{"id":38754,"date":"2026-02-06T20:40:44","date_gmt":"2026-02-06T17:40:44","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=38754"},"modified":"2026-02-06T20:40:44","modified_gmt":"2026-02-06T17:40:44","slug":"seaborn-distplot-kapsamli-bir-rehber","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/seaborn-distplot-kapsamli-bir-rehber\/","title":{"rendered":"Seaborn Distplot: Kapsaml\u0131 Bir Rehber"},"content":{"rendered":"

<\/p>\n

Seaborn Distplot: Kapsaml\u0131 Bir Rehber<\/h2>\n

Giri\u015f<\/h3>\n

Veri analizi ve ke\u015fifsel veri analizi (EDA) s\u00fcrecinde, verilerin da\u011f\u0131l\u0131m\u0131n\u0131 anlamak kritik \u00f6neme sahiptir. Bir de\u011fi\u015fkenin de\u011ferlerinin nas\u0131l yay\u0131ld\u0131\u011f\u0131n\u0131, hangi de\u011ferlerin daha s\u0131k g\u00f6r\u00fcld\u00fc\u011f\u00fcn\u00fc ve potansiyel ayk\u0131r\u0131 de\u011ferleri g\u00f6rselle\u015ftirmek, veri setimiz hakk\u0131nda derinlemesine i\u00e7g\u00f6r\u00fcler sa\u011flar. Python’da veri g\u00f6rselle\u015ftirme denince akla gelen en g\u00fc\u00e7l\u00fc k\u00fct\u00fcphanelerden biri olan Seaborn, bu t\u00fcr da\u011f\u0131l\u0131mlar\u0131 kolay ve estetik bir \u015fekilde g\u00f6rselle\u015ftirmek i\u00e7in \u00e7e\u015fitli ara\u00e7lar sunar. Bu ara\u00e7lardan biri de seaborn.distplot<\/code> fonksiyonudur.<\/p>\n

Bu kapsaml\u0131 rehberde, seaborn.distplot<\/code>‘un ne oldu\u011funu, nas\u0131l kullan\u0131ld\u0131\u011f\u0131n\u0131, temel bile\u015fenlerini, geli\u015fmi\u015f \u00f6zelle\u015ftirme se\u00e7eneklerini ve g\u00fcncel alternatiflerini detayl\u0131 bir \u015fekilde inceleyece\u011fiz. Amac\u0131m\u0131z, tek de\u011fi\u015fkenli da\u011f\u0131l\u0131mlar\u0131 anlamak ve etkili bir \u015fekilde g\u00f6rselle\u015ftirmek isteyen herkese pratik ve teorik bir yol haritas\u0131 sunmakt\u0131r.<\/p>\n

Seaborn ve Matplotlib’e H\u0131zl\u0131 Bir Bak\u0131\u015f<\/h3>\n

Seaborn, Matplotlib \u00fczerine in\u015fa edilmi\u015f y\u00fcksek seviyeli bir veri g\u00f6rselle\u015ftirme k\u00fct\u00fcphanesidir. Matplotlib’in esnekli\u011fini korurken, daha az kodla daha \u00e7ekici ve bilgilendirici istatistiksel grafikler olu\u015fturmay\u0131 hedefler. \u00d6zellikle istatistiksel grafikler i\u00e7in optimize edilmi\u015f olan Seaborn, karma\u015f\u0131k veri setlerindeki ili\u015fkileri, da\u011f\u0131l\u0131mlar\u0131 ve kar\u015f\u0131la\u015ft\u0131rmalar\u0131 g\u00f6rselle\u015ftirmeyi basitle\u015ftirir. distplot<\/code> gibi fonksiyonlar, Matplotlib’in temel \u00e7izim yeteneklerini kullanarak, istatistiksel hesaplamalar\u0131 ve estetik d\u00fczenlemeleri otomatik olarak yapar. Bu sayede, kullan\u0131c\u0131lar grafiklerin teknik detaylar\u0131yla u\u011fra\u015fmak yerine, verilerinin hikayesine odaklanabilirler.<\/p>\n

seaborn.distplot<\/code> Nedir?<\/h3>\n

seaborn.distplot<\/code>, tek de\u011fi\u015fkenli (univariate) veri da\u011f\u0131l\u0131mlar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in tasarlanm\u0131\u015f bir fonksiyondur. Bir de\u011fi\u015fkenin de\u011ferlerinin frekans\u0131n\u0131 veya yo\u011funlu\u011funu g\u00f6stermek i\u00e7in birden fazla g\u00f6rsel \u00f6\u011feyi bir araya getirir. Bu \u00f6zelli\u011fi sayesinde, bir da\u011f\u0131l\u0131m\u0131n genel \u015fekli, merkez e\u011filimi ve yay\u0131l\u0131m\u0131 hakk\u0131nda h\u0131zl\u0131ca bilgi edinmek m\u00fcmk\u00fcnd\u00fcr.<\/p>\n

Temel Bile\u015fenler<\/h4>\n

distplot<\/code> temel olarak \u00fc\u00e7 ana g\u00f6rsel bile\u015feni birle\u015ftirir:<\/p>\n

