<\/p>\n
Giri\u015fleri farkl\u0131 uzunluklara sahip bir s\u00f6zl\u00fckten pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu s\u00f6zdizimi, s\u00f6zl\u00fck dizilerinin bir listesini panda dizilerinin bir listesine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr.<\/span><\/p>\n Bu, bir pandas DataFrame olu\u015fturmam\u0131za ve ortaya \u00e7\u0131kan DataFrame’deki her s\u00fctunun ayn\u0131 uzunlukta olmas\u0131n\u0131 sa\u011flamak i\u00e7in NaN de\u011ferlerini doldurmam\u0131za olanak tan\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Farkl\u0131 uzunluktaki giri\u015fleri i\u00e7eren a\u015fa\u011f\u0131daki s\u00f6zl\u00fc\u011f\u00fcm\u00fcz oldu\u011funu varsayal\u0131m:<\/span><\/p>\n Bu s\u00f6zl\u00fc\u011f\u00fc bir pandas DataFrame’e d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in from_dict()<\/strong> i\u015flevini kullanmaya \u00e7al\u0131\u015f\u0131rsak bir hata al\u0131r\u0131z:<\/span><\/p>\n S\u00f6zl\u00fckteki t\u00fcm dizilerin ayn\u0131 uzunlukta olmas\u0131 gerekti\u011fini s\u00f6yleyen bir hata al\u0131yoruz.<\/span><\/p>\n Bu hataya ge\u00e7ici bir \u00e7\u00f6z\u00fcm bulmak i\u00e7in, Dictionary’yi DataFrame’e d\u00f6n\u00fc\u015ft\u00fcrmek \u00fczere a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Ba\u015far\u0131l\u0131 bir panda DataFrame olu\u015fturabildi\u011fimizi ve her s\u00fctunun ayn\u0131 uzunlukta olmas\u0131n\u0131 sa\u011flamak i\u00e7in NaN de\u011ferlerinin dolduruldu\u011funu unutmay\u0131n.<\/span><\/p>\n Bu NaN de\u011ferlerini ba\u015fka de\u011ferlerle (s\u0131f\u0131r gibi) de\u011fi\u015ftirmek isterseniz, change()<\/strong> fonksiyonunu \u015fu \u015fekilde kullanabilirsiniz:<\/span><\/p>\n Her NaN de\u011ferinin s\u0131f\u0131rla de\u011fi\u015ftirildi\u011fini unutmay\u0131n.<\/span><\/p>\n NaN de\u011ferlerini istedi\u011finiz de\u011ferle de\u011fi\u015ftirmek i\u00e7in change()<\/strong> i\u015flevini kullanmaktan \u00e7ekinmeyin.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalar: DataFrame’i s\u00f6zl\u00fc\u011fe d\u00f6n\u00fc\u015ft\u00fcrme<\/a> Giri\u015fleri farkl\u0131 uzunluklara sahip bir s\u00f6zl\u00fckten pandas DataFrame olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: import pandas as pd df = pd. DataFrame (dict([(key, pd. Series (value)) for key, value in some_dict. items ()])) Bu s\u00f6zdizimi, s\u00f6zl\u00fck dizilerinin bir listesini panda dizilerinin bir listesine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu, bir pandas DataFrame olu\u015fturmam\u0131za ve ortaya \u00e7\u0131kan DataFrame’deki her s\u00fctunun […]<\/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-4245","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n import<\/span> pandas as<\/span> pd\n\ndf = pd. DataFrame<\/span> (dict([(key, pd. Series<\/span> (value)) for<\/span> key, value in<\/span> some_dict. items<\/span> ()]))\n<\/strong><\/pre>\n \u00d6rnek: Farkl\u0131 uzunluklara sahip bir dikteden Pandas DataFrame olu\u015fturun<\/strong><\/span><\/h2>\n
#create dictionary whose entries have different lengths\n<\/span>some_dict = dict(A=[2, 5, 5, 7, 8], B=[9, 3], C=[4, 4, 2])\n\n#view dictionary\n<\/span>print<\/span> (some_dict)\n\n{'A': [2, 5, 5, 7, 8], 'B': [9, 3], 'C': [4, 4, 2]}\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd<\/span>\n\n#attempt to create pandas DataFrame from dictionary<\/span>\ndf = pd. DataFrame<\/span> . from_dict<\/span> (some_dict)\n\nValueError<\/span> : All arrays must be of the same length\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd<\/span>\n\n#create pandas DataFrame from dictionary<\/span>\ndf = pd. DataFrame<\/span> (dict([(key, pd. Series<\/span> (value)) for<\/span> key, value in<\/span> some_dict. items<\/span> ()]))\n\n#view DataFrame<\/span>\nprint<\/span> (df)\n\n ABC\n0 2 9.0 4.0\n1 5 3.0 4.0\n2 5 NaN 2.0\n3 7 NaN NaN\n4 8 NaN NaN\n<\/strong><\/pre>\n #replace all NaNs with zeros\ndf. replace<\/span> (np. nan<\/span> , 0, inplace= True<\/span> )\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n ABC\n0 2 9.0 4.0\n1 5 3.0 4.0\n2 5 0.0 2.0\n3 7 0.0 0.0\n4 8 0.0 0.0\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: Bir s\u00f6zl\u00fckle s\u00fctunlar nas\u0131l yeniden adland\u0131r\u0131l\u0131r?<\/a>
Pandalar: Bir s\u00f6zl\u00fck kullanarak NaN de\u011ferleri nas\u0131l doldurulur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"