{"id":1521,"date":"2025-11-24T18:35:29","date_gmt":"2025-11-24T15:35:29","guid":{"rendered":"https:\/\/www.facadium.com.tr\/blog\/?p=1521"},"modified":"2025-11-27T16:29:47","modified_gmt":"2025-11-27T13:29:47","slug":"100-soruda-numpy","status":"publish","type":"post","link":"https:\/\/www.facadium.com.tr\/blog\/100-soruda-numpy\/","title":{"rendered":"100 Soruda Numpy"},"content":{"rendered":"\n<div class=\"wp-block-rank-math-toc-block\" id=\"rank-math-toc\"><h2>\u0130\u00e7indekiler<\/h2><nav><ul><li class=\"\"><a href=\"#bolum-1-temel-num-py-ve-ndarray-kavramlari\">100 Soruda Numpy B\u00f6l\u00fcm 1 \u2013 Temel NumPy ve ndarray Kavramlar\u0131<\/a><ul><li class=\"\"><a href=\"#num-py-neden-bilimsel-python-ekosisteminin-temeli-olarak-gorulur\">NumPy neden \u201cbilimsel Python ekosisteminin temeli\u201d olarak g\u00f6r\u00fcl\u00fcr?<\/a><\/li><li class=\"\"><a href=\"#ndarray-nedir-ve-python-listelerinden-temel-farklari-nelerdir\">ndarray nedir ve Python listelerinden temel farklar\u0131 nelerdir?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-dtype-neden-bu-kadar-kritiktir\">NumPy\u2019de dtype neden bu kadar kritiktir?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-vektorlestirme-vectorization-ne-anlama-gelir\">NumPy\u2019de \u201cvekt\u00f6rle\u015ftirme (vectorization)\u201d ne anlama gelir?<\/a><\/li><li class=\"\"><a href=\"#num-py-neden-yapay-zeka-veri-bilimi-ve-makine-ogrenmesinde-bu-kadar-yaygindir\">NumPy neden yapay zek\u00e2, veri bilimi ve makine \u00f6\u011frenmesinde bu kadar yayg\u0131nd\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-2-dizi-olusturma-sekil-degistirme-ve-indeksleme\">B\u00f6l\u00fcm 2 \u2013 Dizi Olu\u015fturma, \u015eekil De\u011fi\u015ftirme ve \u0130ndeksleme<\/a><ul><li class=\"\"><a href=\"#num-pyde-dizi-olusturmanin-en-cok-kullanilan-yontemleri-nelerdir\">NumPy\u2019de dizi olu\u015fturman\u0131n en \u00e7ok kullan\u0131lan y\u00f6ntemleri nelerdir?<\/a><\/li><li class=\"\"><a href=\"#reshape-ile-ravel-flatten-arasindaki-fark-nedir\">reshape ile ravel \/ flatten aras\u0131ndaki fark nedir?<\/a><\/li><li class=\"\"><a href=\"#gelismis-indeksleme-advanced-indexing-ile-temel-dilimleme-arasindaki-fark-nedir\">Geli\u015fmi\u015f indeksleme (advanced indexing) ile temel dilimleme aras\u0131ndaki fark nedir?<\/a><\/li><li class=\"\"><a href=\"#boolean-maskeleme-ile-eksik-veya-belirli-kosulu-saglayan-veriler-nasil-secilir\">Boolean maskeleme ile eksik veya belirli ko\u015fulu sa\u011flayan veriler nas\u0131l se\u00e7ilir?<\/a><\/li><li class=\"\"><a href=\"#cok-boyutlu-dizilerde-dilimleme-slicing-ve-eksen-mantigi-nasil-calisir\">\u00c7ok boyutlu dizilerde dilimleme (slicing) ve eksen mant\u0131\u011f\u0131 nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-3-yayinlama-broadcasting-ufunc-ve-matematiksel-islemler\">B\u00f6l\u00fcm 3 \u2013 Yay\u0131nlama (Broadcasting), Ufunc ve Matematiksel \u0130\u015flemler<\/a><ul><li class=\"\"><a href=\"#num-pyde-broadcasting-nedir-ve-hangi-kurala-gore-calisir\">NumPy\u2019de broadcasting nedir ve hangi kurala g\u00f6re \u00e7al\u0131\u015f\u0131r?<\/a><\/li><li class=\"\"><a href=\"#broadcasting-hatalarini-incompatible-shapes-nasil-teshis-eder-ve-duzeltirsiniz\">Broadcasting hatalar\u0131n\u0131 (incompatible shapes) nas\u0131l te\u015fhis eder ve d\u00fczeltirsiniz?<\/a><\/li><li class=\"\"><a href=\"#ufunc-universal-function-nedir-ve-neden-bu-kadar-onemlidir\">Ufunc (universal function) nedir ve neden bu kadar \u00f6nemlidir?<\/a><\/li><li class=\"\"><a href=\"#eleman-bazli-islemler-ile-matris-islemleri-arasindaki-fark-nasil-yonetilir\">Eleman-bazl\u0131 i\u015flemler ile matris i\u015flemleri aras\u0131ndaki fark nas\u0131l y\u00f6netilir?<\/a><\/li><li class=\"\"><a href=\"#kosullu-islemler-icin-np-where-ve-ufunclarin-nasil-kombinasyonu-yapilir\">Ko\u015fullu i\u015flemler i\u00e7in np.where ve ufunc\u2019lar\u0131n nas\u0131l kombinasyonu yap\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-4-lineer-cebir-ve-sayisal-hesaplama\">B\u00f6l\u00fcm 4 \u2013 Lineer Cebir ve Say\u0131sal Hesaplama<\/a><ul><li class=\"\"><a href=\"#num-pyde-temel-lineer-cebir-islemleri-icin-hangi-modul-kullanilir\">NumPy\u2019de temel lineer cebir i\u015flemleri i\u00e7in hangi mod\u00fcl kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#lineer-denklem-sistemi-cozumunde-neden-np-linalg-solve-kullanmak-invden-daha-iyidir\">Lineer denklem sistemi \u00e7\u00f6z\u00fcm\u00fcnde neden np.linalg.solve kullanmak inv&#8217;den daha iyidir?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-ozdeger-ve-ozvektor-hesaplamanin-tipik-kullanim-alanlari-nelerdir\">NumPy ile \u00f6zde\u011fer ve \u00f6zvekt\u00f6r hesaplaman\u0131n tipik kullan\u0131m alanlar\u0131 nelerdir?<\/a><\/li><li class=\"\"><a href=\"#sayisal-kararlilik-numerical-stability-acisindan-hangi-num-py-pratikleri-onerilir\">Say\u0131sal kararl\u0131l\u0131k (numerical stability) a\u00e7\u0131s\u0131ndan hangi NumPy pratikleri \u00f6nerilir?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-buyuk-matrissel-hesaplamalari-hizlandirmak-icin-hangi-stratejiler-kullanilabilir\">NumPy ile b\u00fcy\u00fck matrissel hesaplamalar\u0131 h\u0131zland\u0131rmak i\u00e7in hangi stratejiler kullan\u0131labilir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-5-performans-bellek-yonetimi-ve-diger-kutuphanelerle-entegrasyon\">B\u00f6l\u00fcm 5 \u2013 Performans, Bellek Y\u00f6netimi ve Di\u011fer K\u00fct\u00fcphanelerle Entegrasyon<\/a><ul><li class=\"\"><a href=\"#soru-5-1-num-py-saf-python-listelerine-gore-neden-daha-az-bellek-kullanir\">Soru 5.1 \u2013 NumPy, saf Python listelerine g\u00f6re neden daha az bellek kullan\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-bellek-duzeni-c-vs-fortran-order-performansi-nasil-etkiler\">NumPy\u2019de bellek d\u00fczeni (c vs Fortran order) performans\u0131 nas\u0131l etkiler?<\/a><\/li><li class=\"\"><a href=\"#bellek-eslemeli-dosyalar-memmap-ile-cok-buyuk-diziler-nasil-yonetilir\">Bellek e\u015flemeli dosyalar (memmap) ile \u00e7ok b\u00fcy\u00fck diziler nas\u0131l y\u00f6netilir?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-pandas-ve-matplotlib-arasindaki-iliski-nasildir\">NumPy ile Pandas ve Matplotlib aras\u0131ndaki ili\u015fki nas\u0131ld\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-py-performansini-artirmak-icin-tipik-profil-ve-optimizasyon-adimlari-nelerdir\">NumPy performans\u0131n\u0131 art\u0131rmak i\u00e7in tipik profil ve optimizasyon ad\u0131mlar\u0131 nelerdir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-6-rastgele-sayilar-istatistiksel-dagilimlar-ve-num-py-random\">B\u00f6l\u00fcm 6 \u2013 Rastgele Say\u0131lar, \u0130statistiksel Da\u011f\u0131l\u0131mlar ve NumPy Random<\/a><ul><li class=\"\"><a href=\"#num-pynin-yeni-generator-tabanli-random-ap-isi-neden-eski-np-random-yapisindan-daha-basarilidir\">NumPy\u2019nin yeni Generator tabanl\u0131 Random API\u2019si neden eski np.random yap\u0131s\u0131ndan daha ba\u015far\u0131l\u0131d\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-en-sik-kullanilan-rastgele-dagilimlar-hangileridir-ve-hangi-durumlarda-kullanilir\">NumPy\u2019de en s\u0131k kullan\u0131lan rastgele da\u011f\u0131l\u0131mlar hangileridir ve hangi durumlarda kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#rastgele-sayi-uretiminde-tohumlama-seed-neden-onemlidir\">Rastgele say\u0131 \u00fcretiminde tohumlama (seed) neden \u00f6nemlidir?<\/a><\/li><li class=\"\"><a href=\"#buyuk-orneklemli-istatistiksel-simulasyonlarda-num-py-nasil-avantaj-saglar\">B\u00fcy\u00fck \u00f6rneklemli istatistiksel sim\u00fclasyonlarda NumPy nas\u0131l avantaj sa\u011flar?<\/a><\/li><li class=\"\"><a href=\"#choice-fonksiyonu-ile-rassal-ornekleme-nasil-yapilir\">choice fonksiyonu ile rassal \u00f6rnekleme nas\u0131l yap\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-7-eksik-veri-na-n-standartlastirma-ve-normalizasyon\">B\u00f6l\u00fcm 7 \u2013 Eksik Veri, NaN, Standartla\u015ft\u0131rma ve Normalizasyon<\/a><ul><li class=\"\"><a href=\"#num-pyde-na-n-degerler-neden-sikinti-yaratir\">NumPy\u2019de NaN de\u011ferler neden s\u0131k\u0131nt\u0131 yarat\u0131r?<\/a><\/li><li class=\"\"><a href=\"#na-n-yonetimi-icin-en-iyi-uygulamalar-nelerdir\">NaN y\u00f6netimi i\u00e7in en iyi uygulamalar nelerdir?<\/a><\/li><li class=\"\"><a href=\"#veri-standardizasyonu-z-score-num-py-ile-nasil-yapilir-ve-neden-gereklidir\">Veri standardizasyonu (z-score) NumPy ile nas\u0131l yap\u0131l\u0131r ve neden gereklidir?<\/a><\/li><li class=\"\"><a href=\"#min-max-normalizasyonu-hangi-durumlarda-tercih-edilir\">Min-max normalizasyonu hangi durumlarda tercih edilir?<\/a><\/li><li class=\"\"><a href=\"#aykiri-deger-tespiti-icin-num-py-ile-hangi-teknikler-uygulanabilir\">Ayk\u0131r\u0131 de\u011fer tespiti i\u00e7in NumPy ile hangi teknikler uygulanabilir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-8-hafiza-stride-view-copy-semantigi-ve-dusuk-seviye-mekanizmalar\">B\u00f6l\u00fcm 8 \u2013 Haf\u0131za, Stride, View\/Copy Semanti\u011fi ve D\u00fc\u015f\u00fck Seviye Mekanizmalar<\/a><ul><li class=\"\"><a href=\"#num-pyde-stride-nedir-ve-performansi-nasil-etkiler\">NumPy\u2019de \u201cstride\u201d nedir ve performans\u0131 nas\u0131l etkiler?<\/a><\/li><li class=\"\"><a href=\"#view-ve-copy-farki-neden-bu-kadar-kritiktir\">View ve copy fark\u0131 neden bu kadar kritiktir?<\/a><\/li><li class=\"\"><a href=\"#np-ascontiguousarray-ne-ise-yarar\">np.ascontiguousarray ne i\u015fe yarar?<\/a><\/li><li class=\"\"><a href=\"#num-py-bellek-uzerinde-in-place-islem-yapmayi-nasil-destekler\">NumPy bellek \u00fczerinde in-place i\u015flem yapmay\u0131 nas\u0131l destekler?<\/a><\/li><li class=\"\"><a href=\"#num-py-c-api-hangi-durumlarda-kullanilir\">NumPy C API hangi durumlarda kullan\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-9-gercek-dunya-kullanimlari-bilimsel-uygulamalar-ve-veri-bilimi\">B\u00f6l\u00fcm 9 \u2013 Ger\u00e7ek D\u00fcnya Kullan\u0131mlar\u0131, Bilimsel Uygulamalar ve Veri Bilimi<\/a><ul><li class=\"\"><a href=\"#num-py-goruntu-isleme-alaninda-neden-temel-aractir\">NumPy g\u00f6r\u00fcnt\u00fc i\u015fleme alan\u0131nda neden temel ara\u00e7t\u0131r?<\/a><\/li><li class=\"\"><a href=\"#zaman-serisi-analizinde-num-pynin-rolu-nedir\">Zaman serisi analizinde NumPy\u2019nin rol\u00fc nedir?<\/a><\/li><li class=\"\"><a href=\"#pca-principal-component-analysis-hesaplamasinda-num-py-neden-idealdir\">PCA (Principal Component Analysis) hesaplamas\u0131nda NumPy neden idealdir?<\/a><\/li><li class=\"\"><a href=\"#finans-matematiginde-num-py-hangi-problemler-icin-kullanilir\">Finans matemati\u011finde NumPy hangi problemler i\u00e7in kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#bilimsel-simulasyonlarda-num-pynin-avantajlari-nelerdir\">Bilimsel sim\u00fclasyonlarda NumPy\u2019nin avantajlar\u0131 nelerdir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-10-hatalar-tuzaklar-ve-en-iyi-uygulamalar\">B\u00f6l\u00fcm 10 \u2013 Hatalar, Tuzaklar ve En \u0130yi Uygulamalar<\/a><ul><li class=\"\"><a href=\"#num-pyde-yapilan-en-yaygin-hata-list-comprehension-kullanmak-neden-yanlistir\">NumPy\u2019de yap\u0131lan en yayg\u0131n hata: \u201clist comprehension kullanmak\u201d. Neden yanl\u0131\u015ft\u0131r?<\/a><\/li><li class=\"\"><a href=\"#astype-kullanirken-gizli-kopya-olustugunu-nasil-anlarsiniz\">astype() kullan\u0131rken gizli kopya olu\u015ftu\u011funu nas\u0131l anlars\u0131n\u0131z?<\/a><\/li><li class=\"\"><a href=\"#value-error-operands-could-not-be-broadcast-together-hatasi-nasil-cozulur\">\u201cValueError: operands could not be broadcast together\u201d hatas\u0131 nas\u0131l \u00e7\u00f6z\u00fcl\u00fcr?<\/a><\/li><li class=\"\"><a href=\"#np-concatenate-ve-np-stack-farki-nedir\">np.concatenate ve np.stack fark\u0131 nedir?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-buyuk-dizilerle-calisirken-hangi-optimizasyonlar-kritik-onem-tasir\">NumPy\u2019de b\u00fcy\u00fck dizilerle \u00e7al\u0131\u015f\u0131rken hangi optimizasyonlar kritik \u00f6nem ta\u015f\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#kategori-11-matris-operasyonlari-determinant-ters-rank-ve-svd\">Kategori 11 \u2013 Matris Operasyonlar\u0131, Determinant, Ters, Rank ve SVD<\/a><ul><li class=\"\"><a href=\"#matrisin-rutbesi-rank-numpy-ile-nasil-hesaplanir-ve-neden-onemlidir\">Matrisin r\u00fctbesi (rank) Numpy ile nas\u0131l hesaplan\u0131r ve neden \u00f6nemlidir?<\/a><\/li><li class=\"\"><a href=\"#determinant-neden-bazi-problemlerde-tercih-edilmez\">Determinant neden baz\u0131 problemlerde tercih edilmez?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-svd-singular-value-decomposition-ne-zaman-tercih-edilir\">NumPy\u2019de SVD (Singular Value Decomposition) ne zaman tercih edilir?<\/a><\/li><li class=\"\"><a href=\"#moore-penrose-pseudoinverse-neden-ters-matristen-daha-cok-kullanilir\">Moore\u2013Penrose pseudoinverse neden ters matristen daha \u00e7ok kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#matrislerin-kosul-sayisi-condition-number-neden-onemlidir\">Matrislerin ko\u015ful say\u0131s\u0131 (condition number) neden \u00f6nemlidir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-12-sayisal-turev-integral-yaklasiklik-ve-diferansiyel-denklemler\">B\u00f6l\u00fcm 12 \u2013 Say\u0131sal T\u00fcrev, \u0130ntegral, Yakla\u015f\u0131kl\u0131k ve Diferansiyel Denklemler<\/a><ul><li class=\"\"><a href=\"#num-py-sayisal-turev-hesaplamalari-icin-nasil-kullanilir\">NumPy say\u0131sal t\u00fcrev hesaplamalar\u0131 i\u00e7in nas\u0131l kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#neden-cok-kucuk-h-degerleri-turevde-kararsizliga-yol-acar\">Neden \u00e7ok k\u00fc\u00e7\u00fck h de\u011ferleri t\u00fcrevde karars\u0131zl\u0131\u011fa yol a\u00e7ar?