1. Histogram:<\/strong> Veri aral\u0131klar\u0131n\u0131 (bins) g\u00f6steren \u00e7ubuk grafiklerdir. Her \u00e7ubu\u011fun y\u00fcksekli\u011fi, o aral\u0131ktaki veri noktalar\u0131n\u0131n say\u0131s\u0131n\u0131 veya oran\u0131n\u0131 temsil eder. Bir veri setindeki de\u011ferlerin frekans da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirmenin en temel yollar\u0131ndan biridir.
\n2. KDE (Kernel Density Estimate – \u00c7ekirdek Yo\u011funluk Tahmini):<\/strong> Verinin p\u00fcr\u00fczs\u00fczle\u015ftirilmi\u015f bir yo\u011funluk tahmini \u00e7izgisidir. Histogram\u0131n aksine, KDE, aral\u0131klar yerine s\u00fcrekli bir e\u011fri kullanarak da\u011f\u0131l\u0131m\u0131n \u015feklini tahmin eder. Bu, da\u011f\u0131l\u0131m\u0131n alt\u0131nda yatan olas\u0131l\u0131k yo\u011funluk fonksiyonunu daha net bir \u015fekilde g\u00f6rmemizi sa\u011flar.
\n3. Rug Plot (Hal\u0131 Grafi\u011fi):<\/strong> Veri ekseni \u00fczerinde her veri noktas\u0131n\u0131n konumunu k\u00fc\u00e7\u00fck dikey \u00e7izgilerle g\u00f6sterir. Bu, veri noktalar\u0131n\u0131n yo\u011funla\u015ft\u0131\u011f\u0131 veya seyrek oldu\u011fu b\u00f6lgeleri do\u011frudan g\u00f6zlemlememizi sa\u011flar. \u00d6zellikle KDE’nin veya histogram\u0131n belirli b\u00f6lgelerdeki tahminlerinin ger\u00e7ek veri noktalar\u0131yla ne kadar uyumlu oldu\u011funu anlamak i\u00e7in faydal\u0131d\u0131r.<\/p>\n

Bu \u00fc\u00e7 bile\u015fen, bir da\u011f\u0131l\u0131m hakk\u0131nda hem kaba hem de ince ayr\u0131nt\u0131lar\u0131 ayn\u0131 anda sunarak, veri analistlerine kapsaml\u0131 bir bak\u0131\u015f a\u00e7\u0131s\u0131 sa\u011flar.<\/p>\n

Temel Kullan\u0131m<\/h3>\n

distplot<\/code>‘u kullanmak olduk\u00e7a basittir. Sadece g\u00f6rselle\u015ftirmek istedi\u011finiz tek boyutlu veri dizisini (bir NumPy dizisi, Pandas Serisi veya liste) fonksiyona ge\u00e7irmeniz yeterlidir.<\/p>\n

