\u6570\u636e\u96c6\u4e0d\u4e00\u81f4\u6027\u7684\u8fc7\u7a0b\uff0c\u4ee5\u4fbf\u4e8e\u6570\u636e\u53ef\u4ee5\u8fdb\u884c\u5206\u6790\u3002\u5728\u6b64\u8fc7\u7a0b\u4e2d\uff0c\u6570\u636e\u4e13\u5bb6\u53ef\u4ee5\u66f4\u6e05\u695a\u5730\u4e86\u89e3\u4ed6\u4eec\u7684\u4e1a\u52a1\u4e2d\u6b63\u5728\u53d1\u751f\u7684\u4e8b\u60c5\uff0c\u63d0\u4f9b\u4efb\u4f55\u7528\u6237\u90fd\u53ef\u4ee5\u5229\u7528\u7684\u53ef\u9760\u5206\u6790\uff0c\u5e76\u5e2e\u52a9\u4ed6\u4eec\u7684\u7ec4\u7ec7\u8fd0\u8f6c\u66f4\u9ad8\u6548\u3002<\/h4>\n
\u6570\u636e\u6e05\u6d17\u7684\u7279\u5f81<\/h3>\n
\u4e0d\u540c\u6570\u636e\u7279\u5f81\u548c\u5c5e\u6027\u90fd\u7528\u4e8e\u8861\u91cf\u6570\u636e\u96c6\u7684\u6e05\u6d01\u5ea6\u548c\u6574\u4f53\u8d28\u91cf\uff0c\u5305\u62ec\u4ee5\u4e0b\u65b9\u9762\uff1a\u4e0d\u540c\u6570\u636e\u7279\u5f81\u548c\u5c5e\u6027\u90fd\u7528\u4e8e\u8861\u91cf\u6570\u636e\u96c6\u7684\u6e05\u6d01\u5ea6\u548c\u6574\u4f53\u8d28\u91cf\uff0c\u5305\u62ec\u4ee5\u4e0b\u65b9\u9762\uff1a<\/h4>\n
    \n
  • \u51c6\u786e\u6027<\/li>\n
  • \u5b8c\u6574\u6027<\/li>\n
  • \u4e00\u81f4\u6027<\/li>\n
  • \u6574\u4f53\u6027<\/li>\n
  • \u9002\u65f6\u6027<\/li>\n
  • \u7edf\u4e00\u6027<\/li>\n
  • \u6709\u6548\u6027<\/li>\n<\/ul>\n
    \u5728\u672c\u6587\u5f53\u4e2d\uff0c\u6211\u4eec\u5c06\u8986\u76d6\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d4\u4e2a\u5bbd\u6cdb\u4e3b\u9898\uff0c\u5e76\u901a\u8fc7\u793a\u4f8b\u5c55\u793a\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6e05\u7406\u3002<\/p>\n
    1. \u5e38\u89c1\u6570\u636e\u95ee\u9898<\/h4>\n
    a. \u6570\u636e\u7c7b\u578b\u9650\u5236<\/p>\n
    b. \u6570\u636e\u5e7f\u5ea6\u9650\u5236<\/p>\n
    c. \u7279\u5f02\u6027\u9650\u5236<\/p>\n
    2. \u6587\u672c\u548c\u5206\u7c7b\u6570\u636e\u95ee\u9898<\/h4>\n
    a. \u8d44\u683c\u9650\u5236<\/p>\n
    b. \u5206\u7c7b\u53d8\u91cf<\/p>\n
    c. \u6e05\u6d17\u6587\u672c\u6570\u636e<\/p>\n
    3. \u9ad8\u7ea7\u6570\u636e\u95ee\u9898<\/h4>\n
    a. \u7edf\u4e00\u6027<\/p>\n
    b. \u8de8\u9886\u57df\u9a8c\u8bc1<\/p>\n
    c. \u5b8c\u6574\u6027<\/p>\n
    4. \u8bb0\u5f55\u94fe\u63a5<\/h4>\n
    a. \u5b57\u7b26\u4e32\u6bd4\u8f83<\/p>\n
    b. \u751f\u6210\u914d\u5bf9<\/p>\n
    c. \u94fe\u63a5\u6570\u636e\u6846<\/p>\n
    \"\"<\/p>\n
    1. \u5e38\u89c1\u6570\u636e\u95ee\u9898<\/h1>\n
    \u6211\u4eec\u5c06\u770b\u4e00\u4e0b\u5206\u6790\u8005\u5728\u5904\u7406\u539f\u59cb\u6570\u636e\u548c\u505a\u8fdb\u4e00\u6b65\u5206\u6790\u65f6\u9047\u5230\u7684\u6700\u5e38\u89c1\u7684\u95ee\u9898\u3002<\/p>\n
    a. \u6570\u636e\u7c7b\u578b\u9650\u5236<\/h2>\n
    \u5f53\u7ed9\u5b9a\u6570\u636e\u6846\u7684\u7279\u5b9a\u5217\u662f\u7279\u5b9a\u6570\u636e\u7c7b\u578b\u5e76\u4e14\u9700\u8981\u88ab\u77eb\u6b63\/\u8f6c\u6362\u6210\u53e6\u4e00\u79cd\u4fbf\u4e8e\u8ba1\u7b97\u548c\u5206\u6790\u7684\u6570\u636e\u7c7b\u578b\u6216\u8005\u8fdb\u884c\u7edf\u8ba1\u5904\u7406\u65f6\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u901a\u5e38\u5f88\u8d39\u65f6\u3002<\/p>\n
    \u5728\u4e00\u4e2a\u5178\u578b\u7684\u6570\u636e\u79d1\u5b66\u5de5\u4f5c\u6d41\u7a0b\u5f53\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u83b7\u53d6\u6211\u4eec\u7684\u539f\u59cb\u6570\u636e\uff0c\u63a2\u7d22\u5e76\u52a0\u5de5\u5b83\uff0c\u4f7f\u7528\u53ef\u89c6\u5316\u6216\u8005\u9884\u6d4b\u6a21\u578b\u6765\u83b7\u5f97\u66f4\u6df1\u7684\u6d1e\u5bdf\uff0c\u5e76\u6700\u7ec8\u4f7f\u7528\u62a5\u8868\u6765\u8fdb\u884c\u5c55\u793a\u3002<\/p>\n
    \u7531\u4e8e\u91cd\u590d\u503c\u3001\u62fc\u5199\u9519\u8bef\u3001\u6570\u636e\u7c7b\u578b\u89e3\u6790\u9519\u8bef\u548c\u9057\u7559\u7cfb\u7edf\u7b49\u539f\u56e0\uff0c\u4e5f\u4f1a\u51fa\u73b0\u4e0d\u5e72\u51c0\u7684\u6570\u636e\u3002<\/p>\n
    \u5982\u679c\u4e0d\u80fd\u786e\u4fdd\u6570\u636e\u5df2\u7ecf\u5728\u63a2\u7d22\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u4e86\u5408\u9002\u7684\u6e05\u7406\uff0c\u80af\u5b9a\u4f1a\u5bf9\u6211\u4eec\u4ea7\u751f\u7684\u89c1\u89e3\u548c\u62a5\u544a\u8d28\u91cf\u4ea7\u751f\u5f71\u54cd\u3002<\/p>\n
    \u4f8b1<\/h3>\n
