{"id":31203,"date":"2025-10-07T05:41:11","date_gmt":"2025-10-07T02:41:11","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=31203"},"modified":"2025-10-07T05:41:11","modified_gmt":"2025-10-07T02:41:11","slug":"pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/","title":{"rendered":"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu"},"content":{"rendered":"

<\/p>\n

Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu<\/h2>\n

Python, veri analizi ve manip\u00fclasyonu i\u00e7in g\u00fc\u00e7l\u00fc ve \u00e7ok y\u00f6nl\u00fc bir programlama dilidir. Bir listedeki say\u0131lar\u0131n ortalamas\u0131n\u0131 (aritmetik ortalama) bulmak, bir\u00e7ok veri i\u015fleme g\u00f6revinin temelini olu\u015fturan yayg\u0131n bir i\u015flemdir. \u0130ster basit bir say\u0131 listesiyle \u00e7al\u0131\u015f\u0131n, ister b\u00fcy\u00fck veri k\u00fcmelerini analiz edin, Python bu g\u00f6revi yerine getirmek i\u00e7in \u00e7e\u015fitli y\u00f6ntemler sunar. Bu makalede, Python’da bir listenin ortalamas\u0131n\u0131 bulman\u0131n be\u015f farkl\u0131 yolunu ayr\u0131nt\u0131l\u0131 olarak inceleyece\u011fiz. Her y\u00f6ntemin avantajlar\u0131n\u0131, dezavantajlar\u0131n\u0131, kullan\u0131m senaryolar\u0131n\u0131 ve kod \u00f6rneklerini ele alarak, projeniz i\u00e7in en uygun \u00e7\u00f6z\u00fcm\u00fc se\u00e7menize yard\u0131mc\u0131 olaca\u011f\u0131z.<\/p>\n

Ortalama, bir veri k\u00fcmesindeki t\u00fcm de\u011ferlerin toplam\u0131n\u0131n, veri k\u00fcmesindeki de\u011fer say\u0131s\u0131na b\u00f6l\u00fcnmesiyle elde edilen merkezi e\u011filim \u00f6l\u00e7\u00fcs\u00fcd\u00fcr. Matematiksel olarak $\\frac{\\sum x_i}{n}$ form\u00fcl\u00fcyle ifade edilir; burada $\\sum x_i$ t\u00fcm de\u011ferlerin toplam\u0131n\u0131 ve $n$ de\u011fer say\u0131s\u0131n\u0131 temsil eder. Bu basit hesaplama, finansal analizden bilimsel ara\u015ft\u0131rmalara, m\u00fchendislikten g\u00fcnl\u00fck programlama g\u00f6revlerine kadar geni\u015f bir yelpazede kritik bir rol oynar. Python’\u0131n esnekli\u011fi sayesinde, bu hesaplamay\u0131 ger\u00e7ekle\u015ftirmenin hem temel d\u00f6ng\u00fc tabanl\u0131 yakla\u015f\u0131mlar\u0131n\u0131 hem de optimize edilmi\u015f k\u00fct\u00fcphane fonksiyonlar\u0131n\u0131 ke\u015ffedece\u011fiz.<\/p>\n

1. Geleneksel Bir for<\/code> D\u00f6ng\u00fcs\u00fc Kullanarak Ortalama Hesaplama<\/h3>\n

Bir listenin ortalamas\u0131n\u0131 bulman\u0131n en temel ve anla\u015f\u0131lmas\u0131 kolay yollar\u0131ndan biri, geleneksel bir for<\/code> d\u00f6ng\u00fcs\u00fc kullanmakt\u0131r. Bu y\u00f6ntem, Python’\u0131n temel kontrol yap\u0131lar\u0131n\u0131 kullanarak bir listenin elemanlar\u0131 \u00fczerinde manuel olarak yineleme yapmay\u0131 ve toplam\u0131 hesaplamay\u0131 i\u00e7erir. Ard\u0131ndan, bu toplam\u0131 listenin eleman say\u0131s\u0131na b\u00f6lerek ortalamay\u0131 elde ederiz. Bu yakla\u015f\u0131m, Python’a yeni ba\u015flayanlar i\u00e7in mant\u0131\u011f\u0131 kavramak ad\u0131na m\u00fckemmel bir ba\u015flang\u0131\u00e7 noktas\u0131d\u0131r ve \u00e7o\u011fu programlama dilinde benzer bir \u015fekilde uygulanabilir.<\/p>\n

Y\u00f6ntemin \u00c7al\u0131\u015fma Prensibi<\/h4>\n

Bu y\u00f6ntem, iki ana ad\u0131mdan olu\u015fur:
\n1. Toplam\u0131 Hesaplama:<\/strong> Listenin her bir eleman\u0131 \u00fczerinde tek tek gezinmek ve bu elemanlar\u0131 bir toplam de\u011fi\u015fkeninde biriktirmek.
\n2. Ortalamay\u0131 Hesaplama:<\/strong> Elde edilen toplam\u0131, listenin eleman say\u0131s\u0131na (uzunlu\u011funa) b\u00f6lmek. Python’da listenin uzunlu\u011funu bulmak i\u00e7in len()<\/code> fonksiyonu kullan\u0131l\u0131r.<\/p>\n

Kod \u00d6rne\u011fi<\/h4>\n

A\u015fa\u011f\u0131daki kod par\u00e7ac\u0131\u011f\u0131, bir for<\/code> d\u00f6ng\u00fcs\u00fc kullanarak bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplayaca\u011f\u0131n\u0131z\u0131 g\u00f6stermektedir:<\/p>\n

def ortalama_hesapla_for_dongusu(liste):\n    if not liste:  # Liste bo\u015fsa hata vermemek i\u00e7in kontrol\n        return 0.0  # Veya bir hata f\u0131rlat\u0131labilir, duruma g\u00f6re de\u011fi\u015fir\n\n    toplam = 0\n    for eleman in liste:\n        # Eleman\u0131n say\u0131sal olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek iyi bir pratiktir\n        if not isinstance(eleman, (int, float)):\n            raise TypeError(\"Liste yaln\u0131zca say\u0131sal de\u011ferler i\u00e7ermelidir.\")\n        toplam += eleman\n    \n    ortalama = toplam \/ len(liste)\n    return ortalama\n\nmy_list = [10, 20, 30, 40, 50]\nprint(f\"For d\u00f6ng\u00fcs\u00fc ile ortalama: {ortalama_hesapla_for_dongusu(my_list)}\")\n\nempty_list = []\nprint(f\"Bo\u015f liste i\u00e7in ortalama: {ortalama_hesapla_for_dongusu(empty_list)}\")\n\nmixed_list = [1, 2, \"\u00fc\u00e7\", 4]\ntry:\n    print(f\"Kar\u0131\u015f\u0131k liste i\u00e7in ortalama: {ortalama_hesapla_for_dongusu(mixed_list)}\")\nexcept TypeError as e:\n    print(f\"Hata: {e}\")<\/code><\/pre>\n
A\u00e7\u0131klama<\/h4>\n
1.  ortalama_hesapla_for_dongusu(liste)<\/code> ad\u0131nda bir fonksiyon tan\u0131mlad\u0131k. Bu fonksiyon, ortalamas\u0131n\u0131 hesaplamak istedi\u011fimiz listeyi parametre olarak al\u0131r.
\n2.  Fonksiyonun ba\u015f\u0131nda, listenin bo\u015f olup olmad\u0131\u011f\u0131n\u0131 kontrol ediyoruz (if not liste:<\/code>). Bo\u015f bir listenin eleman say\u0131s\u0131na b\u00f6lmeye \u00e7al\u0131\u015fmak ZeroDivisionError<\/code> hatas\u0131na yol a\u00e7aca\u011f\u0131ndan, bu kontrol \u00f6nemlidir. Bo\u015f liste durumunda 0.0<\/code> d\u00f6nd\u00fcr\u00fcyoruz.
\n3.  toplam<\/code> ad\u0131nda bir de\u011fi\u015fkeni 0<\/code> olarak ba\u015flat\u0131yoruz. Bu de\u011fi\u015fken, listenin elemanlar\u0131n\u0131n toplam\u0131n\u0131 tutacakt\u0131r.
\n4.  for eleman in liste:<\/code> d\u00f6ng\u00fcs\u00fc, listedeki her bir eleman<\/code> \u00fczerinde yineleme yapar.
