{"id":30035,"date":"2025-09-22T19:40:24","date_gmt":"2025-09-22T16:40:24","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/"},"modified":"2025-09-22T19:40:24","modified_gmt":"2025-09-22T16:40:24","slug":"pandas-concat-ornekleri","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/","title":{"rendered":"Pandas concat() \u00d6rnekleri"},"content":{"rendered":"

# Pandas concat() \u0130le Veri Birle\u015ftirme: Kapsaml\u0131 Bir K\u0131lavuz<\/p>\n

Veri bilimi d\u00fcnyas\u0131nda, farkl\u0131 kaynaklardan gelen verileri birle\u015ftirmek s\u0131kl\u0131kla kar\u015f\u0131la\u015f\u0131lan bir ihtiya\u00e7t\u0131r. Pandas k\u00fct\u00fcphanesi, bu i\u015flemi kolayla\u015ft\u0131ran g\u00fc\u00e7l\u00fc bir ara\u00e7 olan concat()<\/code> fonksiyonunu sunar. Bu makalede, Pandas concat()<\/code> fonksiyonunun kullan\u0131m\u0131n\u0131 ad\u0131m ad\u0131m, yeni ba\u015flayanlardan ileri seviye kullan\u0131c\u0131lara kadar herkesin anlayabilece\u011fi \u015fekilde ele alaca\u011f\u0131z. Farkl\u0131 veri yap\u0131lar\u0131yla \u00e7al\u0131\u015f\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz durumlar\u0131, performans optimizasyon tekniklerini ve hata ay\u0131klama stratejilerini kapsaml\u0131 bir \u015fekilde inceleyece\u011fiz. Haz\u0131rsan\u0131z, Pandas concat()<\/code> d\u00fcnyas\u0131na dalal\u0131m!<\/p>\n

Pandas concat() Nedir ve Ne \u0130\u015fe Yarar?<\/strong><\/p>\n

Pandas concat()<\/code> fonksiyonu, bir veya daha fazla Pandas DataFrame veya Series nesnesini dikey veya yatay olarak birle\u015ftirmenizi sa\u011flar. Bu fonksiyon, veri analizi s\u00fcrecinde farkl\u0131 kaynaklardan elde edilen verileri tek bir veri \u00e7er\u00e7evesinde birle\u015ftirmek i\u00e7in olmazsa olmaz bir ara\u00e7t\u0131r. \u00d6rne\u011fin, farkl\u0131 \u015fehirlerden gelen sat\u0131\u015f verilerini tek bir tabloda birle\u015ftirmek, farkl\u0131 zaman dilimlerinde toplanan hava durumu verilerini birle\u015ftirmek veya farkl\u0131 \u00f6zelliklere sahip m\u00fc\u015fteri verilerini birle\u015ftirmek gibi bir\u00e7ok senaryoda kullan\u0131labilir. concat()<\/code> fonksiyonu, verileri birle\u015ftirmenin esnek ve g\u00fc\u00e7l\u00fc bir yolunu sunar, ancak do\u011fru kullan\u0131m\u0131 performans ve do\u011fruluk a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir.<\/p>\n

Pandas concat() Kullan\u0131m\u0131na Giri\u015f: Yeni Ba\u015flayanlar \u0130\u00e7in Ad\u0131m Ad\u0131m K\u0131lavuz<\/strong><\/p>\n

Ad\u0131m 1: Gerekli K\u00fct\u00fcphaneleri \u0130\u00e7e Aktarma:<\/strong><\/p>\n

\u0130lk ad\u0131m\u0131m\u0131z, gerekli k\u00fct\u00fcphaneleri i\u00e7e aktarmakt\u0131r. Bu \u00f6rnekte sadece Pandas’a ihtiyac\u0131m\u0131z var:<\/p>\n