    \u4f8b\u5982\uff1a\u6211\u4eec\u67e5\u770b\u4e00\u4e2a\u865a\u5047\u9500\u552e\u6570\u636e\uff0c\u4e00\u4e2a\u672c\u5e94\u8be5\u662f\u6574\u578b\u53d8\u91cf\u7684\u6570\u503c\u6570\u636e\u88ab\u4fdd\u5b58\u6210\u5b57\u7b26\u4e32\uff08\u4f7f\u7528dtype\u65b9\u6cd5\u8fd4\u56deRevenue\u5217\u5bf9\u8c61\uff09<\/p>\n
    # Import CSV file and output header\nsales = pd.read_csv('sales.csv')\nsales.head(2)\n# Get data types of columns\nsales.dtypes<\/code><\/pre>\n
    \"\u9500\u552e\u6570\u636e\u524d\u4e24\u5217\"
    \n\u9500\u552e\u6570\u636e\u524d\u4e24\u5217<\/p>\n
    \"\"
    \nRevenue\u88ab\u89c6\u4e3a\u5bf9\u8c61\u578b\u6570\u636e\u7c7b\u578b<\/p>\n
    \u73b0\u5728\uff0c\u5982\u679c\u6211\u4eec\u60f3\u8981\u67e5\u770b\u603b\u6536\u5165\u5e76\u5e94\u7528sum\u65b9\u6cd5\uff0c\u7ed3\u679c\u5c06\u662f\u4e00\u4e2a\u8fde\u63a5\u5b57\u7b26\u4e32\uff0c\u800c\u4e0d\u662f\u6240\u6709\u884c\u4e2d\u6240\u6709\u6536\u5165\u503c\u7684\u6570\u503c\u603b\u548c\uff1a<\/p>\n
    sales['Revenue'].sum()<\/code><\/pre>\n
    \u5219\u7ed3\u679c\u4e3a
    \n\"\"<\/p>\n
    \u8fd9\u5f88\u660e\u663e\u662f\u4e0d\u6b63\u786e\u7684\u3002\u4f5c\u4e3a\u4e00\u79cd\u89e3\u51b3\u65b9\u6848\uff0c\u6211\u4eec\u9996\u5148\u5220\u9664$\u7b26\u53f7\uff0c\u7136\u540e\u5728\u6267\u884c\u6c42\u548c\u64cd\u4f5c\u4e4b\u524d\u5c06\u6570\u636e\u7c7b\u578b\u66f4\u6539\u4e3a' int '\uff1a<\/p>\n
    sales['Revenue'] = sales['Revenue'].str.strip('$')\nsales['Revenue'] = sales['Revenue'].astype('int')<\/code><\/pre>\n
    \n
    \u4f8b2<\/h3>\n
    \u6211\u4eec\u7684\u4e0b\u4e00\u4e2a\u4f8b\u5b50\u662f\uff0c\u5f53\u4e00\u4e2a\u5206\u7c7b\u53d8\u91cf\u88ab\u8868\u5f81\u4e3a\u4e00\u4e2a\u6570\u5b57\u5e76\u9519\u8bef\u5730\u53d8\u6210\u4e00\u4e2aint\u53d8\u91cf\u65f6\uff0c\u6211\u4eec\u53ef\u80fd\u4f1a\u5f97\u5230\u8be5\u53d8\u91cf\/\u6570\u636e\u5217\u7684\u975e\u9884\u671f\u7684\u7edf\u8ba1\u6458\u8981\u3002<\/p>\n
    \u793a\u4f8b:\u6211\u4eec\u5c06marriage_status\u503c\u4fdd\u5b58\u4e3a\u6570\u5b57\uff0c\u8868\u793a\uff1a<\/p>\n
    0 = Never married \n1 = Married \n2 = Separated \n3 = Divorced<\/code><\/pre>\n
    \"\"<\/p>\n
    \u7136\u540e\u6211\u4eec\u8bd5\u7740\u770b\u770b\u672c\u5217\u7684\u7edf\u8ba1\u603b\u7ed3:<\/p>\n
    df['marriage_status'].describe()<\/code><\/pre>\n
    \u5219\u7ed3\u679c\u4e3a\uff1a
    \n\"\"<\/p>\n
    \u8fd9\u5bf9\u4e8e\u7406\u89e3\u6211\u4eec\u6240\u5173\u5fc3\u7684\u53d8\u91cf\u5728\u6570\u636e\u96c6\u4e2d\u7684\u5206\u5e03\u6beb\u65e0\u7528\u5904\uff0c\u56e0\u6b64\u6211\u4eec\u9996\u5148\u9700\u8981\u5c06\u5176\u8f6c\u6362\u4e3aint\u7c7b\u578b\u7684\u53d8\u91cf\u3002<\/p>\n
    df["marriage_status"] = df["marriage_status"].astype('category')\ndf.describe()<\/code><\/pre>\n
    \"\"<\/p>\n
    b.\u6570\u636e\u8303\u56f4\u7ea6\u675f<\/h2>\n
    \u5e94\u8be5\u843d\u5728\u4e00\u4e2a\u8303\u56f4\u5185\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0c\u7535\u5f71\u8bc4\u7ea7\u6570\u636e\u53ea\u80fd\u4fdd\u5b581-5\u62161-10\u4e4b\u95f4\u7684\u503c\uff0c\u6216\u8005\u8ba2\u9605\u65e5\u671f\u5217\u4e0d\u80fd\u6709\u672a\u6765\u65e5\u671f\u7684\u503c\u3002<\/p>\n
    \u4f8b3<\/h3>\n
    \u7b2c\u4e00\u4e2a\u4f8b\u5b50\u662f\u6211\u4eec\u7684\u7535\u5f71\u8bc4\u5206\u6570\u636e\uff1a<\/p>\n
    \"\"<\/p>\n
    \u67e5\u770b\u8d85\u51fa\u9884\u671f\u8303\u56f4\u7684\u635f\u574f\u6570\u636e\u7684\u4e00\u79cd\u6709\u6548\u65b9\u6cd5\u662f\u53ef\u89c6\u5316\u8be5\u5217\uff1a<\/p>\n
    import matplotlib.pyplot as plt\nplt.hist(movies['avg_rating'])\nplt.title('Average rating of movies (1-5)')<\/code><\/pre>\n
    \"\"
    \n\u6211\u4eec\u5728\u8fd9\u91cc\u770b\u5230\u51e0\u4e2a\u503c\u5728\u8d85\u51fa\u8303\u56f4\u76846\u5904\u4e0b\u964d\u3002\u6709\u8bb8\u591a\u65b9\u6cd5\u53ef\u4ee5\u5904\u7406\u8fd9\u4e9b\u8d85\u51fa\u8303\u56f4\u7684\u503c\uff0c\u5982\u8f93\u5165\u884c\uff0c\u5c06\u5b83\u4eec\u8bbe\u7f6e\u4e3a\u503c5\uff1b\u6216\u8bbe\u7f6e\u4e3a\u5e73\u5747\u8bc4\u7ea7\u503c\u7b49\uff0c\u8fd9\u53d6\u51b3\u4e8e\u4e1a\u52a1\u6848\u4f8b\u548c\u6211\u4eec\u8bd5\u56fe\u901a\u8fc7\u6b64\u7279\u5b9a\u5206\u6790\u89e3\u51b3\u7684\u95ee\u9898\u3002<\/p>\n
    \u6211\u4eec\u4f7f\u7528\u4e0b\u9762\u7684\u4ee3\u7801\u8fc7\u6ee4\u6389\u9519\u8bef\u7684\u503c\uff1a<\/p>\n
    import pandas as pd\n# Output Movies with rating > 5\nmovies[movies['avg_rating'] > 5]<\/code><\/pre>\n