\n5.  D\u00f6ng\u00fc i\u00e7inde, her eleman<\/code>\u0131n say\u0131sal bir tip (int<\/code> veya float<\/code>) olup olmad\u0131\u011f\u0131n\u0131 kontrol ediyoruz. E\u011fer say\u0131sal de\u011filse, bir TypeError<\/code> f\u0131rlatarak program\u0131n beklenmedik davran\u0131\u015flar sergilemesini \u00f6nl\u00fcyoruz. Bu, daha sa\u011flam bir kod yazmak i\u00e7in \u00f6nemli bir ad\u0131md\u0131r.
\n6.  toplam += eleman<\/code> ifadesi, mevcut eleman<\/code>\u0131 toplam<\/code> de\u011fi\u015fkenine ekler.
\n7.  D\u00f6ng\u00fc tamamland\u0131\u011f\u0131nda, ortalama = toplam \/ len(liste)<\/code> ifadesi toplam\u0131 listenin uzunlu\u011funa b\u00f6lerek ortalamay\u0131 hesaplar. Python 3’te \/<\/code> operat\u00f6r\u00fc her zaman float b\u00f6l\u00fcm\u00fc d\u00f6nd\u00fcr\u00fcr, bu da ortalama hesaplamalar\u0131 i\u00e7in uygundur.
\n8.  Son olarak, hesaplanan ortalama<\/code> de\u011ferini d\u00f6nd\u00fcr\u00fcyoruz.<\/p>\n
Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n
*   Avantajlar\u0131:<\/strong>
\n    *   Anla\u015f\u0131l\u0131rl\u0131k:<\/strong> Ortalama hesaplama mant\u0131\u011f\u0131n\u0131 a\u00e7\u0131k\u00e7a g\u00f6sterir, bu da yeni ba\u015flayanlar i\u00e7in idealdir.
\n    *   Temel Bilgi:<\/strong> Python’\u0131n temel kontrol ak\u0131\u015f\u0131 ve veri yap\u0131lar\u0131 hakk\u0131nda bilgi sa\u011flar.
\n    *   Ba\u011f\u0131ms\u0131zl\u0131k:<\/strong> Herhangi bir harici k\u00fct\u00fcphaneye veya \u00f6zel fonksiyona ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 yoktur.<\/p>\n
*   Dezavantajlar\u0131:<\/strong>
\n    *   Uzunluk:<\/strong> Di\u011fer y\u00f6ntemlere g\u00f6re daha fazla kod sat\u0131r\u0131 gerektirir.
\n    *   Performans:<\/strong> \u00c7ok b\u00fcy\u00fck listeler i\u00e7in Python’\u0131n yerle\u015fik veya k\u00fct\u00fcphane fonksiyonlar\u0131na k\u0131yasla daha yava\u015f olabilir, \u00e7\u00fcnk\u00fc d\u00f6ng\u00fc Python yorumlay\u0131c\u0131s\u0131 taraf\u0131ndan yorumlan\u0131r.
\n    *   Hata Duyarl\u0131l\u0131\u011f\u0131:<\/strong> Bo\u015f liste veya say\u0131sal olmayan elemanlar gibi kenar durumlar\u0131n manuel olarak ele al\u0131nmas\u0131n\u0131 gerektirir.<\/p>\n
Bu y\u00f6ntem, \u00f6zellikle bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplad\u0131\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m anlamak istedi\u011finizde veya belirli bir k\u0131s\u0131tlaman\u0131z oldu\u011funda (\u00f6rne\u011fin, harici k\u00fct\u00fcphane kullanamama) faydal\u0131d\u0131r. Ancak, daha “Pythonic” ve verimli \u00e7\u00f6z\u00fcmler ar\u0131yorsan\u0131z, di\u011fer y\u00f6ntemlere g\u00f6z atman\u0131z \u00f6nerilir.<\/p>\n
2. sum()<\/code> ve len()<\/code> Yerle\u015fik Fonksiyonlar\u0131n\u0131 Kullanarak Ortalama Hesaplama<\/h3>\n
Python’da bir listenin ortalamas\u0131n\u0131 hesaplaman\u0131n en yayg\u0131n, en “Pythonic” ve en verimli yollar\u0131ndan biri, dilin yerle\u015fik sum()<\/code> ve len()<\/code> fonksiyonlar\u0131n\u0131 kullanmakt\u0131r. Bu y\u00f6ntem, hem okunabilirlik hem de performans a\u00e7\u0131s\u0131ndan \u00e7o\u011fu senaryo i\u00e7in idealdir. sum()<\/code> fonksiyonu, bir iterable (\u00f6rne\u011fin bir liste) i\u00e7indeki t\u00fcm say\u0131sal elemanlar\u0131n toplam\u0131n\u0131 d\u00f6nd\u00fcr\u00fcrken, len()<\/code> fonksiyonu bir nesnenin (\u00f6rne\u011fin bir listenin) eleman say\u0131s\u0131n\u0131 d\u00f6nd\u00fcr\u00fcr. Bu iki fonksiyonu birle\u015ftirerek ortalama hesaplamas\u0131n\u0131 son derece k\u0131sa ve etkili bir \u015fekilde ger\u00e7ekle\u015ftirebiliriz.<\/p>\n
Y\u00f6ntemin \u00c7al\u0131\u015fma Prensibi<\/h4>\n
Bu y\u00f6ntem, for<\/code> d\u00f6ng\u00fcs\u00fcyle yap\u0131lan manuel hesaplaman\u0131n ard\u0131ndaki mant\u0131\u011f\u0131 korur, ancak bu i\u015flemleri Python’\u0131n C dilinde optimize edilmi\u015f yerle\u015fik fonksiyonlar\u0131 arac\u0131l\u0131\u011f\u0131yla ger\u00e7ekle\u015ftirir.
\n1.  Toplam\u0131 Hesaplama:<\/strong> sum(liste)<\/code> kullanarak listedeki t\u00fcm elemanlar\u0131n toplam\u0131n\u0131 an\u0131nda elde etmek.
\n2.  Eleman Say\u0131s\u0131n\u0131 Bulma:<\/strong> len(liste)<\/code> kullanarak listenin eleman say\u0131s\u0131n\u0131 almak.
\n3.  Ortalamay\u0131 Hesaplama:<\/strong> Elde edilen toplam\u0131 eleman say\u0131s\u0131na b\u00f6lmek.<\/p>\n
Kod \u00d6rne\u011fi<\/h4>\n
A\u015fa\u011f\u0131daki kod par\u00e7ac\u0131\u011f\u0131, sum()<\/code> ve len()<\/code> fonksiyonlar\u0131n\u0131 kullanarak bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplayaca\u011f\u0131n\u0131z\u0131 g\u00f6stermektedir:<\/p>\n
def ortalama_hesapla_sum_len(liste):\n    if not liste:  # Liste bo\u015fsa ZeroDivisionError hatas\u0131n\u0131 \u00f6nlemek i\u00e7in kontrol\n        return 0.0  # Veya duruma g\u00f6re ba\u015fka bir de\u011fer\/hata d\u00f6nd\u00fcr\u00fclebilir\n    \n    # sum() fonksiyonu otomatik olarak say\u0131sal olmayan elemanlar i\u00e7in TypeError f\u0131rlat\u0131r\n    # bu y\u00fczden manuel kontrol yapmaya gerek kalmaz (ancak hata y\u00f6netimi yap\u0131labilir)\n    \n    toplam = sum(liste)\n    eleman_sayisi = len(liste)\n    ortalama = toplam \/ eleman_sayisi\n    return ortalama\n\nmy_list = [10, 20, 30, 40, 50]\nprint(f\"sum() ve len() ile ortalama: {ortalama_hesapla_sum_len(my_list)}\")\n\nempty_list = []\nprint(f\"Bo\u015f liste i\u00e7in ortalama: {ortalama_hesapla_sum_len(empty_list)}\")\n\nmixed_list_sum_error = [1, 2, \"\u00fc\u00e7\", 4]\ntry:\n    print(f\"Kar\u0131\u015f\u0131k liste i\u00e7in ortalama: {ortalama_hesapla_sum_len(mixed_list_sum_error)}\")\nexcept TypeError as e:\n    print(f\"Hata: {e} (sum() fonksiyonu say\u0131sal olmayan de\u011ferleri toplayamaz)\")\n\nfloat_list = [1.5, 2.5, 3.0, 4.5]\nprint(f\"Float liste i\u00e7in ortalama: {ortalama_hesapla_sum_len(float_list)}\")<\/code><\/pre>\n
A\u00e7\u0131klama<\/h4>\n
1.  ortalama_hesapla_sum_len(liste)<\/code> fonksiyonu, ortalamas\u0131n\u0131 hesaplamak istedi\u011fimiz listeyi al\u0131r.