<\/a><\/li><li class=\"\"><a href=\"#num-py-integral-hesaplamalarinda-nasil-kullanilir\">NumPy integral hesaplamalar\u0131nda nas\u0131l kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-diferansiyel-denklem-cozulebilir-mi\">NumPy ile diferansiyel denklem \u00e7\u00f6z\u00fclebilir mi?<\/a><\/li><li class=\"\"><a href=\"#sayisal-cozumde-stabilite-analizi-neden-kritiktir\">Say\u0131sal \u00e7\u00f6z\u00fcmde stabilite analizi neden kritiktir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-13-fourier-donusumu-sinyal-isleme-ve-filtreleme\">B\u00f6l\u00fcm 13 \u2013 Fourier D\u00f6n\u00fc\u015f\u00fcm\u00fc, Sinyal \u0130\u015fleme ve Filtreleme<\/a><ul><li class=\"\"><a href=\"#num-pyde-fft-np-fft-fft-hangi-durumlarda-kullanilir\">NumPy\u2019de FFT (np.fft.fft) hangi durumlarda kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#fft-sonucunda-kompleks-sayilar-neden-ortaya-cikar\">FFT sonucunda kompleks say\u0131lar neden ortaya \u00e7\u0131kar?<\/a><\/li><li class=\"\"><a href=\"#nyquist-frekansi-ve-aliasing-num-py-ile-nasil-analiz-edilir\">Nyquist frekans\u0131 ve aliasing NumPy ile nas\u0131l analiz edilir?<\/a><\/li><li class=\"\"><a href=\"#dusuk-geciren-filtre-num-py-ile-nasil-uygulanir\">D\u00fc\u015f\u00fck ge\u00e7iren filtre NumPy ile nas\u0131l uygulan\u0131r?<\/a><\/li><li class=\"\"><a href=\"#konvolusyon-islemi-num-pyde-nasil-yapilir\">Konvol\u00fcsyon i\u015flemi NumPy\u2019de nas\u0131l yap\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-14-cok-boyutlu-diziler-tensor-islemleri-ve-ileri-yapilar\">B\u00f6l\u00fcm 14 \u2013 \u00c7ok Boyutlu Diziler, Tensor \u0130\u015flemleri ve \u0130leri Yap\u0131lar<\/a><ul><li class=\"\"><a href=\"#num-py-neden-bir-tensor-kutuphanesi-olarak-kabul-edilir\">NumPy neden bir &#8220;tens\u00f6r&#8221; k\u00fct\u00fcphanesi olarak kabul edilir?<\/a><\/li><li class=\"\"><a href=\"#eksensel-axis-based-islemler-neden-onemlidir\">Eksensel (axis-based) i\u015flemler neden \u00f6nemlidir?<\/a><\/li><li class=\"\"><a href=\"#np-transpose-ile-swapaxes-farki-nedir\">np.transpose ile swapaxes fark\u0131 nedir?<\/a><\/li><li class=\"\"><a href=\"#tensor-cogullama-tensor-contraction-nedir\">Tens\u00f6r \u00e7o\u011fullama (tensor contraction) nedir?<\/a><\/li><li class=\"\"><a href=\"#num-pyde-einsum-neden-profesyoneller-tarafindan-sikca-tercih-edilir\">NumPy\u2019de einsum neden profesyoneller taraf\u0131ndan s\u0131k\u00e7a tercih edilir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-15-istatistiksel-hesaplamalar-korelasyon-kovaryans-ve-regresyon-temelleri\">B\u00f6l\u00fcm 15 \u2013 \u0130statistiksel Hesaplamalar, Korelasyon, Kovaryans ve Regresyon Temelleri<\/a><ul><li class=\"\"><a href=\"#num-pyde-kovaryans-nasil-hesaplanir-ve-neyi-ifade-eder\">NumPy\u2019de kovaryans nas\u0131l hesaplan\u0131r ve neyi ifade eder?<\/a><\/li><li class=\"\"><a href=\"#korelasyon-matrisi-nasil-elde-edilir\">Korelasyon matrisi nas\u0131l elde edilir?<\/a><\/li><li class=\"\"><a href=\"#dogrusal-regresyon-parametreleri-num-py-ile-nasil-elde-edilir\">Do\u011frusal regresyon parametreleri NumPy ile nas\u0131l elde edilir?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-varyans-ve-standart-sapma-hesaplamalarinda-dikkat-edilmesi-gereken-parametre-nedir\">NumPy ile varyans ve standart sapma hesaplamalar\u0131nda dikkat edilmesi gereken parametre nedir?<\/a><\/li><li class=\"\"><a href=\"#histogram-hesaplamalari-num-pyde-nasil-yapilir\">Histogram hesaplamalar\u0131 NumPy\u2019de nas\u0131l yap\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-16-veri-yapilari-arasi-donusum-dosya-islemleri-ve-dis-kaynaklarla-entegrasyon\">B\u00f6l\u00fcm 16 \u2013 Veri Yap\u0131lar\u0131 Aras\u0131 D\u00f6n\u00fc\u015f\u00fcm, Dosya \u0130\u015flemleri ve D\u0131\u015f Kaynaklarla Entegrasyon<\/a><ul><li class=\"\"><a href=\"#num-py-dizileri-listelere-nasil-donusturulur-ve-bu-islem-maliyetli-midir\">NumPy dizileri listelere nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr ve bu i\u015flem maliyetli midir?<\/a><\/li><li class=\"\"><a href=\"#num-py-dizileri-nasil-dosyaya-kaydedilir\">NumPy dizileri nas\u0131l dosyaya kaydedilir?<\/a><\/li><li class=\"\"><a href=\"#csv-dosyalari-num-py-ile-nasil-okunur\">CSV dosyalar\u0131 NumPy ile nas\u0131l okunur?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-json-formati-dogrudan-desteklenir-mi\">NumPy ile JSON format\u0131 do\u011frudan desteklenir mi?<\/a><\/li><li class=\"\"><a href=\"#num-py-ve-pandas-birlikte-nasil-en-verimli-sekilde-kullanilir\">NumPy ve Pandas birlikte nas\u0131l en verimli \u015fekilde kullan\u0131l\u0131r?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-17-buyuk-veri-paralel-isleme-gpu-ve-performans-iyilestirme\">B\u00f6l\u00fcm 17 \u2013 B\u00fcy\u00fck Veri, Paralel \u0130\u015fleme, GPU ve Performans \u0130yile\u015ftirme<\/a><ul><li class=\"\"><a href=\"#num-py-neden-tek-cekirdekli-calisir\">NumPy neden tek \u00e7ekirdekli \u00e7al\u0131\u015f\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-paralel-isleme-nasil-yapilir\">NumPy ile paralel i\u015fleme nas\u0131l yap\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#num-py-gpu-hizlandirma-destekler-mi\">NumPy GPU h\u0131zland\u0131rma destekler mi?<\/a><\/li><li class=\"\"><a href=\"#numba-ile-num-py-nasil-hizlandirilir\">Numba ile NumPy nas\u0131l h\u0131zland\u0131r\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#bellek-eslemeli-memory-mapped-diziler-ne-zaman-zorunludur\">Bellek e\u015flemeli (memory-mapped) diziler ne zaman zorunludur?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-18-gelismis-hatalar-debugging-teknikleri-ve-test-edilebilirlik\">B\u00f6l\u00fcm 18 \u2013 Geli\u015fmi\u015f Hatalar, Debugging Teknikleri ve Test Edilebilirlik<\/a><ul><li class=\"\"><a href=\"#floating-point-hatalari-num-pyde-nasil-tespit-edilir\">Floating-point hatalar\u0131 NumPy\u2019de nas\u0131l tespit edilir?<\/a><\/li><li class=\"\"><a href=\"#num-py-islemleri-neden-bazen-farkli-platformlarda-farkli-sonuc-verir\">NumPy i\u015flemleri neden bazen farkl\u0131 platformlarda farkl\u0131 sonu\u00e7 verir?<\/a><\/li><li class=\"\"><a href=\"#copy-view-hatalarini-onlemenin-en-iyi-yolu-nedir\">Copy\u2013view hatalar\u0131n\u0131 \u00f6nlemenin en iyi yolu nedir?<\/a><\/li><li class=\"\"><a href=\"#tip-donusumlerinde-astype-sessiz-veri-kaybi-yasanabilir-mi\">Tip d\u00f6n\u00fc\u015f\u00fcmlerinde (astype) sessiz veri kayb\u0131 ya\u015fanabilir mi?<\/a><\/li><li class=\"\"><a href=\"#num-py-kodu-nasil-test-edilir\">NumPy kodu nas\u0131l test edilir?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-19-uygulama-ornekleri-modelleme-ve-bilimsel-kullanim-senaryolari\">B\u00f6l\u00fcm 19 \u2013 Uygulama \u00d6rnekleri, Modelleme ve Bilimsel Kullan\u0131m Senaryolar\u0131<\/a><ul><li class=\"\"><a href=\"#num-py-fizik-simulasyonlarinda-nasil-kullanilir\">NumPy fizik sim\u00fclasyonlar\u0131nda nas\u0131l kullan\u0131l\u0131r?<\/a><\/li><li class=\"\"><a href=\"#makine-ogrenmesinde-num-pynin-rolu-nedir\">Makine \u00f6\u011frenmesinde NumPy\u2019nin rol\u00fc nedir?<\/a><\/li><li class=\"\"><a href=\"#derin-ogrenme-kutuphaneleri-neden-num-py-ap-isini-taklit-eder\">Derin \u00f6\u011frenme k\u00fct\u00fcphaneleri neden NumPy API\u2019sini taklit eder?<\/a><\/li><li class=\"\"><a href=\"#monte-carlo-simulasyonlarinda-num-py-neden-vazgecilmezdir\">Monte Carlo sim\u00fclasyonlar\u0131nda NumPy neden vazge\u00e7ilmezdir?<\/a><\/li><li class=\"\"><a href=\"#doga-bilimlerinde-veri-analizi-neden-num-py-uzerine-kuruludur\">Do\u011fa bilimlerinde veri analizi neden NumPy \u00fczerine kuruludur?<\/a><\/li><\/ul><\/li><li class=\"\"><a href=\"#bolum-20-en-iyi-uygulamalar-kod-stili-tasarim-prensipleri-ve-uzun-omurlu-projeler\">B\u00f6l\u00fcm 20 \u2013 En \u0130yi Uygulamalar, Kod Stili, Tasar\u0131m Prensipleri ve Uzun \u00d6m\u00fcrl\u00fc Projeler<\/a><ul><li class=\"\"><a href=\"#num-py-kodu-yazarken-en-onemli-stil-kurali-nedir\">NumPy kodu yazarken en \u00f6nemli stil kural\u0131 nedir?<\/a><\/li><li class=\"\"><a href=\"#buyuk-projelerde-dtype-stratejisi-nasil-belirlenir\">B\u00fcy\u00fck projelerde dtype stratejisi nas\u0131l belirlenir?<\/a><\/li><li class=\"\"><a href=\"#num-py-ile-yazilmis-bir-fonksiyon-nasil-daha-okunabilir-hale-getirilir\">NumPy ile yaz\u0131lm\u0131\u015f bir fonksiyon nas\u0131l daha okunabilir hale getirilir?<\/a><\/li><li class=\"\"><a href=\"#num-py-tabanli-projeler-nasil-surdurulebilir-olur\">NumPy tabanl\u0131 projeler nas\u0131l s\u00fcrd\u00fcr\u00fclebilir olur?<\/a><\/li><li class=\"\"><a href=\"#num-py-ogrenmenin-en-verimli-yolu-nedir\">NumPy \u00f6\u011frenmenin en verimli yolu nedir?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-1-temel-num-py-ve-ndarray-kavramlari\">100 Soruda Numpy B\u00f6l\u00fcm 1 \u2013 Temel NumPy ve ndarray Kavramlar\u0131<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-neden-bilimsel-python-ekosisteminin-temeli-olarak-gorulur\">NumPy neden \u201cbilimsel Python ekosisteminin temeli\u201d olarak g\u00f6r\u00fcl\u00fcr?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy, Python\u2019daki say\u0131sal hesaplamalar\u0131n merkezinde yer al\u0131r \u00e7\u00fcnk\u00fc veriyi <strong><code>ndarray<\/code><\/strong> ad\u0131 verilen yo\u011fun (contiguous) \u00e7ok boyutlu dizilerde saklar ve bu diziler \u00fczerinde vekt\u00f6rle\u015ftirilmi\u015f, C d\u00fczeyinde optimize edilmi\u015f i\u015flemler sunar. Bu sayede saf Python listelerine g\u00f6re genellikle <strong>b\u00fcy\u00fckl\u00fck mertebesinde<\/strong> h\u0131z kazan\u0131m\u0131 elde edilir. Ayr\u0131ca Pandas, SciPy, scikit-learn, Matplotlib gibi k\u00fct\u00fcphaneler NumPy dizilerini ortak veri modeli olarak kullan\u0131r. Bu ekosistem yakla\u015f\u0131m\u0131, verinin kopyalanmadan bile\u015fenler aras\u0131nda aktar\u0131lmas\u0131n\u0131, bellek kullan\u0131m\u0131n\u0131n d\u00fc\u015fmesini ve algoritmalar\u0131n yeniden kullan\u0131labilirli\u011fini sa\u011flar. K\u0131sacas\u0131 NumPy, <strong>h\u0131z, bellek verimlili\u011fi ve birlikte \u00e7al\u0131\u015fabilirlik<\/strong> kombinasyonu nedeniyle \u00e7ekirdek bile\u015fen kabul edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ndarray-nedir-ve-python-listelerinden-temel-farklari-nelerdir\">ndarray nedir ve Python listelerinden temel farklar\u0131 nelerdir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>ndarray<\/code>, NumPy\u2019nin temel veri yap\u0131s\u0131 olan, sabit boyutlu ve <strong>homojen tipte<\/strong> (tek <code>dtype<\/code>) elemanlar i\u00e7eren N-boyutlu bir dizidir. Python listeleri heterojen tipte veriye izin verir ve her eleman ayr\u0131 bir Python nesnesi oldu\u011fu i\u00e7in bellek par\u00e7al\u0131 (non-contiguous) yap\u0131dad\u0131r. <code>ndarray<\/code> ise elemanlar\u0131 bellekte tek blok h\u00e2linde tutar; bu da CPU cache verimlili\u011fini art\u0131r\u0131r. Ayr\u0131ca <code>ndarray<\/code> \u00fczerinde toplama, \u00e7arpma gibi i\u015flemler <strong>eleman seviyesinde vekt\u00f6rle\u015ftirilmi\u015f<\/strong> \u015fekilde \u00e7al\u0131\u015f\u0131r ve C ile yaz\u0131lm\u0131\u015f d\u00f6ng\u00fcler kullan\u0131r; bu da Python d\u00f6ng\u00fclerine g\u00f6re \u00e7ok daha h\u0131zl\u0131d\u0131r. Sonu\u00e7 olarak <code>ndarray<\/code>, b\u00fcy\u00fck say\u0131sal veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken hem daha h\u0131zl\u0131 hem de daha az bellek t\u00fcketen bir yap\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-dtype-neden-bu-kadar-kritiktir\">NumPy\u2019de dtype neden bu kadar kritiktir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>dtype<\/code>, bir <code>ndarray<\/code> i\u00e7indeki t\u00fcm elemanlar\u0131n veri t\u00fcr\u00fcn\u00fc tan\u0131mlar ve hem <strong>performans<\/strong> hem de <strong>say\u0131sal do\u011fruluk<\/strong> a\u00e7\u0131s\u0131ndan belirleyicidir. \u00d6rne\u011fin <code>float32<\/code> se\u00e7mek bellek kullan\u0131m\u0131n\u0131 azalt\u0131rken, <code>float64<\/code> daha y\u00fcksek say\u0131sal hassasiyet sa\u011flar; b\u00fcy\u00fck matris hesaplamalar\u0131nda bu se\u00e7im, hem h\u0131z hem de n\u00fcmerik kararl\u0131l\u0131k \u00fczerinde ciddi farklar yaratabilir. Tamsay\u0131 dizilerinde <code>int8<\/code>, <code>int16<\/code>, <code>int32<\/code>, <code>int64<\/code> gibi se\u00e7enekler depolayabilece\u011finiz en k\u00fc\u00e7\u00fck ve en b\u00fcy\u00fck de\u011fer aral\u0131klar\u0131n\u0131 belirler; ta\u015fma (overflow) hatalar\u0131 <code>dtype<\/code>\u2019\u0131n uygun se\u00e7ilmemesiyle s\u0131k g\u00f6r\u00fcl\u00fcr. Ayr\u0131ca <code>dtype<\/code>\u2019\u0131n sabit olmas\u0131, SIMD ve vekt\u00f6rle\u015ftirilmi\u015f i\u015flemler i\u00e7in CPU\u2019nun optimize edilmesine imk\u00e2n tan\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-vektorlestirme-vectorization-ne-anlama-gelir\">NumPy\u2019de \u201cvekt\u00f6rle\u015ftirme (vectorization)\u201d ne anlama gelir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Vekt\u00f6rle\u015ftirme, Python seviyesinde a\u00e7\u0131k <code>for<\/code> d\u00f6ng\u00fcleri yazmak yerine i\u015flemleri do\u011frudan <strong>t\u00fcm dizi \u00fczerinde tek seferde<\/strong> tan\u0131mlamay\u0131 ifade eder. \u00d6rne\u011fin bir liste \u00fczerinde tek tek dola\u015fmak yerine <code>y = 3*x + 2<\/code> gibi bir ifade ile t\u00fcm <code>ndarray<\/code> i\u00e7in i\u015flem yap\u0131l\u0131r. Bu yakla\u015f\u0131m, iki sebeple performans kazand\u0131r\u0131r: (1) D\u00f6ng\u00fcler C\u2019de, d\u00fc\u015f\u00fck seviyede \u00e7al\u0131\u015f\u0131r; Python yorumlay\u0131c\u0131s\u0131n\u0131n her iterasyonda devreye girmesine gerek kalmaz. (2) Bellek eri\u015fimi d\u00fczenlidir ve CPU cache daha etkin kullan\u0131l\u0131r. M\u00fcfredat ve m\u00fclakat sorular\u0131nda, NumPy\u2019nin g\u00fcc\u00fcn\u00fcn b\u00fcy\u00fck k\u0131sm\u0131n\u0131n bu <strong>vekt\u00f6rle\u015ftirilmi\u015f hesaplama modelinden<\/strong> geldi\u011fi s\u0131k\u00e7a vurgulan\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-neden-yapay-zeka-veri-bilimi-ve-makine-ogrenmesinde-bu-kadar-yaygindir\">NumPy neden yapay zek\u00e2, veri bilimi ve makine \u00f6\u011frenmesinde bu kadar yayg\u0131nd\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Makine \u00f6\u011frenmesi ve derin \u00f6\u011frenme algoritmalar\u0131, tipik olarak <strong>b\u00fcy\u00fck tens\u00f6rler<\/strong> \u00fczerinde lineer cebir i\u015flemleri yapar. NumPy, bu tens\u00f6rleri (matris ve vekt\u00f6rleri) temsil etmek i\u00e7in standart <code>ndarray<\/code> yap\u0131s\u0131, BLAS\/LAPACK gibi y\u00fcksek performansl\u0131 k\u00fct\u00fcphanelere k\u00f6pr\u00fcler ve zengin lineer cebir fonksiyonlar\u0131 sunar. Bir\u00e7ok framework (\u00f6r. scikit-learn, baz\u0131 durumlarda PyTorch\/TF \u00f6ncesi veri haz\u0131rlama katman\u0131) veri \u00f6n-i\u015fleme, normalizasyon, \u00f6zellik m\u00fchendisli\u011fi gibi ad\u0131mlarda NumPy dizilerini kullan\u0131r. Ayr\u0131ca NumPy API\u2019si, GPU tabanl\u0131 k\u00fct\u00fcphanelere (CuPy, JAX) de model olmu\u015f; b\u00f6ylece ara\u015ft\u0131rmac\u0131lar CPU ve GPU aras\u0131nda benzer s\u00f6zdizimiyle \u00e7al\u0131\u015fabilmi\u015ftir. Bu nedenle NumPy, veri bilimi ekosisteminde <strong>ortak dil<\/strong> g\u00f6revini g\u00f6r\u00fcr.