    \"\"<\/p>\n
    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528\u8fc7\u6ee4\u6765\u5220\u9664\u503c\uff1a<\/p>\n
    movies = movies[movies['avg_rating'] <= 5]\n# Drop values using .drop()\nmovies.drop(movies[movies['avg_rating'] > 5].index, inplace = True)<\/code><\/pre>\n
    \u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u5c06\u8fd9\u4e9b\u503c\u66ff\u6362\u4e3a\u6700\u5927\u503c\uff0c\u53735<\/p>\n
    # Convert avg_rating > 5 to 5\nmovies.loc[movies['avg_rating'] > 5, 'avg_rating'] = 5<\/code><\/pre>\n
    \n
    \u4f8b4<\/h3>\n
    \u5728\u6211\u4eec\u7b2c\u4e8c\u4e2a\u4f8b\u5b50\u5f53\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6570\u636e\u8303\u56f4\u8d85\u51fa\u9884\u671f\u503c\uff1a<\/p>\n
    \u4e3a\u4e86\u8fc7\u6ee4\u7ed9\u5b9a\u6570\u636e\u6846\u4e2d\u9519\u8bef\/\u5c06\u6765\u7684\u65e5\u671f\u503c\uff0c\u6211\u4eec\u8fd0\u884c\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
    import datetime as dt\ntoday_date = dt.date.today()\nuser_signups[user_signups['subscription_date'] > dt.date.today()]\n# this is an old code and 'today's date' was 1\/1\/2020 , result has been filtered accordingly<\/code><\/pre>\n
    \"\"
    \n\u5047\u5b9a\u4eca\u5929\u65e5\u671f\u662f2020\/01\/02\uff0csubscription_date\u5217\u91cc\u6240\u6709\u8fc7\u6ee4\u503c\u90fd\u662f\u672a\u6765\u65f6\u95f4<\/p>\n
    \u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\u6211\u4eec\u9996\u5148\u68c0\u67e5\u4e00\u4e0b\u8be5\u5217\u7684\u6570\u636e\u7c7b\u578b\uff1a<\/p>\n
    import datetime as dt\nimport pandas as pd\n# Output data types\nuser_signups.dtypes<\/code><\/pre>\n
    \"\"
    \n\u6211\u4eec\u770b\u5230subscription_date\u5217\u6709\u5bf9\u8c61\u6570\u636e\u7c7b\u578b\uff0c\u663e\u793a\u53ef\u80fd\u6709\u5b57\u7b26\u4e32\u7c7b\u578b\u7684\u503c\uff0c\u6240\u4ee5\u9996\u5148\u6211\u4eec\u9700\u8981\u5c06\u5b83\u4eec\u8f6c\u6362\u4e3a\u65e5\u671f\u7c7b\u578b\uff1a<\/p>\n
    import datetime as dt\nimport pandas as pd\n# Output data types\nuser_signups.dtypes<\/code><\/pre>\n
    \u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528loc\u51fd\u6570\u5c06\u672c\u5217\u4e2d\u663e\u793a\u672a\u6765\u65e5\u671f\u7684\u503c\u8fdb\u884c\u66ff\u6362\uff1a<\/p>\n
    today_date = dt.date.today()\n# Drop values using filtering\nuser_signups = user_signups[user_signups['subscription_date'] < today_date]\n# Drop values using .drop()\nuser_signups.drop(user_signups[user_signups['subscription_date'] > today_date].index, inplace = True)\n# Drop values using filtering\nuser_signups.loc[user_signups['subscription_date'] > today_date, 'subscription_date'] = today_date\n# Assert its true\nassert user_signups.subscription_date.max().date() <= today_date<\/code><\/pre>\n
    c.\u7279\u5f02\u6027\u9650\u5236<\/h2>\n
    \u5f53\u6211\u4eec\u6709\u76f8\u540c\u7684\u660e\u786e\u4fe1\u606f\u5728\u4e00\u4e9b\u6216\u6240\u6709\u5217\u5f53\u4e2d\u51fa\u73b0\u8de8\u884c\u91cd\u590d\u65f6\uff0c\u53ef\u4ee5\u88ab\u8bca\u65ad\u4e3a\u91cd\u590d\u503c\u3002<\/p>\n
    \u4f8b\u5982\uff0c\u4ee5\u4e0b\u4e2a\u4eba\u4fe1\u606f\uff1a
    \n\"\"
    \n\u8981\u4e48\u662f\u6240\u6709\u5217\u90fd\u91cd\u590d\u6216\u8005\u5927\u90e8\u5206\u5217\u503c\u91cd\u590d\u3002<\/p>\n
    \u539f\u56e0\uff1a<\/p>\n
    a. \u6570\u636e\u8f93\u5165\u9519\u8bef<\/p>\n
    b. \u8fde\u63a5\u6216\u5408\u5e76\u9519\u8bef<\/p>\n
    c. \u6f0f\u6d1e\u6216\u8bbe\u8ba1\u9519\u8bef<\/p>\n
    \u5982\u4f55\u53d1\u73b0\u91cd\u590d\u503c\uff1f<\/p>\n
    \u7b2c\u4e00\u6b65\uff0c\u68c0\u67e5\u6570\u636e\u6846<\/p>\n
    \u4f8b5<\/h3>\n
    \u4f8b\u5982\uff0c\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u5904\u7406\u7528\u6237\u4fe1\u606f\u6570\u636e\u7684\u91cd\u590d\u503c\uff1a<\/p>\n
    height_weight.head()<\/code><\/pre>\n
    \"\"
    \n\u4f7f\u7528duplicated()\u65b9\u6cd5\uff1a<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528duplicated()\u65b9\u6cd5\u6765\u5904\u7406\u6570\u636e\u6846\u4e2d\u7684\u91cd\u590d\u503c\u3002\u8fd4\u56de\u4e00\u7cfb\u5217\u5e03\u5c14\u503c\uff0cTrue\u8868\u793a\u6709\u91cd\u590d\uff0cFalse\u8868\u793a\u975e\u91cd\u590d\u503c\u3002<\/p>\n