\n2.  Yine, listenin bo\u015f olup olmad\u0131\u011f\u0131n\u0131 kontrol ediyoruz (if not liste:<\/code>). Bo\u015f bir liste i\u00e7in len(liste)<\/code> 0<\/code> d\u00f6nd\u00fcrecektir ve toplam \/ 0<\/code> i\u015flemi ZeroDivisionError<\/code> hatas\u0131na yol a\u00e7ar. Bu nedenle, bu kontrol kritik \u00f6neme sahiptir.
\n3.  sum(liste)<\/code> \u00e7a\u011fr\u0131s\u0131, listedeki t\u00fcm say\u0131sal elemanlar\u0131n toplam\u0131n\u0131 hesaplar ve toplam<\/code> de\u011fi\u015fkenine atar. E\u011fer listede say\u0131sal olmayan bir eleman varsa (\u00f6rne\u011fin bir string), sum()<\/code> fonksiyonu do\u011frudan bir TypeError<\/code> f\u0131rlat\u0131r, bu da hatal\u0131 veri tipleriyle u\u011fra\u015f\u0131rken kodu daha sa\u011flam hale getirir.
\n4.  len(liste)<\/code> \u00e7a\u011fr\u0131s\u0131, listenin eleman say\u0131s\u0131n\u0131 hesaplar ve eleman_sayisi<\/code> de\u011fi\u015fkenine atar.
\n5.  ortalama = toplam \/ eleman_sayisi<\/code> ifadesi, toplam\u0131 eleman say\u0131s\u0131na b\u00f6lerek ortalamay\u0131 hesaplar.
\n6.  Son olarak, hesaplanan ortalama<\/code> de\u011ferini d\u00f6nd\u00fcr\u00fcyoruz.<\/p>\n
Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n
*   Avantajlar\u0131:<\/strong>
\n    *   K\u0131sal\u0131k ve Okunabilirlik:<\/strong> Tek bir sat\u0131rda ortalama hesaplamas\u0131 yap\u0131labilir, bu da kodu son derece k\u0131sa ve anla\u015f\u0131l\u0131r k\u0131lar.
\n    *   Performans:<\/strong> Python’\u0131n yerle\u015fik fonksiyonlar\u0131 C dilinde optimize edildi\u011fi i\u00e7in, b\u00fcy\u00fck listeler \u00fczerinde for<\/code> d\u00f6ng\u00fcs\u00fcne k\u0131yasla \u00e7ok daha h\u0131zl\u0131 \u00e7al\u0131\u015f\u0131r.
\n    *   “Pythonic”:<\/strong> Bu, Python geli\u015ftiricileri aras\u0131nda ortalama hesaplamak i\u00e7in standart ve tercih edilen bir y\u00f6ntemdir.
\n    *   Say\u0131sal Olmayan Hata Y\u00f6netimi:<\/strong> sum()<\/code> fonksiyonu, say\u0131sal olmayan elemanlar i\u00e7in otomatik olarak TypeError<\/code> f\u0131rlatarak, veri temizli\u011fi veya do\u011frulama yap\u0131lmad\u0131\u011f\u0131nda bile program\u0131n beklenmedik sonu\u00e7lar \u00fcretmesini engeller.<\/p>\n
*   Dezavantajlar\u0131:<\/strong>
\n    *   Bo\u015f Liste Kontrol\u00fc:<\/strong> ZeroDivisionError<\/code>‘\u0131 \u00f6nlemek i\u00e7in hala manuel olarak bo\u015f liste kontrol\u00fc yap\u0131lmas\u0131 gerekir.
\n    *   Sadece Say\u0131sal Tipler:<\/strong> sum()<\/code> fonksiyonu yaln\u0131zca say\u0131sal tipleri toplayabilir. Kar\u0131\u015f\u0131k tipli listelerde (say\u0131 ve string gibi), TypeError<\/code> f\u0131rlatacakt\u0131r.<\/p>\n
\u00c7o\u011fu genel ama\u00e7l\u0131 Python programlama g\u00f6revi i\u00e7in, sum()<\/code> ve len()<\/code> kombinasyonu, bir listenin ortalamas\u0131n\u0131 bulmak i\u00e7in en iyi ve en dengeli \u00e7\u00f6z\u00fcm\u00fc sunar. Hem basit hem de etkilidir, bu da onu Python ara\u00e7 kutunuzda vazge\u00e7ilmez bir ara\u00e7 haline getirir.<\/p>\n
3. statistics<\/code> Mod\u00fcl\u00fc Kullanarak Ortalama Hesaplama<\/h3>\n
Python’\u0131n standart k\u00fct\u00fcphanesi, istatistiksel hesaplamalar i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015f bir statistics<\/code> mod\u00fcl\u00fc i\u00e7erir. Bu mod\u00fcl, ortalama (mean<\/code>), medyan (median<\/code>), mod (mode<\/code>), standart sapma (stdev<\/code>) gibi \u00e7e\u015fitli istatistiksel fonksiyonlar\u0131 sunar. Bir listenin aritmetik ortalamas\u0131n\u0131 bulmak i\u00e7in statistics.mean()<\/code> fonksiyonunu kullanmak, \u00f6zellikle istatistiksel do\u011fruluk ve g\u00fcvenilirlik \u00f6nemli oldu\u011funda veya zaten ba\u015fka istatistiksel hesaplamalar yapman\u0131z gerekti\u011finde m\u00fckemmel bir se\u00e7enektir.<\/p>\n
Y\u00f6ntemin \u00c7al\u0131\u015fma Prensibi<\/h4>\n
statistics.mean()<\/code> fonksiyonu, bir veri k\u00fcmesinin (\u00f6rne\u011fin bir liste veya tuple) aritmetik ortalamas\u0131n\u0131 hesaplar. Fonksiyon, verilen veri k\u00fcmesini dahili olarak i\u015fler ve ortalama de\u011feri d\u00f6nd\u00fcr\u00fcr. Bu fonksiyon, kayan nokta say\u0131lar\u0131 i\u00e7in hassasiyeti koruma konusunda da \u00f6zen g\u00f6sterir.<\/p>\n
Kod \u00d6rne\u011fi<\/h4>\n
statistics<\/code> mod\u00fcl\u00fcn\u00fc kullanarak bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplayaca\u011f\u0131n\u0131z\u0131 g\u00f6steren kod par\u00e7ac\u0131\u011f\u0131 a\u015fa\u011f\u0131dad\u0131r:<\/p>\n
import statistics\n\ndef ortalama_hesapla_statistics(liste):\n    if not liste:  # statistics.mean() bo\u015f liste i\u00e7in StatisticsError f\u0131rlat\u0131r\n        # Bu durumu burada yakalayabilir veya fonksiyonun f\u0131rlatmas\u0131na izin verebiliriz\n        # Fonksiyonun StatisticsError f\u0131rlatmas\u0131na izin vermek daha Pythonic olabilir\n        # return 0.0 # E\u011fer bo\u015f liste i\u00e7in 0.0 d\u00f6nd\u00fcrmek istiyorsak\n        raise statistics.StatisticsError(\"Bo\u015f bir listenin ortalamas\u0131 hesaplanamaz.\")\n\n    # statistics.mean() otomatik olarak say\u0131sal olmayan elemanlar i\u00e7in TypeError f\u0131rlat\u0131r\n    ortalama = statistics.mean(liste)\n    return ortalama\n\nmy_list = [10, 20, 30, 40, 50]\nprint(f\"statistics.mean() ile ortalama: {ortalama_hesapla_statistics(my_list)}\")\n\nfloat_list = [1.1, 2.2, 3.3, 4.4]\nprint(f\"Float liste i\u00e7in ortalama: {ortalama_hesapla_statistics(float_list)}\")\n\n
Bo\u015f liste \u00f6rne\u011fi<\/h2>\nempty_list = []\ntry:\n    print(f\"Bo\u015f liste i\u00e7in ortalama: {ortalama_hesapla_statistics(empty_list)}\")\nexcept statistics.StatisticsError as e:\n    print(f\"Hata: {e}\")\n\n
Say\u0131sal olmayan eleman \u00f6rne\u011fi<\/h2>\nmixed_list_stat_error = [1, 2, \"\u00fc\u00e7\", 4]\ntry:\n    print(f\"Kar\u0131\u015f\u0131k liste i\u00e7in ortalama: {ortalama_hesapla_statistics(mixed_list_stat_error)}\")\nexcept TypeError as e:\n    print(f\"Hata: {e} (statistics.mean() say\u0131sal olmayan de\u011ferleri i\u015fleyemez)\")<\/code><\/pre>\n
A\u00e7\u0131klama<\/h4>\n
1.  \u00d6ncelikle import statistics<\/code> ifadesiyle statistics<\/code> mod\u00fcl\u00fcn\u00fc i\u00e7e aktar\u0131yoruz.
