<\/p>\n
Bir pandan\u0131n DataFrame’indeki iki s\u00fctunu \u00e7arpmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: \u0130ki s\u00fctunu \u00e7arpma<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Ko\u015fula g\u00f6re iki s\u00fctunu \u00e7arpma<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler her y\u00f6ntemin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/span><\/p>\n Fiyat<\/strong> ve tutar<\/strong> s\u00fctunlar\u0131n\u0131 \u00e7arpmak ve gelir<\/strong> ad\u0131 verilen yeni bir s\u00fctun olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Yeni gelir<\/b> s\u00fctunundaki de\u011ferlerin Fiyat<\/strong> ve Tutar<\/strong> s\u00fctunlar\u0131ndaki de\u011ferlerin \u00e7arp\u0131m\u0131 oldu\u011funu unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n Fiyat<\/strong> ve tutar<\/strong> s\u00fctunlar\u0131n\u0131 birlikte \u00e7arpabilir, ard\u0131ndan type<\/strong> s\u00fctununun de\u011ferine g\u00f6re sonu\u00e7lar\u0131 de\u011fi\u015ftirmek i\u00e7in Where ()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Gelir<\/b> s\u00fctununun a\u015fa\u011f\u0131daki de\u011ferleri ald\u0131\u011f\u0131n\u0131 unutmay\u0131n:<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandas DataFrame’de dizine g\u00f6re s\u00fctunlar nas\u0131l se\u00e7ilir<\/a> Bir pandan\u0131n DataFrame’indeki iki s\u00fctunu \u00e7arpmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: \u0130ki s\u00fctunu \u00e7arpma df[‘ new_column ‘] = df. column1 * df. column2 Y\u00f6ntem 2: Ko\u015fula g\u00f6re iki s\u00fctunu \u00e7arpma new_column = df. column1 * df. column2 #update values based on condition df[‘ new_column ‘] = new_column. where (df. column2 == ‘ value1 ‘, […]<\/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-3515","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' new_column<\/span> '] = df. column1<\/span> * df. column2<\/span><\/strong><\/pre>\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 \u00d6rnek 1: \u0130ki s\u00fctunu \u00e7arpma<\/strong><\/span><\/h2>\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 #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 \u00d6rnek 2: \u0130ki s\u00fctunu ko\u015fula g\u00f6re \u00e7arpma<\/strong><\/span><\/h2>\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 #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\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas DataFrame’de dizin nas\u0131l yeniden adland\u0131r\u0131l\u0131r<\/a>
Pandalar’da s\u00fctunlar\u0131 dizine g\u00f6re nas\u0131l silebilirim?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"