    \u63a5\u4e0b\u6765\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u54ea\u4e9b\u884c\u53d7\u5230\u4e86\u5f71\u54cd\u3002<\/p>\n
    duplicates = height_weight.duplicated()\nprint(duplicates)<\/code><\/pre>\n
    \u6837\u4f8b\u8f93\u51fa\u5982\u4e0b\uff1a
    \n\"\"<\/p>\n
    \u7136\u800c\uff0c\u4f7f\u7528duplicated()\u4f46\u4e0d\u4f7f\u7528\u53c2\u6570\u7684\u65b9\u6cd5\u53ef\u80fd\u4f1a\u5bfc\u81f4\u9519\u8bef\u7684\u7ed3\u679c\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u6240\u6709\u5217\u90fd\u8981\u6c42\u6709\u91cd\u590d\u503c\uff0c\u9664\u4e86\u7b2c\u4e00\u6b21\u51fa\u73b0\u5916\uff0c\u6240\u6709\u91cd\u590d\u7684\u503c\u90fd\u88ab\u6807\u8bb0\u4e3aTrue\u3002\u8fd9\u9650\u5236\u4e86\u6211\u4eec\u8bca\u65ad\u91cd\u590d\u7c7b\u578b\u4ee5\u53ca\u5982\u4f55\u6709\u6548\u5904\u7406\u7684\u80fd\u529b\u3002<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6240\u6709\u884c\u90fd\u4f1a\u53d7\u5230[]\u7684\u5f71\u54cd\uff1a<\/p>\n
    duplicates = height_weight.duplicated()\nheight_weight[duplicates]<\/code><\/pre>\n
    \"\"<\/p>\n
    \u4e3a\u4e86\u6b63\u786e\u6821\u51c6\u67e5\u627e\u91cd\u590d\u9879\u7684\u65b9\u5f0f\uff0c\u6211\u4eec\u5c06\u4f7f\u7528duplicate()\u65b9\u6cd5\u4e2d\u76842\u4e2a\u53c2\u6570\u3002<\/p>\n
    a. \u5b50\u96c6\u53c2\u6570\u5141\u8bb8\u6211\u4eec\u8bbe\u7f6e\u4e00\u4e2a\u5217\u540d\u5217\u8868\u6765\u68c0\u67e5\u91cd\u590d\u3002\u4f8b\u5982\uff0c\u5b83\u53ea\u5141\u8bb8\u6211\u4eec\u67e5\u627e\u59d3\u548c\u540d\u5217\u7684\u91cd\u590d\u9879\u3002<\/p>\n
    b. keep\u53c2\u6570\u8ba9\u6211\u4eec\u901a\u8fc7\u5c06\u91cd\u590d\u503c\u7684\u7b2c\u4e00\u6b21\u51fa\u73b0\u8bbe\u7f6e\u4e3a\u5b57\u7b26\u4e32\u7684\u7b2c\u4e00\u4e2a\uff0c\u5c06\u91cd\u590d\u503c\u7684\u6700\u540e\u4e00\u6b21\u51fa\u73b0\u8bbe\u7f6e\u4e3a\u5b57\u7b26\u4e32\u7684\u6700\u540e\u4e00\u4e2a\uff0c\u6216\u5c06\u6240\u6709\u91cd\u590d\u503c\u7684\u51fa\u73b0\u8bbe\u7f6e\u4e3aFalse\u6765\u4fdd\u7559\u3002<\/p>\n
    \u5728\u672c\u4f8b\u4e2d\uff0c\u6211\u4eec\u68c0\u67e5\u540d\u5b57\u3001\u59d3\u6c0f\u548c\u5730\u5740\u53d8\u91cf\u4e4b\u95f4\u7684\u91cd\u590d\u9879\uff0c\u5e76\u9009\u62e9\u4fdd\u7559\u6240\u6709\u91cd\u590d\u9879\u3002<\/p>\n
    column_names = ['first_name','last_name','address']\nduplicates = height_weight.duplicated(subset = column_names, keep = False)<\/code><\/pre>\n
    \"\"
    \n\u73b0\u5728\uff0c\u4e3a\u4e86\u83b7\u5f97\u66f4\u597d\u5730\u4e86\u89e3\u91cd\u590d\u503c\uff0c\u6211\u4eec\u4f7f\u7528\u6392\u5e8f\u65b9\u6cd5\uff1a<\/p>\n
    height_weight[duplicates].sort_values(by = 'first_name')<\/code><\/pre>\n
    \"\"<\/p>\n
    \u6211\u4eec\u53d1\u73b0\u6709\u56db\u4e2a\u91cd\u590d\u884c\u7684\u96c6\uff0c\u524d\u4e24\u884c\u5728\u6240\u6709\u5217\u4e0a\u90fd\u91cd\u590d\uff081\uff0c2\uff0c7\uff0c8\u884c\uff09<\/p>\n
    \u53e6\u5916\u4e24\u4e2a\u4e0d\u5b8c\u5168\u91cd\u590d\uff083\uff0c4\uff0c5\uff0c\u884c\uff09<\/p>\n
    \u5904\u7406\u91cd\u590d\u503c\uff1a<\/p>\n
    \u5b8c\u5168\u91cd\u590d\u7684\u6bd4\u8f83\u5bb9\u6613\u5904\u7406\u3002\u6240\u9700\u8981\u505a\u7684\u5c31\u662f\u4fdd\u7559\u5176\u4e2d\u4e00\u4e2a\uff0c\u53bb\u6389\u5176\u4ed6\u7684\u3002<\/p>\n
    drop_duplicates\u65b9\u6cd5\u2014\u2014\u4e0eduplicates\u6cd5\u53c2\u6570\u76f8\u540c\u2014\u2014\u5b50\u96c6\u548c\u539f\u4f4d<\/p>\n
    height_weight.drop_duplicates(inplace = True)<\/code><\/pre>\n
    \u8fd9\u7ed9\u6211\u4eec\u7559\u4e0b\u4e86\u53e6\u5916\u4e24\u7ec4\u91cd\u590d\u9879\uff0c\u5b83\u4eec\u5bf9\u4e8efirst_name\u3001last_name\u548caddress\u662f\u76f8\u540c\u7684\uff0c\u4f46\u662f\u5305\u542b\u9ad8\u5ea6\u548c\u91cd\u91cf\u7684\u5dee\u5f02\u3002\u9664\u4e86\u5220\u9664\u5dee\u5f02\u5f88\u5c0f\u7684\u884c\u4e4b\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u7edf\u8ba1\u5ea6\u91cf\u6765\u7ec4\u5408\u6bcf\u7ec4\u91cd\u590d\u7684\u503c\u3002<\/p>\n