import pandas as pd<\/code><\/pre>\n
Ad\u0131m 2: \u00d6rnek DataFrame’ler Olu\u015fturma:<\/strong><\/p>\n
\u0130ki basit DataFrame olu\u015ftural\u0131m:<\/p>\n
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})\ndf2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})<\/code><\/pre>\n
Ad\u0131m 3: concat() Fonksiyonunu Kullanarak DataFrame’leri Birle\u015ftirme:<\/strong><\/p>\n
concat()<\/code> fonksiyonunu kullanarak df1<\/code> ve df2<\/code> DataFrame’lerini dikey olarak birle\u015ftirelim:<\/p>\n
combined_df = pd.concat([df1, df2])\nprint(combined_df)<\/code><\/pre>\n
Bu kod, df1<\/code> ve df2<\/code> DataFrame’lerini dikey olarak birle\u015ftirip combined_df<\/code> adl\u0131 yeni bir DataFrame olu\u015fturur.  Sonu\u00e7 a\u015fa\u011f\u0131daki gibi olacakt\u0131r:<\/p>\n
A  B\n0  1  3\n1  2  4\n0  5  7\n1  6  8<\/code><\/pre>\n
G\u00f6rd\u00fc\u011f\u00fcn\u00fcz gibi, indexler korunmu\u015ftur.  E\u011fer indexleri s\u0131f\u0131rlamak isterseniz ignore_index=True<\/code> parametresini kullanabilirsiniz:<\/p>\n
combined_df = pd.concat([df1, df2], ignore_index=True)\nprint(combined_df)<\/code><\/pre>\n
Bu kod, indexleri s\u0131f\u0131rlayarak a\u015fa\u011f\u0131daki \u00e7\u0131kt\u0131y\u0131 verir:<\/p>\n
A  B\n0  1  3\n1  2  4\n2  5  7\n3  6  8<\/code><\/pre>\n
Yatay Birle\u015ftirme: Nas\u0131l Yap\u0131l\u0131r?<\/strong><\/p>\n
concat()<\/code> fonksiyonu ile yatay birle\u015ftirme yapmak i\u00e7in axis=1<\/code> parametresini kullan\u0131r\u0131z.  \u00d6rne\u011fin:<\/p>\n
df3 = pd.DataFrame({'C': [9, 10], 'D': [11, 12]})\nhorizontal_combined_df = pd.concat([df1, df3], axis=1)\nprint(horizontal_combined_df)<\/code><\/pre>\n
Bu kod, df1<\/code> ve df3<\/code> DataFrame’lerini yatay olarak birle\u015ftirir.  Sonu\u00e7:<\/p>\n
A  B  C   D\n0  1  3  9  11\n1  2  4  10 12<\/code><\/pre>\n
Ger\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka Analizleri<\/strong><\/p>\n
Vaka 1: E-ticaret Veri Analizi:<\/strong><\/p>\n
Bir e-ticaret \u015firketinin farkl\u0131 ma\u011fazalar\u0131ndan gelen sat\u0131\u015f verilerini birle\u015ftirelim. Her ma\u011faza, \u00fcr\u00fcn ID’si, sat\u0131\u015f adedi ve sat\u0131\u015f tarihi gibi bilgileri i\u00e7eren ayr\u0131 bir CSV dosyas\u0131nda sakl\u0131yor olsun.  concat()<\/code> fonksiyonu, bu dosyalar\u0131 tek bir DataFrame’e birle\u015ftirerek toplam sat\u0131\u015flar\u0131 analiz etmemizi sa\u011flar.<\/p>\n
import glob\n\nfiles = glob.glob("sales_data\/*.csv") # sales_data klas\u00f6r\u00fcndeki t\u00fcm csv dosyalar\u0131n\u0131 bulur\n\ndfs = []\nfor file in files:\n    dfs.append(pd.read_csv(file))\n\ncombined_sales_data = pd.concat(dfs, ignore_index=True)\nprint(combined_sales_data.head()) # \u0130lk birka\u00e7 sat\u0131r\u0131 g\u00f6r\u00fcnt\u00fcler<\/code><\/pre>\n
Bu \u00f6rnekte, glob<\/code> mod\u00fcl\u00fc ile t\u00fcm CSV dosyalar\u0131n\u0131 okuyup, concat()<\/code> ile birle\u015ftiriyoruz.  ignore_index=True<\/code> sayesinde yeni bir index olu\u015fturuyoruz.<\/p>\n
Vaka 2: Sosyal Medya Veri Analizi:<\/strong><\/p>\n
Farkl\u0131 sosyal medya platformlar\u0131ndan (\u00f6rne\u011fin, Twitter, Instagram) toplanan verileri birle\u015ftirelim.  Her platformdan gelen veriler farkl\u0131 s\u00fctunlara sahip olabilir. concat()<\/code> fonksiyonu, bu farkl\u0131 s\u00fctunlar\u0131 i\u00e7eren bir DataFrame olu\u015fturmam\u0131z\u0131 sa\u011flar.  Eksik de\u011ferlerle ba\u015fa \u00e7\u0131kmak i\u00e7in ek i\u015flemler gerekebilir (\u00f6rne\u011fin, fillna()<\/code> fonksiyonu).<\/p>\n
Orta Seviye: Ger\u00e7ek Hayat \u00d6rne\u011fi ve Optimizasyon \u0130pu\u00e7lar\u0131<\/strong><\/p>\n