Gerekli K\u00fct\u00fcphanelerin \u0130\u00e7e Aktar\u0131lmas\u0131<\/h4>\n

\u0130lk olarak, gerekli k\u00fct\u00fcphaneleri i\u00e7e aktaral\u0131m:<\/p>\n

import seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd<\/code><\/pre>\n
Basit Bir distplot<\/code> Olu\u015fturma<\/h4>\n
\u015eimdi, rastgele olu\u015fturulmu\u015f bir veri seti \u00fczerinde distplot<\/code>‘un temel kullan\u0131m\u0131n\u0131 g\u00f6relim:<\/p>\n
# Rastgele veri olu\u015fturma\ndata = np.random.normal(loc=0, scale=1, size=1000) # Ortalama 0, standart sapma 1 olan 1000 veri noktas\u0131\n\n
distplot olu\u015fturma<\/h2>\nsns.distplot(data)\n\n
Grafi\u011fi g\u00f6sterme<\/h2>\nplt.title(\"Temel Da\u011f\u0131l\u0131m Grafi\u011fi\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()<\/code><\/pre>\n
Bu kod par\u00e7as\u0131, varsay\u0131lan olarak bir histogram, bir KDE ve bir rug plot i\u00e7eren bir grafik olu\u015fturacakt\u0131r. \u00c7\u0131kt\u0131, verilerin yakla\u015f\u0131k olarak normal bir da\u011f\u0131l\u0131ma sahip oldu\u011funu g\u00f6sterecektir.<\/p>\n
distplot<\/code>‘un Bile\u015fenlerini Anlamak ve Kontrol Etmek<\/h3>\n
distplot<\/code>, kullan\u0131c\u0131lar\u0131n her bir bile\u015feni ayr\u0131 ayr\u0131 etkinle\u015ftirmesine veya devre d\u0131\u015f\u0131 b\u0131rakmas\u0131na ve bunlar\u0131n g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc \u00f6zelle\u015ftirmesine olanak tan\u0131yan bir dizi parametreye sahiptir.<\/p>\n
Histogram\u0131 \u00d6zelle\u015ftirme<\/h4>\n
Histogram, distplot<\/code>‘un temel bile\u015fenlerinden biridir. hist<\/code> parametresi ile etkinle\u015ftirilebilir veya devre d\u0131\u015f\u0131 b\u0131rak\u0131labilir. bins<\/code> parametresi, histogramdaki \u00e7ubuklar\u0131n say\u0131s\u0131n\u0131 veya aral\u0131klar\u0131n\u0131 belirler. hist_kws<\/code> s\u00f6zl\u00fc\u011f\u00fc ise Matplotlib’in hist<\/code> fonksiyonuna iletilen anahtar kelime arg\u00fcmanlar\u0131n\u0131 bar\u0131nd\u0131r\u0131r.<\/p>\n
# Sadece histogram\u0131 g\u00f6ster\nsns.distplot(data, hist=True, kde=False, rug=False)\nplt.title(\"Sadece Histogram\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Frekans\")\nplt.show()\n\n
\u00c7ubuk say\u0131s\u0131n\u0131 (bins) ayarlama<\/h2>\nsns.distplot(data, bins=30, kde=False, rug=False) # 30 \u00e7ubuk\nplt.title(\"Histogram (30 \u00c7ubuk)\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Frekans\")\nplt.show()\n\n
Histogram\u0131n rengini ve kenarl\u0131klar\u0131n\u0131 de\u011fi\u015ftirme<\/h2>\nsns.distplot(data, hist_kws={\n    \"color\": \"skyblue\",\n    \"edgecolor\": \"black\",\n    \"linewidth\": 1.5,\n    \"alpha\": 0.7\n}, kde=False, rug=False)\nplt.title(\"\u00d6zelle\u015ftirilmi\u015f Histogram\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Frekans\")\nplt.show()<\/code><\/pre>\n
bins<\/code> parametresi, da\u011f\u0131l\u0131m\u0131n ne kadar ayr\u0131nt\u0131l\u0131 g\u00f6sterilece\u011fini belirler. Daha fazla bins<\/code>, daha ince ayr\u0131nt\u0131lar sunar ancak g\u00fcr\u00fclt\u00fcl\u00fc olabilir; daha az bins<\/code> ise genel \u015fekli daha p\u00fcr\u00fczs\u00fcz g\u00f6sterir ancak ayr\u0131nt\u0131lar\u0131 gizleyebilir.<\/p>\n
KDE’yi (Kernel Density Estimate) \u00d6zelle\u015ftirme<\/h4>\n
KDE, kde<\/code> parametresi ile etkinle\u015ftirilebilir veya devre d\u0131\u015f\u0131 b\u0131rak\u0131labilir. kde_kws<\/code> s\u00f6zl\u00fc\u011f\u00fc, Matplotlib’in plot<\/code> fonksiyonuna iletilen anahtar kelime arg\u00fcmanlar\u0131n\u0131 bar\u0131nd\u0131r\u0131r.<\/p>\n
# Sadece KDE'yi g\u00f6ster\nsns.distplot(data, hist=False, kde=True, rug=False)\nplt.title(\"Sadece KDE\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()\n\n
KDE \u00e7izgisini \u00f6zelle\u015ftirme<\/h2>\nsns.distplot(data, hist=False, kde_kws={\n    \"color\": \"red\",\n    \"linewidth\": 2,\n    \"linestyle\": \"--\",\n    \"alpha\": 0.8,\n    \"shade\": True # KDE'nin alt\u0131n\u0131 doldurma\n}, rug=False)\nplt.title(\"\u00d6zelle\u015ftirilmi\u015f KDE\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()<\/code><\/pre>\n