    \u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u5e73\u5747\u503c\uff0c\u6216\u6700\u5927\u503c\uff0c\u6216\u5176\u4ed6\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5c06\u8fd9\u4e24\u884c\u5408\u5e76\u4e3a\u4e00\u884c\uff0c\u8fd9\u9ad8\u5ea6\u4f9d\u8d56\u4e8e\u5bf9\u6570\u636e\u7684\u5e38\u8bc6\u7406\u89e3\uff0c\u4ee5\u53ca\u6211\u4eec\u62e5\u6709\u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528groupby\u65b9\u6cd5\u8f7b\u677e\u5730\u505a\u5230\u8fd9\u4e00\u70b9\uff0c\u5f53\u5b83\u4e0eagg\u65b9\u6cd5\u94fe\u63a5\u5728\u4e00\u8d77\u65f6\uff0c\u53ef\u4ee5\u6309\u4e00\u7ec4\u516c\u5171\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5728\u6267\u884c\u805a\u5408\u65f6\u8fd4\u56de\u7279\u5b9a\u5217\u7684\u7edf\u8ba1\u503c\u3002
    \n\"\"<\/p>\n
    \u4ee3\u7801\u8bf4\u660e\uff1a<\/p>\n
    \u4f8b\u5982\uff0c\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u540d\u4e3asummary\u7684\u5b57\u5178\uff0c\u5b83\u6307\u793agroupby\u8fd4\u56de\u9ad8\u5ea6\u5217\u7684\u6700\u5927\u91cd\u590d\u884c\u6570\uff0c\u4ee5\u53ca\u6743\u91cd\u5217\u7684\u5e73\u5747\u91cd\u590d\u884c\u6570\u3002\u7136\u540e\uff0c\u6211\u4eec\u6839\u636e\u524d\u9762\u5b9a\u4e49\u7684\u5217\u540d\u5bf9highight_weight\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5c06\u5176\u4e0eagg\u65b9\u6cd5\u94fe\u63a5\u8d77\u6765\uff0c\u8be5\u65b9\u6cd5\u4f7f\u7528\u6211\u4eec\u521b\u5efa\u7684\u6458\u8981\u5b57\u5178\u3002\u6211\u4eec\u7528dot-reset_index()\u65b9\u6cd5\u94fe\u63a5\u6574\u884c\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u5728\u6700\u7ec8\u8f93\u51fa\u4e2d\u62e5\u6709\u7f16\u53f7\u7684\u7d22\u5f15\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u518d\u6b21\u8fd0\u884cduplicate\u65b9\u6cd5\u6765\u9a8c\u8bc1\u6ca1\u6709\u91cd\u590d\u7684\u503c\uff0c\u5e76\u4f7f\u7528brackets\u6765\u8f93\u51fa\u91cd\u590d\u884c\u3002<\/p>\n
    2. \u6587\u672c\u548c\u5206\u7c7b\u6570\u636e\u95ee\u9898<\/h1>\n
    \u63a5\u4e0b\u6765\uff0c\u5728\u8fdb\u884c\u5206\u6790\u4e4b\u524d\uff0c\u6211\u4eec\u5c06\u7814\u7a76\u53ef\u80fd\u9762\u4e34\u7684\u53e6\u5916\u4e24\u79cd\u7c7b\u578b\u7684\u6570\u636e\u6e05\u7406\u8303\u56f4\u3002<\/p>\n
    \u5206\u7c7b\u53d8\u91cf\u662f\u4e00\u79cd\u53ef\u4ee5\u53d6\u6709\u9650\u7684\uff08\u901a\u5e38\u662f\u56fa\u5b9a\u7684\uff09\u53ef\u80fd\u503c\u4e2d\u7684\u4e00\u4e2a\u7684\u53d8\u91cf\u3002\u56e0\u6b64\uff0c\u5206\u7c7b\u6570\u636e\u53ef\u4ee5\u8868\u793a\u9884\u5b9a\u4e49\u6709\u9650\u7c7b\u522b\u96c6\u7684\u53d8\u91cf\u3002\u8fd9\u65b9\u9762\u7684\u4f8b\u5b50\u5305\u62ec\u5a5a\u59fb\u72b6\u51b5\u3001\u5bb6\u5ead\u6536\u5165\u7c7b\u522b\u3001\u8d37\u6b3e\u72b6\u51b5\u7b49\u3002\u4e3a\u4e86\u5728\u5206\u7c7b\u6570\u636e\u4e0a\u8fd0\u884c\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5b83\u4eec\u901a\u5e38\u88ab\u7f16\u7801\u4e3a\u6570\u5b57\u3002\u7531\u4e8e\u5206\u7c7b\u6570\u636e\u8868\u793a\u4e00\u7ec4\u9884\u5b9a\u4e49\u7684\u7c7b\u522b\uff0c\u56e0\u6b64\u5b83\u4eec\u4e0d\u80fd\u5177\u6709\u8d85\u51fa\u8fd9\u4e9b\u9884\u5b9a\u4e49\u7c7b\u522b\u7684\u503c\u3002<\/p>\n
    a. \u5173\u7cfb\u9650\u5236<\/h2>\n
    \u7531\u4e8e\u5404\u79cd\u539f\u56e0\uff0c\u6211\u4eec\u7684\u5206\u7c7b\u6570\u636e\u53ef\u80fd\u4e0d\u4e00\u81f4\u3002\u8fd9\u53ef\u80fd\u662f\u7531\u4e8e\u81ea\u7531\u6587\u672c\u4e0e\u4e0b\u62c9\u5b57\u6bb5\u7684\u6570\u636e\u8f93\u5165\u95ee\u9898\uff0c\u6570\u636e\u89e3\u6790\u9519\u8bef\u548c\u5176\u4ed6\u7c7b\u578b\u7684\u9519\u8bef\u3002<\/p>\n
    \u4e0d\u540c\u7c7b\u578b\u7684\u77db\u76fe\u9700\u8981\u4e0d\u540c\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n
    \u4f8b6<\/h3>\n
    \u4e0b\u9762\u662f\u4e00\u4e2a\u540d\u4e3astudy_data\u7684\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542b\u540d\u5b57\u3001\u51fa\u751f\u65e5\u671f\u548c\u8840\u578b\u7684\u5217\u8868\u3002\u6b64\u5916\uff0c\u8fd8\u521b\u5efa\u4e86\u4e00\u4e2a\u540d\u4e3acategories\u7684\u6570\u636e\u6846\uff0c\u5176\u4e2d\u5305\u542b\u8840\u578b\u5217\u53ef\u80fd\u7684\u6b63\u786e\u7c7b\u522b\u3002<\/p>\n
    study_data = pd.read_csv('study.csv')\nstudy_data<\/code><\/pre>\n
    \"\"<\/p>\n
    # Correct possible blood types\ncategories<\/code><\/pre>\n
    \"\"
    \n\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u91cc\u770b\u5230\uff0c\u7814\u7a76\u6570\u636e\u7684\u7b2c\u4e94\u884c\u6709\u4e00\u4e2a\u8840\u578bZ+\uff0c\u8fd9\u4e0d\u662f\u4ece\u7c7b\u522b\u6570\u636e\u3002\u7c7b\u522b\u6570\u636e\u6846\u67b6\u5c06\u5e2e\u52a9\u6211\u4eec\u7cfb\u7edf\u5730\u53d1\u73b0\u6240\u6709\u8fd9\u4e9b\u4e0d\u4e00\u81f4\u7684\u884c\u3002<\/p>\n
