<\/p>\n
Bir pandan\u0131n DataFrame’indeki de\u011fi\u015fkenler i\u00e7in tan\u0131mlay\u0131c\u0131 istatistikler<\/a> olu\u015fturmak amac\u0131yla define()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n A\u00e7\u0131klama()<\/strong> i\u015flevinin \u00e7\u0131kt\u0131s\u0131ndan bilimsel g\u00f6sterimi kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: Bir s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel g\u00f6sterimi kald\u0131r\u0131n<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Birden fazla s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel g\u00f6sterimi kald\u0131r\u0131n<\/strong><\/span><\/p>\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 Sat\u0131\u015f<\/strong> s\u00fctununa ili\u015fkin tan\u0131mlay\u0131c\u0131 istatistikleri hesaplamak i\u00e7in define()<\/strong> i\u015flevini kullan\u0131rsak, \u00e7\u0131kt\u0131 de\u011ferleri bilimsel g\u00f6sterimle g\u00f6r\u00fcnt\u00fclenecektir:<\/span><\/p>\n \u00c7\u0131k\u0131\u015f de\u011ferlerinin her birinin bilimsel g\u00f6sterim kullan\u0131larak g\u00f6r\u00fcnt\u00fclendi\u011fini unutmay\u0131n.<\/span><\/span><\/p>\n \u00c7\u0131kt\u0131dan bilimsel g\u00f6sterimi kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/span><\/p>\n \u00c7\u0131kt\u0131daki de\u011ferlerin art\u0131k bilimsel g\u00f6sterim olmadan g\u00f6r\u00fcnt\u00fclendi\u011fini unutmay\u0131n.<\/span><\/p>\n Her say\u0131sal s\u00fctuna ili\u015fkin tan\u0131mlay\u0131c\u0131 istatistikleri hesaplamak i\u00e7in define()<\/strong> i\u015flevini kullan\u0131rsak, \u00e7\u0131kt\u0131 de\u011ferleri bilimsel g\u00f6sterimle g\u00f6r\u00fcnt\u00fclenecektir:<\/span><\/p>\n \u00c7\u0131k\u0131\u015f de\u011ferlerinin her birinin bilimsel g\u00f6sterim kullan\u0131larak g\u00f6r\u00fcnt\u00fclendi\u011fini unutmay\u0131n.<\/span><\/p>\n \u00c7\u0131kt\u0131dan bilimsel g\u00f6sterimi kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n \u00c7\u0131kt\u0131daki de\u011ferlerin art\u0131k bilimsel g\u00f6sterim olmadan g\u00f6r\u00fcnt\u00fclendi\u011fini unutmay\u0131n.<\/span><\/p>\n Bu \u00f6rnekte \u00e7\u0131kt\u0131da 5<\/strong> ondal\u0131k basamak g\u00f6r\u00fcnt\u00fclemek i\u00e7in 0:.5f<\/strong> kulland\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n.<\/span><\/p>\n Farkl\u0131 say\u0131da ondal\u0131k basamak g\u00f6r\u00fcnt\u00fclemek i\u00e7in 5’i<\/strong> farkl\u0131 bir say\u0131yla de\u011fi\u015ftirmekten \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: Grup ba\u015f\u0131na k\u00fcm\u00fclatif toplam nas\u0131l hesaplan\u0131r?<\/a> Bir pandan\u0131n DataFrame’indeki de\u011fi\u015fkenler i\u00e7in tan\u0131mlay\u0131c\u0131 istatistikler olu\u015fturmak amac\u0131yla define() i\u015flevini kullanabilirsiniz. A\u00e7\u0131klama() i\u015flevinin \u00e7\u0131kt\u0131s\u0131ndan bilimsel g\u00f6sterimi kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: Bir s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel g\u00f6sterimi kald\u0131r\u0131n df[‘ my_column ‘]. describe (). apply ( lambda x: format (x, ‘ f ‘)) Y\u00f6ntem 2: Birden fazla s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel […]<\/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-4319","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' my_column<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))<\/strong><\/span><\/pre>\n df. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n<\/strong><\/span><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' sales<\/span> ': [8450550, 406530, 53000, 6000, 2000, 4000, 5400, 6500],\n ' returns<\/span> ':[2212200, 145200, 300, 2500, 700, 600, 800, 1200]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n store sales returns\n0 A 8450550 2212200\n1 A 406530 145200\n2 A 53000 300\n3 A 6000 2500\n4 B 2000 700\n5 B 4000 600\n6 B 5400 800\n7 B 6500 1200<\/strong><\/span><\/pre>\n \u00d6rnek 1: Bir s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel g\u00f6sterimi kald\u0131r\u0131n<\/strong><\/span><\/h2>\n
#calculate descriptive statistics for sales column\ndf[' sales<\/span> ']. describe<\/span> ()\n<\/span>\n<\/span>count 8.000000e+00\nmean 1.116748e+06\nstd 2.966552e+06\nmin 2.000000e+03\n25% 5.050000e+03\n50% 6.250000e+03\n75% 1.413825e+05\nmax 8.450550e+06\nName: sales, dtype: float64<\/strong><\/pre>\n #calculate descriptive statistics for sales column and suppress scientific notation<\/span>\ndf[' sales<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))\n\ncount 8.000000\nmean 1116747.500000\nstd 2966551.594104\nmin 2000.000000\n25% 5050.000000\n50% 6250.000000\n75% 141382.500000\nmax 8450550.000000\nName: sales, dtype: object\n<\/strong><\/span><\/pre>\n \u00d6rnek 2: Birden fazla s\u00fctunla define() i\u015flevini kullan\u0131rken bilimsel g\u00f6sterimi kald\u0131r\u0131n<\/strong><\/span><\/h2>\n
#calculate descriptive statistics for each numeric column\ndf. describe<\/span> ()\n<\/span><\/span>\n sales returns\ncount 8.000000e+00 8.000000e+00\nmean 1.116748e+06 2.954375e+05\nstd 2.966552e+06 7.761309e+05\nmin 2.000000e+03 3.000000e+02\n25% 5.050000e+03 6.750000e+02\n50% 6.250000e+03 1.000000e+03\n75% 1.413825e+05 3.817500e+04\nmax 8.450550e+06 2.212200e+06<\/strong><\/pre>\n #calculate descriptive statistics for numerical columns and suppress scientific notation<\/span>\ndf. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n\n sales returns\ncount 8.00000 8.00000\nmean 1116747.50000 295437.50000\nstd 2966551.59410 776130.93692\nmin 2000.00000 300.00000\n25% 5050.00000 675.00000\n50% 6250.00000 1000.00000\n75% 141382.50000 38175.00000\nmax 8450550.00000 2212200.00000\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar: benzersiz de\u011ferlerin gruba g\u00f6re nas\u0131l say\u0131laca\u011f\u0131<\/a>
Pandalar: gruba g\u00f6re korelasyon nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"