<\/p>\n
Bir s\u00fctuna g\u00f6re gruplamak ve ba\u015fka bir s\u00fctunun ortalama de\u011ferini hesaplamak i\u00e7in pandas groupby()<\/strong> i\u015flevini kullan\u0131rken, pandalar varsay\u0131lan olarak NaN<\/strong> de\u011ferlerini g\u00f6z ard\u0131 edecektir.<\/span><\/p>\n Bunun yerine, bir s\u00fctunda NaN<\/strong> de\u011ferleri varsa NaN’i<\/strong> g\u00f6r\u00fcnt\u00fclemek istiyorsan\u0131z a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, DataFrame s\u00fctununun sat\u0131rlar\u0131n\u0131 Tak\u0131ma<\/strong> g\u00f6re grupland\u0131racak ve ard\u0131ndan NaN<\/strong> de\u011ferlerini g\u00f6z ard\u0131 etmeden puan<\/strong> s\u00fctununun ortalama de\u011ferini hesaplayacakt\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/span><\/p>\n \u00c7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/span><\/p>\n Tak\u0131ma<\/strong> g\u00f6re grupland\u0131r\u0131lm\u0131\u015f ortalama puan<\/strong> de\u011ferini hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kulland\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Puan s\u00fctununda A Tak\u0131m\u0131 i\u00e7in bir NaN<\/strong> de\u011feri olsa bile, her tak\u0131m<\/strong> i\u00e7in ortalama puan<\/strong> de\u011ferinin d\u00f6nd\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc unutmay\u0131n.<\/span><\/span><\/p>\n Varsay\u0131lan olarak pandalar ortalamay\u0131 hesaplarken NaN<\/strong> de\u011ferini g\u00f6z ard\u0131 eder.<\/span><\/p>\n Bunun yerine, ger\u00e7ekten NaN’ler<\/strong> varsa, NaN’i<\/strong> ortalama de\u011fer olarak g\u00f6r\u00fcnt\u00fclemek istiyorsan\u0131z a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu kez A Tak\u0131m\u0131 i\u00e7in ortalama puan de\u011feri olarak bir NaN<\/strong> de\u011ferinin d\u00f6nd\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc unutmay\u0131n.<\/span><\/p>\n skipna=False<\/strong> arg\u00fcman\u0131n\u0131 kullanarak pandalara ortalamay\u0131 hesaplarken NaN<\/strong> de\u011ferlerini g\u00f6z ard\u0131 etmemelerini s\u00f6yledik.<\/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 GroupBy kullan\u0131larak benzersiz de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a> Bir s\u00fctuna g\u00f6re gruplamak ve ba\u015fka bir s\u00fctunun ortalama de\u011ferini hesaplamak i\u00e7in pandas groupby() i\u015flevini kullan\u0131rken, pandalar varsay\u0131lan olarak NaN de\u011ferlerini g\u00f6z ard\u0131 edecektir. Bunun yerine, bir s\u00fctunda NaN de\u011ferleri varsa NaN’i g\u00f6r\u00fcnt\u00fclemek istiyorsan\u0131z a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby (‘ team ‘). agg ({‘ points ‘: lambda x: x. mean (skipna= False )}) Bu […]<\/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-4313","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> (' team<\/span> '). agg<\/span> ({' points<\/span> ': lambda<\/span> x: x. mean<\/span> (skipna= False<\/span> )})\n<\/strong><\/pre>\n \u00d6rnek: pandas groupby() kullan\u0131n ve NaN’leri g\u00f6z ard\u0131 etmeyin<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n ' points<\/span> ': [15, np.nan, 24, 25, 20, 35, 34, 19, 14, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points\n0 to 15.0\n1 A NaN\n2 A 24.0\n3 A 25.0\n4 A 20.0\n5 B 35.0\n6 B 34.0\n7B 19.0\n8B 14.0\n9B 12.0<\/span><\/span><\/strong><\/pre>\n #calculate mean of points, grouped by team\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. mean<\/span> ()\n\nteam\nAt 21.0\nB 22.8\nName: points, dtype: float64<\/span><\/span><\/strong><\/pre>\n #calculate mean points value grouped by team and don't ignore NaNs<\/span>\ndf. groupby<\/span> (' team<\/span> '). agg<\/span> ({' points<\/span> ': lambda<\/span> x: x. mean<\/span> (skipna= False<\/span> )})\n\n points\nteam\t\nTo NaN\nB 22.8\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas Groupby’ye bir i\u015flev nas\u0131l uygulan\u0131r?<\/a>
Pandas GroupBy’den Bar Grafi\u011fi Nas\u0131l Olu\u015fturulur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"