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-2-dizi-olusturma-sekil-degistirme-ve-indeksleme\">B\u00f6l\u00fcm 2 \u2013 Dizi Olu\u015fturma, \u015eekil De\u011fi\u015ftirme ve \u0130ndeksleme<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-dizi-olusturmanin-en-cok-kullanilan-yontemleri-nelerdir\">NumPy\u2019de dizi olu\u015fturman\u0131n en \u00e7ok kullan\u0131lan y\u00f6ntemleri nelerdir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> En temel y\u00f6ntem <code>np.array()<\/code> ile Python liste veya tuple\u2019lar\u0131ndan dizi olu\u015fturmakt\u0131r. Ancak pratikte <strong>say\u0131sal aral\u0131k ve \u015fablon fonksiyonlar\u0131<\/strong> \u00e7ok daha yayg\u0131n kullan\u0131l\u0131r. \u00d6rne\u011fin <code>np.zeros<\/code>, <code>np.ones<\/code>, <code>np.full<\/code> sabit de\u011ferli diziler \u00fcretirken; <code>np.arange<\/code> ve <code>np.linspace<\/code> d\u00fczenli aral\u0131kl\u0131 say\u0131lar olu\u015fturur. Rastgele ba\u015flang\u0131\u00e7l\u0131 modeller i\u00e7in <code>np.random<\/code> mod\u00fcl\u00fcyle rastgele diziler yarat\u0131l\u0131r. Matrissel yap\u0131lar i\u00e7in <code>np.eye<\/code> ve <code>np.identity<\/code> birim matris \u00fcretir. Bu fonksiyonlar, hem kodun okunabilirli\u011fini art\u0131r\u0131r hem de dizi boyutlar\u0131n\u0131, tiplerini ve ba\u015flang\u0131\u00e7 de\u011ferlerini tek sat\u0131rda belirlemenizi sa\u011flar; bu da bilimsel deneyleri tekrarlanabilir ve sistematik h\u00e2le getirir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"reshape-ile-ravel-flatten-arasindaki-fark-nedir\">reshape ile ravel \/ flatten aras\u0131ndaki fark nedir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>reshape<\/code>, bir dizinin <strong>g\u00f6r\u00fcn\u00fc\u015f \u015feklini<\/strong> (shape) de\u011fi\u015ftirir; m\u00fcmk\u00fcnse ayn\u0131 bellek blo\u011funu kullanarak g\u00f6r\u00fcn\u00fcm\u00fc yeniden d\u00fczenler. <code>ravel<\/code>, diziyi tek boyutlu h\u00e2le getirir ve \u00e7o\u011fu durumda <strong>g\u00f6r\u00fcn\u00fcm (view)<\/strong> d\u00f6nd\u00fcr\u00fcr; yani orijinal veriyle ayn\u0131 bellek alan\u0131n\u0131 payla\u015f\u0131r. <code>flatten<\/code> ise her zaman <strong>kopya (copy)<\/strong> \u00fcretir, bu nedenle \u00fczerinde yap\u0131lan de\u011fi\u015fiklikler orijinali etkilemez. B\u00fcy\u00fck dizilerde gereksiz kopyalardan ka\u00e7\u0131nmak i\u00e7in, belle\u011fi payla\u015fan <code>reshape<\/code> ve <code>ravel<\/code> tercih edilir; ancak orijinal veriyi korumak istiyorsan\u0131z <code>flatten<\/code> g\u00fcvenli se\u00e7enektir. Bu n\u00fcans, performans odakl\u0131 uygulamalarda s\u0131k\u00e7a sorulan bir m\u00fclakat konusudur.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"gelismis-indeksleme-advanced-indexing-ile-temel-dilimleme-arasindaki-fark-nedir\">Geli\u015fmi\u015f indeksleme (advanced indexing) ile temel dilimleme aras\u0131ndaki fark nedir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Temel dilimleme (<code>:<\/code> operat\u00f6r\u00fc, tam say\u0131l\u0131 aral\u0131klar) \u00e7o\u011funlukla <strong>g\u00f6r\u00fcn\u00fcm<\/strong> d\u00f6nd\u00fcr\u00fcr; yani elde edilen alt dizi, orijinal dizinin bellek alan\u0131n\u0131 payla\u015f\u0131r. Geli\u015fmi\u015f indeksleme ise liste, <code>ndarray<\/code> veya boolean maske kullan\u0131larak yap\u0131l\u0131r ve her zaman <strong>yeni bir kopya<\/strong> olu\u015fturur. \u00d6rne\u011fin <code>a[mask]<\/code> veya <code>a[[0, 2, 5]]<\/code> geli\u015fmi\u015f indekslemedir. Bu fark, hem performans hem de yan etki y\u00f6netimi a\u00e7\u0131s\u0131ndan kritiktir: B\u00fcy\u00fck veri k\u00fcmelerinde istemeden kopya olu\u015fturmak bellek bask\u0131s\u0131 yarat\u0131rken; g\u00f6r\u00fcn\u00fcm \u00fczerinde de\u011fi\u015fiklik yapmak da beklenmedik sonu\u00e7lara yol a\u00e7abilir. Bu nedenle, hangi indeksleme t\u00fcr\u00fcn\u00fcn kullan\u0131ld\u0131\u011f\u0131n\u0131 bilmek ileri seviye NumPy kullan\u0131m\u0131n\u0131n temelidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"boolean-maskeleme-ile-eksik-veya-belirli-kosulu-saglayan-veriler-nasil-secilir\">Boolean maskeleme ile eksik veya belirli ko\u015fulu sa\u011flayan veriler nas\u0131l se\u00e7ilir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Boolean maskeleme, eleman baz\u0131nda bir ko\u015ful ifadesi kullanarak <code>True\/False<\/code> de\u011ferlerden olu\u015fan bir maske dizisi \u00fcretmek ve bu maske ile alt k\u00fcme se\u00e7mek anlam\u0131na gelir. \u00d6rne\u011fin <code>mask = (a &gt; 0) &amp; (a &lt; 1)<\/code> ifadesi, 0 ile 1 aras\u0131ndaki t\u00fcm elemanlar\u0131 se\u00e7en bir maske \u00fcretir; <code>a[mask]<\/code> ise yaln\u0131zca bu elemanlardan olu\u015fan yeni bir dizi d\u00f6nd\u00fcr\u00fcr. Bu yakla\u015f\u0131m, \u00f6zellikle eksik de\u011ferleri (<code>np.isnan<\/code>, <code>np.isfinite<\/code>) veya u\u00e7 de\u011ferleri filtrelemede olduk\u00e7a yayg\u0131nd\u0131r. Ayr\u0131ca maske kullanarak do\u011frudan atama da yap\u0131labilir: <code>a[mask] = 0<\/code> gibi. Bu tarz vekt\u00f6rle\u015ftirilmi\u015f ko\u015fullu i\u015flemler, d\u00f6ng\u00fc ve <code>if<\/code> yap\u0131lar\u0131na g\u00f6re \u00e7ok daha okunabilir ve performansl\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"cok-boyutlu-dizilerde-dilimleme-slicing-ve-eksen-mantigi-nasil-calisir\">\u00c7ok boyutlu dizilerde dilimleme (slicing) ve eksen mant\u0131\u011f\u0131 nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> \u00c7ok boyutlu dizilerde her eksen i\u00e7in ayr\u0131 bir dilim verilir: <code>a[ sat\u0131r_dilimi , s\u00fctun_dilimi , ... ]<\/code>. \u00d6rne\u011fin <code>a[0, :]<\/code> ilk sat\u0131r\u0131, <code>a[:, 0]<\/code> ilk s\u00fctunu, <code>a[1:3, 2:5]<\/code> ise belirli bir alt blok matrisini se\u00e7er. NumPy\u2019de eksenler, indeks s\u0131ras\u0131na g\u00f6re soldan sa\u011fa tan\u0131mlan\u0131r ve her eksen ba\u011f\u0131ms\u0131z dilimlenebilir. Bu yap\u0131, g\u00f6r\u00fcnt\u00fc i\u015fleme (y\u00fckseklik \u00d7 geni\u015flik \u00d7 kanal) veya zaman serisi tens\u00f6rlerinde (zaman \u00d7 \u00f6rnek \u00d7 \u00f6zellik) s\u0131k kullan\u0131l\u0131r. Dilimleme sonucu genellikle g\u00f6r\u00fcn\u00fcm d\u00f6nd\u00fcrd\u00fc\u011f\u00fc i\u00e7in, elde edilen alt dizi \u00fczerinde yap\u0131lan de\u011fi\u015fiklikler orijinal diziyi de g\u00fcncelleyebilir; bu davran\u0131\u015f hem performans avantaj\u0131 hem de dikkat edilmesi gereken bir yan etkidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-3-yayinlama-broadcasting-ufunc-ve-matematiksel-islemler\">B\u00f6l\u00fcm 3 \u2013 Yay\u0131nlama (Broadcasting), Ufunc ve Matematiksel \u0130\u015flemler<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-broadcasting-nedir-ve-hangi-kurala-gore-calisir\">NumPy\u2019de broadcasting nedir ve hangi kurala g\u00f6re \u00e7al\u0131\u015f\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Broadcasting, farkl\u0131 \u015fekillere sahip diziler aras\u0131nda eleman-bazl\u0131 i\u015flemi m\u00fcmk\u00fcn k\u0131lan mekanizmad\u0131r. Temel kural, son eksenlerden ba\u015flayarak \u015fekilleri kar\u015f\u0131la\u015ft\u0131rmak ve eksenlerin ya <strong>e\u015fit boyutta<\/strong> ya da <strong>1<\/strong> olmas\u0131 gerekti\u011fini kabul etmektir. Boyutu 1 olan eksen, di\u011fer eksenin boyutuna \u201cgeni\u015fletilerek\u201d sanal olarak kopyalan\u0131r; fiziksel kopya olu\u015fmad\u0131\u011f\u0131 i\u00e7in bellek verimlidir. \u00d6rne\u011fin <code>(3, 1)<\/code> \u015fekilli bir dizi ile <code>(1, 4)<\/code> \u015fekilli bir dizi topland\u0131\u011f\u0131nda sonu\u00e7 <code>(3, 4)<\/code> olur. Broadcasting, skaler ekleme, sat\u0131r\/s\u00fctun-bazl\u0131 normalizasyon ve \u00f6zellik \u00f6l\u00e7ekleme gibi veri bilimi senaryolar\u0131nda yayg\u0131n olarak kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"broadcasting-hatalarini-incompatible-shapes-nasil-teshis-eder-ve-duzeltirsiniz\">Broadcasting hatalar\u0131n\u0131 (incompatible shapes) nas\u0131l te\u015fhis eder ve d\u00fczeltirsiniz?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Broadcasting hatalar\u0131 genellikle \u201coperands could not be broadcast together with shapes \u2026\u201d mesaj\u0131yla ortaya \u00e7\u0131kar. Bu durumda yap\u0131lacak ilk \u015fey, i\u015flem yap\u0131lan dizilerin <code>shape<\/code>\u2019lerini yazd\u0131rarak son eksenlerden itibaren kar\u015f\u0131la\u015ft\u0131rmakt\u0131r. Boyutlar\u0131n e\u015fit olmad\u0131\u011f\u0131 ve ikisinden birinin 1 olmad\u0131\u011f\u0131 eksen, uyumsuzlu\u011fun kayna\u011f\u0131d\u0131r. \u00c7\u00f6z\u00fcm olarak <code>np.expand_dims<\/code>, <code>np.newaxis<\/code> veya <code>reshape<\/code> ile eksen eklenebilir ya da boyutlar yeniden d\u00fczenlenebilir. \u00d6rne\u011fin <code>(N,)<\/code> ile <code>(N, 1)<\/code> kar\u0131\u015f\u0131kl\u0131klar\u0131 s\u0131k rastlan\u0131r; bu durumda vekt\u00f6r a\u00e7\u0131k\u00e7a s\u00fctun veya sat\u0131r vekt\u00f6r\u00fcne \u00e7evrilmelidir. Bu t\u00fcr te\u015fhis ve d\u00fczeltmeler, \u00f6zellikle istatistiksel \u00f6zelliklerin eksen bazl\u0131 hesapland\u0131\u011f\u0131 veri \u00f6n-i\u015fleme a\u015famalar\u0131nda kritiktir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"ufunc-universal-function-nedir-ve-neden-bu-kadar-onemlidir\">Ufunc (universal function) nedir ve neden bu kadar \u00f6nemlidir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Ufunc\u2019lar, <code>ndarray<\/code> \u00fczerinde <strong>eleman-bazl\u0131<\/strong> \u00e7al\u0131\u015fan, C d\u00fczeyinde uygulanm\u0131\u015f, vekt\u00f6rle\u015ftirilmi\u015f fonksiyonlard\u0131r. \u00d6rne\u011fin <code>np.sin<\/code>, <code>np.exp<\/code>, <code>np.add<\/code>, <code>np.maximum<\/code> birer ufunc\u2019t\u0131r. Ufunc\u2019lar, broadcasting kurallar\u0131na otomatik uyar, tip d\u00f6n\u00fc\u015ft\u00fcrme (type casting) ve \u00e7\u0131kt\u0131 i\u00e7in <code>dtype<\/code> y\u00f6netimi yapar, ayr\u0131ca <code>out<\/code> parametresi ile bellek \u00fczerinde yerinde (in-place) \u00e7al\u0131\u015fabilir. Bu \u00f6zellikler, yo\u011fun matematiksel hesaplamalarda hem h\u0131z hem de esneklik sa\u011flar. Ek olarak, <code>np.vectorize<\/code> veya <code>frompyfunc<\/code> ile Python fonksiyonlar\u0131 ufunc benzeri davranacak \u015fekilde sarmalanabilir; ancak ger\u00e7ek performans kazan\u0131m\u0131, C ile yaz\u0131lm\u0131\u015f yerle\u015fik ufunc\u2019larda elde edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"eleman-bazli-islemler-ile-matris-islemleri-arasindaki-fark-nasil-yonetilir\">Eleman-bazl\u0131 i\u015flemler ile matris i\u015flemleri aras\u0131ndaki fark nas\u0131l y\u00f6netilir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019de <code>*<\/code> operat\u00f6r\u00fc <strong>eleman-bazl\u0131 \u00e7arpma<\/strong> yapar; lineer cebirsel matris \u00e7arp\u0131m\u0131 i\u00e7in <code>np.dot<\/code> veya <code>@<\/code> (matmul) operat\u00f6r\u00fc kullan\u0131l\u0131r. Bu ayr\u0131m \u00f6zellikle makine \u00f6\u011frenmesi ve istatistikte kritik \u00f6neme sahiptir; yanl\u0131\u015fl\u0131kla element-wise yerine matris \u00e7arp\u0131m\u0131 yapmak, sonu\u00e7lar\u0131 tamamen bozabilir. Benzer \u015fekilde <code>np.power<\/code> ile eleman-bazl\u0131 \u00fcs alma yap\u0131l\u0131rken, matris kuvveti i\u00e7in <code>scipy.linalg<\/code> fonksiyonlar\u0131 gibi farkl\u0131 ara\u00e7lara ba\u015fvurmak gerekir. Kod yazarken, de\u011fi\u015fken isimlerini ve yorum sat\u0131rlar\u0131n\u0131 a\u00e7\u0131k tutmak, bu iki i\u015flem t\u00fcr\u00fcn\u00fcn kar\u0131\u015fmas\u0131n\u0131 engellemek a\u00e7\u0131s\u0131ndan iyi bir pratiktir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"kosullu-islemler-icin-np-where-ve-ufunclarin-nasil-kombinasyonu-yapilir\">Ko\u015fullu i\u015flemler i\u00e7in <code>np.where<\/code> ve ufunc\u2019lar\u0131n nas\u0131l kombinasyonu yap\u0131l\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.where<\/code> fonksiyonu, vekt\u00f6rle\u015ftirilmi\u015f bir <code>if-else<\/code> yap\u0131s\u0131 gibi \u00e7al\u0131\u015f\u0131r: <code>np.where(cond, x, y)<\/code> ifadesi, <code>cond<\/code> do\u011fru oldu\u011funda <code>x<\/code>, yanl\u0131\u015f oldu\u011funda <code>y<\/code> de\u011ferini se\u00e7er. Bu, ufunc\u2019lar\u0131n \u00e7\u0131kt\u0131lar\u0131n\u0131 ko\u015fullu olarak birle\u015ftirmek i\u00e7in idealdir. \u00d6rne\u011fin <code>z = np.where(a &gt; 0, np.log(a), 0.0)<\/code> ifadesi, pozitif elemanlara logaritma uygularken di\u011ferlerini s\u0131f\u0131r yapar. Bu yakla\u015f\u0131m, finansal risk modelleri, par\u00e7a-par\u00e7a tan\u0131ml\u0131 fonksiyonlar ve eksik veri y\u00f6netiminde yayg\u0131n olarak kullan\u0131l\u0131r. D\u00f6ng\u00fc ve i\u00e7 i\u00e7e <code>if<\/code> yap\u0131s\u0131 yerine tek sat\u0131rl\u0131k vekt\u00f6rle\u015ftirilmi\u015f ifade ile hem kod sadele\u015fir hem de \u00f6nemli performans avantaj\u0131 elde edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-4-lineer-cebir-ve-sayisal-hesaplama\">B\u00f6l\u00fcm 4 \u2013 Lineer Cebir ve Say\u0131sal Hesaplama<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-temel-lineer-cebir-islemleri-icin-hangi-modul-kullanilir\">NumPy\u2019de temel lineer cebir i\u015flemleri i\u00e7in hangi mod\u00fcl kullan\u0131l\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019nin lineer cebir fonksiyonlar\u0131 <code>np.linalg<\/code> mod\u00fcl\u00fcnde toplanm\u0131\u015ft\u0131r. Bu mod\u00fcl, matris determinanti (<code>np.linalg.det<\/code>), tersini alma (<code>np.linalg.inv<\/code>), \u00f6zde\u011fer\/\u00f6zvekt\u00f6r hesaplama (<code>np.linalg.eig<\/code>), tekil de\u011fer ayr\u0131\u015f\u0131m\u0131 (<code>np.linalg.svd<\/code>) ve lineer denklem sistemlerini \u00e7\u00f6zme (<code>np.linalg.solve<\/code>) gibi fonksiyonlar i\u00e7erir. Bu fonksiyonlar, \u00e7o\u011funlukla BLAS\/LAPACK gibi optimize edilmi\u015f k\u00fct\u00fcphaneleri kullan\u0131r ve bu nedenle saf Python implementasyonlar\u0131na g\u00f6re \u00e7ok daha h\u0131zl\u0131 ve g\u00fcvenilirdir. Uygulamada, istatistiksel modelleme, PCA, regresyon ve say\u0131sal sim\u00fclasyonlar gibi pek \u00e7ok alanda bu fonksiyonlara do\u011frudan ba\u015fvurulur.