{"id":3513,"date":"2023-07-17T02:11:32","date_gmt":"2023-07-17T02:11:32","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%80%e1%85%a9%e1%86%b8%e1%84%92%e1%85%a1\/"},"modified":"2023-07-17T02:11:32","modified_gmt":"2023-07-17T02:11:32","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%80%e1%85%a9%e1%86%b8%e1%84%92%e1%85%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%80%e1%85%a9%e1%86%b8%e1%84%92%e1%85%a1\/","title":{"rendered":"Pandas\uc5d0\uc11c \ub450 \uc5f4\uc744 \uacf1\ud558\ub294 \ubc29\ubc95: \uc608 \ud3ec\ud568"},"content":{"rendered":"

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\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ub450 \uc5f4\uc744 \uacf1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \ub450 \uc5f4 \uacf1\ud558\uae30<\/strong><\/span><\/p>\n

 df[' new_column<\/span> '] = df. column1<\/span> * df. column2<\/span><\/strong><\/pre>\n
 \ubc29\ubc95 2: \uc870\uac74\uc5d0 \ub530\ub77c \ub450 \uc5f4 \uacf1\ud558\uae30<\/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
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 1: \ub450 \uc5f4 \uacf1\ud558\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac00\uaca9<\/strong> \ubc0f \uae08\uc561<\/strong> \uc5f4\uc744 \uacf1\ud558\uace0 \uc218\uc775<\/strong> \uc774\ub77c\ub294 \uc0c8 \uc5f4\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc0c8 \uc218\uc775<\/b> \uc5f4\uc758 \uac12\uc740 \uac00\uaca9<\/strong> \ubc0f \uae08\uc561<\/strong> \uc5f4\uc758 \uac12\uc744 \uacf1\ud55c \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \uc870\uac74\uc5d0 \ub530\ub77c \ub450 \uc5f4 \uacf1\ud558\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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
 \uac00\uaca9<\/strong> \uacfc \uae08\uc561<\/strong> \uc5f4\uc744 \ud568\uaed8 \uacf1\ud55c \ub2e4\uc74c where ()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc720\ud615<\/strong> \uc5f4\uc758 \uac12\uc5d0 \ub530\ub77c \uacb0\uacfc\ub97c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc18c\ub4dd<\/b> \uc5f4\uc5d0\ub294 \ub2e4\uc74c \uac12\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n