{"id":3933,"date":"2023-07-14T16:38:03","date_gmt":"2023-07-14T16:38:03","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e6%9b%b4%e6%94%b9%e5%88%97%e7%b1%bb%e5%9e%8b\/"},"modified":"2023-07-14T16:38:03","modified_gmt":"2023-07-14T16:38:03","slug":"pandas-%e6%9b%b4%e6%94%b9%e5%88%97%e7%b1%bb%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e6%9b%b4%e6%94%b9%e5%88%97%e7%b1%bb%e5%9e%8b\/","title":{"rendered":"\u5982\u4f55\u66f4\u6539 pandas \u4e2d\u7684\u5217\u7c7b\u578b\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">pandas DataFrame \u7684\u5217\u53ef\u4ee5\u91c7\u7528\u4ee5\u4e0b\u7c7b\u578b\u4e4b\u4e00\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5bf9\u8c61<\/strong>\uff08\u5b57\u7b26\u4e32\uff09<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>int64<\/strong> \uff08\u6574\u6570\uff09<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>float64<\/strong> \uff08\u5e26\u5c0f\u6570\u7684\u6570\u503c\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5e03\u5c14\u503c<\/strong>\uff08\u771f\u503c\u6216\u5047\u503c\uff09<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>datetime64<\/strong> \uff08\u65e5\u671f\u548c\u65f6\u95f4\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5c06\u5217\u4ece\u4e00\u79cd\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u4f7f\u7528<strong>astype()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u5c06\u4ee5\u4e0b\u65b9\u6cd5\u4e0e<strong>astype()<\/strong>\u51fd\u6570\u7ed3\u5408\u4f7f\u7528\uff0c\u5c06\u5217\u4ece\u4e00\u79cd\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1\uff1a\u5c06\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">col1<\/span> '] = df[' <span style=\"color: #ff0000;\">col1<\/span> ']. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2\uff1a\u5c06\u591a\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']] = df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']]. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 3\uff1a\u5c06\u6240\u6709\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u4ee5\u4e0b pandas DataFrame \u7684\u6bcf\u79cd\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">\n\n#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">ID<\/span> ': ['1', '2', '3', '4', '5', '6'],\n                   ' <span style=\"color: #ff0000;\">tenure<\/span> ': [12.443, 15.8, 16.009, 5.06, 11.075, 12.9546],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [5, 7, 7, 9, 12, 9]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  ID tenure sales\n0 1 12.4430 5\n1 2 15.8000 7\n2 3 16.0090 7\n3 4 5.0600 9\n4 5 11.0750 12\n5 6 12.9546 9\n\n<span style=\"color: #008080;\">#view data type of each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\nobject ID\ntenure float64\ndirty int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5c06\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>astype()<\/strong>\u51fd\u6570\u5c06<strong>tenure<\/strong>\u5217\u4ece\u6d6e\u70b9\u578b\u8f6c\u6362\u4e3a\u6574\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert tenure column to int64\n<\/span>df[' <span style=\"color: #ff0000;\">tenure<\/span> '] = df[' <span style=\"color: #ff0000;\">tenure<\/span> ']. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')\n\n<span style=\"color: #008080;\">#view updated data type for each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\nobject ID\ntenure int64\ndirty int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c <strong>tenure<\/strong>\u5217\u5df2\u8f6c\u6362\u4e3a int64\uff0c\u800c\u6240\u6709\u5176\u4ed6\u5217\u4fdd\u7559\u5176\u539f\u59cb\u6570\u636e\u7c7b\u578b\u3002<\/span><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u5c06\u591a\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>astype()<\/strong>\u51fd\u6570\u5c06<strong>ID<\/strong>\u548c<strong>tenure<\/strong>\u5217\u8f6c\u6362\u4e3a\u6574\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert ID and tenure columns to int64\n<\/span>df[[' <span style=\"color: #ff0000;\">ID<\/span> ', ' <span style=\"color: #ff0000;\">tenure<\/span> ']] = df[[' <span style=\"color: #ff0000;\">ID<\/span> ', ' <span style=\"color: #ff0000;\">tenure<\/span> ']]. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')\n\n<span style=\"color: #008080;\">#view updated data type for each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\nID int64\ntenure int64\ndirty int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c <strong>ID<\/strong>\u548c<strong>tenure<\/strong>\u5217\u5df2\u8f6c\u6362\u4e3a int64\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 3\uff1a\u5c06\u6240\u6709\u5217\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u4f7f\u7528<strong>astype()<\/strong>\u51fd\u6570\u5c06 DataFrame \u4e2d\u7684\u6240\u6709\u5217\u8f6c\u6362\u4e3a\u6574\u6570\u6570\u636e\u7c7b\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert all columns to int64\n<\/span>df = df. <span style=\"color: #3366ff;\">astype<\/span> (' <span style=\"color: #ff0000;\">int64<\/span> ')\n\n<span style=\"color: #008080;\">#view updated data type for each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\nID int64\ntenure int64\ndirty int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6240\u6709\u5217\u5747\u5df2\u8f6c\u6362\u4e3a int64\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.astype.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 pandas <strong>astype()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u8f6c\u6362\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u4e32\u4e0a\u7684\u718a\u732b\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5c06 Pandas DataFrame \u5217\u8f6c\u6362\u4e3a\u5b57\u7b26\u4e32<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u718a\u732b\u65f6\u95f4\u6233\u65e5\u671f\u65f6\u95f4\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u65f6\u95f4\u6233\u8f6c\u6362\u4e3a\u65e5\u671f\/\u65f6\u95f4<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5c06\u65e5\u671f\u65f6\u95f4\u8f6c\u6362\u4e3a\u65e5\u671f\u718a\u732b\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5c06 DateTime \u8f6c\u6362\u4e3a Pandas \u4e2d\u7684\u65e5\u671f<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u6d6e\u52a8\u718a\u732b\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>pandas DataFrame \u7684\u5217\u53ef\u4ee5\u91c7\u7528\u4ee5\u4e0b\u7c7b\u578b\u4e4b\u4e00\uff1a \u5bf9\u8c61\uff08\u5b57\u7b26\u4e32\uff09 int64 \uff08\u6574\u6570\uff09 float [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3933","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u66f4\u6539 Pandas \u4e2d\u7684\u5217\u7c7b\u578b\uff08\u9644\u793a\u4f8b\uff09-Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u66f4\u6539 pandas \u4e2d\u7684\u5217\u7c7b\u578b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/pandas-\u66f4\u6539\u5217\u7c7b\u578b\/\" \/>\n<meta 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