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"lineer-denklem-sistemi-cozumunde-neden-np-linalg-solve-kullanmak-invden-daha-iyidir\">Lineer denklem sistemi \u00e7\u00f6z\u00fcm\u00fcnde neden np.linalg.solve kullanmak inv&#8217;den daha iyidir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>Ax = b<\/code> denklemi i\u00e7in matematiksel olarak <code>x = A\u207b\u00b9b<\/code> do\u011fru olsa da, say\u0131sal hesaplamada \u00f6nce <code>A<\/code>\u2019n\u0131n tersini hesaplay\u0131p sonra \u00e7arpmak hem <strong>daha maliyetli<\/strong> hem de <strong>daha az kararl\u0131<\/strong> bir yakla\u015f\u0131md\u0131r. <code>np.linalg.solve<\/code>, sistemi fakt\u00f6rizasyon (\u00f6r. LU) ile do\u011frudan \u00e7\u00f6zer ve ters matrisin a\u00e7\u0131k\u00e7a hesaplanmas\u0131ndan ka\u00e7\u0131n\u0131r. Bu, hem hesaplama s\u00fcresini azalt\u0131r hem de yuvarlama hatalar\u0131n\u0131n birikmesini engeller. B\u00fcy\u00fck boyutlu sistemlerde bu fark \u00e7ok daha belirgindir. Bu nedenle, akademik ve end\u00fcstriyel uygulamalarda \u201cters matris hesaplamay\u0131n, sistemi do\u011frudan \u00e7\u00f6z\u00fcn\u201d prensibi temel bir en iyi uygulama olarak kabul edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-ozdeger-ve-ozvektor-hesaplamanin-tipik-kullanim-alanlari-nelerdir\">NumPy ile \u00f6zde\u011fer ve \u00f6zvekt\u00f6r hesaplaman\u0131n tipik kullan\u0131m alanlar\u0131 nelerdir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> \u00d6zde\u011fer ve \u00f6zvekt\u00f6rler, \u00f6zellikle <strong>boyut indirgeme (PCA)<\/strong>, spektral k\u00fcmelenme, Markov zincirleri ve diferansiyel denklem \u00e7\u00f6z\u00fcmlerinde merkezi rol oynar. <code>np.linalg.eig<\/code>, kare matrislerin \u00f6zde\u011fer ve \u00f6zvekt\u00f6rlerini hesaplar; simetrik\/hermitian matrisler i\u00e7in daha kararl\u0131 ve verimli olan <code>np.linalg.eigh<\/code> tercih edilir. PCA\u2019de kovaryans matrisi \u00fczerinden elde edilen en b\u00fcy\u00fck \u00f6zde\u011ferlere kar\u015f\u0131l\u0131k gelen \u00f6zvekt\u00f6rler, verinin ana bile\u015fenlerini olu\u015fturur. Bu t\u00fcr i\u015flemler, veri bilimi ve istatistiksel \u00f6\u011frenme derslerinin NumPy i\u00e7eren \u00f6rneklerinde s\u0131k\u00e7a kar\u015f\u0131m\u0131za \u00e7\u0131kar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"sayisal-kararlilik-numerical-stability-acisindan-hangi-num-py-pratikleri-onerilir\">Say\u0131sal kararl\u0131l\u0131k (numerical stability) a\u00e7\u0131s\u0131ndan hangi NumPy pratikleri \u00f6nerilir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Say\u0131sal kararl\u0131l\u0131k i\u00e7in birka\u00e7 temel ilke \u00f6ne \u00e7\u0131kar: (1) \u00c7ok b\u00fcy\u00fck veya \u00e7ok k\u00fc\u00e7\u00fck \u00f6l\u00e7ekli verileri normalle\u015ftirmek\/standartla\u015ft\u0131rmak, (2) Matrix inversion yerine <code>solve<\/code> gibi daha kararl\u0131 algoritmalar\u0131 tercih etmek, (3) Toplama i\u015flemlerinde b\u00fcy\u00fck ve k\u00fc\u00e7\u00fck b\u00fcy\u00fckl\u00fckteki say\u0131lar\u0131 kar\u0131\u015ft\u0131r\u0131rken Kahan toplam\u0131 benzeri teknikler (veya <code>dtype<\/code>\u2019\u0131 <code>float64<\/code> se\u00e7mek) kullanmak, (4) Log-uzay\u0131nda hesaplama yaparak ta\u015fma\/alt-ta\u015fma riskini azaltmak. NumPy, bu pratikleri uygulamak i\u00e7in gerekli fonksiyonlar\u0131 sa\u011flar; ancak kararl\u0131l\u0131k \u00e7o\u011fu zaman <strong>algoritma tasar\u0131m\u0131<\/strong> d\u00fczeyinde al\u0131nan kararlara ba\u011fl\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-buyuk-matrissel-hesaplamalari-hizlandirmak-icin-hangi-stratejiler-kullanilabilir\">NumPy ile b\u00fcy\u00fck matrissel hesaplamalar\u0131 h\u0131zland\u0131rmak i\u00e7in hangi stratejiler kullan\u0131labilir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> B\u00fcy\u00fck matrislerde h\u0131z i\u00e7in \u00f6ncelikle <strong>vekt\u00f6rle\u015ftirme<\/strong> ve <strong>broadcasting<\/strong> ile saf Python d\u00f6ng\u00fclerinden ka\u00e7\u0131nmak gerekir. Ard\u0131ndan, operasyonlar\u0131 m\u00fcmk\u00fcn oldu\u011funca <strong>toplu<\/strong> h\u00e2le getirmek (\u00f6rne\u011fin birden fazla matris \u00e7arp\u0131m\u0131n\u0131 tek <code>@<\/code> zincirine s\u0131k\u0131\u015ft\u0131rmak) \u00f6nemlidir. BLAS seviyesini y\u00fcksek tutan NumPy da\u011f\u0131t\u0131mlar\u0131 (MKL, OpenBLAS) tercih edilebilir. Bellek a\u00e7\u0131s\u0131ndan, gereksiz kopyalardan ka\u00e7\u0131nmak i\u00e7in <code>out<\/code> parametresi ve yerinde (in-place) i\u015flemler kullan\u0131lmal\u0131d\u0131r. Son olarak, \u00e7ok b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in, par\u00e7al\u0131 i\u015flem (chunking), bellek e\u015flemeli dosyalar (<code>memmap<\/code>) veya GPU tabanl\u0131 NumPy benzerleri (CuPy, JAX) devreye al\u0131narak hesaplama daha da \u00f6l\u00e7eklenebilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-5-performans-bellek-yonetimi-ve-diger-kutuphanelerle-entegrasyon\">B\u00f6l\u00fcm 5 \u2013 Performans, Bellek Y\u00f6netimi ve Di\u011fer K\u00fct\u00fcphanelerle Entegrasyon<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"soru-5-1-num-py-saf-python-listelerine-gore-neden-daha-az-bellek-kullanir\">Soru 5.1 \u2013 NumPy, saf Python listelerine g\u00f6re neden daha az bellek kullan\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Python listelerinde her eleman ayr\u0131 bir Python nesnesi oldu\u011fundan, her biri i\u00e7in ek meta veri (tip bilgisi, referans sayac\u0131 vb.) tutulur ve bellek adresleri par\u00e7al\u0131 yap\u0131dad\u0131r. NumPy <code>ndarray<\/code> ise elemanlar\u0131 <strong>sabit boyutlu ham veri<\/strong> olarak tek bir contiguous blokta saklar; tip bilgisi yaln\u0131zca <code>dtype<\/code>\u2019ta tutulur. Bu, hem nesne ba\u015f\u0131na overhead\u2019i ortadan kald\u0131r\u0131r hem de CPU cache dostu eri\u015fim sa\u011flar. \u00d6zellikle milyonlarca eleman i\u00e7eren dizilerde, bu fark \u00e7ok dramatiktir: Ayn\u0131 veri miktar\u0131, listelere g\u00f6re \u00e7o\u011fu zaman kat kat daha az bellekle temsil edilebilir ve bu da algoritmalar\u0131n daha b\u00fcy\u00fck veri setleri \u00fczerinde \u00e7al\u0131\u015fmas\u0131na imk\u00e2n verir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-bellek-duzeni-c-vs-fortran-order-performansi-nasil-etkiler\">NumPy\u2019de bellek d\u00fczeni (c vs Fortran order) performans\u0131 nas\u0131l etkiler?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy dizileri varsay\u0131lan olarak <strong>C-order<\/strong> (sat\u0131r-\u00f6ncelikli) format\u0131nda tutulur; bu durumda ayn\u0131 sat\u0131rdaki elemanlar bellekte yan yana yer al\u0131r. Fortran-order (s\u00fctun-\u00f6ncelikli) dizilerde ise s\u00fctun elemanlar\u0131 contiguous\u2019tur. \u00c7ok boyutlu diziler \u00fczerinde d\u00f6ng\u00fc veya vekt\u00f6rle\u015ftirilmi\u015f i\u015flemler yaparken, <strong>i\u00e7 d\u00f6ng\u00fcn\u00fcn contiguous y\u00f6nde ilerlemesi<\/strong> performans\u0131 belirgin bi\u00e7imde art\u0131r\u0131r; \u00e7\u00fcnk\u00fc CPU cache, ard\u0131\u015f\u0131k bellek eri\u015fimlerinde en verimli \u015fekilde kullan\u0131l\u0131r. NumPy\u2019de <code>order='F'<\/code> ile Fortran d\u00fczeninde dizi olu\u015fturabilir veya <code>np.asfortranarray<\/code> ile d\u00f6n\u00fc\u015ft\u00fcrebilirsiniz. \u00d6zellikle lineer cebir rutinleri ve <code>BLAS<\/code> arka plan\u0131nda \u00e7al\u0131\u015fan fonksiyonlar i\u00e7in uygun bellek d\u00fczeni se\u00e7imi \u00f6nemli h\u0131z kazan\u0131mlar\u0131 sa\u011flayabilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"bellek-eslemeli-dosyalar-memmap-ile-cok-buyuk-diziler-nasil-yonetilir\">Bellek e\u015flemeli dosyalar (memmap) ile \u00e7ok b\u00fcy\u00fck diziler nas\u0131l y\u00f6netilir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.memmap<\/code>, diskteki bir dosyay\u0131 sanki RAM\u2019deki bir <code>ndarray<\/code>\u2019mi\u015f gibi kullanman\u0131z\u0131 sa\u011flar. Bu yap\u0131, \u00f6zellikle RAM\u2019e s\u0131\u011fmayacak kadar b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken kullan\u0131l\u0131r. NumPy, sadece ihtiya\u00e7 duyulan par\u00e7alar\u0131 diskten okur; i\u015fletim sisteminin sayfalama mekanizmas\u0131 devreye girer. \u00d6rne\u011fin \u00e7ok b\u00fcy\u00fck g\u00f6r\u00fcnt\u00fc veri setleri veya zaman serileri i\u00e7in, tamam\u0131n\u0131 RAM\u2019e almadan blok blok i\u015flem yapmak m\u00fcmk\u00fcnd\u00fcr. Dezavantaj\u0131, disk eri\u015fimi RAM\u2019e g\u00f6re \u00e7ok daha yava\u015f oldu\u011fundan, rastgele eri\u015fimin (random access) pahal\u0131 olmas\u0131d\u0131r. Bu y\u00fczden <code>memmap<\/code> kullan\u0131rken, algoritmay\u0131 m\u00fcmk\u00fcn oldu\u011funca <strong>s\u0131ral\u0131 eri\u015fimi<\/strong> tercih edecek \u015fekilde tasarlamak gerekir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-pandas-ve-matplotlib-arasindaki-iliski-nasildir\">NumPy ile Pandas ve Matplotlib aras\u0131ndaki ili\u015fki nas\u0131ld\u0131r?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Pandas\u2019\u0131n <code>Series<\/code> ve <code>DataFrame<\/code> yap\u0131lar\u0131, altta \u00e7o\u011funlukla NumPy <code>ndarray<\/code>\u2019lerini kullan\u0131r; bu nedenle istatistiksel ve analitik fonksiyonlar\u0131n bir\u00e7o\u011fu NumPy fonksiyonlar\u0131na delegasyon yapar. Veri haz\u0131rlama s\u00fcrecinde, kompleks indeksleme ve eksik veri y\u00f6netimi i\u00e7in Pandas; d\u00fc\u015f\u00fck seviyeli say\u0131sal i\u015flemler i\u00e7in NumPy tercih edilir. Matplotlib ise \u00e7izim fonksiyonlar\u0131na genellikle NumPy dizileri veya dizimsi (array-like) nesneler bekler. B\u00f6ylece, veriyi NumPy ile i\u015fleyip, Pandas ile organize edip, Matplotlib ile g\u00f6rselle\u015ftirmek, veri bilimi i\u015f ak\u0131\u015flar\u0131nda standart bir model h\u00e2line gelmi\u015ftir. Bu s\u0131k\u0131 entegrasyon, NumPy\u2019nin ekosistem i\u00e7indeki stratejik konumunu daha da g\u00fc\u00e7lendirir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-performansini-artirmak-icin-tipik-profil-ve-optimizasyon-adimlari-nelerdir\">NumPy performans\u0131n\u0131 art\u0131rmak i\u00e7in tipik profil ve optimizasyon ad\u0131mlar\u0131 nelerdir?