Yukar\u0131daki \u00f6rnekler basittir, ancak ger\u00e7ek d\u00fcnya verileri genellikle daha karma\u015f\u0131kt\u0131r.  \u00d6rne\u011fin, birle\u015ftirmek istedi\u011finiz DataFrame’lerin indexleri ayn\u0131 olmayabilir veya baz\u0131 s\u00fctunlar eksik olabilir.  Bu gibi durumlarda, concat()<\/code> fonksiyonunun keys<\/code>, join<\/code>, ve verify_integrity<\/code> gibi parametrelerini kullanman\u0131z gerekebilir.<\/p>\n
keys<\/code> parametresi, her DataFrame’i bir anahtarla etiketlemenizi sa\u011flar.  Bu, birle\u015ftirme sonras\u0131nda hangi DataFrame’in hangi veriyi sa\u011flad\u0131\u011f\u0131n\u0131 takip etmenizi kolayla\u015ft\u0131r\u0131r.<\/p>\n
join<\/code> parametresi, birle\u015ftirme i\u015fleminin nas\u0131l yap\u0131laca\u011f\u0131n\u0131 belirler ('inner'<\/code>, 'outer'<\/code>, 'left'<\/code>, 'right'<\/code>).  'outer'<\/code> birle\u015fimi, t\u00fcm verileri i\u00e7erirken, 'inner'<\/code> birle\u015fimi sadece ortak indexlere sahip sat\u0131rlar\u0131 i\u00e7erir.<\/p>\n
verify_integrity<\/code> parametresi, birle\u015ftirme sonras\u0131nda tekrar eden indexlerin olup olmad\u0131\u011f\u0131n\u0131 kontrol eder.  E\u011fer tekrar eden indexler varsa, bir hata verir.<\/p>\n
Performans Optimizasyonu:<\/strong><\/p>\n
B\u00fcy\u00fck veri setleriyle \u00e7al\u0131\u015f\u0131rken, concat()<\/code> i\u015fleminin performans\u0131 \u00f6nemli bir fakt\u00f6rd\u00fcr.  Performans\u0131 art\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki ipu\u00e7lar\u0131n\u0131 kullanabilirsiniz:<\/p>\n
* Dtype’lar\u0131 Kontrol Edin:<\/strong> DataFrame’lerin s\u00fctunlar\u0131n\u0131n veri tiplerini (dtype<\/code>) kontrol edin ve gerekiyorsa d\u00f6n\u00fc\u015ft\u00fcr\u00fcn.  Uygun olmayan veri tipleri performans\u0131 olumsuz etkileyebilir.
\n* Chunk’lar Halinde Okuma:<\/strong> \u00c7ok b\u00fcy\u00fck dosyalar\u0131 tek seferde okumak yerine, chunksize<\/code> parametresini kullanarak par\u00e7alara b\u00f6l\u00fcn ve her par\u00e7ay\u0131 ayr\u0131 ayr\u0131 i\u015fleyin.  Daha sonra concat()<\/code> ile birle\u015ftirin.
\n* append()<\/code> yerine concat()<\/code> kullan\u0131n:<\/strong>  K\u00fc\u00e7\u00fck DataFrame’leri birle\u015ftirmek i\u00e7in append()<\/code> fonksiyonu kullanmak cazip gelse de, concat()<\/code> daha verimlidir.
\n* Liste Anlama (List Comprehension):<\/strong> Bir\u00e7ok k\u00fc\u00e7\u00fck DataFrame’i birle\u015ftirmeniz gerekiyorsa, liste anlama kullanarak daha okunabilir ve h\u0131zl\u0131 bir kod yazabilirsiniz.<\/p>\n
\u00d6rnek:<\/p>\n
dfs = [pd.DataFrame({'A': [i]}) for i in range(1000)]  # 1000 k\u00fc\u00e7\u00fck DataFrame olu\u015fturur\ncombined_df = pd.concat(dfs, ignore_index=True)<\/code><\/pre>\n
\u0130leri D\u00fczey: Performans Analizi ve Edge Case’ler<\/strong><\/p>\n
B\u00fcy\u00fck veri setleriyle \u00e7al\u0131\u015f\u0131rken performans kritik \u00f6nem ta\u015f\u0131r.  concat()<\/code> i\u015fleminin s\u00fcresini \u00f6l\u00e7mek i\u00e7in timeit<\/code> mod\u00fcl\u00fcn\u00fc kullanabilirsiniz:<\/p>\n
import timeit\n\n# ... (DataFrame'leri olu\u015fturma kodu) ...\n\ntime_taken = timeit.timeit(lambda: pd.concat(dfs, ignore_index=True), number=10)\nprint(f"concat() i\u015flemi {time_taken:.4f} saniye s\u00fcrd\u00fc.")<\/code><\/pre>\n
Bu kod, concat()<\/code> i\u015fleminin 10 kez \u00e7al\u0131\u015ft\u0131r\u0131lma s\u00fcresini \u00f6l\u00e7er.<\/p>\n
Edge Case’ler:<\/strong><\/p>\n
* Farkl\u0131 Indexler:<\/strong>  DataFrame’lerin indexleri farkl\u0131ysa, join<\/code> parametresini kullanarak birle\u015ftirme i\u015flemini kontrol etmeniz gerekir.