shade=True<\/code> parametresi, KDE e\u011frisinin alt\u0131ndaki alan\u0131 doldurarak da\u011f\u0131l\u0131m\u0131n g\u00f6rsel etkisini art\u0131r\u0131r. bw_method<\/code> (bandwidth method) ve gridsize<\/code> gibi daha geli\u015fmi\u015f kde_kws<\/code> parametreleri, KDE tahmininin p\u00fcr\u00fczs\u00fczl\u00fc\u011f\u00fcn\u00fc ve hassasiyetini kontrol eder. bw_method<\/code> genellikle ‘scott’, ‘silverman’ veya bir say\u0131sal de\u011fer alabilir; bu, \u00e7ekirdek fonksiyonunun geni\u015fli\u011fini belirler ve e\u011frinin ne kadar p\u00fcr\u00fczs\u00fcz olaca\u011f\u0131n\u0131 etkiler.<\/p>\n
Rug Plot’u \u00d6zelle\u015ftirme<\/h4>\n
Rug plot, rug<\/code> parametresi ile etkinle\u015ftirilebilir veya devre d\u0131\u015f\u0131 b\u0131rak\u0131labilir. rug_kws<\/code> s\u00f6zl\u00fc\u011f\u00fc, Matplotlib’in plot<\/code> fonksiyonuna iletilen anahtar kelime arg\u00fcmanlar\u0131n\u0131 bar\u0131nd\u0131r\u0131r.<\/p>\n
# Sadece rug plot'u g\u00f6ster\nsns.distplot(data, hist=False, kde=False, rug=True)\nplt.title(\"Sadece Rug Plot\")\nplt.xlabel(\"De\u011ferler\")\nplt.show()\n\n
Rug plot'u \u00f6zelle\u015ftirme<\/h2>\nsns.distplot(data, hist=False, kde=False, rug_kws={\n    \"color\": \"purple\",\n    \"linewidth\": 0.5,\n    \"height\": 0.05 # \u00c7izgilerin y\u00fcksekli\u011fi\n})\nplt.title(\"\u00d6zelle\u015ftirilmi\u015f Rug Plot\")\nplt.xlabel(\"De\u011ferler\")\nplt.show()<\/code><\/pre>\n
height<\/code> parametresi, rug \u00e7izgilerinin eksen \u00fczerindeki y\u00fcksekli\u011fini kontrol eder. Rug plot, \u00f6zellikle veri noktalar\u0131n\u0131n yo\u011funla\u015ft\u0131\u011f\u0131 veya bo\u015fluklar\u0131n oldu\u011fu yerleri do\u011frudan g\u00f6stermesi a\u00e7\u0131s\u0131ndan faydal\u0131d\u0131r.<\/p>\n
Geli\u015fmi\u015f \u00d6zellikler ve \u00d6zelle\u015ftirmeler<\/h3>\n
distplot<\/code>, yaln\u0131zca temel bile\u015fenleri kontrol etmekle kalmaz, ayn\u0131 zamanda grafiklerinizi daha bilgilendirici hale getirmek i\u00e7in \u00e7e\u015fitli geli\u015fmi\u015f \u00f6zelle\u015ftirme se\u00e7enekleri sunar.<\/p>\n
Normalizasyon ve Yo\u011funluk<\/h4>\n
Varsay\u0131lan olarak, distplot<\/code> histogram\u0131 yo\u011funluk (density) olarak normalle\u015ftirir, yani \u00e7ubuklar\u0131n toplam alan\u0131 1’e e\u015fittir. Bu, KDE e\u011frisi ile histogram\u0131n ayn\u0131 \u00f6l\u00e7ekte olmas\u0131n\u0131 sa\u011flar. Ancak, frekanslar\u0131 g\u00f6rmek istiyorsan\u0131z norm_hist=False<\/code> olarak ayarlayabilirsiniz.<\/p>\n
# Histogram\u0131 frekans olarak g\u00f6sterme (KDE ile birlikte)\nsns.distplot(data, norm_hist=False, kde_kws={\"color\": \"red\"})\nplt.title(\"Histogram (Frekans) ve KDE\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Frekans \/ Yo\u011funluk\") # \u0130ki \u00f6l\u00e7ekli bir grafik i\u00e7in etiket\nplt.show()<\/code><\/pre>\n
norm_hist=False<\/code> yap\u0131ld\u0131\u011f\u0131nda, histogram \u00e7ubuklar\u0131 frekanslar\u0131 temsil ederken, KDE e\u011frisi hala olas\u0131l\u0131k yo\u011funlu\u011funu temsil edecektir. Bu durumda y ekseni etiketi “Frekans \/ Yo\u011funluk” gibi bir ifadeyle iki farkl\u0131 \u00f6l\u00e7e\u011fi ima etmelidir.<\/p>\n
Farkl\u0131 Renkler ve Stiller<\/h4>\n
color<\/code> parametresi, t\u00fcm grafik bile\u015fenlerinin (histogram, KDE, rug plot) ana rengini belirler. Ancak, her bile\u015fenin kendi _kws<\/code> s\u00f6zl\u00fc\u011f\u00fc arac\u0131l\u0131\u011f\u0131yla daha spesifik renk ve stil ayarlamalar\u0131 yapabilirsiniz.<\/p>\n
sns.distplot(data, color=\"green\",\n             hist_kws={\"edgecolor\": \"black\", \"alpha\": 0.6},\n             kde_kws={\"linewidth\": 2, \"linestyle\": \"-.\"},\n             rug_kws={\"color\": \"darkgreen\", \"height\": 0.03})\nplt.title(\"Tamamen \u00d6zelle\u015ftirilmi\u015f Da\u011f\u0131l\u0131m\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()<\/code><\/pre>\n
Eksen Limitleri ve Etiketler<\/h4>\n
Grafi\u011fin okunabilirli\u011fini art\u0131rmak i\u00e7in eksen limitlerini ve etiketlerini ayarlamak \u00f6nemlidir. Bu, Matplotlib’in plt<\/code> mod\u00fcl\u00fc arac\u0131l\u0131\u011f\u0131yla yap\u0131l\u0131r.<\/p>\n