<\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> \u0130lk ad\u0131m, <code>timeit<\/code> veya profil ara\u00e7lar\u0131yla (\u00f6r. <code>line_profiler<\/code>) kodun en \u00e7ok zaman harcayan b\u00f6l\u00fcmlerini tespit etmektir. Sonras\u0131nda, bu b\u00f6l\u00fcmlerdeki Python d\u00f6ng\u00fcleri vekt\u00f6rle\u015ftirilmi\u015f NumPy i\u015flemlerine d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr; kopya olu\u015fumunu azaltmak i\u00e7in <code>copy()<\/code> \u00e7a\u011fr\u0131lar\u0131, gereksiz <code>astype<\/code> ve yeniden \u015fekillendirmeler g\u00f6zden ge\u00e7irilir. Bellek eri\u015fim desenleri analiz edilerek, contiguous olmayan diziler <code>np.ascontiguousarray<\/code> ile d\u00fczenlenebilir. E\u011fer h\u00e2l\u00e2 performans yetersizse, numba ile JIT derleme, Cython, C\/Fortran geni\u015fletmeleri veya GPU tabanl\u0131 NumPy benzerleri (CuPy) devreye al\u0131nabilir. B\u00f6ylece, NumPy hem prototipleme hem de \u00fcretim ortam\u0131na yak\u0131n performans i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7 h\u00e2line gelir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-6-rastgele-sayilar-istatistiksel-dagilimlar-ve-num-py-random\"><strong>B\u00f6l\u00fcm 6 \u2013 Rastgele Say\u0131lar, \u0130statistiksel Da\u011f\u0131l\u0131mlar ve NumPy Random<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pynin-yeni-generator-tabanli-random-ap-isi-neden-eski-np-random-yapisindan-daha-basarilidir\"><strong>NumPy\u2019nin yeni Generator tabanl\u0131 Random API\u2019si neden eski np.random yap\u0131s\u0131ndan daha ba\u015far\u0131l\u0131d\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>Generator<\/code> s\u0131n\u0131f\u0131, ba\u011f\u0131ms\u0131z ve yeniden \u00fcretilebilir (reproducible) rastgele say\u0131 ak\u0131\u015flar\u0131 y\u00f6netir. Eski <code>RandomState<\/code> tek bir global durum kulland\u0131\u011f\u0131 i\u00e7in \u00e7ok \u00e7ekirdekli sim\u00fclasyonlarda veri s\u0131z\u0131nt\u0131s\u0131na ve deterministik olmayan davran\u0131\u015flara yol a\u00e7abiliyordu. <code>Generator<\/code>, farkl\u0131 da\u011f\u0131l\u0131mlar i\u00e7in daha h\u0131zl\u0131 algoritmalar (\u00f6r. PCG64) kullan\u0131r; ayr\u0131ca paralel i\u015fleme, Monte Carlo sim\u00fclasyonlar\u0131 ve istatistiksel modelleme i\u00e7in izole random stream\u2019leri olu\u015fturmay\u0131 kolayla\u015ft\u0131r\u0131r. Modern bilimsel hesaplamalarda ba\u011f\u0131ms\u0131z ak\u0131\u015f \u00fcretimi kritik oldu\u011fundan yeni API b\u00fcy\u00fck avantaj sa\u011flar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-en-sik-kullanilan-rastgele-dagilimlar-hangileridir-ve-hangi-durumlarda-kullanilir\"><strong>NumPy\u2019de en s\u0131k kullan\u0131lan rastgele da\u011f\u0131l\u0131mlar hangileridir ve hangi durumlarda kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Normal da\u011f\u0131l\u0131m (<code>normal<\/code>) regresyon modelleri ve g\u00fcr\u00fclt\u00fc eklemede, uniform da\u011f\u0131l\u0131m (<code>uniform<\/code>) sim\u00fclasyon ve ba\u015flang\u0131\u00e7 de\u011ferlerinde, binom da\u011f\u0131l\u0131m\u0131 (<code>binomial<\/code>) olas\u0131l\u0131k temelli karar sistemlerinde, Poisson da\u011f\u0131l\u0131m\u0131 (<code>poisson<\/code>) olay say\u0131m\u0131 modellerinde, beta da\u011f\u0131l\u0131m\u0131 (<code>beta<\/code>) Bayesyen modellerde yo\u011fun kullan\u0131l\u0131r. <code>Generator<\/code> API\u2019si bu da\u011f\u0131l\u0131mlar\u0131 y\u00fcksek performansl\u0131 algoritmalarla \u00fcretir ve veri bilimi, istatistik ve yapay zek\u00e2 uygulamalar\u0131nda yayg\u0131n tercih edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"rastgele-sayi-uretiminde-tohumlama-seed-neden-onemlidir\"><strong>Rastgele say\u0131 \u00fcretiminde tohumlama (seed) neden \u00f6nemlidir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Tohumlama, deneylerin <strong>yeniden \u00fcretilebilirli\u011fini<\/strong> sa\u011flar. Bilimsel \u00e7al\u0131\u015fmalarda ayn\u0131 modelin farkl\u0131 ortamlarda ayn\u0131 sonu\u00e7lar\u0131 vermesi gerekir; rastgele ba\u015flang\u0131\u00e7 de\u011ferleri sonu\u00e7lar\u0131 etkileyebilir. <code>Generator(PCG64(1234))<\/code> gibi sabit bir tohum, e\u011fitim\/test b\u00f6lme, veri art\u0131rma, optimizasyon ve Monte Carlo sim\u00fclasyonlar\u0131nda deterministik sonu\u00e7 \u00fcretir. Bu, akademik yay\u0131nlar ve end\u00fcstriyel Ar-Ge \u00e7al\u0131\u015fmalar\u0131nda zorunluluktur.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"buyuk-orneklemli-istatistiksel-simulasyonlarda-num-py-nasil-avantaj-saglar\"><strong>B\u00fcy\u00fck \u00f6rneklemli istatistiksel sim\u00fclasyonlarda NumPy nas\u0131l avantaj sa\u011flar?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019nin random mod\u00fcl\u00fc C d\u00fczeyinde optimize edildi\u011fi i\u00e7in milyonlarca \u00f6rne\u011fi Python d\u00f6ng\u00fcleri olmadan \u00fcretebilir. <code>Generator<\/code> improve edilmi\u015f PRNG algoritmas\u0131 (PCG64) ile daha h\u0131zl\u0131d\u0131r. Ayr\u0131ca vekt\u00f6rle\u015ftirme sayesinde tek \u00e7a\u011fr\u0131da b\u00fcy\u00fck diziler olu\u015fturulur. Bellek kullan\u0131m\u0131n\u0131 azaltmak i\u00e7in <code>dtype<\/code> optimizasyonu yap\u0131labilir. Bu \u00f6zellikler, bootstrap, Monte Carlo ve MCMC analizleri i\u00e7in NumPy\u2019yi standart ara\u00e7 yapar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"choice-fonksiyonu-ile-rassal-ornekleme-nasil-yapilir\"><strong>choice fonksiyonu ile rassal \u00f6rnekleme nas\u0131l yap\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>choice<\/code> hem e\u015fit olas\u0131l\u0131kl\u0131 hem de a\u011f\u0131rl\u0131kl\u0131 \u00f6rnekleme yapabilir. \u00d6rneklem tekrarl\u0131 (<code>replace=True<\/code>) veya tekrars\u0131z (<code>replace=False<\/code>) al\u0131nabilir. B\u00fcy\u00fck veri setlerinde a\u011f\u0131rl\u0131kl\u0131 \u00f6rnekleme, veri art\u0131rma, s\u0131n\u0131f dengesizli\u011fi d\u00fczeltme ve istatistiksel yeniden \u00f6rnekleme (resampling) i\u015flemlerinde kritik rol oynar. NumPy\u2019nin vekt\u00f6rle\u015ftirilmi\u015ftir ve b\u00fcy\u00fck \u00f6l\u00e7ekli \u00e7al\u0131\u015fmalarda olduk\u00e7a h\u0131zl\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-7-eksik-veri-na-n-standartlastirma-ve-normalizasyon\"><strong>B\u00f6l\u00fcm 7 \u2013 Eksik Veri, NaN, Standartla\u015ft\u0131rma ve Normalizasyon<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-na-n-degerler-neden-sikinti-yaratir\"><strong>NumPy\u2019de NaN de\u011ferler neden s\u0131k\u0131nt\u0131 yarat\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NaN, IEEE 754 standard\u0131na g\u00f6re \u00f6zel bir \u201ctan\u0131mlanamayan say\u0131\u201dd\u0131r ve \u00e7o\u011fu matematiksel i\u015flem NaN i\u00e7erdi\u011finde t\u00fcm sonucu NaN yapar. Toplam, ortalama, standart sapma gibi i\u015flemler bozulur. Bu nedenle veri analizi yaparken NaN\u2019lerin tespit edilmesi (<code>np.isnan<\/code>, <code>np.isfinite<\/code>) ve uygun y\u00f6ntemlerle d\u00fczeltilmesi gerekir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"na-n-yonetimi-icin-en-iyi-uygulamalar-nelerdir\"><strong>NaN y\u00f6netimi i\u00e7in en iyi uygulamalar nelerdir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> \u0130lk ad\u0131m NaN\u2019leri tespit etmektir. Sonras\u0131nda:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Silme (listwise deletion),<\/li>\n\n\n\n<li>Ortalama\/medyan ile doldurma,<\/li>\n\n\n\n<li>Grup bazl\u0131 doldurma,<\/li>\n\n\n\n<li>\u0130leri\/geri doldurma,<\/li>\n\n\n\n<li>Model tabanl\u0131 tahmin (regresyon imputation)<br>gibi y\u00f6ntemler uygulanabilir. NumPy tek ba\u015f\u0131na s\u0131n\u0131rl\u0131d\u0131r; bu nedenle Pandas veya scikit-learn ile birlikte kullan\u0131ld\u0131\u011f\u0131nda etkisi artar.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"veri-standardizasyonu-z-score-num-py-ile-nasil-yapilir-ve-neden-gereklidir\"><strong>Veri standardizasyonu (z-score) NumPy ile nas\u0131l yap\u0131l\u0131r ve neden gereklidir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Z-score standardizasyonu <code>(x - mean) \/ std<\/code> form\u00fcl\u00fcyle yap\u0131l\u0131r. Bu i\u015flem, \u00f6zellikle makine \u00f6\u011frenmesi modellerinde de\u011fi\u015fkenler aras\u0131ndaki \u00f6l\u00e7ek farklar\u0131n\u0131 ortadan kald\u0131rarak e\u011fitimi stabilize eder. PCA gibi algoritmalar \u00f6l\u00e7ek duyarl\u0131d\u0131r; bu nedenle NumPy\u2019nin vekt\u00f6rle\u015ftirilmi\u015f matematiksel fonksiyonlar\u0131yla \u00f6l\u00e7ekleme h\u0131zl\u0131 ve g\u00fcvenilir bi\u00e7imde yap\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"min-max-normalizasyonu-hangi-durumlarda-tercih-edilir\"><strong>Min-max normalizasyonu hangi durumlarda tercih edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Min-max, veri de\u011ferlerini belirli bir aral\u0131\u011fa (genellikle 0\u20131) d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. G\u00f6r\u00fcnt\u00fc i\u015fleme, sinyal i\u015fleme ve n\u00f6ral a\u011f modellerinde s\u0131k kullan\u0131l\u0131r; \u00e7\u00fcnk\u00fc aktivasyon fonksiyonlar\u0131 belirli aral\u0131klara duyarl\u0131d\u0131r. Ayk\u0131r\u0131 de\u011ferlerden etkilendi\u011fi i\u00e7in dikkatli kullan\u0131lmal\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"aykiri-deger-tespiti-icin-num-py-ile-hangi-teknikler-uygulanabilir\"><strong>Ayk\u0131r\u0131 de\u011fer tespiti i\u00e7in NumPy ile hangi teknikler uygulanabilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> En yayg\u0131n y\u00f6ntem IQR tekni\u011fidir. Q1 ve Q3 hesaplan\u0131r ve IQR = Q3 \u2013 Q1 bulunur. Alt\/\u00fcst s\u0131n\u0131rlar:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Alt s\u0131n\u0131r = Q1 \u2013 1.5\u00d7IQR<\/strong><\/li>\n\n\n\n<li><strong>\u00dcst s\u0131n\u0131r = Q3 + 1.5\u00d7IQR<\/strong><br>Bu s\u0131n\u0131rlar\u0131n d\u0131\u015f\u0131nda kalan de\u011ferler ayk\u0131r\u0131 kabul edilir. NumPy\u2019nin h\u0131zl\u0131 istatistiksel fonksiyonlar\u0131, milyonluk veri setlerinde bile bu i\u015flemi verimli \u015fekilde yapar.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-8-hafiza-stride-view-copy-semantigi-ve-dusuk-seviye-mekanizmalar\"><strong>B\u00f6l\u00fcm 8 \u2013 Haf\u0131za, Stride, View\/Copy Semanti\u011fi ve D\u00fc\u015f\u00fck Seviye Mekanizmalar<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-stride-nedir-ve-performansi-nasil-etkiler\"><strong>NumPy\u2019de \u201cstride\u201d nedir ve performans\u0131 nas\u0131l etkiler?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Stride, bir sonraki elemana ge\u00e7mek i\u00e7in bellekte ka\u00e7 byte ilerlenmesi gerekti\u011fini tan\u0131mlar. Contiguous dizilerde stride d\u00fczenlidir; bu CPU cache\u2019i maksimize eder. Contiguous olmayan diziler (\u00f6r. dilimlenmi\u015f matrisler) daha d\u00fc\u015f\u00fck performans verir. Stride yap\u0131s\u0131n\u0131n anla\u015f\u0131lmas\u0131, NumPy\u2019nin bellek modelini \u00e7\u00f6zmenin anahtar\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"view-ve-copy-farki-neden-bu-kadar-kritiktir\"><strong>View ve copy fark\u0131 neden bu kadar kritiktir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> View (g\u00f6r\u00fcn\u00fcm) orijinal veriyle ayn\u0131 belle\u011fi payla\u015f\u0131r; copy ise yeni bir bellek olu\u015fturur. \u00d6rne\u011fin <code>reshape<\/code>, <code>ravel<\/code>, dilimleme i\u015flemleri genellikle view d\u00f6nd\u00fcr\u00fcr. Bu, performans avantaj\u0131 sa\u011flar ancak alt dizide yap\u0131lan de\u011fi\u015fikliklerin ana diziyi etkilemesine yol a\u00e7abilir. B\u00fcy\u00fck projelerde bu n\u00fcans g\u00f6zden ka\u00e7t\u0131\u011f\u0131nda kritik hatalar do\u011fabilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"np-ascontiguousarray-ne-ise-yarar\"><strong>np.ascontiguousarray ne i\u015fe yarar?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Bu fonksiyon, contiguous olmayan dizileri contiguous d\u00fczene \u00e7evirir. Stride d\u00fczensiz oldu\u011funda BLAS \u00e7a\u011fr\u0131lar\u0131n\u0131n yava\u015flamas\u0131 veya hata vermesi m\u00fcmk\u00fcn oldu\u011fundan, matris \u00e7arp\u0131m\u0131 gibi i\u015flemlerden \u00f6nce contiguous bellek d\u00fczeni istenebilir. Bilimsel hesaplamalarda performans optimizasyonu i\u00e7in s\u0131k kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-bellek-uzerinde-in-place-islem-yapmayi-nasil-destekler\"><strong>NumPy bellek \u00fczerinde in-place i\u015flem yapmay\u0131 nas\u0131l destekler?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Ufunc\u2019larda <code>out=<\/code> parametresi ile \u00e7\u0131kt\u0131 ayn\u0131 dizinin \u00fczerine yaz\u0131labilir:<br><code>np.add(a, b, out=a)<\/code> gibi. Bu, yeni dizi olu\u015fturmay\u0131 engeller ve bellek kullan\u0131m\u0131n\u0131 dramatik bi\u00e7imde d\u00fc\u015f\u00fcr\u00fcr. Deep learning preprocessing a\u015famalar\u0131nda s\u0131k\u00e7a kullan\u0131lan bir tekniktir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-c-api-hangi-durumlarda-kullanilir\"><strong>NumPy C API hangi durumlarda kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy C API, Python d\u0131\u015f\u0131ndaki dillerden NumPy dizileriyle do\u011frudan etkile\u015fim kurmak i\u00e7in kullan\u0131l\u0131r. \u00d6rne\u011fin performans-kritik C mod\u00fclleri geli\u015ftirmek, Python\u2013C++ k\u00f6pr\u00fcleri kurmak, \u00f6zel derleyiciler veya hesaplama motorlar\u0131 yazmak gibi ileri seviye \u00e7al\u0131\u015fmalarda NumPy dizilerine eri\u015fmek gerekir. Bu API, b\u00fcy\u00fck Ar-Ge projelerinde h\u0131z optimizasyonu i\u00e7in \u00f6nemlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-9-gercek-dunya-kullanimlari-bilimsel-uygulamalar-ve-veri-bilimi\"><strong>B\u00f6l\u00fcm 9 \u2013 Ger\u00e7ek D\u00fcnya Kullan\u0131mlar\u0131, Bilimsel Uygulamalar ve Veri Bilimi<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-goruntu-isleme-alaninda-neden-temel-aractir\"><strong>NumPy g\u00f6r\u00fcnt\u00fc i\u015fleme alan\u0131nda neden temel ara\u00e7t\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> G\u00f6r\u00fcnt\u00fcler, piksel matrislerinden olu\u015fur ve do\u011fal olarak <code>ndarray<\/code> yap\u0131s\u0131na uygundur. Renkli g\u00f6r\u00fcnt\u00fcler genellikle (Y\u00fckseklik \u00d7 Geni\u015flik \u00d7 Kanal) \u015feklindedir. G\u00f6r\u00fcnt\u00fc filtreleri, konvol\u00fcsyonlar, histogram e\u015fitleme gibi i\u015flemler NumPy\u2019nin vekt\u00f6rle\u015ftirilmi\u015f fonksiyonlar\u0131yla y\u00fcksek performansla yap\u0131labilir. OpenCV, scikit-image gibi k\u00fct\u00fcphaneler de veri modelinde NumPy dizilerini kullan\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"zaman-serisi-analizinde-num-pynin-rolu-nedir\"><strong>Zaman serisi analizinde NumPy\u2019nin rol\u00fc nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Zaman serileri matris bi\u00e7iminde modellendi\u011fi i\u00e7in NumPy spinelidir. Kayd\u0131rmal\u0131 pencereler, fark alma, hareketli ortalama gibi i\u015flemler NumPy ufunc\u2019lar\u0131yla h\u0131zl\u0131 bi\u00e7imde yap\u0131l\u0131r. Veri \u00f6n-i\u015fleme, g\u00fcr\u00fclt\u00fc giderme ve regresyon modellerine veri haz\u0131rlama a\u015famalar\u0131nda yo\u011fun \u015fekilde kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"pca-principal-component-analysis-hesaplamasinda-num-py-neden-idealdir\"><strong>PCA (Principal Component Analysis) hesaplamas\u0131nda NumPy neden idealdir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> PCA, kovaryans matrisi \u2192 SVD\/\u00f6zde\u011fer ayr\u0131\u015f\u0131m\u0131 dizisini i\u00e7erir. NumPy\u2019nin <code>np.cov<\/code>, <code>np.linalg.eig<\/code>, <code>np.linalg.svd<\/code> fonksiyonlar\u0131 C seviyesinde optimize edilmi\u015ftir. Bu nedenle y\u00fcksek boyutlu matrislerde bile h\u0131zl\u0131, kararl\u0131 ve tekrarlanabilir sonu\u00e7lar verir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"finans-matematiginde-num-py-hangi-problemler-icin-kullanilir\"><strong>Finans matemati\u011finde NumPy hangi problemler i\u00e7in kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Opsiyon fiyatlama (Black-Scholes), portf\u00f6y optimizasyonu, risk sim\u00fclasyonlar\u0131 (Monte Carlo), VaR\/CVaR hesaplamalar\u0131, korelasyon\u2013kovaryans matrisleri gibi bir\u00e7ok problemde NumPy dizileri ve lineer cebir fonksiyonlar\u0131 \u00e7ekirdek bile\u015fen olarak kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"bilimsel-simulasyonlarda-num-pynin-avantajlari-nelerdir\"><strong>Bilimsel sim\u00fclasyonlarda NumPy\u2019nin avantajlar\u0131 nelerdir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Diferansiyel denklem \u00e7\u00f6z\u00fcmleri, \u0131zgara tabanl\u0131 fizik modelleri, par\u00e7ac\u0131k sim\u00fclasyonlar\u0131 gibi i\u015flemlerde NumPy\u2019nin h\u0131zl\u0131 array i\u015flemleri, g\u00fc\u00e7l\u00fc lineer cebir ara\u00e7lar\u0131 ve vekt\u00f6rle\u015ftirilmi\u015f fonksiyonlar\u0131 en iyi performans\u0131 sa\u011flar. Ayr\u0131ca sonu\u00e7lar kolayca di\u011fer bilimsel k\u00fct\u00fcphanelere aktar\u0131labilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-10-hatalar-tuzaklar-ve-en-iyi-uygulamalar\"><strong>B\u00f6l\u00fcm 10 \u2013 Hatalar, Tuzaklar ve En \u0130yi Uygulamalar<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-yapilan-en-yaygin-hata-list-comprehension-kullanmak-neden-yanlistir\"><strong>NumPy\u2019de yap\u0131lan en yayg\u0131n hata: \u201clist comprehension kullanmak\u201d. Neden yanl\u0131\u015ft\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019nin g\u00fcc\u00fc vekt\u00f6rle\u015ftirmededir. <code>for<\/code> d\u00f6ng\u00fcs\u00fc veya list comprehension kullanmak, hesaplamay\u0131 Python seviyesine \u00e7eker ve performans\u0131 dramatik bi\u00e7imde d\u00fc\u015f\u00fcr\u00fcr. Bunun yerine do\u011frudan <code>ndarray<\/code> ifadeleri veya ufunc\u2019lar kullan\u0131lmal\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"astype-kullanirken-gizli-kopya-olustugunu-nasil-anlarsiniz\"><strong>astype() kullan\u0131rken gizli kopya olu\u015ftu\u011funu nas\u0131l anlars\u0131n\u0131z?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>astype<\/code> her zaman yeni bir kopya \u00fcretir. B\u00fcy\u00fck veri setlerinde bu b\u00fcy\u00fck maliyet olu\u015fturur. Bunu kontrol etmek i\u00e7in <code>a is a.astype(...)<\/code> ifadesi daima <code>False<\/code> d\u00f6ner. Dtype d\u00f6n\u00fc\u015f\u00fcm\u00fcn\u00fc minimumda tutmak performans i\u00e7in \u00f6nemlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"value-error-operands-could-not-be-broadcast-together-hatasi-nasil-cozulur\"><strong>\u201cValueError: operands could not be broadcast together\u201d hatas\u0131 nas\u0131l \u00e7\u00f6z\u00fcl\u00fcr?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Bu hata, shape uyumsuzlu\u011fundan kaynaklan\u0131r. \u00c7\u00f6z\u00fcm:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><code>a.shape<\/code>, <code>b.shape<\/code> de\u011ferlerini kontrol et.<\/li>\n\n\n\n<li>Son eksenlerden ba\u015flayarak kar\u015f\u0131la\u015ft\u0131r.<\/li>\n\n\n\n<li><code>np.newaxis<\/code>, <code>reshape<\/code> veya eksen ekleme ile uygun hale getir.<br>Broadcasting kural\u0131n\u0131 do\u011fru anlamak kritik \u00f6nem ta\u015f\u0131r.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"np-concatenate-ve-np-stack-farki-nedir\"><strong>np.concatenate ve np.stack fark\u0131 nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>concatenate<\/code>, mevcut eksenlerden birinde dizileri birle\u015ftirir. <code>stack<\/code> ise yeni bir eksen ekleyerek birle\u015ftirir. \u00d6rne\u011fin iki (3, 4) matris <code>concatenate<\/code> ile yine (3, 8) olabilirken, <code>stack<\/code> ile (2, 3, 4) olur. Derin \u00f6\u011frenme batching i\u015flemlerinde <code>stack<\/code> s\u0131k kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-buyuk-dizilerle-calisirken-hangi-optimizasyonlar-kritik-onem-tasir\"><strong>NumPy\u2019de b\u00fcy\u00fck dizilerle \u00e7al\u0131\u015f\u0131rken hangi optimizasyonlar kritik \u00f6nem ta\u015f\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> (1) Gereksiz kopyalardan ka\u00e7\u0131nmak, (2) dtype\u2019\u0131 do\u011fru se\u00e7mek, (3) in-place i\u015flemleri tercih etmek, (4) vekt\u00f6rle\u015ftirme ve broadcasting kullanmak, (5) contiguous bellek d\u00fczeni sa\u011flamak, (6) m\u00fcmk\u00fcnse BLAS h\u0131zland\u0131rmal\u0131 NumPy da\u011f\u0131t\u0131mlar\u0131n\u0131 kullanmak, (7) b\u00fcy\u00fck veri i\u00e7in memmap veya chunk y\u00f6ntemiyle par\u00e7a par\u00e7a i\u015flem yapmak. Bu teknikler milyonlarca elemanl\u0131 veri setlerinde performans\u0131 katlar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"kategori-11-matris-operasyonlari-determinant-ters-rank-ve-svd\"><strong>Kategori 11 \u2013 Matris Operasyonlar\u0131, Determinant, Ters, Rank ve SVD<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"matrisin-rutbesi-rank-numpy-ile-nasil-hesaplanir-ve-neden-onemlidir\"><strong>Matrisin r\u00fctbesi (rank) Numpy ile nas\u0131l hesaplan\u0131r ve neden \u00f6nemlidir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Matris rank\u2019\u0131, matrisin sat\u0131r\/s\u00fctun uzay\u0131n\u0131n boyutudur ve do\u011frusal ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 \u00f6l\u00e7er. NumPy\u2019de <code>np.linalg.matrix_rank(A)<\/code> ile hesaplan\u0131r. Rank, lineer sistemlerin \u00e7\u00f6z\u00fcm yap\u0131s\u0131n\u0131 belirler: tam rank bir matris tekil olmayan \u00e7\u00f6z\u00fcmler \u00fcretir; d\u00fc\u015f\u00fck rank ise sonsuz \u00e7\u00f6z\u00fcm veya \u00e7\u00f6z\u00fcms\u00fczl\u00fck ihtimalini g\u00f6sterir. Regresyon, PCA ve bilgi s\u0131k\u0131\u015ft\u0131rma gibi alanlarda rank kritik parametredir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"determinant-neden-bazi-problemlerde-tercih-edilmez\"><strong>Determinant neden baz\u0131 problemlerde tercih edilmez?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Determinant \u00e7ok b\u00fcy\u00fck veya \u00e7ok k\u00fc\u00e7\u00fck say\u0131lara d\u00f6n\u00fc\u015febilir ve say\u0131sal karars\u0131zl\u0131\u011fa yol a\u00e7abilir. Ayr\u0131ca determinant\u0131n s\u0131f\u0131ra yak\u0131n olmas\u0131 durumunda matris neredeyse tekildir ancak determinant bunu a\u00e7\u0131k\u00e7a g\u00f6stermez. Bu y\u00fczden tekillik testi i\u00e7in \u00e7o\u011fu zaman <code>cond<\/code> (condition number) veya rank kullanmak daha g\u00fcvenilirdir. NumPy\u2019de <code>np.linalg.det(A)<\/code> h\u0131zl\u0131 olsa da bilimsel uygulamalarda do\u011frudan tan\u0131 i\u00e7in \u00f6nerilmez.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-svd-singular-value-decomposition-ne-zaman-tercih-edilir\"><strong>NumPy\u2019de SVD (Singular Value Decomposition) ne zaman tercih edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> SVD, matrisleri kararl\u0131 \u015fekilde ayr\u0131\u015ft\u0131r\u0131r ve PCA, d\u00fc\u015f\u00fck-rank yakla\u015f\u0131m\u0131, g\u00fcr\u00fclt\u00fc azaltma, \u00f6neri sistemleri ve boyut indirgeme i\u00e7in standart y\u00f6ntemdir. <code>np.linalg.svd(A, full_matrices=False)<\/code> bilimsel hesaplamalarda en \u00e7ok kullan\u0131lan bi\u00e7imidir. SVD, \u00f6zde\u011fer ayr\u0131\u015f\u0131m\u0131na g\u00f6re daha kararl\u0131d\u0131r ve her matrise uygulanabilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"moore-penrose-pseudoinverse-neden-ters-matristen-daha-cok-kullanilir\"><strong>Moore\u2013Penrose pseudoinverse neden ters matristen daha \u00e7ok kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Ger\u00e7ek d\u00fcnyada, modeller tam kare veya tam rank olmayan matrislerle \u00e7al\u0131\u015f\u0131r. Pseudoinverse (<code>pinv<\/code>), bu t\u00fcr durumlarda en k\u00fc\u00e7\u00fck kareler \u00e7\u00f6z\u00fcm\u00fcn\u00fc verir ve g\u00fcr\u00fclt\u00fcye dayan\u0131kl\u0131d\u0131r. <code>np.linalg.pinv(A)<\/code> SVD tabanl\u0131d\u0131r, bu da numerik kararl\u0131l\u0131\u011f\u0131 art\u0131r\u0131r. Regresyon, sinyal i\u015fleme ve kontrol teori uygulamalar\u0131nda standartt\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"matrislerin-kosul-sayisi-condition-number-neden-onemlidir\"><strong>Matrislerin ko\u015ful say\u0131s\u0131 (condition number) neden \u00f6nemlidir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Ko\u015ful say\u0131s\u0131, matrisin \u00e7\u00f6z\u00fcm hassasiyetini \u00f6l\u00e7er. <code>np.linalg.cond(A)<\/code> \u00e7ok y\u00fcksekse (\u00f6r. 10\u2079+) matris k\u00f6t\u00fc ko\u015fulludur ve k\u00fc\u00e7\u00fck giri\u015f hatalar\u0131 b\u00fcy\u00fck \u00e7\u0131k\u0131\u015f hatalar\u0131na d\u00f6n\u00fc\u015febilir. Bu, say\u0131sal analizde en kritik kararl\u0131l\u0131k \u00f6l\u00e7\u00fctlerinden biridir. \u00d6zellikle regresyon ve PDE \u00e7\u00f6z\u00fcmlerinde kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-12-sayisal-turev-integral-yaklasiklik-ve-diferansiyel-denklemler\"><strong>B\u00f6l\u00fcm 12 \u2013 Say\u0131sal T\u00fcrev, \u0130ntegral, Yakla\u015f\u0131kl\u0131k ve Diferansiyel Denklemler<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-sayisal-turev-hesaplamalari-icin-nasil-kullanilir\"><strong>NumPy say\u0131sal t\u00fcrev hesaplamalar\u0131 i\u00e7in nas\u0131l kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> T\u00fcrev, <code>f'(x) \u2248 (f(x+h) \u2013 f(x\u2013h))\/(2h)<\/code> merkez fark form\u00fcl\u00fcyle hesaplanabilir. NumPy\u2019nin vekt\u00f6rle\u015ftirilmi\u015f i\u015flemleri ile bu hesaplamalar d\u00f6ng\u00fcs\u00fcz yap\u0131l\u0131r. B\u00fcy\u00fck veri k\u00fcmelerinde say\u0131sal diferansiyasyon h\u0131z kazan\u0131r. Sinyal i\u015fleme, fiziksel sim\u00fclasyonlar ve optimizasyon problemlerinde temel ara\u00e7t\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"neden-cok-kucuk-h-degerleri-turevde-kararsizliga-yol-acar\"><strong>Neden \u00e7ok k\u00fc\u00e7\u00fck h de\u011ferleri t\u00fcrevde karars\u0131zl\u0131\u011fa yol a\u00e7ar?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> h k\u00fc\u00e7\u00fcld\u00fck\u00e7e fark ifadesi iki b\u00fcy\u00fck say\u0131n\u0131n fark\u0131na d\u00f6n\u00fc\u015f\u00fcr ve floating-point hassasiyeti nedeniyle yuvarlama hatas\u0131 b\u00fcy\u00fcr. Bu durum \u201ccancellation error\u201d olarak bilinir. Say\u0131sal t\u00fcrevde optimum h de\u011feri se\u00e7mek gerekir. NumPy\u2019nin <code>float64<\/code> hassasiyeti \u00e7o\u011fu uygulama i\u00e7in yeterlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-integral-hesaplamalarinda-nasil-kullanilir\"><strong>NumPy integral hesaplamalar\u0131nda nas\u0131l kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Riemann toplam\u0131 yakla\u015f\u0131m\u0131yla integral hesaplanabilir:<br><code>integral \u2248 np.sum(f(x) * dx)<\/code>.<br>Yeterince k\u00fc\u00e7\u00fck dx ve yo\u011fun aral\u0131k se\u00e7imi ile y\u00fcksek do\u011fruluk elde edilir. Bilimsel sim\u00fclasyonlarda y\u00fcksek performans sa\u011flar. Ancak daha sofistike y\u00f6ntemler i\u00e7in SciPy\u2019nin <code>quad<\/code> fonksiyonlar\u0131 tercih edilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-diferansiyel-denklem-cozulebilir-mi\"><strong>NumPy ile diferansiyel denklem \u00e7\u00f6z\u00fclebilir mi?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy temel diferansiyel denklem \u00e7\u00f6z\u00fcc\u00fclerini i\u00e7ermez fakat ODE \u00e7\u00f6z\u00fcmleri i\u00e7in gerekli olan matris hesaplamalar\u0131, e\u011fim fonksiyonlar\u0131 ve zaman ad\u0131mlamalar\u0131 NumPy ile uygulanabilir. Euler veya Runge\u2013Kutta gibi y\u00f6ntemler NumPy ile y\u00fcksek performansl\u0131 \u015fekilde kodlanabilir. Geli\u015fmi\u015f \u00e7\u00f6z\u00fcc\u00fcler i\u00e7in SciPy kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"sayisal-cozumde-stabilite-analizi-neden-kritiktir\"><strong>Say\u0131sal \u00e7\u00f6z\u00fcmde stabilite analizi neden kritiktir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Yinelemeli ODE\/PDE \u00e7\u00f6z\u00fcmlerinde zaman ad\u0131m\u0131 (\u0394t) \u00e7ok b\u00fcy\u00fck se\u00e7ilirse \u00e7\u00f6z\u00fcm \u201cpatlar\u201d. Bu nedenle stabilite s\u0131n\u0131rlar\u0131 (\u00f6r. CFL Condition) kontrol edilmelidir. NumPy h\u0131zl\u0131 matris i\u015flemlerini sa\u011flad\u0131\u011f\u0131ndan stabilite testi yapmak kolayla\u015f\u0131r. Bilimsel hesaplamalarda do\u011frulu\u011fun kilit par\u00e7as\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-13-fourier-donusumu-sinyal-isleme-ve-filtreleme\"><strong>B\u00f6l\u00fcm 13 \u2013 Fourier D\u00f6n\u00fc\u015f\u00fcm\u00fc, Sinyal \u0130\u015fleme ve Filtreleme<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-fft-np-fft-fft-hangi-durumlarda-kullanilir\"><strong>NumPy\u2019de FFT (np.fft.fft) hangi durumlarda kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> FFT zaman domenindeki sinyalleri frekans domenine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Sinyal i\u015fleme, titre\u015fim analizi, ses i\u015fleme, g\u00f6r\u00fcnt\u00fc filtrasyonu ve spektral analizde en temel ara\u00e7t\u0131r. NumPy\u2019nin FFT uygulamas\u0131 optimize edilmi\u015ftir ve milyonlarca \u00f6rnek \u00fczerinde h\u0131zl\u0131 \u00e7al\u0131\u015f\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"fft-sonucunda-kompleks-sayilar-neden-ortaya-cikar\"><strong>FFT sonucunda kompleks say\u0131lar neden ortaya \u00e7\u0131kar?