    \u4fdd\u5b58\u5206\u7c7b\u6570\u636e\u7684\u6240\u6709\u53ef\u80fd\u503c\u7684\u65e5\u5fd7\u59cb\u7ec8\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u505a\u6cd5\uff0c\u56e0\u4e3a\u5b83\u5c06\u4f7f\u5904\u7406\u8fd9\u4e9b\u7c7b\u578b\u7684\u4e0d\u4e00\u81f4\u66f4\u5bb9\u6613\u3002<\/p>\n
    \u5982\u4f55\u4f7f\u7528\u8fde\u63a5\u6765\u4fee\u590d\u6570\u636e\u4e2d\u7684\u5206\u7c7b\u4e0d\u4e00\u81f4\uff1a<\/p>\n
    \u6211\u4eec\u5728\u8fd9\u91cc\u5173\u5fc3\u7684\u4e24\u79cd\u4e3b\u8981\u8fde\u63a5\u7c7b\u578b\u662f\u53cd\u8fde\u63a5\u548c\u5185\u8fde\u63a5\u3002<\/p>\n
    \u6211\u4eec\u5728\u5b83\u4eec\u4e4b\u95f4\u7684\u516c\u5171\u5217\u4e0a\u8fde\u63a5\u6570\u636e\u6846\u3002\u53cd\u8fde\u63a5\uff0c\u5408\u5e76\u4e24\u4e2a\u6570\u636e\u6846 A\u548cB\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u6570\u636e\u6846\u4e2d\u4e0d\u5305\u542b\u5728\u53e6\u4e00\u4e2a\u6570\u636e\u6846\u4e2d\u7684\u6570\u636e\u3002<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u6b63\u5728\u6267\u884cA\u548cB\u7684\u5de6\u53cd\u8fde\u63a5\uff0c\u5e76\u4e14\u8fd4\u56de\u6570\u636e\u6846 A\u548cB\u7684\u5217\uff0c\u8fd9\u4e9b\u5217\u7684\u503c\u53ea\u80fd\u5728\u8fde\u63a5\u5b83\u4eec\u4e4b\u95f4\u7684\u516c\u5171\u5217\u7684A\u4e2d\u627e\u5230\u3002<\/p>\n
    \u5185\u90e8\u8fde\u63a5\uff0c\u53ea\u8fd4\u56de\u4e24\u4e2a\u6570\u636e\u6846\u4e2d\u5305\u542b\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0cA\u548cB\u7684\u5185\u90e8\u8fde\u63a5\u5c06\u4ece\u4e24\u4e2a\u6570\u636e\u6846\u8fd4\u56de\u4ec5\u5728A\u548cB\u4e2d\u627e\u5230\u7684\u503c\u7684\u5217\uff0c\u5b83\u4eec\u4e4b\u95f4\u7684\u516c\u5171\u5217\u88ab\u8fde\u63a5\u5728\u4e00\u8d77\u3002
    \n\"\"
    \n\u5982\u4f55\u4f7f\u7528\u8fde\u63a5\u6765\u6e05\u9664\u6570\u636e\u4e2d\u7684\u5206\u7c7b\u4e0d\u4e00\u81f4<\/p>\n
    \u5728\u6211\u4eec\u7684\u793a\u4f8b\u4e2d\uff0c\u5de6\u53cd\u8fde\u63a5\u5b9e\u8d28\u4e0a\u8fd4\u56de\u6240\u6709\u8840\u578b\u4e0d\u4e00\u81f4\u7684\u7814\u7a76\u6570\u636e\u4e2d\u7684\u6570\u636e\uff1a
    \n\"\"<\/p>\n
    \u5728Python\u4e2d\uff1a<\/p>\n
    \u9996\u5148\u6211\u4eec\u8981\u627e\u5230\u7ed9\u5b9a\u6570\u636e\u4e2d\u4e0d\u4e00\u81f4\u7684\u5206\u7c7b\uff1a<\/p>\n
    inconsistent_categories = \nset(study_data['blood_type']).difference(categories['blood_type'])\nprint(inconsistent_categories)<\/code><\/pre>\n
    \"\"
    \n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u627e\u5230\u5177\u6709\u8fd9\u4e2a\u4e0d\u4e00\u81f4\u503c\u7684\u884c\uff1a<\/p>\n
    # Get and print rows with inconsistent categories\ninconsistent_rows = \nstudy_data['blood_type'].isin(inconsistent_categories)\nstudy_data[inconsistent_rows]<\/code><\/pre>\n
    \"\"
    \n\u5220\u9664\u4e0d\u4e00\u81f4\u7684\u884c\uff0c\u53ea\u4fdd\u7559\u4e00\u81f4\u7684\u884c\u3002\u6211\u4eec\u53ea\u662f\u5728\u5b50\u96c6\u65f6\u4f7f\u7528\u6ce2\u6d6a\u7b26\u53f7\uff0c\u5b83\u8fd4\u56de\u9664\u4e86\u4e0d\u4e00\u81f4\u884c\u7684\u6240\u6709\u5185\u5bb9\u3002<\/p>\n
    inconsistent_categories = \nset(study_data['blood_type']).difference(categories['blood_type'])\ninconsistent_rows =\n study_data['blood_type'].isin(inconsistent_categories)\ninconsistent_data = study_data[inconsistent_rows]\n# Drop inconsistent categories and get consistent data only\nconsistent_data = study_data[~inconsistent_rows]<\/code><\/pre>\n
    \"\"<\/p>\n
    b.\u5206\u7c7b\u53d8\u91cf<\/h2>\n
    \u5728\u6e05\u7406\u5206\u7c7b\u6570\u636e\u65f6\uff0c\u6211\u4eec\u53ef\u80fd\u9047\u5230\u7684\u4e00\u4e9b\u95ee\u9898\u5305\u62ec\u503c\u4e0d\u4e00\u81f4\uff0c\u5b58\u5728\u592a\u591a\u53ef\u4ee5\u5408\u5e76\u4e3a\u4e00\u4e2a\u7684\u7c7b\u522b\uff0c\u4ee5\u53ca\u786e\u4fdd\u6570\u636e\u5177\u6709\u6b63\u786e\u7684\u7c7b\u578b\u3002<\/p>\n
    \u4f8b7<\/h3>\n
    \u4e3a\u6b64\uff0c\u6211\u4eec\u5c06\u4ee5\u4eba\u53e3\u7edf\u8ba1\u6570\u636e\u4e3a\u4f8b\uff0c\u5176\u4e2d\u4e00\u4e2a\u5217\u662f\u201cmarriage_status\u201d\uff1a<\/p>\n
