<\/p>\n
Pandas DataFrame’de y\u0131l, ay ve g\u00fcn s\u00fctunlar\u0131ndan bir tarih s\u00fctunu olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Diyelim ki bir \u015firketin farkl\u0131 tarihlerde yapt\u0131\u011f\u0131 sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’imiz var:<\/span><\/p>\n Her sat\u0131r i\u00e7in bir tarih olu\u015fturmak amac\u0131yla DataFrame’deki y\u0131l<\/strong> , ay<\/strong> ve g\u00fcn<\/strong> s\u00fctunlar\u0131n\u0131n de\u011ferlerini birle\u015ftiren tarih<\/strong> ad\u0131 verilen yeni bir s\u00fctun olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Tarih<\/strong> s\u00fctununun, her sat\u0131rdaki y\u0131l<\/strong> , ay<\/strong> ve g\u00fcn<\/strong> s\u00fctunlar\u0131ndaki de\u011ferlere g\u00f6re tarih de\u011ferleri i\u00e7erdi\u011fini unutmay\u0131n.<\/span><\/p>\n DataFrame’deki her bir s\u00fctun hakk\u0131nda bilgi almak i\u00e7in df.info()<\/strong> i\u015flevini kullan\u0131rsak, yeni tarih<\/strong> s\u00fctununun datetime64<\/strong> veri t\u00fcr\u00fcne sahip oldu\u011funu g\u00f6rebiliriz:<\/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’da bir tarihe g\u00fcn ekleme ve \u00e7\u0131karma<\/a> Pandas DataFrame’de y\u0131l, ay ve g\u00fcn s\u00fctunlar\u0131ndan bir tarih s\u00fctunu olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df[‘ date ‘] = pd. to_datetime (dict(year=df. year , month=df. month , day=df. day )) A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: Pandas’ta y\u0131l, ay ve g\u00fcnden bir tarih s\u00fctunu olu\u015fturun Diyelim ki bir \u015firketin farkl\u0131 tarihlerde […]<\/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-3728","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' date<\/span> '] = pd. to_datetime<\/span> (dict(year=df. year<\/span> , month=df. month<\/span> , day=df. day<\/span> ))\n<\/strong><\/pre>\n \u00d6rnek: Pandas’ta y\u0131l, ay ve g\u00fcnden bir tarih s\u00fctunu olu\u015fturun<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' year<\/span> ': [2021, 2022, 2022, 2022, 2022, 2022, 2022, 2022],\n ' month<\/span> ': [7, 1, 1, 2, 5, 10, 11, 12],\n ' day<\/span> ': [4, 15, 25, 27, 27, 24, 10, 18],\n ' sales<\/span> ': [140, 200, 250, 180, 130, 87, 90, 95]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n year month day sales\n0 2021 7 4 140\n1 2022 1 15 200\n2 2022 1 25 250\n3 2022 2 27 180\n4 2022 5 27 130\n5 2022 10 24 87\n6 2022 11 10 90\n7 2022 12 18 95\n<\/strong><\/span><\/pre>\n #create date columns from year, month, and day columns\n<\/span>df[' date<\/span> '] = pd. to_datetime<\/span> (dict(year=df. year<\/span> , month=df. month<\/span> , day=df. day<\/span> ))\n\n#view updated DataFrame\nprint<\/span><\/span> (df)\n\n year month day sales date\n0 2021 7 4 140 2021-07-04\n1 2022 1 15 200 2022-01-15\n2 2022 1 25 250 2022-01-25\n3 2022 2 27 180 2022-02-27\n4 2022 5 27 130 2022-05-27\n5 2022 10 24 87 2022-10-24\n6 2022 11 10 90 2022-11-10\n7 2022 12 18 95 2022-12-18\n<\/span><\/span><\/strong><\/pre>\n #display information about each column in DataFrame\ndf. info<\/span> ()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8 entries, 0 to 7\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 year 8 non-null int64 \n 1 month 8 non-null int64 \n 2 day 8 non-null int64 \n 3 dirty 8 non-null int64 \n 4 date 8 non-null datetime64[ns]\ndtypes: datetime64[ns](1), int64(4)\nmemory usage: 388.0 bytes<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalarda iki tarih aras\u0131ndaki sat\u0131rlar nas\u0131l se\u00e7ilir<\/a>
Pandalarda iki tarih aras\u0131ndaki fark nas\u0131l hesaplan\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"