{"id":3858,"date":"2023-07-15T03:28:54","date_gmt":"2023-07-15T03:28:54","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalar-berbat-degildir\/"},"modified":"2023-07-15T03:28:54","modified_gmt":"2023-07-15T03:28:54","slug":"pandalar-berbat-degildir","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalar-berbat-degildir\/","title":{"rendered":"\u201cbo\u015f de\u011fil\u201d nas\u0131l kullan\u0131l\u0131r? pandalarda (\u00f6rneklerle)"},"content":{"rendered":"

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Bir pandas DataFrame’in \u00f6\u011felerinin bo\u015f olup olmad\u0131\u011f\u0131n\u0131 test etmek i\u00e7in pandas notnull()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n

Bir \u00f6\u011fe NaN veya Yok’a e\u015fitse i\u015flev False de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n

Aksi takdirde i\u015flev True de\u011ferini<\/strong> d\u00f6nd\u00fcr\u00fcr.<\/span><\/p>\n

Bu i\u015flevi pratikte kullanman\u0131n birka\u00e7 yayg\u0131n yolu \u015funlard\u0131r:<\/span><\/p>\n

Y\u00f6ntem 1: Herhangi bir s\u00fctunda bo\u015f de\u011fer i\u00e7ermeyen sat\u0131rlar\u0131 filtreleyin<\/strong><\/span><\/p>\n

 df[df. notnull<\/span> (). all<\/span> ( 1<\/span> )]\n<\/strong><\/pre>\n
 Y\u00f6ntem 2: Belirli bir s\u00fctunda bo\u015f de\u011fer i\u00e7ermeyen sat\u0131rlar\u0131 filtreleyin<\/strong><\/span><\/p>\n
 df[df[[' this_column<\/span> ']]. notnull<\/span> (). all<\/span> ( 1<\/span> )]<\/strong><\/pre>\n
 Y\u00f6ntem 3: Her s\u00fctundaki s\u0131f\u0131r olmayan de\u011ferlerin say\u0131s\u0131n\u0131 say\u0131n<\/strong><\/span><\/p>\n
 df. notnull<\/span> (). sum<\/span> ()<\/strong><\/pre>\n
 Y\u00f6ntem 4: DataFrame’in tamam\u0131ndaki s\u0131f\u0131r olmayan de\u011ferlerin say\u0131s\u0131n\u0131 say\u0131n<\/strong><\/span><\/p>\n
 df. notnull<\/span> (). sum<\/span> (). sum<\/span> ()<\/strong><\/pre>\n
 A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\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', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, np.nan],\n                   ' assists<\/span> ': [5, np.nan, 7, 9, 12, 9, 9, np.nan],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, np.nan, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds\n0 A 18.0 5.0 11.0\n1 B 22.0 NaN 8.0\n2 C 19.0 7.0 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 12.0 6.0\n5 F 11.0 9.0 5.0\n6G 20.0 9.0 NaN\n7 H NaN NaN 12.0\n<\/strong><\/pre>\n
 \u00d6rnek 1: Herhangi bir s\u00fctunda bo\u015f de\u011fer bulunmayan sat\u0131rlar\u0131 filtreleme<\/strong><\/span><\/h2>\n
 A\u015fa\u011f\u0131daki kod, herhangi bir s\u00fctunda yaln\u0131zca bo\u015f de\u011fer i\u00e7ermeyen sat\u0131rlar\u0131 g\u00f6r\u00fcnt\u00fclemek i\u00e7in DataFrame’in nas\u0131l filtrelenece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #filter for rows with no null values in any column\n<\/span>df[df. notnull<\/span> (). all<\/span> ( 1<\/span> )]\n\n\n        team points assists rebounds\n0 A 18.0 5.0 11.0\n2 C 19.0 7.0 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 12.0 6.0\n5 F 11.0 9.0 5.0<\/span><\/span><\/strong><\/pre>\n
 Bu filtrelenmi\u015f DataFrame’deki sat\u0131rlar\u0131n her birinin, hi\u00e7bir s\u00fctunda bo\u015f de\u011fer i\u00e7ermedi\u011fini unutmay\u0131n.<\/span><\/p>\n
 \u00d6rnek 2: Belirli bir s\u00fctunda bo\u015f de\u011fer i\u00e7ermeyen sat\u0131rlar\u0131 filtreleme<\/strong><\/span><\/h2>\n
 A\u015fa\u011f\u0131daki kod, yard\u0131mc\u0131<\/strong> s\u00fctunda yaln\u0131zca bo\u015f de\u011ferleri olmayan sat\u0131rlar\u0131 g\u00f6r\u00fcnt\u00fclemek i\u00e7in DataFrame’in nas\u0131l filtrelenece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #filter for rows with no null values in the 'assists' column\n<\/span>df[df[[' assists<\/span> ']]. notnull<\/span> (). all<\/span> ( 1<\/span> )]\n\n\tteam points assists rebounds\n0 A 18.0 5.0 11.0\n2 C 19.0 7.0 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 12.0 6.0\n5 F 11.0 9.0 5.0\n6G 20.0 9.0 NaN<\/span><\/span><\/strong><\/pre>\n
 Bu filtrelenmi\u015f DataFrame’deki sat\u0131rlar\u0131n her birinin Yard\u0131mc\u0131<\/strong> s\u00fctununda bo\u015f de\u011fer bulunmad\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n
 \u00d6rnek 3: Her s\u00fctundaki s\u0131f\u0131r olmayan de\u011ferlerin say\u0131s\u0131n\u0131 say\u0131n<\/strong><\/span><\/h2>\n
 A\u015fa\u011f\u0131daki kod, DataFrame’in her s\u00fctunundaki s\u0131f\u0131r olmayan de\u011ferlerin say\u0131s\u0131n\u0131n nas\u0131l say\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n
 #count number of non-null values in each column<\/span>\ndf. notnull<\/span> (). sum<\/span> ()\n\nteam 8\npoints 7\nassists 6\nrebounds 7\ndtype: int64\n<\/span><\/span><\/strong><\/pre>\n
 Sonu\u00e7tan \u015funu g\u00f6rebiliriz:<\/span><\/span><\/p>\n