    \u9996\u5148\uff0c\u6211\u4eec\u4ece\u8ba1\u7b97\u8fd9\u4e00\u5217\u4e2d\u7684\u552f\u4e00\u503c\u5f00\u59cb\uff0c\u4ee5\u67e5\u770b\u8fd9\u4e00\u7c7b\u522b\u4e2d\u7684\u6240\u6709\u53ef\u80fd\u503c:(\u68c0\u67e5\u4e00\u81f4\u6027)\u3002<\/p>\n
    \u6ce8\u610f\uff0cdot-value_counts()\u65b9\u6cd5\u53ea\u9002\u7528\u4e8eSeries\u3002<\/p>\n
    # Get marriage status column\nmarriage_status = demographics['marriage_status']\nmarriage_status.value_counts()<\/code><\/pre>\n
    \"\"
    \n\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06marriage_status\u5217\u5927\u5199\u6216\u5c0f\u5199\u3002\u8fd9\u53ef\u4ee5\u5206\u522b\u4f7f\u7528str-dot-upper()\u6216dot-lower()\u51fd\u6570\u6765\u5b8c\u6210\u3002<\/p>\n
    \u9009\u62e91\uff1a<\/p>\n
    # Capitalize\nmarriage_status['marriage_status'] =\n marriage_status['marriage_status'].str.upper()\nmarriage_status['marriage_status'].valu_e_counts()<\/code><\/pre>\n
    \"\"
    \n\u9009\u62e92\uff1a<\/p>\n
    # Lowercase\nmarriage_status['marriage_status'] = marriage_status['marriage_status'].str.lower()\nmarriage_status['marriage_status'].value_counts()<\/code><\/pre>\n
    \"\"
    \n\u6211\u4eec\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\u770b\u5230\u7684\u53e6\u4e00\u4e2a\u95ee\u9898\u662f:\u524d\u5bfc\u6216\u5c3e\u968f\u7a7a\u683c:'\u5df2\u5a5a'\uff0c'\u5df2\u5a5a'\uff0c'\u672a\u5a5a'\uff0c'\u672a\u5a5a' ..<\/p>\n
    # Get marriage status column\nmarriage_status = demographics['marriage_status']\nmarriage_status.value_counts()<\/code><\/pre>\n
    \"\"<\/p>\n
    \u8981\u5220\u9664\u524d\u5bfc\u7a7a\u683c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528str-dot-strip()\u65b9\u6cd5\uff0c\u5f53\u6ca1\u6709\u8f93\u5165\u65f6\uff0c\u8be5\u65b9\u6cd5\u4f1a\u5220\u9664\u6240\u6709\u524d\u5bfc\u548c\u540e\u5bfc\u7a7a\u683c\u3002<\/p>\n
    # Strip all spaces\ndemographics = demographics['marriage_status'].str.strip()\ndemographics['marriage_status'].value_counts()<\/code><\/pre>\n
    \"\"<\/p>\n
    \u7ee7\u7eed\u2026<\/p>\n
    \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u5e0c\u671b\u5c06\u5217\u4e2d\u7684\u503c\u6298\u53e0\u5230\u7c7b\u522b\u6216\u7bb1\u4e2d\u3002<\/p>\n
    \u6709\u65f6\u4e5f\u53ef\u80fd\u5b58\u5728\u7c7b\u522b\u6570\u91cf\u975e\u5e38\u9ad8\u7684\u60c5\u51b5\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u901a\u8fc7\u5c06\u5c11\u6570\u7c7b\u522b\u6620\u5c04\u5230\u66f4\u5e7f\u6cdb\u7684\u7c7b\u522b\u4e2d\u6765\u5c06\u5b83\u4eec\u6298\u53e0\u6210\u66f4\u5c0f\u7684\u7ec4\u3002<\/p>\n
    \u4f8b8<\/h3>\n
    \u5bf9\u4e8e\u6211\u4eec\u7684\u7b2c\u4e00\u4e2a\u4f8b\u5b50\uff0c\u8ba9\u6211\u4eec\u4ece\u4eba\u53e3\u7edf\u8ba1\u6570\u636e\u4e2d\u63d0\u53d6\u6536\u5165\u7fa4\u4f53\uff1a<\/p>\n
    (\u4ee3\u8868income_group\u5217)<\/p>\n
    \u4e3a\u4e86\u8282\u7701\u7a7a\u95f4\uff0c\u6211\u4eec\u5c31\u4e0d\u8bb2\u592a\u591a\u7ec6\u8282\u4e86\uff0c\u6211\u4eec\u53ea\u770b\u8fd9\u90e8\u5206\u4ee3\u7801\uff1a<\/p>\n
    \u4f7f\u7528cut()\u65b9\u6cd5:\u8ba9\u6211\u4eec\u4f7f\u7528bins\u53c2\u6570\u5b9a\u4e49\u7c7b\u522b\u622a\u65ad\u3002<\/p>\n
    \u8303\u56f4\u3002\u5b83\u63a5\u53d7\u6bcf\u4e2a\u7c7b\u522b\u7684\u622a\u65ad\u70b9\u5217\u8868\uff0c\u6700\u540e\u4e00\u4e2a\u662f\u65e0\u7a77\u5927\uff0c\u7528np-dot-inf()\u8868\u793a\u3002<\/p>\n
    # Using cut() - create category ranges and names\nranges = [0,200000,500000,np.inf]\ngroup_names = ['0-200K', '200K-500K', '500K+']\n# Create income group column\ndemographics['income_group'] = pd.cut(demographics['household_income'], bins=ranges,\nlabels=group_names)\ndemographics[['income_group', 'household_income']]<\/code><\/pre>\n
    \"\"<\/p>\n
    \u8fd9\u6837\u6211\u4eec\u5c31\u628a\u6240\u6709\u7684\u6536\u5165\u90fd\u5206\u7ec4\u4e86\u3002<\/p>\n
    \u4f8b9<\/h3>\n
    \u5c06\u7c7b\u522b\u6620\u5c04\u5230\u66f4\u5c11\u7684\u7c7b\u522b\uff1a<\/p>\n
    \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u64cd\u4f5c\u7cfb\u7edf\u5217\uff0c\u5176\u4e2d\u7684\u503c\u5982\u4e0b\uff1a<\/p>\n
    operating_system\u5217\u4e3a:' Microsoft '\uff0c ' MacOS '\uff0c ' IOS '\uff0c ' Android '\uff0c ' Linux '\noperating_system\u5217\u5e94\u8be5\u53d8\u6210:' DesktopOS '\uff0c ' MobileOS '\n\n# Create mapping dictionary and replace\nmapping = {'Microsoft':'DesktopOS', 'MacOS':'DesktopOS', 'Linux':'DesktopOS',\n'IOS':'MobileOS', 'Android':'MobileOS'}\ndevices['operating_system'] = devices['operating_system'].replace(mapping)\ndevices['operating_system'].unique() # to check result<\/code><\/pre>\n