sns.distplot(data, color=\"blue\")\nplt.title(\"\u00d6zel Ba\u015fl\u0131k\")\nplt.xlabel(\"\u00d6zel X Ekseni Etiketi\")\nplt.ylabel(\"\u00d6zel Y Ekseni Etiketi\")\nplt.xlim(-4, 4) # X ekseni limitlerini belirleme\nplt.ylim(0, 0.5) # Y ekseni limitlerini belirleme\nplt.show()<\/code><\/pre>\n
Dikey distplot<\/code><\/h4>\n
Bazen, \u00f6zellikle birden fazla da\u011f\u0131l\u0131m\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131rken veya belirli bir g\u00f6rsel d\u00fczeni korurken, da\u011f\u0131l\u0131m\u0131 dikey olarak g\u00f6rselle\u015ftirmek isteyebilirsiniz. vertical=True<\/code> parametresi bu i\u015flevi sa\u011flar.<\/p>\n
sns.distplot(data, vertical=True, color=\"orange\")\nplt.title(\"Dikey Da\u011f\u0131l\u0131m Grafi\u011fi\")\nplt.xlabel(\"Yo\u011funluk\")\nplt.ylabel(\"De\u011ferler\")\nplt.show()<\/code><\/pre>\n
Dikey grafiklerde eksen etiketlerinin de de\u011fi\u015fti\u011fine dikkat edin.<\/p>\n
Ortalama ve Medyan \u00c7izgileri Ekleme<\/h4>\n
Da\u011f\u0131l\u0131m\u0131n merkez e\u011filimini vurgulamak i\u00e7in ortalama veya medyan gibi istatistiksel \u00f6l\u00e7\u00fctleri grafik \u00fczerine \u00e7izmek \u00e7ok faydal\u0131d\u0131r. Matplotlib’in axvline<\/code> (dikey \u00e7izgi) fonksiyonu bu ama\u00e7la kullan\u0131labilir.<\/p>\n
mean_val = np.mean(data)\nmedian_val = np.median(data)\n\nsns.distplot(data, color=\"teal\")\nplt.axvline(mean_val, color='red', linestyle='--', label=f'Ortalama: {mean_val:.2f}')\nplt.axvline(median_val, color='purple', linestyle=':', label=f'Medyan: {median_val:.2f}')\nplt.title(\"Da\u011f\u0131l\u0131m Ortalamas\u0131 ve Medyan\u0131 ile\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.legend()\nplt.show()<\/code><\/pre>\n
Bu, da\u011f\u0131l\u0131m\u0131n simetrisini veya \u00e7arp\u0131kl\u0131\u011f\u0131n\u0131 h\u0131zl\u0131ca de\u011ferlendirmek i\u00e7in harika bir yoldur.<\/p>\n
Birden Fazla Da\u011f\u0131l\u0131m\u0131 Tek Grafikte G\u00f6sterme (Overlapping)<\/h4>\n
Farkl\u0131 gruplara ait da\u011f\u0131l\u0131mlar\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in ayn\u0131 eksen \u00fczerinde birden fazla distplot<\/code> \u00e7izebilirsiniz. Her distplot<\/code> \u00e7a\u011fr\u0131s\u0131 i\u00e7in label<\/code> parametresini kullanarak bir a\u00e7\u0131klama eklemeyi ve plt.legend()<\/code> ile bunlar\u0131 g\u00f6stermeyi unutmay\u0131n.<\/p>\n
data_group1 = np.random.normal(loc=-1, scale=1, size=500)\ndata_group2 = np.random.normal(loc=1, scale=0.8, size=500)\n\nsns.distplot(data_group1, color=\"blue\", label=\"Grup 1\", hist=False, rug=False, kde_kws={\"shade\": True})\nsns.distplot(data_group2, color=\"red\", label=\"Grup 2\", hist=False, rug=False, kde_kws={\"shade\": True})\n\nplt.title(\"\u0130ki Grubun Da\u011f\u0131l\u0131m Kar\u015f\u0131la\u015ft\u0131rmas\u0131\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.legend()\nplt.show()<\/code><\/pre>\n
Bu \u00f6rnekte, sadece KDE’leri kullanarak da\u011f\u0131l\u0131mlar\u0131 kar\u015f\u0131la\u015ft\u0131rd\u0131k. Histogramlar\u0131 da dahil edebilirsiniz, ancak bu, grafi\u011fi daha karma\u015f\u0131k hale getirebilir.<\/p>\n
distplot<\/code>‘un S\u0131n\u0131rlamalar\u0131 ve Alternatifleri<\/h3>\n
seaborn.distplot<\/code> g\u00fc\u00e7l\u00fc bir ara\u00e7 olmas\u0131na ra\u011fmen, Seaborn’un daha yeni s\u00fcr\u00fcmleriyle birlikte baz\u0131 s\u0131n\u0131rlamalar\u0131 ortaya \u00e7\u0131km\u0131\u015f ve yerine daha esnek ve \u00f6zelle\u015ftirilebilir alternatifler sunulmu\u015ftur. \u00d6nemli bir not olarak, distplot<\/code> fonksiyonu Seaborn’un gelecekteki s\u00fcr\u00fcmlerinde kald\u0131r\u0131lmas\u0131 planlanan eski bir fonksiyondur.<\/strong> Bu nedenle, yeni projelerinizde veya mevcut kodunuzu g\u00fcncellerken alternatiflerini kullanman\u0131z \u015fiddetle tavsiye edilir.<\/p>\n
distplot<\/code>‘un S\u0131n\u0131rlamalar\u0131<\/h4>\n
1.  Tek De\u011fi\u015fkenli Odak:<\/strong> distplot<\/code> sadece tek bir de\u011fi\u015fkenin da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in tasarlanm\u0131\u015ft\u0131r. Birden fazla kategorik veya say\u0131sal de\u011fi\u015fkenin da\u011f\u0131l\u0131m\u0131n\u0131 ayn\u0131 anda incelemek i\u00e7in do\u011frudan bir hue<\/code> veya col<\/code> parametresi yoktur.
