{"id":3511,"date":"2023-07-17T02:11:32","date_gmt":"2023-07-17T02:11:32","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af2%e3%81%a4%e3%81%ae%e5%88%97%e3%82%92%e6%8e%9b%e3%81%91%e3%81%be%e3%81%99\/"},"modified":"2023-07-17T02:11:32","modified_gmt":"2023-07-17T02:11:32","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af2%e3%81%a4%e3%81%ae%e5%88%97%e3%82%92%e6%8e%9b%e3%81%91%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af2%e3%81%a4%e3%81%ae%e5%88%97%e3%82%92%e6%8e%9b%e3%81%91%e3%81%be%e3%81%99\/","title":{"rendered":"Pandas \u3067 2 \u3064\u306e\u5217\u3092\u4e57\u7b97\u3059\u308b\u65b9\u6cd5: \u4f8b\u4ed8\u304d"},"content":{"rendered":"

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\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e 2 \u3064\u306e\u5217\u3092\u4e57\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u65b9\u6cd5 1: 2 \u3064\u306e\u5217\u3092\u4e57\u7b97\u3059\u308b<\/strong><\/span><\/p>\n

 df[' new_column<\/span> '] = df. column1<\/span> * df. column2<\/span><\/strong><\/pre>\n
\u65b9\u6cd5 2: \u6761\u4ef6\u306b\u57fa\u3065\u3044\u3066 2 \u3064\u306e\u5217\u3092\u4e57\u7b97\u3059\u308b<\/strong><\/span><\/p>\n
 new_column = df. column1<\/span> * df. column2<\/span>\n\n#update values based on condition\n<\/span>df[' new_column<\/span> '] = new_column. where<\/span> (df. column2<\/span> == ' value1<\/span> ', other= 0<\/span> )\n<\/strong><\/pre>\n
\u6b21\u306e\u4f8b\u306f\u3001\u5404\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b 1: 2 \u3064\u306e\u5217\u306e\u4e57\u7b97<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' price<\/span> ': [22, 20, 25, 30, 4, 8, 12, 10],\n                   ' amount<\/span> ': [3, 1, 3, 3, 2, 4, 3, 5]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   price amount\n0 22 3\n1 20 1\n2 25 3\n3 30 3\n4 4 2\n5 8 4\n6 12 3\n7 10 5<\/strong><\/pre>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001 price \u5217<\/strong>\u3068amount<\/strong>\u5217\u3092\u4e57\u7b97\u3057\u3001\u53ce\u76ca<\/strong>\u3068\u3044\u3046\u65b0\u3057\u3044\u5217\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #multiply price and amount columns\n<\/span>df[' revenue<\/span> '] = df. price<\/span> * df. amount<\/span>\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n   price amount revenue\n0 22 3 66\n1 20 1 20\n2 25 3 75\n3 30 3 90\n4 4 2 8\n5 8 4 32\n6 12 3 36\n7 10 5 50<\/strong><\/pre>\n
\u65b0\u3057\u3044\u53ce\u76ca<\/b>\u5217\u306e\u5024\u306f\u3001\u4fa1\u683c\u5217<\/strong>\u3068\u91d1\u984d<\/strong>\u5217\u306e\u5024\u306e\u7a4d\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u4f8b 2: \u6761\u4ef6\u306b\u57fa\u3065\u3044\u3066 2 \u3064\u306e\u5217\u3092\u4e57\u7b97\u3057\u307e\u3059\u3002<\/strong><\/span><\/h2>\n
\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<\/span>\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' price<\/span> ': [22, 20, 25, 30, 4, 8, 12, 10],\n                   ' amount<\/span> ': [3, 1, 3, 3, 2, 4, 3, 5],\n                   ' type<\/span> ': ['Sale', 'Refund', 'Sale', 'Sale',\n                            'Sale', 'Refund', 'Refund', 'Sale']})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   price amount type\n0 22 3 Dirty\n1 20 1 Refund\n2 25 3 Dirty\n3 30 3 Dirty\n4 4 2 Dirty\n5 8 4 Refund\n6 12 3 Return\n7 10 5 Dirty<\/strong><\/pre>\n
\u4fa1\u683c\u5217<\/strong>\u3068\u91d1\u984d<\/strong>\u5217\u3092\u639b\u3051\u5408\u308f\u305b\u3066\u304b\u3089\u3001where ()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001 type<\/strong>\u5217\u306e\u5024\u306b\u57fa\u3065\u3044\u3066\u7d50\u679c\u3092\u5909\u66f4\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #multiply price and amount columns\n<\/span>income = df. price<\/span> * df. amount<\/span>\n\n#update values based on type\n<\/span>df[' revenue<\/span> '] = revenue. where<\/span> (df. type<\/span> == ' Sale<\/span> ', other= 0<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n   price amount type revenue\n0 22 3 Dirty 66\n1 20 1 Refund 0\n2 25 3 Dirty 75\n3 30 3 Dirty 90\n4 4 2 Dirty 8\n5 8 4 Refund 0\n6 12 3 Refund 0\n7 10 5 Dirty 50<\/strong><\/pre>\n
 \u300c\u53ce\u5165\u300d<\/b>\u5217\u306b\u306f\u6b21\u306e\u5024\u304c\u542b\u307e\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n