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Zaman domenindeki reel sinyaller \u00e7o\u011funlukla sin\u00fcs ve kosin\u00fcs bile\u015fenlerinin s\u00fcperpozisyonudur. Bu bile\u015fenlerin faz ve genlik bilgisini temsil etmek i\u00e7in karma\u015f\u0131k say\u0131 kullan\u0131l\u0131r. Kompleks spektrum frekans analizi i\u00e7in zorunludur ve bilgi kayb\u0131 yaratmaz.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"nyquist-frekansi-ve-aliasing-num-py-ile-nasil-analiz-edilir\"><strong>Nyquist frekans\u0131 ve aliasing NumPy ile nas\u0131l analiz edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019nin FFT fonksiyonlar\u0131 ile sinyalin frekans spektrumu elde edilir. \u00d6rnekleme frekans\u0131n\u0131n yar\u0131s\u0131 Nyquist frekans\u0131d\u0131r; sinyal bu s\u0131n\u0131r\u0131n \u00fczerinde \u00f6rneklendi\u011finde aliasing olu\u015fur. Bu durum yanl\u0131\u015f frekans bile\u015fenlerinin olu\u015fmas\u0131na neden olur. FFT sonu\u00e7lar\u0131n\u0131n incelenmesi aliasing tespiti i\u00e7in temel yakla\u015f\u0131md\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"dusuk-geciren-filtre-num-py-ile-nasil-uygulanir\"><strong>D\u00fc\u015f\u00fck ge\u00e7iren filtre NumPy ile nas\u0131l uygulan\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> FFT al\u0131n\u0131r, y\u00fcksek frekans bile\u015fenleri <code>mask<\/code> ile s\u0131f\u0131rlan\u0131r ve ters FFT (<code>ifft<\/code>) uygulan\u0131r. Bu y\u00f6ntem h\u0131zl\u0131d\u0131r ve sinyal d\u00fczenleme (noise reduction) i\u00e7in etkilidir. NumPy\u2019nin vekt\u00f6rle\u015ftirilmi\u015f yap\u0131s\u0131 filtre tasar\u0131m\u0131n\u0131 kolayla\u015ft\u0131r\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"konvolusyon-islemi-num-pyde-nasil-yapilir\"><strong>Konvol\u00fcsyon i\u015flemi NumPy\u2019de nas\u0131l yap\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.convolve<\/code> veya FFT tabanl\u0131 konvol\u00fcsyonlar kullan\u0131labilir. G\u00f6r\u00fcnt\u00fc i\u015flemede 2D konvol\u00fcsyon i\u00e7in SciPy\u2019nin <code>signal.convolve2d<\/code> fonksiyonlar\u0131 daha uygundur ancak temel matris \u00e7arp\u0131mlar\u0131 NumPy ile y\u00fcksek verimle uygulanabilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-14-cok-boyutlu-diziler-tensor-islemleri-ve-ileri-yapilar\"><strong>B\u00f6l\u00fcm 14 \u2013 \u00c7ok Boyutlu Diziler, Tensor \u0130\u015flemleri ve \u0130leri Yap\u0131lar<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-neden-bir-tensor-kutuphanesi-olarak-kabul-edilir\"><strong>NumPy neden bir &#8220;tens\u00f6r&#8221; k\u00fct\u00fcphanesi olarak kabul edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>ndarray<\/code> s\u0131n\u0131rs\u0131z say\u0131da eksene izin verir ve tens\u00f6r i\u015flemlerine uygun yap\u0131da tasarlanm\u0131\u015ft\u0131r. Derin \u00f6\u011frenme k\u00fct\u00fcphanelerindeki tens\u00f6r yap\u0131lar\u0131na temel te\u015fkil eder; eksen bazl\u0131 operasyonlar, broadcasting ve matris algebra NumPy \u00fczerinden modellenmi\u015ftir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"eksensel-axis-based-islemler-neden-onemlidir\"><strong>Eksensel (axis-based) i\u015flemler neden \u00f6nemlidir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Birden fazla boyutlu veride ortalama, toplam veya standardizasyon i\u015flemleri belirli eksenlere g\u00f6re yap\u0131l\u0131r. \u00d6rne\u011fin g\u00f6r\u00fcnt\u00fclerde kanal ekseni \u00fczerinden normalize etmek gerekir. NumPy\u2019nin <code>axis<\/code> parametresi bu esnekli\u011fi sa\u011flar. Eksensel operasyonlar\u0131 anlamak veri i\u015fleme i\u00e7in zorunludur.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"np-transpose-ile-swapaxes-farki-nedir\"><strong>np.transpose ile swapaxes fark\u0131 nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>transpose<\/code>, t\u00fcm eksenleri yeniden s\u0131ralar; <code>swapaxes<\/code> yaln\u0131zca iki eksenin yerini de\u011fi\u015ftirir. \u00c7ok boyutlu dizilerde, \u00f6zellikle tens\u00f6r bi\u00e7imlendirmelerinde do\u011fru d\u00f6n\u00fc\u015f\u00fcm\u00fc se\u00e7mek kritik \u00f6nem ta\u015f\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"tensor-cogullama-tensor-contraction-nedir\"><strong>Tens\u00f6r \u00e7o\u011fullama (tensor contraction) nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Tens\u00f6r kontraksiyonu, belirli eksenler boyunca toplama i\u015flemidir; matmul bunun \u00f6zel bir halidir. NumPy\u2019de <code>np.tensordot<\/code> bu i\u015flemleri g\u00fc\u00e7l\u00fc ve verimli \u015fekilde yapar. Fizik sim\u00fclasyonlar\u0131 (kuantum hesaplama vb.) ve deep learning mimarilerinde yayg\u0131n \u015fekilde kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-einsum-neden-profesyoneller-tarafindan-sikca-tercih-edilir\"><strong>NumPy\u2019de einsum neden profesyoneller taraf\u0131ndan s\u0131k\u00e7a tercih edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>einsum<\/code>, Einstein toplam notasyonunu kullanarak karma\u015f\u0131k tens\u00f6r i\u015flemlerini tek sat\u0131rda tan\u0131mlamaya izin verir. Kod okunabilirli\u011fini art\u0131r\u0131r, gereksiz ara kopyalar\u0131 engeller ve \u00e7o\u011fu zaman daha h\u0131zl\u0131d\u0131r. Matris \u00e7arp\u0131m\u0131, outer product, batched operation ve tens\u00f6r kontraksiyonlar\u0131nda \u00fcst d\u00fczey performans sa\u011flar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-15-istatistiksel-hesaplamalar-korelasyon-kovaryans-ve-regresyon-temelleri\"><strong>B\u00f6l\u00fcm 15 \u2013 \u0130statistiksel Hesaplamalar, Korelasyon, Kovaryans ve Regresyon Temelleri<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-pyde-kovaryans-nasil-hesaplanir-ve-neyi-ifade-eder\"><strong>NumPy\u2019de kovaryans nas\u0131l hesaplan\u0131r ve neyi ifade eder?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.cov<\/code> fonksiyonu de\u011fi\u015fkenler aras\u0131ndaki ortak de\u011fi\u015fim miktar\u0131n\u0131 \u00f6l\u00e7er. Kovaryans matrisi PCA, \u00e7ok de\u011fi\u015fkenli istatistik, portf\u00f6y teorisi ve regresyon modellerinin temel bile\u015fenidir. Matrisin diyagonal elemanlar\u0131 varyans, off-diagonal elemanlar\u0131 de\u011fi\u015fken \u00e7iftlerinin kovaryans\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"korelasyon-matrisi-nasil-elde-edilir\"><strong>Korelasyon matrisi nas\u0131l elde edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Korelasyon = kovaryans\u0131n standart sapmalara b\u00f6l\u00fcnm\u00fc\u015f h\u00e2lidir. NumPy\u2019de <code>np.corrcoef<\/code> ile hesaplan\u0131r. Bu matris, de\u011fi\u015fkenlerin do\u011frusal ili\u015fkilerini \u00f6l\u00e7ekten ba\u011f\u0131ms\u0131z \u015fekilde g\u00f6sterir. Veri ke\u015ffi, \u00f6zellik se\u00e7imi ve risk analizi i\u00e7in kritik bir ara\u00e7t\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"dogrusal-regresyon-parametreleri-num-py-ile-nasil-elde-edilir\"><strong>Do\u011frusal regresyon parametreleri NumPy ile nas\u0131l elde edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Regresyon denklemi <code>\u03b2 = (X\u1d40X)\u207b\u00b9 X\u1d40 y<\/code> form\u00fcl\u00fcyle \u00e7\u00f6z\u00fcl\u00fcr. Bu i\u015flem <code>np.linalg.pinv<\/code> veya <code>np.linalg.solve<\/code> ile yap\u0131l\u0131r. Makine \u00f6\u011frenmesi teorisinin temelinde bu \u00e7\u00f6z\u00fcm vard\u0131r. NumPy bu hesaplamalar\u0131 BLAS\/LAPACK h\u0131z\u0131nda ger\u00e7ekle\u015ftirir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-varyans-ve-standart-sapma-hesaplamalarinda-dikkat-edilmesi-gereken-parametre-nedir\"><strong>NumPy ile varyans ve standart sapma hesaplamalar\u0131nda dikkat edilmesi gereken parametre nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>ddof<\/code> parametresi. Varsay\u0131lan <code>ddof=0<\/code> pop\u00fclasyon varyans\u0131 verir; \u00f6rnek varyans\u0131 i\u00e7in <code>ddof=1<\/code> gerekir. Bilimsel ara\u015ft\u0131rmalarda genellikle <code>ddof=1<\/code> kullan\u0131l\u0131r. Bu n\u00fcans yanl\u0131\u015f raporlamalara yol a\u00e7abilece\u011finden \u00f6nemlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"histogram-hesaplamalari-num-pyde-nasil-yapilir\"><strong>Histogram hesaplamalar\u0131 NumPy\u2019de nas\u0131l yap\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.histogram<\/code> veriyi belirli aral\u0131klara (bin) b\u00f6lerek frekans da\u011f\u0131l\u0131m\u0131 \u00e7\u0131kar\u0131r. Veri ke\u015ffi, yo\u011funluk tahmini ve g\u00f6rselle\u015ftirme \u00f6ncesi haz\u0131rl\u0131k i\u00e7in kritik bir ad\u0131md\u0131r. Bin say\u0131s\u0131 se\u00e7imi (Sturges, Freedman-Diaconis) da\u011f\u0131l\u0131m\u0131n do\u011fru temsil edilmesi i\u00e7in \u00f6nemlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-16-veri-yapilari-arasi-donusum-dosya-islemleri-ve-dis-kaynaklarla-entegrasyon\"><strong>B\u00f6l\u00fcm 16 \u2013 Veri Yap\u0131lar\u0131 Aras\u0131 D\u00f6n\u00fc\u015f\u00fcm, Dosya \u0130\u015flemleri ve D\u0131\u015f Kaynaklarla Entegrasyon<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-dizileri-listelere-nasil-donusturulur-ve-bu-islem-maliyetli-midir\"><strong>NumPy dizileri listelere nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr ve bu i\u015flem maliyetli midir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>ndarray.tolist()<\/code> fonksiyonu NumPy dizisini Python listesine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Ancak bu i\u015flem <strong>her eleman i\u00e7in Python nesnesi olu\u015fturdu\u011fundan<\/strong> maliyetlidir. B\u00fcy\u00fck dizilerde bellek kullan\u0131m\u0131n\u0131 katlar ve performans\u0131 d\u00fc\u015f\u00fcr\u00fcr. Veri i\u015fleme a\u015famas\u0131nda m\u00fcmk\u00fcn oldu\u011funca NumPy yap\u0131s\u0131ndan \u00e7\u0131kmamak en do\u011fru yakla\u015f\u0131md\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-dizileri-nasil-dosyaya-kaydedilir\"><strong>NumPy dizileri nas\u0131l dosyaya kaydedilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> En yayg\u0131n y\u00f6ntemler:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>np.save<\/code> \u2192 .npy (tek dizi i\u00e7in)<\/li>\n\n\n\n<li><code>np.savez<\/code> \u2192 .npz (\u00e7oklu dizi)<\/li>\n\n\n\n<li><code>np.savetxt<\/code> \u2192 metin temelli formatlar<br><code>.npy<\/code> format\u0131 en h\u0131zl\u0131s\u0131 ve do\u011fruluk kayb\u0131 olmayan metottur. B\u00fcy\u00fck projelerde model a\u011f\u0131rl\u0131klar\u0131, ara hesaplamalar veya \u00f6n i\u015fleme ad\u0131mlar\u0131 i\u00e7in standartt\u0131r.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"csv-dosyalari-num-py-ile-nasil-okunur\"><strong>CSV dosyalar\u0131 NumPy ile nas\u0131l okunur?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.loadtxt<\/code> veya daha esnek bi\u00e7imde <code>np.genfromtxt<\/code> ile CSV okunabilir. <code>genfromtxt<\/code> eksik de\u011ferler (<code>nan<\/code>), farkl\u0131 ayra\u00e7lar ve dtype kar\u0131\u015f\u0131kl\u0131klar\u0131 i\u00e7in daha dayan\u0131kl\u0131d\u0131r. Ancak \u00e7ok b\u00fcy\u00fck CSV\u2019lerde Pandas daha verimli olabilir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-json-formati-dogrudan-desteklenir-mi\"><strong>NumPy ile JSON format\u0131 do\u011frudan desteklenir mi?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> JSON bi\u00e7imi yaln\u0131zca temel Python tiplerini destekler (list, dict vb.). NumPy dizileri JSON\u2019a g\u00f6m\u00fclmeden \u00f6nce <strong>listeye d\u00f6n\u00fc\u015ft\u00fcr\u00fclmelidir<\/strong>. Ancak bu hem performans hem bellek i\u00e7in zay\u0131f bir yakla\u015f\u0131md\u0131r. B\u00fcy\u00fck verilerde HDF5, Parquet veya NumPy\u2019nin kendi .npy formatlar\u0131 tercih edilmelidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ve-pandas-birlikte-nasil-en-verimli-sekilde-kullanilir\"><strong>NumPy ve Pandas birlikte nas\u0131l en verimli \u015fekilde kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Pandas veri okuma, temizleme ve etiketli veri y\u00f6netimi i\u00e7in iyidir; NumPy ise yo\u011fun say\u0131sal i\u015flemlerde daha h\u0131zl\u0131d\u0131r. Tipik ak\u0131\u015f:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Veri Pandas ile okunur<\/li>\n\n\n\n<li>Say\u0131sal kolonlar NumPy\u2019ye aktar\u0131l\u0131r<\/li>\n\n\n\n<li>A\u011f\u0131r hesaplamalar NumPy\u2019de yap\u0131l\u0131r<\/li>\n\n\n\n<li>Sonu\u00e7 tekrar DataFrame\u2019e \u00e7evrilir<br>Bu hibrit yakla\u015f\u0131m veri biliminde standartt\u0131r.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-17-buyuk-veri-paralel-isleme-gpu-ve-performans-iyilestirme\"><strong>B\u00f6l\u00fcm 17 \u2013 B\u00fcy\u00fck Veri, Paralel \u0130\u015fleme, GPU ve Performans \u0130yile\u015ftirme<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-neden-tek-cekirdekli-calisir\"><strong>NumPy neden tek \u00e7ekirdekli \u00e7al\u0131\u015f\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy\u2019nin ana hesaplamalar\u0131 BLAS\/LAPACK \u00fczerinden yap\u0131l\u0131r. Bu k\u00fct\u00fcphaneler \u00e7ok \u00e7ekirdek destekleyebilir; ancak NumPy\u2019nin Python taraf\u0131ndaki i\u015flemleri Global Interpreter Lock (GIL) nedeniyle tek \u00e7ekirdeklidir. B\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131nda BLAS sayesinde paralellik m\u00fcmk\u00fcnd\u00fcr; ancak saf Python seviyesinde paralellik yoktur.