    \u5c31\u8fd9\u4e48\u7b80\u5355\uff01
    \n\"\"<\/p>\n
    c.\u6e05\u7406\u6587\u672c\u6570\u636e<\/h2>\n
    \u6587\u672c\u6570\u636e\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u7c7b\u578b\u3002\u5b83\u7684\u4f8b\u5b50\u5305\u62ec\u59d3\u540d\u3001\u7535\u8bdd\u53f7\u7801\u3001\u5730\u5740\u3001\u7535\u5b50\u90ae\u4ef6\u7b49\u7b49\u3002\u5e38\u89c1\u7684\u6587\u672c\u6570\u636e\u95ee\u9898\u5305\u62ec\u5904\u7406\u4e0d\u4e00\u81f4\u3001\u786e\u4fdd\u6587\u672c\u6570\u636e\u5177\u6709\u4e00\u5b9a\u957f\u5ea6\u3001\u62fc\u5199\u9519\u8bef\u7b49\u3002<\/p>\n
    \u4f8b10<\/h3>\n
    \u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u5c06\u9009\u62e9\u4e00\u4e2a\u793a\u4f8b' phone.csv '\uff0c\u5176\u4e2d\u5305\u542b\u6709\u6570\u636e\u9519\u8bef\u7684\u7535\u8bdd\u53f7\u7801\u5217\uff1a<\/p>\n
    phones = pd.read_csv('phones.csv')\nprint(phones)<\/code><\/pre>\n
    \"\"
    \n\u5982\u679c\u6211\u4eec\u60f3\u8981\u5c06\u8fd9\u4e9b\u7535\u8bdd\u53f7\u7801\u8f93\u5165\u5230\u81ea\u52a8\u547c\u53eb\u7cfb\u7edf\u4e2d\uff0c\u6216\u8005\u521b\u5efa\u4e00\u4e2a\u6309\u533a\u53f7\u8ba8\u8bba\u7528\u6237\u5206\u5e03\u7684\u62a5\u544a\uff0c\u90a3\u4e48\u5982\u679c\u6ca1\u6709\u7edf\u4e00\u7684\u7535\u8bdd\u53f7\u7801\uff0c\u6211\u4eec\u5c31\u65e0\u6cd5\u771f\u6b63\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n
    \u6211\u4eec\u671f\u671b\uff1a<\/p>\n
      \n
    1. \n
      \u7535\u8bdd\u53f7\u7801\u4ee500\u5f00\u59cb\u6392\u5217<\/p>\n<\/li>\n
    2. \n
      \u4f4e\u4e8e10\u4f4d\u6570\u503c\u7684\u4efb\u4f55\u6570\u5b57\u90fd\u5c06\u88abNaN\u66ff\u6362\u4ee5\u8868\u793a\u7f3a\u5931\u7684\u503c\uff0c\u5e76\u4e14<\/p>\n<\/li>\n
    3. \n
      \u6240\u6709\u7684\u7834\u6298\u53f7\u90fd\u88ab\u53bb\u6389\u4e86<\/p>\n<\/li>\n<\/ol>\n
      \u6211\u4eec\u5c06\u4e00\u6b65\u4e00\u6b65\u5730\u8fdb\u884c\uff1a<\/p>\n
      # Replace "+" with "00"\nphones["Phone number"] = phones["Phone number"].str.replace("+","00") \nphones<\/code><\/pre>\n
      \"\"<\/p>\n
      # Replace "-" with nothing\nphones["Phone number"] = phones["Phone number"].str.replace("-","")\nphones<\/code><\/pre>\n
      \"\"<\/p>\n
      # Replace phone numbers with lower than 10 digits to NaN\ndigits = phones['Phone number'].str.len() phones.loc[digits <10,"Phone number"] = np.nan\nphones<\/code><\/pre>\n
      \"\"<\/p>\n
      \u81f3\u6b64\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u4e0a\u7bc7\u6587\u7ae0\u4e2d\u5bf9\u6570\u636e\u6e05\u7406\u7684\u8ba8\u8bba\u3002<\/p>\n
      \u5982\u679c\u4f60\u559c\u6b22\u8fd9\u4e2a\u5185\u5bb9\uff0c\u8bf7\u9f13\u638c\/\u8bc4\u8bba\uff1a\uff09<\/p>\n","protected":false},"excerpt":{"rendered":"
      \u672c\u6587\u7ea65500\u5b57\uff0c\u5efa\u8bae\u9605\u8bfb15\u5206\u949f \u672c\u6587\u4e3a\u4e0a\u7bc7\uff0c\u4ecb\u7ecd\u4e86\u4e3a\u6570\u636e\u5206\u6790\u800c\u51c6\u5907\u7684\u6570\u636e\u6e05\u6d17\u768410\u4e2aPython\u6848\u4f8b\u53ca\u4ee3\u7801 … <\/p>\n
      \u7ee7\u7eed\u9605\u8bfb\u201c\u4e3a\u6570\u636e\u5206\u6790\u800c\u6e05\u6d17\u6570\u636e\u2014\u2014Python\u768421\u4e2a\u6848\u4f8b\u548c\u4ee3\u7801\uff08\u4e0a\uff09\u201d<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[137],"tags":[],"_links":{"self":[{"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/posts\/39"}],"collection":[{"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/comments?post=39"}],"version-history":[{"count":5,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/posts\/39\/revisions"}],"predecessor-version":[{"id":84,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/posts\/39\/revisions\/84"}],"wp:attachment":[{"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/media?parent=39"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/categories?post=39"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nickchan.cn\/index.php\/wp-json\/wp\/v2\/tags?post=39"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}