\n2.  \u00d6zelle\u015ftirme Zorlu\u011fu:<\/strong> Her ne kadar _kws<\/code> s\u00f6zl\u00fckleri arac\u0131l\u0131\u011f\u0131yla \u00f6zelle\u015ftirmeler m\u00fcmk\u00fcn olsa da, bu y\u00f6ntem bazen karma\u015f\u0131k olabilir ve daha az sezgiseldir.
\n3.  API Karma\u015f\u0131kl\u0131\u011f\u0131:<\/strong> Tek bir fonksiyonun hem histogram, hem KDE, hem de rug plot’u i\u00e7ermesi, baz\u0131 durumlarda gereksiz \u00f6zelliklerin etkin kalmas\u0131na veya kontrol edilmesinin zorla\u015fmas\u0131na neden olabilir.<\/p>\n
Modern Alternatifler<\/h4>\n
Seaborn, distplot<\/code>‘un yerini almak \u00fczere tasarlanm\u0131\u015f, daha mod\u00fcler ve g\u00fc\u00e7l\u00fc alternatifler sunar: histplot<\/code>, kdeplot<\/code>, ecdfplot<\/code> ve figure-level displot<\/code>.<\/p>\n
seaborn.histplot<\/code><\/h5>\n
histplot<\/code>, distplot<\/code>‘un histogram bile\u015feninin daha geli\u015fmi\u015f ve esnek bir versiyonudur.<\/p>\n
*   Hue Deste\u011fi:<\/strong> Kategorik de\u011fi\u015fkenlere g\u00f6re farkl\u0131 histogramlar \u00e7izebilir.
\n*   \u00c7ift De\u011fi\u015fkenli Histogramlar:<\/strong> \u0130ki say\u0131sal de\u011fi\u015fkenin da\u011f\u0131l\u0131m\u0131n\u0131 2D histogram olarak g\u00f6sterebilir.
\n*   Normalizasyon \u00c7e\u015fitlili\u011fi:<\/strong> stat<\/code> parametresi ile count<\/code>, frequency<\/code>, density<\/code>, probability<\/code> gibi farkl\u0131 normalizasyon t\u00fcrlerini se\u00e7ebilirsiniz.
\n*   \u00c7oklu bins<\/code> Stratejileri:<\/strong> Daha esnek binwidth<\/code> ve bins<\/code> ayarlamalar\u0131 sunar.<\/p>\n
# histplot ile temel kullan\u0131m\nsns.histplot(data, kde=True, color=\"blue\")\nplt.title(\"histplot ile Histogram ve KDE\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Say\u0131\")\nplt.show()\n\n
Hue ile gruplara g\u00f6re histogram<\/h2>\ndf = pd.DataFrame({\n    'value': np.concatenate([np.random.normal(0, 1, 500), np.random.normal(2, 1, 500)]),\n    'group': ['A']500 + ['B']<\/em>500\n})\nsns.histplot(data=df, x='value', hue='group', kde=True)\nplt.title(\"histplot ile Hue Deste\u011fi\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Say\u0131\")\nplt.show()<\/code><\/pre>\n
seaborn.kdeplot<\/code><\/h5>\n
kdeplot<\/code>, distplot<\/code>‘un KDE bile\u015feninin daha geli\u015fmi\u015f bir versiyonudur. Tek ba\u015f\u0131na veya iki de\u011fi\u015fkenli (bivariate) yo\u011funluk haritalar\u0131 olu\u015fturmak i\u00e7in kullan\u0131labilir.<\/p>\n
*   2D KDE:<\/strong> \u0130ki de\u011fi\u015fkenin ortak da\u011f\u0131l\u0131m\u0131n\u0131 kontur \u00e7izgileri veya renkli alanlar olarak g\u00f6sterebilir.
\n*   Daha Fazla Kontrol:<\/strong> fill<\/code>, levels<\/code>, thresh<\/code> gibi parametrelerle KDE g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc daha detayl\u0131 kontrol edebilirsiniz.<\/p>\n
# kdeplot ile tek de\u011fi\u015fkenli\nsns.kdeplot(x=data, fill=True, color=\"purple\")\nplt.title(\"kdeplot ile Tek De\u011fi\u015fkenli KDE\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()\n\n
kdeplot ile iki de\u011fi\u015fkenli (\u00f6rnek veri)<\/h2>\ndata_x = np.random.normal(0, 1, 500)\ndata_y = np.random.normal(0, 1, 500)\nsns.kdeplot(x=data_x, y=data_y, fill=True, cmap=\"Reds\")\nplt.title(\"kdeplot ile \u0130ki De\u011fi\u015fkenli KDE\")\nplt.xlabel(\"X De\u011feri\")\nplt.ylabel(\"Y De\u011feri\")\nplt.show()<\/code><\/pre>\n
seaborn.ecdfplot<\/code><\/h5>\n
ecdfplot<\/code>, k\u00fcm\u00fclatif da\u011f\u0131l\u0131m fonksiyonunu (Empirical Cumulative Distribution Function – ECDF) g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131l\u0131r. Bu, bir de\u011ferin veya daha az\u0131n\u0131n g\u00f6zlemlenme olas\u0131l\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/p>\n
sns.ecdfplot(data, color=\"brown\")\nplt.title(\"ECDF Plot\")\nplt.xlabel(\"De\u011ferler\")\nplt.ylabel(\"K\u00fcm\u00fclatif Olas\u0131l\u0131k\")\nplt.show()<\/code><\/pre>\n
seaborn.displot<\/code> (Dikkat: distplot<\/code> de\u011fil, displot<\/code>!)<\/h5>\n