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-paralel-isleme-nasil-yapilir\"><strong>NumPy ile paralel i\u015fleme nas\u0131l yap\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Do\u011frudan NumPy i\u00e7inde de\u011fil, Python\u2019\u0131n <code>multiprocessing<\/code> mod\u00fcl\u00fcyle yap\u0131l\u0131r. B\u00fcy\u00fck veri birden fazla proses aras\u0131nda b\u00f6l\u00fcn\u00fcr ve NumPy her proseste ba\u011f\u0131ms\u0131z hesaplama yapar. Ara sonu\u00e7lar birle\u015ftirilir. Bu yakla\u015f\u0131m \u00f6zellikle b\u00fcy\u00fck matrisler ve veri par\u00e7alama (chunking) i\u00e7in kullan\u0131l\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-gpu-hizlandirma-destekler-mi\"><strong>NumPy GPU h\u0131zland\u0131rma destekler mi?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy do\u011frudan GPU desteklemez. Ancak s\u00f6zdizimi NumPy ile <strong>tam uyumlu olan CuPy<\/strong>, GPU \u00fczerinde NumPy h\u0131z\u0131n\u0131n y\u00fczlerce kat\u0131na ula\u015fabilir. Derin \u00f6\u011frenme \u00f6ncesi, g\u00f6r\u00fcnt\u00fc i\u015fleme ve matris a\u011f\u0131rl\u0131kl\u0131 g\u00f6revlerde CuPy g\u00fc\u00e7l\u00fc bir alternatiftir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"numba-ile-num-py-nasil-hizlandirilir\"><strong>Numba ile NumPy nas\u0131l h\u0131zland\u0131r\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>@njit<\/code> dekorat\u00f6r\u00fc ile Python fonksiyonlar\u0131 JIT-compile edilir ve NumPy kodu makine diline \u00e7evrilir. Bu \u00f6zellikle d\u00f6ng\u00fcl\u00fc i\u015flemler i\u00e7in dramatik h\u0131z sa\u011flar. NumPy + Numba kombinasyonu CPU tabanl\u0131 bilimsel hesaplamalarda son derece etkilidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"bellek-eslemeli-memory-mapped-diziler-ne-zaman-zorunludur\"><strong>Bellek e\u015flemeli (memory-mapped) diziler ne zaman zorunludur?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> \u00c7ok b\u00fcy\u00fck veri setlerinin RAM\u2019e s\u0131\u011fmad\u0131\u011f\u0131 durumlarda kullan\u0131l\u0131r. <code>np.memmap<\/code> yaln\u0131zca gerekli k\u0131sm\u0131 diskteki dosyadan \u00e7eker. Bu teknik, g\u00f6r\u00fcnt\u00fc veri tabanlar\u0131, astronomi verileri ve y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc zaman serileri gibi devasa veri kaynaklar\u0131 i\u00e7in vazge\u00e7ilmezdir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-18-gelismis-hatalar-debugging-teknikleri-ve-test-edilebilirlik\"><strong>B\u00f6l\u00fcm 18 \u2013 Geli\u015fmi\u015f Hatalar, Debugging Teknikleri ve Test Edilebilirlik<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"floating-point-hatalari-num-pyde-nasil-tespit-edilir\"><strong>Floating-point hatalar\u0131 NumPy\u2019de nas\u0131l tespit edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>np.seterr(all='raise')<\/code> ile floating-point hatalar\u0131 exception\u2019a d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. Bu sayede overflow, underflow, division-by-zero gibi hatalar sessizce kaybolmaz, do\u011frudan test a\u015famas\u0131nda yakalan\u0131r. Bilimsel hesaplamalar i\u00e7in kritik bir uygulamad\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-islemleri-neden-bazen-farkli-platformlarda-farkli-sonuc-verir\"><strong>NumPy i\u015flemleri neden bazen farkl\u0131 platformlarda farkl\u0131 sonu\u00e7 verir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> BLAS\/LAPACK varyasyonlar\u0131 (MKL, OpenBLAS), CPU mimarisi ve floating-point optimizasyonlar\u0131 sistemler aras\u0131 farkl\u0131l\u0131k yaratabilir. \u00d6zellikle SVD, eigen ve \u00e7\u00f6z\u00fcm algoritmalar\u0131 platforma g\u00f6re \u00e7ok k\u00fc\u00e7\u00fck farklarla de\u011fi\u015febilir. Bu durum numerik algoritmalarda normaldir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"copy-view-hatalarini-onlemenin-en-iyi-yolu-nedir\"><strong>Copy\u2013view hatalar\u0131n\u0131 \u00f6nlemenin en iyi yolu nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>array.flags['OWNDATA']<\/code> kontrol edilerek dizinin kendi belle\u011fine sahip olup olmad\u0131\u011f\u0131 anla\u015f\u0131labilir. Ayr\u0131ca karma\u015f\u0131k dilimleme i\u015flemlerinde <code>copy()<\/code> a\u00e7\u0131k\u00e7a \u00e7a\u011fr\u0131larak hatal\u0131 g\u00fcncellemeler engellenir. Bilimsel projelerde test edilmeden view kullanmak risklidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"tip-donusumlerinde-astype-sessiz-veri-kaybi-yasanabilir-mi\"><strong>Tip d\u00f6n\u00fc\u015f\u00fcmlerinde (astype) sessiz veri kayb\u0131 ya\u015fanabilir mi?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Evet. \u00d6rne\u011fin <code>float \u2192 int<\/code> d\u00f6n\u00fc\u015f\u00fcm\u00fcnde kesirli k\u0131s\u0131m kaybolur. <code>int16<\/code> gibi k\u00fc\u00e7\u00fck tamsay\u0131 t\u00fcrlerinde overflow olu\u015fabilir. NumPy d\u00f6n\u00fc\u015f\u00fcmlerde uyar\u0131 vermez; bu nedenle dtype se\u00e7iminde test yapmak gereklidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-kodu-nasil-test-edilir\"><strong>NumPy kodu nas\u0131l test edilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> <code>pytest + numpy.testing<\/code> kombinasyonu en do\u011fru yakla\u015f\u0131md\u0131r. <code>assert_allclose<\/code>, <code>assert_array_equal<\/code> gibi fonksiyonlar say\u0131sal kar\u015f\u0131la\u015ft\u0131rmalar i\u00e7in optimize edilmi\u015ftir. Y\u00fczer nokta hesaplamalar\u0131nda tolerans (<code>rtol<\/code>, <code>atol<\/code>) belirlemek \u00f6nemlidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-19-uygulama-ornekleri-modelleme-ve-bilimsel-kullanim-senaryolari\"><strong>B\u00f6l\u00fcm 19 \u2013 Uygulama \u00d6rnekleri, Modelleme ve Bilimsel Kullan\u0131m Senaryolar\u0131<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-fizik-simulasyonlarinda-nasil-kullanilir\"><strong>NumPy fizik sim\u00fclasyonlar\u0131nda nas\u0131l kullan\u0131l\u0131r?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Par\u00e7ac\u0131k pozisyonlar\u0131, h\u0131z vekt\u00f6rleri ve kuvvet hesaplamalar\u0131 tens\u00f6rler halinde saklan\u0131r ve vekt\u00f6rle\u015ftirilmi\u015f i\u015flemlerle g\u00fcncellenir. Bu yap\u0131 klasik mekanik, ak\u0131\u015fkanlar mekani\u011fi ve molek\u00fcler dinamik sim\u00fclasyonlar\u0131n\u0131n temelidir.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"makine-ogrenmesinde-num-pynin-rolu-nedir\"><strong>Makine \u00f6\u011frenmesinde NumPy\u2019nin rol\u00fc nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> E\u011fitim \u00f6ncesi veri haz\u0131rlama, normalizasyon, matris \u00e7arp\u0131mlar\u0131, hipotez hesaplamalar\u0131 ve temel optimizasyon ad\u0131mlar\u0131nda NumPy kullan\u0131l\u0131r. scikit-learn gibi k\u00fct\u00fcphaneler i\u00e7sel olarak NumPy\u2019yi temel al\u0131r. NumPy, ML projelerinin altyap\u0131 ta\u015f\u0131d\u0131r.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"derin-ogrenme-kutuphaneleri-neden-num-py-ap-isini-taklit-eder\"><strong>Derin \u00f6\u011frenme k\u00fct\u00fcphaneleri neden NumPy API\u2019sini taklit eder?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> NumPy API \u00f6\u011frenme kolayl\u0131\u011f\u0131 sa\u011flar ve geli\u015ftiriciler i\u00e7in tan\u0131d\u0131k bir arabirimdir. PyTorch, TensorFlow, JAX gibi k\u00fct\u00fcphaneler NumPy ile uyumlu tasarlanarak ara\u015ft\u0131rmac\u0131lar\u0131n CPU\u2013GPU aras\u0131nda kolay ge\u00e7i\u015f yapabilmesini sa\u011flar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"monte-carlo-simulasyonlarinda-num-py-neden-vazgecilmezdir\"><strong>Monte Carlo sim\u00fclasyonlar\u0131nda NumPy neden vazge\u00e7ilmezdir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Milyonlarca rastgele \u00f6rnek olu\u015fturarak da\u011f\u0131l\u0131m tahmini, finansal risk \u00f6l\u00e7\u00fcm\u00fc, fiziksel s\u00fcre\u00e7 benzetimi gibi g\u00f6revlerde NumPy\u2019nin random ve vekt\u00f6rle\u015ftirilmi\u015f fonksiyonlar\u0131 ola\u011fan\u00fcst\u00fc performans sunar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"doga-bilimlerinde-veri-analizi-neden-num-py-uzerine-kuruludur\"><strong>Do\u011fa bilimlerinde veri analizi neden NumPy \u00fczerine kuruludur?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Fizik, kimya, astronomi ve biyoinformati\u011fin b\u00fcy\u00fck b\u00f6l\u00fcm\u00fc matris-tens\u00f6r tabanl\u0131 hesaplama gerektirir. NumPy\u2019nin h\u0131zl\u0131 C tabanl\u0131 motoru bu alanlarda standart hale gelmi\u015ftir. Bir\u00e7ok bilimsel yaz\u0131l\u0131m NumPy dizilerini I\/O format\u0131 olarak kabul eder.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"bolum-20-en-iyi-uygulamalar-kod-stili-tasarim-prensipleri-ve-uzun-omurlu-projeler\"><strong>B\u00f6l\u00fcm 20 \u2013 En \u0130yi Uygulamalar, Kod Stili, Tasar\u0131m Prensipleri ve Uzun \u00d6m\u00fcrl\u00fc Projeler<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-kodu-yazarken-en-onemli-stil-kurali-nedir\"><strong>NumPy kodu yazarken en \u00f6nemli stil kural\u0131 nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Vekt\u00f6rle\u015ftirilmi\u015f i\u015flem tasarlamak. D\u00f6ng\u00fc, liste \u00fcretimi, tek tek eleman i\u015flemek yerine \u201ct\u00fcm dizi \u00fczerinde i\u015flem\u201d yakla\u015f\u0131m\u0131 temel kurald\u0131r. Bu hem okunabilirli\u011fi hem h\u0131z\u0131 katlar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"buyuk-projelerde-dtype-stratejisi-nasil-belirlenir\"><strong>B\u00fcy\u00fck projelerde dtype stratejisi nas\u0131l belirlenir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Hassas hesaplamalarda <code>float64<\/code>, b\u00fcy\u00fck veri + haf\u0131za optimizasyonu gereken yerlerde <code>float32<\/code>, say\u0131m verilerinde <code>int32\/int16<\/code> tercih edilir. Dtype tutarl\u0131l\u0131\u011f\u0131n\u0131 korumak hatalar\u0131 azalt\u0131r. Veri seti b\u00fcy\u00fcd\u00fck\u00e7e dtype se\u00e7imi performans\u0131 dramatik etkiler.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ile-yazilmis-bir-fonksiyon-nasil-daha-okunabilir-hale-getirilir\"><strong>NumPy ile yaz\u0131lm\u0131\u015f bir fonksiyon nas\u0131l daha okunabilir hale getirilir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>De\u011fi\u015fken adlar\u0131 matematiksel kavramlarla uyumlu olmal\u0131<\/li>\n\n\n\n<li>Ara hesaplamalar anlaml\u0131 sat\u0131rlara b\u00f6l\u00fcnmeli<\/li>\n\n\n\n<li>Karma\u015f\u0131k broadcasting i\u015flemleri a\u00e7\u0131klay\u0131c\u0131 yorumlarla desteklenmeli<\/li>\n\n\n\n<li><code>einsum<\/code> kullan\u0131l\u0131yorsa i\u015flem denklemi belirtilmeli<br>Bu stil ara\u015ft\u0131rma projelerinde \u00f6nemlidir.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-tabanli-projeler-nasil-surdurulebilir-olur\"><strong>NumPy tabanl\u0131 projeler nas\u0131l s\u00fcrd\u00fcr\u00fclebilir olur?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong> Kodun mod\u00fcler yaz\u0131lmas\u0131, testlerin otomatik olmas\u0131, dtype ve bellek y\u00f6netimi politikalar\u0131n\u0131n belirlenmesi, veri formatlar\u0131n\u0131n standartla\u015ft\u0131r\u0131lmas\u0131 ve dok\u00fcmantasyonun g\u00fcncel tutulmas\u0131 projeyi uzun \u00f6m\u00fcrl\u00fc yapar. B\u00fcy\u00fck Ar-Ge projelerinde bu uygulamalar kritik rol oynar.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"num-py-ogrenmenin-en-verimli-yolu-nedir\"><strong>NumPy \u00f6\u011frenmenin en verimli yolu nedir?<\/strong><\/h3>\n\n\n\n<p><strong>Cevap:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>K\u00fc\u00e7\u00fck dizi manip\u00fclasyonlar\u0131yla ba\u015flamak<\/li>\n\n\n\n<li>Fonksiyonlar\u0131n davran\u0131\u015f\u0131n\u0131 test ederek stride, view, axis gibi kavramlar\u0131 \u00f6\u011frenmek<\/li>\n\n\n\n<li>Matris ve tens\u00f6r \u00f6rnekleri \u00e7\u00f6zmek<\/li>\n\n\n\n<li>Ger\u00e7ek d\u00fcnya veri setleri \u00fczerinde PCA, regresyon, sinyal i\u015fleme gibi uygulamalar yapmak<br>Bu yol, NumPy\u2019i teoriden prati\u011fe tam kapsaml\u0131 \u00f6\u011fretir.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>E\u011fitimlerimize kat\u0131larak bu ve di\u011fer projeleri uygulamal\u0131 olarak \u00f6\u011frenebilirsiniz. E\u011fitimlerimize ve di\u011fer bilgilere\u00a0<a href=\"https:\/\/www.facadium.com.tr\/\">buradaki linkten<\/a>\u00a0(<a href=\"https:\/\/www.facadium.com.tr\/\">https:\/\/www.facadium.com.tr\/<\/a>) ula\u015fabilirsiniz<a href=\"http:\/\/www.stemkits.com.tr\" target=\"_blank\" rel=\"noopener\">.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>100 Soruda Numpy B\u00f6l\u00fcm 1 \u2013 Temel NumPy ve ndarray Kavramlar\u0131 NumPy neden \u201cbilimsel Python ekosisteminin temeli\u201d olarak g\u00f6r\u00fcl\u00fcr? Cevap: NumPy, Python\u2019daki say\u0131sal hesaplamalar\u0131n merkezinde [&#8230;]<\/p>\n","protected":false},"author":3,"featured_media":1522,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[39],"tags":[32,31,34,44,8,30,9],"class_list":["post-1521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy","tag-data-analysis","tag-data-mining","tag-data-science","tag-numpy","tag-python","tag-veri-madenciligi","tag-yazilim"],"_links":{"self":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/1521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/comments?post=1521"}],"version-history":[{"count":9,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/1521\/revisions"}],"predecessor-version":[{"id":1547,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/posts\/1521\/revisions\/1547"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/media\/1522"}],"wp:attachment":[{"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/media?parent=1521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/categories?post=1521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.facadium.com.tr\/blog\/wp-json\/wp\/v2\/tags?post=1521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}