\n2.  ortalama_hesapla_statistics(liste)<\/code> fonksiyonunu tan\u0131mlad\u0131k.
\n3.  statistics.mean()<\/code> fonksiyonu, bo\u015f bir liste verildi\u011finde statistics.StatisticsError<\/code> hatas\u0131 f\u0131rlat\u0131r. Bu nedenle, fonksiyonumuzda bu hatay\u0131 yakalamak veya \u00f6nceden bir kontrol yapmak mant\u0131kl\u0131 olabilir. Yukar\u0131daki \u00f6rnekte, fonksiyonun kendisinin bir StatisticsError<\/code> f\u0131rlatmas\u0131na izin veriyoruz, bu da mod\u00fcl\u00fcn kendi davran\u0131\u015f\u0131na daha uygun bir yakla\u015f\u0131md\u0131r.
\n4.  ortalama = statistics.mean(liste)<\/code> \u00e7a\u011fr\u0131s\u0131, verilen listenin ortalamas\u0131n\u0131 do\u011frudan hesaplar. Bu fonksiyon, listedeki elemanlar\u0131n say\u0131sal olmas\u0131n\u0131 bekler; say\u0131sal olmayan bir elemanla kar\u015f\u0131la\u015ft\u0131\u011f\u0131nda TypeError<\/code> f\u0131rlat\u0131r.
\n5.  Hesaplanan ortalama<\/code> de\u011ferini d\u00f6nd\u00fcr\u00fcyoruz.<\/p>\n
Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n
*   Avantajlar\u0131:<\/strong>
\n    *   \u0130statistiksel Do\u011fruluk:<\/strong> \u0130statistiksel hesaplamalar i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015ft\u0131r ve kayan nokta hassasiyetini koruma konusunda \u00f6zen g\u00f6sterir.
\n    *   Okunabilirlik:<\/strong> Kodun amac\u0131 (istatistiksel ortalama hesaplamak) statistics.mean()<\/code> \u00e7a\u011fr\u0131s\u0131yla a\u00e7\u0131k\u00e7a belirtilir.
\n    *   G\u00fcvenilirlik:<\/strong> Python’\u0131n standart k\u00fct\u00fcphanesinin bir par\u00e7as\u0131 oldu\u011fu i\u00e7in g\u00fcvenilirdir ve geni\u015f \u00f6l\u00e7\u00fcde test edilmi\u015ftir.
\n    *   Hata Y\u00f6netimi:<\/strong> Bo\u015f listeler i\u00e7in \u00f6zel bir StatisticsError<\/code> f\u0131rlat\u0131r, bu da hata durumlar\u0131n\u0131n net bir \u015fekilde ay\u0131rt edilmesini sa\u011flar.<\/p>\n
*   Dezavantajlar\u0131:<\/strong>
\n    *   Mod\u00fcl \u0130\u00e7e Aktar\u0131m\u0131:<\/strong> Kullanmak i\u00e7in statistics<\/code> mod\u00fcl\u00fcn\u00fc i\u00e7e aktarman\u0131z gerekir, bu da \u00e7ok basit durumlar i\u00e7in k\u00fc\u00e7\u00fck bir ek ad\u0131m anlam\u0131na gelebilir.
\n    *   Performans:<\/strong> \u00c7ok b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in, \u00f6zellikle say\u0131sal hesaplamalar i\u00e7in optimize edilmi\u015f NumPy gibi harici k\u00fct\u00fcphanelerden daha yava\u015f olabilir. Ancak, \u00e7o\u011fu pratik kullan\u0131m durumu i\u00e7in performans\u0131 yeterlidir.
\n    *   Sadece Say\u0131sal Tipler:<\/strong> sum()<\/code> gibi, yaln\u0131zca say\u0131sal tiplerle \u00e7al\u0131\u015f\u0131r ve say\u0131sal olmayan elemanlar i\u00e7in TypeError<\/code> f\u0131rlat\u0131r.<\/p>\n
statistics<\/code> mod\u00fcl\u00fc, \u00f6zellikle bir projenin istatistiksel analiz gereksinimleri oldu\u011funda ve Python’\u0131n yerle\u015fik fonksiyonlar\u0131n\u0131n \u00f6tesinde daha spesifik istatistiksel ara\u00e7lara ihtiya\u00e7 duyuldu\u011funda tercih edilmelidir. Veri biliminde veya analitik uygulamalarda, bu mod\u00fcl s\u0131k\u00e7a kullan\u0131l\u0131r.<\/p>\n
4. numpy<\/code> K\u00fct\u00fcphanesi Kullanarak Ortalama Hesaplama<\/h3>\n
B\u00fcy\u00fck say\u0131sal veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken, Python’\u0131n yerle\u015fik listeleri ve fonksiyonlar\u0131 bazen performans a\u00e7\u0131s\u0131ndan yetersiz kalabilir. Bu gibi durumlarda, numpy<\/code> (Numerical Python) k\u00fct\u00fcphanesi devreye girer. NumPy, \u00e7ok boyutlu diziler (array’ler) \u00fczerinde y\u00fcksek performansl\u0131 say\u0131sal i\u015flemler yapmak i\u00e7in tasarlanm\u0131\u015f, Python’\u0131n temel bilimsel hesaplama k\u00fct\u00fcphanesidir. \u00d6zellikle veri bilimi, makine \u00f6\u011frenimi ve bilimsel ara\u015ft\u0131rmalar alanlar\u0131nda yayg\u0131n olarak kullan\u0131l\u0131r. NumPy dizileri, C tabanl\u0131 kodda uyguland\u0131\u011f\u0131 i\u00e7in Python listelerinden \u00e7ok daha h\u0131zl\u0131d\u0131r ve bir\u00e7ok matematiksel i\u015flemi do\u011frudan destekler, bunlardan biri de ortalama hesaplamas\u0131d\u0131r.<\/p>\n
Y\u00f6ntemin \u00c7al\u0131\u015fma Prensibi<\/h4>\n
NumPy, verileri ndarray<\/code> ad\u0131 verilen \u00f6zel bir dizi yap\u0131s\u0131nda saklar. Bu diziler, homojen veri tipleriyle \u00e7al\u0131\u015f\u0131r ve \u00fczerinde vekt\u00f6rle\u015ftirilmi\u015f i\u015flemler yap\u0131lmas\u0131na olanak tan\u0131r. NumPy’nin mean()<\/code> fonksiyonu veya ndarray<\/code> nesnelerinin .mean()<\/code> metodu, dizideki t\u00fcm elemanlar\u0131n ortalamas\u0131n\u0131 son derece h\u0131zl\u0131 bir \u015fekilde hesaplar.<\/p>\n
Kod \u00d6rne\u011fi<\/h4>\n
NumPy k\u00fct\u00fcphanesini kullanarak bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplayaca\u011f\u0131n\u0131z\u0131 g\u00f6steren kod par\u00e7ac\u0131\u011f\u0131 a\u015fa\u011f\u0131dad\u0131r:<\/p>\n
import numpy as np\n\ndef ortalama_hesapla_numpy(liste):\n    # NumPy array'i olu\u015ftur\n    np_array = np.array(liste)\n    \n    # Bo\u015f array kontrol\u00fc (NumPy'nin mean'i bo\u015f array i\u00e7in NaN d\u00f6nd\u00fcrebilir veya uyar\u0131 verebilir)\n    if np_array.size == 0:\n        # Bo\u015f array i\u00e7in ortalama tan\u0131ml\u0131 de\u011fildir, genellikle NaN (Not a Number) d\u00f6nd\u00fcr\u00fcl\u00fcr.\n        # Ya da kullan\u0131c\u0131ya bilgi verilebilir.\n        return np.nan # NaN d\u00f6nd\u00fcrmek NumPy'nin genel yakla\u015f\u0131m\u0131d\u0131r.\n\n    # Ortalama hesapla\n    ortalama = np_array.mean() # Veya np.mean(np_array)\n    return ortalama\n\nmy_list = [10, 20, 30, 40, 50]\nprint(f\"NumPy ile ortalama: {ortalama_hesapla_numpy(my_list)}\")\n\nfloat_list = [1.1, 2.2, 3.3, 4.4]\nprint(f\"NumPy ile float liste ortalamas\u0131: {ortalama_hesapla_numpy(float_list)}\")\n\n
Bo\u015f liste \u00f6rne\u011fi<\/h2>\nempty_list = []\nprint(f\"NumPy ile bo\u015f liste ortalamas\u0131: {ortalama_hesapla_numpy(empty_list)}\")\n\n
Say\u0131sal olmayan eleman \u00f6rne\u011fi<\/h2>\n
NumPy array'i olu\u015fturulurken tip d\u00f6n\u00fc\u015f\u00fcm\u00fc ba\u015far\u0131s\u0131z olursa hata verir.<\/h2>\n
\u00d6rne\u011fin, string i\u00e7eren bir liste do\u011frudan say\u0131sal bir array'e d\u00f6n\u00fc\u015ft\u00fcr\u00fclemez.<\/h2>\nmixed_list_numpy_error = [1, 2, \"\u00fc\u00e7\", 4]\ntry:\n    print(f\"NumPy ile kar\u0131\u015f\u0131k liste ortalamas\u0131: {ortalama_hesapla_numpy(mixed_list_numpy_error)}\")\nexcept ValueError as e:\n    print(f\"Hata: {e} (NumPy array'i say\u0131sal olmayan de\u011ferleri d\u00f6n\u00fc\u015ft\u00fcremedi)\")\n\n
B\u00fcy\u00fck liste performans testi (\u00f6rnek)<\/h2>\nlarge_list = list(range(1, 1000001)) # 1 milyon eleman\n
Zamanlama kodlar\u0131 burada g\u00f6sterilmiyor, ancak bu y\u00f6ntem \u00e7ok daha h\u0131zl\u0131 olacakt\u0131r.<\/h2>\n
print(f\"NumPy ile b\u00fcy\u00fck liste ortalamas\u0131: {ortalama_hesapla_numpy(large_list)}\")<\/code><\/pre>\n<\/h2>\n
A\u00e7\u0131klama<\/h4>\n
1.  \u00d6ncelikle import numpy as np<\/code> ifadesiyle numpy<\/code> k\u00fct\u00fcphanesini np<\/code> takma ad\u0131yla i\u00e7e aktar\u0131yoruz.