seaborn.displot<\/code>, figure-level bir fonksiyondur ve kind<\/code> parametresi arac\u0131l\u0131\u011f\u0131yla histplot<\/code>, kdeplot<\/code> veya ecdfplot<\/code>‘u \u00e7a\u011f\u0131rabilir. En b\u00fcy\u00fck avantaj\u0131, FacetGrid kullanarak birden fazla alt grafik (subplot) olu\u015fturma yetene\u011fidir. Bu, kategorik de\u011fi\u015fkenlere g\u00f6re da\u011f\u0131l\u0131mlar\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in son derece kullan\u0131\u015fl\u0131d\u0131r.<\/p>\n
*   Figure-level:<\/strong> Matplotlib’in Figure ve Axes nesnelerini y\u00f6netir, bu da birden fazla alt grafik olu\u015fturmay\u0131 kolayla\u015ft\u0131r\u0131r.
\n*   kind<\/code> parametresi:<\/strong> hist<\/code>, kde<\/code>, ecdf<\/code> se\u00e7enekleriyle istedi\u011finiz grafik t\u00fcr\u00fcn\u00fc belirtirsiniz.
\n*   col<\/code> ve row<\/code> parametreleri:<\/strong> Kategorik de\u011fi\u015fkenlere g\u00f6re grafikleri s\u00fctunlara veya sat\u0131rlara ay\u0131r\u0131r.
\n*   hue<\/code> parametresi:<\/strong> Farkl\u0131 gruplar\u0131 ayn\u0131 alt grafik i\u00e7inde renklerle ay\u0131r\u0131r.<\/p>\n
# displot ile histogram (hue ve col deste\u011fi ile)\ntips = sns.load_dataset(\"tips\") # \u00d6rnek veri seti\nsns.displot(data=tips, x=\"total_bill\", kind=\"hist\", hue=\"sex\", col=\"time\")\nplt.suptitle(\"displot ile total_bill da\u011f\u0131l\u0131m\u0131 (Cinsiyet ve Zaman'a G\u00f6re)\", y=1.02)\nplt.show()\n\n
displot ile KDE<\/h2>\nsns.displot(data=tips, x=\"total_bill\", kind=\"kde\", hue=\"sex\", col=\"time\", fill=True)\nplt.suptitle(\"displot ile total_bill KDE (Cinsiyet ve Zaman'a G\u00f6re)\", y=1.02)\nplt.show()<\/code><\/pre>\n
displot<\/code>, karma\u015f\u0131k kar\u015f\u0131la\u015ft\u0131rmal\u0131 da\u011f\u0131l\u0131m analizleri i\u00e7in tercih edilen fonksiyondur ve distplot<\/code>‘un yerini almas\u0131 gereken ana ara\u00e7t\u0131r.<\/p>\n
Pratik Bir Uygulama \u00d6rne\u011fi<\/h3>\n
\u015eimdi, ger\u00e7ek bir veri seti \u00fczerinde distplot<\/code>‘un ve alternatiflerinin nas\u0131l kullan\u0131labilece\u011fine dair pratik bir \u00f6rnek yapal\u0131m. Seaborn’un iris<\/code> veri setini kullanarak \u00e7i\u00e7eklerin ta\u00e7 yapra\u011f\u0131 uzunluklar\u0131n\u0131n da\u011f\u0131l\u0131m\u0131n\u0131 inceleyelim.<\/p>\n
# Iris veri setini y\u00fckle\niris = sns.load_dataset(\"iris\")\n\n
Genel ta\u00e7 yapra\u011f\u0131 uzunlu\u011fu da\u011f\u0131l\u0131m\u0131 (distplot ile)<\/h2>\nplt.figure(figsize=(10, 6))\nsns.distplot(iris['petal_length'], color=\"purple\",\n             bins=20,\n             hist_kws={\"edgecolor\": \"black\", \"alpha\": 0.7},\n             kde_kws={\"linewidth\": 2, \"linestyle\": \"-\"},\n             rug=True, rug_kws={\"color\": \"darkblue\", \"height\": 0.05})\nplt.title(\"Iris Petal Length Da\u011f\u0131l\u0131m\u0131 (distplot)\")\nplt.xlabel(\"Ta\u00e7 Yapra\u011f\u0131 Uzunlu\u011fu (cm)\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()\n\n
T\u00fcrlere g\u00f6re ta\u00e7 yapra\u011f\u0131 uzunlu\u011fu da\u011f\u0131l\u0131m\u0131 (displot ile)<\/h2>\nplt.figure(figsize=(12, 6))\nsns.displot(data=iris, x=\"petal_length\", kind=\"kde\", hue=\"species\", fill=True,\n            palette=\"viridis\", height=5, aspect=1.2)\nplt.suptitle(\"Iris Petal Length Da\u011f\u0131l\u0131m\u0131 T\u00fcrlere G\u00f6re (displot - KDE)\", y=1.02)\nplt.xlabel(\"Ta\u00e7 Yapra\u011f\u0131 Uzunlu\u011fu (cm)\")\nplt.ylabel(\"Yo\u011funluk\")\nplt.show()\n\n
T\u00fcrlere g\u00f6re ta\u00e7 yapra\u011f\u0131 uzunlu\u011fu da\u011f\u0131l\u0131m\u0131 (displot - hist)<\/h2>\nplt.figure(figsize=(12, 6))\nsns.displot(data=iris, x=\"petal_length\", kind=\"hist\", hue=\"species\",\n            multiple=\"stack\", palette=\"viridis\", height=5, aspect=1.2)\nplt.suptitle(\"Iris Petal Length Da\u011f\u0131l\u0131m\u0131 T\u00fcrlere G\u00f6re (displot - Histogram)\", y=1.02)\nplt.xlabel(\"Ta\u00e7 Yapra\u011f\u0131 Uzunlu\u011fu (cm)\")\nplt.ylabel(\"Say\u0131\")\nplt.show()<\/code><\/pre>\n
\u0130lk grafikte, distplot<\/code> kullanarak t\u00fcm iris t\u00fcrlerinin ta\u00e7 yapra\u011f\u0131 uzunluklar\u0131n\u0131n genel da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6r\u00fcyoruz. \u0130ki belirgin tepe noktas\u0131, veri setinde farkl\u0131 ta\u00e7 yapra\u011f\u0131 uzunluklar\u0131na sahip iki ana grup oldu\u011funu d\u00fc\u015f\u00fcnd\u00fcr\u00fcyor.
