{"id":3557,"date":"2023-07-16T20:14:19","date_gmt":"2023-07-16T20:14:19","guid":{"rendered":"https:\/\/statorials.org\/ja\/1%e6%99%82%e9%96%93%e3%81%82%e3%81%9f%e3%82%8a%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a\/"},"modified":"2023-07-16T20:14:19","modified_gmt":"2023-07-16T20:14:19","slug":"1%e6%99%82%e9%96%93%e3%81%82%e3%81%9f%e3%82%8a%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/1%e6%99%82%e9%96%93%e3%81%82%e3%81%9f%e3%82%8a%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a\/","title":{"rendered":"Pandas \u3067\u30c7\u30fc\u30bf\u3092\u6642\u9593\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)"},"content":{"rendered":"

<\/p>\n

\n

\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u3092\u6642\u9593\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3001\u30d1\u30f3\u30c0\u3067\u96c6\u8a08\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 df. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> ]). dirty<\/span> . sum<\/span> ()\n<\/strong><\/pre>\n
\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001\u5024\u3092\u6642\u9593\u3054\u3068\u306bHour<\/strong>\u3068\u3044\u3046\u5217\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3001\u5404\u6642\u9593\u306eSales<\/strong>\u5217\u306e\u5024\u306e\u5408\u8a08\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: Pandas \u3067\u6642\u9593\u3054\u3068\u306b\u30c7\u30fc\u30bf\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3059\u308b<\/strong><\/span><\/h2>\n
\u5e97\u8217\u306e 1 \u65e5\u306e\u3055\u307e\u3056\u307e\u306a\u6642\u9593\u5e2f\u306e\u8ca9\u58f2\u6570\u3092\u793a\u3059\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' time<\/span> ': ['2022-01-01 01:14:00', '2022-01-01 01:24:15',\n                            '2022-01-01 02:52:19', '2022-01-01 02:54:00',\n                            '2022-01-01 04:05:10', '2022-01-01 05:35:09'],\n                   ' sales<\/span> ': [18, 20, 15, 14, 10, 9]})\n\n#convert date column to datetime\n<\/span>df[' time<\/span> '] = pd. to_datetime<\/span> (df[' time<\/span> '])\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n                 time sales\n0 2022-01-01 01:14:00 18\n1 2022-01-01 01:24:15 20\n2 2022-01-01 02:52:19 15\n3 2022-01-01 02:54:00 14\n4 2022-01-01 04:05:10 10\n5 2022-01-01 05:35:09 9\n<\/span><\/span><\/strong><\/pre>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u6642\u523b<\/strong>\u5217\u3092\u6642\u9593\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3001\u6642\u9593\u3054\u3068\u306e\u58f2\u4e0a<\/strong>\u306e\u5408\u8a08\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #group by hours in time column and calculate sum of sales<\/span>\ndf. groupby<\/span> ([df[' time<\/span> ']. dt<\/span> . hour<\/span> ]). dirty<\/span> . sum<\/span> ()\n\ntime\n1 38\n2 29\n4 10\n5 9\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n
\u7d50\u679c\u304b\u3089\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n