\n2.  ortalama_hesapla_numpy(liste)<\/code> fonksiyonu, bir Python listesini al\u0131r.
\n3.  np_array = np.array(liste)<\/code> ifadesi, verilen Python listesini bir NumPy ndarray<\/code>‘ine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. NumPy, bu d\u00f6n\u00fc\u015f\u00fcm s\u0131ras\u0131nda elemanlar\u0131n tipini infer etmeye \u00e7al\u0131\u015f\u0131r. E\u011fer listede say\u0131sal olmayan bir eleman varsa ve NumPy bunu say\u0131sal bir tipe d\u00f6n\u00fc\u015ft\u00fcremezse, bir ValueError<\/code> f\u0131rlat\u0131r.
\n4.  if np_array.size == 0:<\/code> kontrol\u00fc ile NumPy dizisinin bo\u015f olup olmad\u0131\u011f\u0131n\u0131 kontrol ediyoruz. Bo\u015f bir NumPy dizisinin mean()<\/code> metodu \u00e7a\u011fr\u0131ld\u0131\u011f\u0131nda genellikle np.nan<\/code> (Not a Number) d\u00f6nd\u00fcr\u00fcr veya RuntimeWarning<\/code> verebilir. np.nan<\/code> d\u00f6nd\u00fcrmek, bo\u015f bir veri k\u00fcmesi i\u00e7in ortalaman\u0131n tan\u0131ms\u0131z oldu\u011funu belirtmenin standart bir yoludur.
\n5.  ortalama = np_array.mean()<\/code> veya ortalama = np.mean(np_array)<\/code> ifadelerinden biriyle NumPy dizisinin ortalamas\u0131n\u0131 hesaplar\u0131z. Her iki kullan\u0131m da ayn\u0131 sonucu verir: ilki bir dizi metodu, ikincisi ise bir NumPy fonksiyonudur.
\n6.  Hesaplanan ortalama<\/code> de\u011ferini d\u00f6nd\u00fcr\u00fcyoruz.<\/p>\n
Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n
*   Avantajlar\u0131:<\/strong>
\n    *   Y\u00fcksek Performans:<\/strong> \u00c7ok b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde di\u011fer y\u00f6ntemlere k\u0131yasla belirgin \u015fekilde daha h\u0131zl\u0131d\u0131r. Vekt\u00f6rle\u015ftirilmi\u015f i\u015flemler sayesinde d\u00f6ng\u00fc overhead’ini ortadan kald\u0131r\u0131r.
\n    *   Kompakt Kod:<\/strong> Ortalamay\u0131 hesaplamak i\u00e7in tek bir fonksiyon \u00e7a\u011fr\u0131s\u0131 yeterlidir.
\n    *   Geni\u015f Fonksiyon Yelpazesi:<\/strong> NumPy, ortalama d\u0131\u015f\u0131nda standart sapma, varyans, medyan gibi bir\u00e7ok di\u011fer matematiksel ve istatistiksel fonksiyonu da i\u00e7erir.
\n    *   Veri Bilimi Entegrasyonu:<\/strong> Pandas gibi di\u011fer veri bilimi k\u00fct\u00fcphaneleriyle sorunsuz bir \u015fekilde entegre olur.<\/p>\n
*   Dezavantajlar\u0131:<\/strong>
\n    *   Harici K\u00fct\u00fcphane Ba\u011f\u0131ml\u0131l\u0131\u011f\u0131:<\/strong> Kullanmak i\u00e7in NumPy’yi y\u00fcklemeniz gerekir (pip install numpy<\/code>). Bu, k\u00fc\u00e7\u00fck, ba\u011f\u0131ms\u0131z komut dosyalar\u0131 i\u00e7in gereksiz bir ba\u011f\u0131ml\u0131l\u0131k olabilir.
\n    *   Bellek Kullan\u0131m\u0131:<\/strong> NumPy dizileri genellikle homojen tiplidir. Farkl\u0131 tiplerdeki verileri depolarken (\u00f6rne\u011fin, int ve float), NumPy t\u00fcm elemanlar\u0131 en geni\u015f tipe (float) d\u00f6n\u00fc\u015ft\u00fcrerek bellek kullan\u0131m\u0131n\u0131 art\u0131rabilir.
\n    *   Karma\u015f\u0131kl\u0131k:<\/strong> Sadece ortalama hesaplamak i\u00e7in NumPy kullanmak, \u00f6\u011frenme e\u011frisi ve kurulum maliyeti a\u00e7\u0131s\u0131ndan a\u015f\u0131r\u0131ya ka\u00e7abilir.