\n\u0130kinci grafikte ise, displot<\/code>‘un kind=\"kde\"<\/code> ve hue=\"species\"<\/code> parametrelerini kullanarak her bir iris t\u00fcr\u00fcn\u00fcn (setosa, versicolor, virginica) ta\u00e7 yapra\u011f\u0131 uzunluklar\u0131n\u0131n yo\u011funluk da\u011f\u0131l\u0131m\u0131n\u0131 ayr\u0131 ayr\u0131 ve kar\u015f\u0131la\u015ft\u0131rmal\u0131 olarak g\u00f6r\u00fcyoruz. Bu, ilk tepe noktas\u0131n\u0131n Setosa t\u00fcr\u00fcne, di\u011fer iki tepe noktas\u0131n\u0131n ise Versicolor ve Virginica t\u00fcrlerine ait oldu\u011funu net bir \u015fekilde ortaya koyuyor.
\n\u00dc\u00e7\u00fcnc\u00fc grafikte, displot<\/code>‘un kind=\"hist\"<\/code> ve multiple=\"stack\"<\/code> parametrelerini kullanarak histogramlar\u0131 \u00fcst \u00fcste y\u0131\u011fd\u0131k, bu da her t\u00fcr\u00fcn toplam da\u011f\u0131l\u0131ma nas\u0131l katk\u0131da bulundu\u011funu g\u00f6rselle\u015ftirmemizi sa\u011flad\u0131.<\/p>\n
Bu \u00f6rnek, distplot<\/code>‘un tek de\u011fi\u015fkenli genel bir bak\u0131\u015f i\u00e7in hala kullan\u0131\u015fl\u0131 olabilece\u011fini, ancak kategorik de\u011fi\u015fkenlere g\u00f6re kar\u015f\u0131la\u015ft\u0131rmal\u0131 analizler i\u00e7in displot<\/code> gibi daha modern ve esnek ara\u00e7lar\u0131n \u00e7ok daha g\u00fc\u00e7l\u00fc oldu\u011funu g\u00f6stermektedir.<\/p>\n
Sonu\u00e7<\/h3>\n
seaborn.distplot<\/code>, tek de\u011fi\u015fkenli veri da\u011f\u0131l\u0131mlar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in g\u00fc\u00e7l\u00fc ve kullan\u0131\u015fl\u0131 bir ara\u00e7 olarak Seaborn k\u00fct\u00fcphanesinin \u00f6nemli bir par\u00e7as\u0131 olmu\u015ftur. Histogram, KDE ve rug plot gibi bile\u015fenleri bir araya getirerek, verilerinizin \u015fekli, merkez e\u011filimi ve yay\u0131l\u0131m\u0131 hakk\u0131nda h\u0131zl\u0131 ve kapsaml\u0131 i\u00e7g\u00f6r\u00fcler sunar. \u00c7e\u015fitli parametreler arac\u0131l\u0131\u011f\u0131yla bu bile\u015fenlerin g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc \u00f6zelle\u015ftirmek m\u00fcmk\u00fcnd\u00fcr, bu da analistlere grafiklerini ihtiya\u00e7lar\u0131na g\u00f6re uyarlama esnekli\u011fi sa\u011flar.<\/p>\n
Ancak, veri g\u00f6rselle\u015ftirme k\u00fct\u00fcphaneleri s\u00fcrekli geli\u015fti\u011fi i\u00e7in, distplot<\/code>‘un yerini daha mod\u00fcler, esnek ve \u00e7ok y\u00f6nl\u00fc alternatifler alm\u0131\u015ft\u0131r. seaborn.histplot<\/code>, seaborn.kdeplot<\/code>, seaborn.ecdfplot<\/code> ve \u00f6zellikle figure-level seaborn.displot<\/code>, modern veri analizi i\u015f ak\u0131\u015flar\u0131nda da\u011f\u0131l\u0131m g\u00f6rselle\u015ftirmeleri i\u00e7in tercih edilen ara\u00e7lard\u0131r. Bu yeni fonksiyonlar, \u00f6zellikle kategorik de\u011fi\u015fkenlere g\u00f6re da\u011f\u0131l\u0131mlar\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve birden fazla alt grafik olu\u015fturmak gibi karma\u015f\u0131k senaryolarda distplot<\/code>‘tan \u00e7ok daha \u00fcst\u00fcn yetenekler sunar.<\/p>\n
\u00d6zetle, distplot<\/code>‘u anlamak ve kullanabilmek, Seaborn’un tarihsel geli\u015fimini ve temel da\u011f\u0131l\u0131m g\u00f6rselle\u015ftirme prensiplerini kavramak i\u00e7in faydal\u0131d\u0131r. Ancak, g\u00fcncel ve gelece\u011fe d\u00f6n\u00fck projelerinizde histplot<\/code>, kdeplot<\/code> ve displot<\/code> gibi modern alternatiflere y\u00f6nelmeniz, hem daha esnek hem de daha g\u00fc\u00e7l\u00fc g\u00f6rselle\u015ftirmeler olu\u015fturman\u0131z\u0131 sa\u011flayacakt\u0131r. Bu rehberin, tek de\u011fi\u015fkenli veri da\u011f\u0131l\u0131mlar\u0131n\u0131 etkili bir \u015fekilde analiz etme ve g\u00f6rselle\u015ftirme yolculu\u011funuzda size sa\u011flam bir temel sunmas\u0131n\u0131 umuyoruz.<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Seaborn Distplot: Kapsaml\u0131 Bir Rehber\nGiri\u015f\nVeri analizi ve ke\u015fifsel veri analizi (EDA) s\u00fcrecinde, verilerin da\u011f\u0131l\u0131m\u0131n\u0131 anlamak kritik \u00f6nem","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":[1],"tags":[],"class_list":{"0":"post-38754","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nSeaborn Distplot: Kapsaml\u0131 Bir Rehber - 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