\n    *   Bo\u015f Array Y\u00f6netimi:<\/strong> Bo\u015f bir array’in ortalamas\u0131 NaN<\/code> d\u00f6nd\u00fcrd\u00fc\u011f\u00fcnden, bu de\u011feri uygun \u015fekilde i\u015flemek gerekebilir.<\/p>\n
NumPy, \u00f6zellikle bilimsel hesaplama, veri analizi ve makine \u00f6\u011frenimi projelerinde b\u00fcy\u00fck say\u0131sal veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken ortalama hesaplamas\u0131 i\u00e7in vazge\u00e7ilmez bir ara\u00e7t\u0131r. Performans ve geni\u015f matematiksel fonksiyon setleri, onu bu alanlarda standart bir se\u00e7im haline getirir.<\/p>\n
5. pandas<\/code> K\u00fct\u00fcphanesi Kullanarak Ortalama Hesaplama<\/h3>\n
Python’da veri analizi denince akla gelen ilk k\u00fct\u00fcphanelerden biri pandas<\/code>‘t\u0131r. Pandas, yap\u0131land\u0131r\u0131lm\u0131\u015f verilerle \u00e7al\u0131\u015fmak i\u00e7in g\u00fc\u00e7l\u00fc ve esnek veri yap\u0131lar\u0131 (DataFrame ve Series) sunar. \u00d6zellikle tablolar, zaman serileri ve matris benzeri verilerle u\u011fra\u015f\u0131rken Pandas, veri manip\u00fclasyonu, temizli\u011fi ve analizi i\u00e7in kapsaml\u0131 ara\u00e7lar sa\u011flar. Bir listenin ortalamas\u0131n\u0131 bulmak i\u00e7in Pandas kullanmak, \u00f6zellikle verileriniz zaten bir Pandas Series veya DataFrame i\u00e7indeyse veya daha karma\u015f\u0131k veri analizi ad\u0131mlar\u0131n\u0131n bir par\u00e7as\u0131 olarak ortalama hesaplamas\u0131 yapman\u0131z gerekiyorsa \u00e7ok mant\u0131kl\u0131d\u0131r.<\/p>\n
Y\u00f6ntemin \u00c7al\u0131\u015fma Prensibi<\/h4>\n
Pandas’ta, tek boyutlu bir veri k\u00fcmesi genellikle bir Series<\/code> nesnesi olarak temsil edilir. Bir Pandas Series<\/code> nesnesi, NumPy dizileri \u00fczerine in\u015fa edilmi\u015ftir ve kendi \u00fczerinde bir\u00e7ok istatistiksel metod i\u00e7erir, bunlardan biri de .mean()<\/code> metodudur. Bu metod, Series i\u00e7indeki t\u00fcm say\u0131sal elemanlar\u0131n ortalamas\u0131n\u0131 hesaplar. Pandas, eksik veriler (NaN<\/code> – Not a Number) ile \u00e7al\u0131\u015f\u0131rken de olduk\u00e7a yeteneklidir ve .mean()<\/code> gibi metodlar varsay\u0131lan olarak bu de\u011ferleri g\u00f6z ard\u0131 edebilir.<\/p>\n
Kod \u00d6rne\u011fi<\/h4>\n
Pandas k\u00fct\u00fcphanesini kullanarak bir listenin ortalamas\u0131n\u0131 nas\u0131l hesaplayaca\u011f\u0131n\u0131z\u0131 g\u00f6steren kod par\u00e7ac\u0131\u011f\u0131 a\u015fa\u011f\u0131dad\u0131r:<\/p>\n
import pandas as pd\nimport numpy as np # NaN de\u011ferler i\u00e7in\n\ndef ortalama_hesapla_pandas(liste):\n    # Pandas Series olu\u015ftur\n    pd_series = pd.Series(liste)\n    \n    # Bo\u015f Series kontrol\u00fc (Pandas'\u0131n mean'i bo\u015f Series i\u00e7in NaN d\u00f6nd\u00fcr\u00fcr)\n    if pd_series.empty:\n        return np.nan # Bo\u015f Series i\u00e7in NaN d\u00f6nd\u00fcrmek standartt\u0131r.\n\n    # Ortalama hesapla\n    ortalama = pd_series.mean()\n    return ortalama\n\nmy_list = [10, 20, 30, 40, 50]\nprint(f\"Pandas ile ortalama: {ortalama_hesapla_pandas(my_list)}\")\n\nfloat_list = [1.1, 2.2, 3.3, 4.4]\nprint(f\"Pandas ile float liste ortalamas\u0131: {ortalama_hesapla_pandas(float_list)}\")\n\n
Bo\u015f liste \u00f6rne\u011fi<\/h2>\nempty_list = []\nprint(f\"Pandas ile bo\u015f liste ortalamas\u0131: {ortalama_hesapla_pandas(empty_list)}\")\n\n
NaN de\u011fer i\u00e7eren liste \u00f6rne\u011fi<\/h2>\nlist_with_nan = [10, 20, np.nan, 40, 50]\nprint(f\"Pandas ile NaN i\u00e7eren liste ortalamas\u0131: {ortalama_hesapla_pandas(list_with_nan)}\")\n\n
Say\u0131sal olmayan eleman \u00f6rne\u011fi<\/h2>\nmixed_list_pandas_error = [1, 2, \"\u00fc\u00e7\", 4]\ntry:\n    print(f\"Pandas ile kar\u0131\u015f\u0131k liste ortalamas\u0131: {ortalama_hesapla_pandas(mixed_list_pandas_error)}\")\nexcept TypeError as e:\n    print(f\"Hata: {e} (Pandas Series say\u0131sal olmayan de\u011ferleri i\u015fleyemedi veya d\u00f6n\u00fc\u015ft\u00fcremedi)\")<\/code><\/pre>\n
A\u00e7\u0131klama<\/h4>\n
1.  \u00d6ncelikle import pandas as pd<\/code> ifadesiyle pandas<\/code> k\u00fct\u00fcphanesini pd<\/code> takma ad\u0131yla i\u00e7e aktar\u0131yoruz. numpy<\/code>‘\u0131 da np<\/code> olarak i\u00e7e aktar\u0131yoruz, \u00e7\u00fcnk\u00fc bo\u015f Series i\u00e7in np.nan<\/code> kullanaca\u011f\u0131z.
\n2.  ortalama_hesapla_pandas(liste)<\/code> fonksiyonu, bir Python listesini al\u0131r.
\n3.  pd_series = pd.Series(liste)<\/code> ifadesi, verilen Python listesini bir Pandas Series<\/code> nesnesine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Pandas, bu d\u00f6n\u00fc\u015f\u00fcm s\u0131ras\u0131nda elemanlar\u0131n tipini infer etmeye \u00e7al\u0131\u015f\u0131r. E\u011fer listede say\u0131sal olmayan bir eleman varsa ve Pandas bunu say\u0131sal bir tipe d\u00f6n\u00fc\u015ft\u00fcremezse, genellikle TypeError<\/code> f\u0131rlat\u0131r veya elemanlar\u0131 object<\/code> (genel Python nesnesi) tipinde tutar, bu da .mean()<\/code> metodunun \u00e7al\u0131\u015fmamas\u0131na neden olabilir.
\n4.  if pd_series.empty:<\/code> kontrol\u00fc ile Pandas Series’in bo\u015f olup olmad\u0131\u011f\u0131n\u0131 kontrol ediyoruz. Bo\u015f bir Series’in .mean()<\/code> metodu \u00e7a\u011fr\u0131ld\u0131\u011f\u0131nda np.nan<\/code> d\u00f6nd\u00fcr\u00fcr. Bu, bo\u015f bir veri k\u00fcmesi i\u00e7in ortalaman\u0131n tan\u0131ms\u0131z oldu\u011funu belirtmenin standart bir yoludur.
\n5.  ortalama = pd_series.mean()<\/code> \u00e7a\u011fr\u0131s\u0131, Pandas Series’in ortalamas\u0131n\u0131 do\u011frudan hesaplar. Bu metod, varsay\u0131lan olarak NaN<\/code> (Not a Number) de\u011ferlerini ortalama hesaplamas\u0131ndan hari\u00e7 tutar, bu da eksik verilerle \u00e7al\u0131\u015f\u0131rken \u00e7ok kullan\u0131\u015fl\u0131d\u0131r.
\n6.  Hesaplanan ortalama<\/code> de\u011ferini d\u00f6nd\u00fcr\u00fcyoruz.<\/p>\n
Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n
*   Avantajlar\u0131:<\/strong>
\n    *   Veri Analizi \u0130\u00e7in \u0130deal:<\/strong> Yap\u0131land\u0131r\u0131lm\u0131\u015f verilerle \u00e7al\u0131\u015f\u0131rken \u00fcst\u00fcn performans ve esneklik sunar.
\n    *   Eksik Veri Y\u00f6netimi:<\/strong> NaN<\/code> de\u011ferlerini otomatik olarak g\u00f6z ard\u0131 ederek ortalama hesaplamas\u0131n\u0131 kolayla\u015ft\u0131r\u0131r.
\n    *   Kapsaml\u0131 Fonksiyonellik:<\/strong> Ortalama d\u0131\u015f\u0131nda bir\u00e7ok di\u011fer istatistiksel, veri manip\u00fclasyonu ve analiz fonksiyonunu i\u00e7erir.
\n    *   Okunabilirlik:<\/strong> Veri analizi ba\u011flam\u0131nda kodun amac\u0131n\u0131 a\u00e7\u0131k\u00e7a belirtir.
\n    *   Performans:<\/strong> B\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde NumPy gibi y\u00fcksek performansl\u0131d\u0131r.<\/p>\n
*   Dezavantajlar\u0131:<\/strong>
\n    *   Harici K\u00fct\u00fcphane Ba\u011f\u0131ml\u0131l\u0131\u011f\u0131:<\/strong> Kullanmak i\u00e7in Pandas’\u0131 y\u00fcklemeniz gerekir (pip install pandas<\/code>). Bu, sadece ortalama hesaplamak i\u00e7in gereksiz bir ba\u011f\u0131ml\u0131l\u0131k olabilir.
\n    *   A\u015f\u0131r\u0131ya Ka\u00e7ma:<\/strong> Yaln\u0131zca basit bir listenin ortalamas\u0131n\u0131 bulmak i\u00e7in Pandas kullanmak, k\u00fct\u00fcphanenin geni\u015f kapsam\u0131 ve bellek ayak izi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda a\u015f\u0131r\u0131ya ka\u00e7mak olabilir.
\n    *   \u00d6\u011frenme E\u011frisi:<\/strong> Pandas’\u0131n t\u00fcm \u00f6zelliklerini \u00f6\u011frenmek, sadece ortalama hesaplamaktan \u00e7ok daha fazlas\u0131n\u0131 gerektirir.
\n    *   Bo\u015f Series Y\u00f6netimi:<\/strong> Bo\u015f bir Series’in ortalamas\u0131 NaN<\/code> d\u00f6nd\u00fcrd\u00fc\u011f\u00fcnden, bu de\u011feri uygun \u015fekilde i\u015flemek gerekebilir.<\/p>\n
Pandas, \u00f6zellikle veri temizli\u011fi, d\u00f6n\u00fc\u015f\u00fcm\u00fc ve analizi gerektiren b\u00fcy\u00fck ve karma\u015f\u0131k veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken ortalama hesaplamas\u0131 i\u00e7in tercih edilen y\u00f6ntemdir. E\u011fer projeniz zaten Pandas kullan\u0131yorsa veya veri analizi odakl\u0131ysa, bu y\u00f6ntem en mant\u0131kl\u0131 ve g\u00fc\u00e7l\u00fc \u00e7\u00f6z\u00fcm\u00fc sunar.<\/p>\n
Y\u00f6ntemlerin Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 ve En \u0130yi Uygulamalar<\/h3>\n
Python’da bir listenin ortalamas\u0131n\u0131 bulmak i\u00e7in be\u015f farkl\u0131 y\u00f6ntemi inceledik. Her bir y\u00f6ntemin kendine \u00f6zg\u00fc avantajlar\u0131 ve dezavantajlar\u0131 vard\u0131r ve “en iyi” y\u00f6ntem, projenizin \u00f6zel gereksinimlerine, veri setinizin boyutuna ve mevcut ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131za ba\u011fl\u0131d\u0131r. A\u015fa\u011f\u0131daki tablo, bu y\u00f6ntemleri temel \u00f6zelliklerine g\u00f6re kar\u015f\u0131la\u015ft\u0131rmaktad\u0131r:<\/p>\n
| Y\u00f6ntem                   | Okunabilirlik | Performans (K\u00fc\u00e7\u00fck Liste) | Performans (B\u00fcy\u00fck Liste) | Ba\u011f\u0131ml\u0131l\u0131k  | Hata Y\u00f6netimi (Bo\u015f Liste) | Hata Y\u00f6netimi (Say\u0131sal Olmayan) | Kullan\u0131m Senaryosu                                        |
\n| :———————– | :———— | :———————– | :———————– | :———- | :———————— | :—————————— | :——————————————————– |
\n| for<\/code> D\u00f6ng\u00fcs\u00fc            | Y\u00fcksek        | Orta                     | D\u00fc\u015f\u00fck                    | Yok         | Manuel (ZeroDivisionError<\/code>) | Manuel (TypeError<\/code>)            | Temel \u00f6\u011frenme, ba\u011f\u0131ms\u0131zl\u0131k gerektiren durumlar              |
\n| sum()<\/code> ve len()<\/code>       | \u00c7ok Y\u00fcksek    | Y\u00fcksek                   | Y\u00fcksek                   | Yok         | Manuel (ZeroDivisionError<\/code>) | Otomatik (TypeError<\/code>)          | \u00c7o\u011fu genel ama\u00e7l\u0131 kullan\u0131m, basit ve h\u0131zl\u0131 \u00e7\u00f6z\u00fcmler        |
\n| statistics<\/code> Mod\u00fcl\u00fc      | Y\u00fcksek        | Y\u00fcksek                   | Orta                     | Standart    | Otomatik (StatisticsError<\/code>) | Otomatik (TypeError<\/code>)          | \u0130statistiksel do\u011fruluk, standart istatistiksel hesaplamalar |
\n| numpy<\/code> K\u00fct\u00fcphanesi      | Orta-Y\u00fcksek   | Y\u00fcksek                   | \u00c7ok Y\u00fcksek               | Harici      | Otomatik (NaN<\/code> veya Uyar\u0131)  | Otomatik (ValueError<\/code>)         | B\u00fcy\u00fck say\u0131sal veri, bilimsel hesaplama, veri bilimi        |
\n| pandas<\/code> K\u00fct\u00fcphanesi     | Orta-Y\u00fcksek   | Y\u00fcksek                   | \u00c7ok Y\u00fcksek               | Harici      | Otomatik (NaN<\/code>)             | Otomatik (TypeError<\/code>)          | Yap\u0131land\u0131r\u0131lm\u0131\u015f veri analizi, eksik veri y\u00f6netimi         |<\/p>\n
Hangi Y\u00f6ntemi Ne Zaman Kullanmal\u0131?<\/h4>\n
1.  K\u00fc\u00e7\u00fck, Basit Listeler ve Genel Ama\u00e7l\u0131 Kullan\u0131m (sum()<\/code> ve len()<\/code>):<\/strong>
\n    *   E\u011fer sadece birka\u00e7 say\u0131dan olu\u015fan k\u00fc\u00e7\u00fck bir listenin ortalamas\u0131n\u0131 bulman\u0131z gerekiyorsa ve harici k\u00fct\u00fcphanelere ba\u011f\u0131ml\u0131l\u0131k eklemek istemiyorsan\u0131z, sum()<\/code> ve len()<\/code> kombinasyonu en iyi se\u00e7imdir.
\n    *   K\u0131sa, okunabilir ve Python’\u0131n yerle\u015fik C implementasyonlar\u0131 sayesinde olduk\u00e7a h\u0131zl\u0131d\u0131r. \u00c7o\u011fu g\u00fcnl\u00fck programlama g\u00f6revi i\u00e7in fazlas\u0131yla yeterlidir.
\n    *   Bo\u015f liste durumunu manuel olarak ele almay\u0131 unutmay\u0131n.<\/p>\n
2.  \u0130statistiksel Do\u011fruluk ve Standart \u0130statistikler (statistics<\/code> mod\u00fcl\u00fc):<\/strong>
\n    *   E\u011fer ortalama hesaplamas\u0131, daha geni\u015f bir istatistiksel analiz g\u00f6revinin bir par\u00e7as\u0131ysa veya kayan nokta hassasiyeti gibi istatistiksel do\u011fruluk \u00f6nemliyse, statistics.mean()<\/code> fonksiyonu tercih edilmelidir.
\n    *   Bu mod\u00fcl, ortalama d\u0131\u015f\u0131nda medyan, mod, standart sapma gibi di\u011fer istatistiksel \u00f6l\u00e7\u00fcmleri de sunar, bu da onu istatistiksel projeler i\u00e7in kapsaml\u0131 bir ara\u00e7 haline getirir.
\n    *   Bo\u015f liste i\u00e7in StatisticsError<\/code> f\u0131rlatmas\u0131, hata y\u00f6netimini netle\u015ftirir.<\/p>\n
3.  B\u00fcy\u00fck Say\u0131sal Veri K\u00fcmeleri ve Bilimsel Hesaplama (numpy<\/code>):<\/strong>
\n    *   Milyonlarca eleman i\u00e7eren b\u00fcy\u00fck say\u0131sal listelerle veya \u00e7ok boyutlu dizilerle \u00e7al\u0131\u015f\u0131yorsan\u0131z, numpy<\/code> vazge\u00e7ilmezdir. Performans\u0131, di\u011fer y\u00f6ntemlere k\u0131yasla kat kat \u00fcst\u00fcnd\u00fcr.
\n    *   Veri bilimi, makine \u00f6\u011frenimi ve bilimsel ara\u015ft\u0131rma gibi alanlarda standartt\u0131r.
\n    *   E\u011fer projeniz zaten NumPy kullan\u0131yorsa, ortalama hesaplamak i\u00e7in de bu k\u00fct\u00fcphaneyi kullanmak mant\u0131kl\u0131d\u0131r.
\n    *   Bo\u015f array’lerin NaN<\/code> d\u00f6nd\u00fcrd\u00fc\u011f\u00fcn\u00fc ve say\u0131sal olmayan elemanlar i\u00e7in ValueError<\/code> f\u0131rlatt\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/p>\n
4.  Yap\u0131land\u0131r\u0131lm\u0131\u015f Veri Analizi ve Eksik Veri Y\u00f6netimi (pandas<\/code>):<\/strong>
\n    *   Verileriniz tablolar, zaman serileri veya di\u011fer yap\u0131land\u0131r\u0131lm\u0131\u015f formatlardaysa ve veri manip\u00fclasyonu, temizli\u011fi veya analizi yapman\u0131z gerekiyorsa pandas<\/code> idealdir.
\n    *   Pandas Series<\/code>‘in .mean()<\/code> metodu, eksik verileri (NaN<\/code>) otomatik olarak g\u00f6z ard\u0131 etme gibi pratik \u00f6zelliklere sahiptir.
\n    *   E\u011fer projeniz zaten Pandas kullan\u0131yorsa veya veri analizi odakl\u0131ysa, bu y\u00f6ntem en kapsaml\u0131 \u00e7\u00f6z\u00fcm\u00fc sunar.
\n    *   Bo\u015f Series’in NaN<\/code> d\u00f6nd\u00fcrd\u00fc\u011f\u00fcn\u00fc ve say\u0131sal olmayan elemanlar i\u00e7in TypeError<\/code> f\u0131rlatabilece\u011fini g\u00f6z \u00f6n\u00fcnde bulundurun.<\/p>\n
5.  \u00d6\u011frenme ve Temel Anlay\u0131\u015f (for<\/code> d\u00f6ng\u00fcs\u00fc):<\/strong>
\n    *   Python’a yeni ba\u015fl\u0131yorsan\u0131z ve ortalama hesaplama mant\u0131\u011f\u0131n\u0131 ad\u0131m ad\u0131m anlamak istiyorsan\u0131z for<\/code> d\u00f6ng\u00fcs\u00fc kullanmak m\u00fckemmel bir e\u011fitim arac\u0131d\u0131r.
\n    *   \u00c7ok k\u00fc\u00e7\u00fck listeler i\u00e7in veya harici k\u00fct\u00fcphane ba\u011f\u0131ml\u0131l\u0131\u011f\u0131na izin verilmeyen \u00e7ok k\u0131s\u0131tl\u0131 ortamlarda kullan\u0131labilir.<\/p>\n
Ortak Hata Y\u00f6netimi \u0130pu\u00e7lar\u0131<\/h4>\n
*   Bo\u015f Liste Kontrol\u00fc:<\/strong> T\u00fcm y\u00f6ntemlerde, bo\u015f bir liste i\u00e7in ortalama hesaplamaya \u00e7al\u0131\u015fmak ya bir hataya (ZeroDivisionError<\/code>, StatisticsError<\/code>, ValueError<\/code>) ya da tan\u0131ms\u0131z bir sonuca (NaN<\/code>) yol a\u00e7ar. Bu nedenle, ortalama hesaplamadan \u00f6nce listenin bo\u015f olup olmad\u0131\u011f\u0131n\u0131 kontrol etmek her zaman iyi bir uygulamad\u0131r.
\n*   Say\u0131sal Olmayan Elemanlar:<\/strong> T\u00fcm ortalama hesaplama y\u00f6ntemleri say\u0131sal elemanlar bekler. Listenizde say\u0131sal olmayan elemanlar (string, boolean vb.) varsa, \u00e7o\u011fu y\u00f6ntem TypeError<\/code> veya ValueError<\/code> f\u0131rlatacakt\u0131r. Bu t\u00fcr durumlar\u0131 ele almak i\u00e7in veri temizli\u011fi veya tip kontrol\u00fc yapman\u0131z gerekebilir.<\/p>\n
Sonu\u00e7<\/h3>\n
Python, bir listenin ortalamas\u0131n\u0131 bulmak i\u00e7in geni\u015f bir ara\u00e7 yelpazesi sunar. Basit for<\/code> d\u00f6ng\u00fcs\u00fcnden ba\u015flayarak, Python’\u0131n yerle\u015fik sum()<\/code> ve len()<\/code> fonksiyonlar\u0131na, istatistiksel analiz i\u00e7in statistics<\/code> mod\u00fcl\u00fcne ve b\u00fcy\u00fck \u00f6l\u00e7ekli say\u0131sal i\u015flemler i\u00e7in numpy<\/code> ile pandas<\/code> k\u00fct\u00fcphanelerine kadar her bir y\u00f6ntemin kendine \u00f6zg\u00fc avantajlar\u0131 ve uygun kullan\u0131m senaryolar\u0131 bulunmaktad\u0131r.<\/p>\n
Se\u00e7iminiz, projenizin \u00f6zel gereksinimlerine, performans beklentilerinize, veri setinizin boyutuna ve mevcut k\u00fct\u00fcphane ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131za g\u00f6re \u015fekillenmelidir. \u00c7o\u011fu genel ama\u00e7l\u0131 g\u00f6rev i\u00e7in sum()<\/code> ve len()<\/code> kombinasyonu en dengeli ve “Pythonic” \u00e7\u00f6z\u00fcm\u00fc sunarken, veri bilimi veya bilimsel hesaplama gibi alanlarda numpy<\/code> ve pandas<\/code> vazge\u00e7ilmezdir. \u0130statistiksel do\u011fruluk \u00f6nemliyse statistics<\/code> mod\u00fcl\u00fc devreye girer. Hangi y\u00f6ntemi se\u00e7erseniz se\u00e7in, her zaman bo\u015f liste ve say\u0131sal olmayan elemanlar gibi kenar durumlar\u0131 ele almay\u0131 unutmay\u0131n. Bu, kodunuzu daha sa\u011flam ve g\u00fcvenilir hale getirecektir. Bu makaledeki bilgilerle, Python’da ortalama hesaplama g\u00f6revinizi en verimli ve uygun \u015fekilde ger\u00e7ekle\u015ftirmek i\u00e7in gerekli ara\u00e7lara sahipsiniz.<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu\nPython, veri analizi ve manip\u00fclasyonu i\u00e7in g\u00fc\u00e7l\u00fc ve \u00e7ok y\u00f6nl\u00fc bir programlama dilidir.","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1403],"tags":[],"class_list":{"0":"post-31203","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-python","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nPython'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu\" \/>\n<meta property=\"og:description\" content=\"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu Python, veri analizi ve manip\u00fclasyonu i\u00e7in g\u00fc\u00e7l\u00fc ve \u00e7ok y\u00f6nl\u00fc bir programlama dilidir.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-07T02:41:11+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"26 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu\",\"datePublished\":\"2025-10-07T02:41:11+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\"},\"wordCount\":3969,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"articleSection\":[\"Python\"],\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\",\"name\":\"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-10-07T02:41:11+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/","og_locale":"tr_TR","og_type":"article","og_title":"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu","og_description":"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu Python, veri analizi ve manip\u00fclasyonu i\u00e7in g\u00fc\u00e7l\u00fc ve \u00e7ok y\u00f6nl\u00fc bir programlama dilidir.","og_url":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2025-10-07T02:41:11+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"26 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu","datePublished":"2025-10-07T02:41:11+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/"},"wordCount":3969,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"articleSection":["Python"],"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#respond"]}],"copyrightYear":"2025","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/","url":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/","name":"Python'da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu - Kodlar\u0131n Gizemli D\u00fcnyas\u0131","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2025-10-07T02:41:11+00:00","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/pythonda-bir-listenin-ortalamasini-bulmanin-5-yolu\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Python’da Bir Listenin Ortalamas\u0131n\u0131 Bulman\u0131n 5 Yolu"}]},{"@type":"WebSite","@id":"https:\/\/fatihsoysal.com\/blog\/#website","url":"https:\/\/fatihsoysal.com\/blog\/","name":"Fatihsoysal.com","description":"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim","publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"tr"},{"@type":["Person","Organization"],"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1","name":"Fatih Soysal","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","contentUrl":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","width":512,"height":512,"caption":"Fatih Soysal"},"logo":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/"},"description":"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.","url":"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/"}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/31203","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/comments?post=31203"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/31203\/revisions"}],"predecessor-version":[{"id":31204,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/31203\/revisions\/31204"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=31203"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=31203"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=31203"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}