\n* Eksik Veriler:<\/strong>  Eksik veriler (NaN<\/code>) birle\u015ftirme i\u015flemini etkileyebilir.  fillna()<\/code> fonksiyonu ile eksik verileri doldurabilirsiniz.
\n* Veri Tipi Uyu\u015fmazl\u0131\u011f\u0131:<\/strong>  Farkl\u0131 DataFrame’lerin s\u00fctunlar\u0131n\u0131n veri tipleri farkl\u0131ysa, concat()<\/code> hata verebilir.  Veri tiplerini kontrol edip d\u00f6n\u00fc\u015ft\u00fcrmeniz gerekebilir.
\n* Bellek Y\u00f6netimi:<\/strong>  \u00c7ok b\u00fcy\u00fck veri setleriyle \u00e7al\u0131\u015f\u0131rken, bellek y\u00f6netimine dikkat etmeniz gerekir.  Gerekirse, verileri par\u00e7alara b\u00f6lerek i\u015fleyin.<\/p>\n
Sonu\u00e7<\/strong><\/p>\n
Pandas concat()<\/code> fonksiyonu, farkl\u0131 kaynaklardan gelen verileri birle\u015ftirmek i\u00e7in g\u00fc\u00e7l\u00fc ve esnek bir ara\u00e7t\u0131r.  Bu makalede, concat()<\/code> fonksiyonunun temel kullan\u0131m\u0131ndan ileri seviye tekniklere kadar geni\u015f bir yelpazeyi kapsad\u0131k.  Farkl\u0131 senaryolar i\u00e7in optimizasyon ipu\u00e7lar\u0131 ve performans analizi tekniklerini ele ald\u0131k.  Unutmay\u0131n ki, verilerinizin yap\u0131s\u0131na ve b\u00fcy\u00fckl\u00fc\u011f\u00fcne ba\u011fl\u0131 olarak, en uygun yakla\u015f\u0131m\u0131 se\u00e7mek \u00f6nemlidir.  Daha fazla bilgi i\u00e7in [Fatih Soysal’\u0131n veri bilimi kaynaklar\u0131na](https:\/\/fatihsoysal.com) g\u00f6z atabilirsiniz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular:<\/strong><\/p>\n
1. concat() fonksiyonu hangi veri tiplerini destekler?<\/strong>  DataFrame’ler ve Series’leri destekler.
\n2. concat() fonksiyonu ile birle\u015ftirme i\u015flemi s\u0131ras\u0131nda indexler nas\u0131l davran\u0131r?<\/strong>  Varsay\u0131lan olarak indexler korunur. ignore_index=True<\/code> parametresi ile s\u0131f\u0131rlanabilir.
\n3. concat() fonksiyonu ile b\u00fcy\u00fck dosyalar\u0131 nas\u0131l verimli bir \u015fekilde birle\u015ftiririm?<\/strong>  chunksize<\/code> parametresini kullanarak par\u00e7alara b\u00f6l\u00fcn ve her par\u00e7ay\u0131 ayr\u0131 ayr\u0131 i\u015fleyin.
\n4. concat() fonksiyonu s\u0131ras\u0131nda hata al\u0131rsam ne yapmal\u0131y\u0131m?<\/strong>  Hata mesaj\u0131n\u0131 dikkatlice inceleyin ve veri tiplerini, indexleri ve eksik verileri kontrol edin.
\n5. concat() fonksiyonunun performans\u0131n\u0131 nas\u0131l iyile\u015ftirebilirim?<\/strong>  Veri tiplerini optimize edin, append()<\/code> yerine concat()<\/code> kullan\u0131n ve b\u00fcy\u00fck dosyalar\u0131 par\u00e7alara b\u00f6l\u00fcn.<\/p>\n
Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"# Pandas concat() \u0130le Veri Birle\u015ftirme: Kapsaml\u0131 Bir K\u0131lavuz Veri bilimi d\u00fcnyas\u0131nda, farkl\u0131 kaynaklardan gelen verileri birle\u015ftirmek s\u0131kl\u0131kla…","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-30035","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nPandas concat() \u00d6rnekleri<\/title>\n<meta name=\"description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\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\/pandas-concat-ornekleri\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas concat() \u00d6rnekleri\" \/>\n<meta property=\"og:description\" content=\"Hi\u00e7bir etiketi bulunamad\u0131.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-22T16:40:24+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=\"7 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Pandas concat() \u00d6rnekleri\",\"datePublished\":\"2025-09-22T16:40:24+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\"},\"wordCount\":1111,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\",\"name\":\"Pandas concat() \u00d6rnekleri\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-09-22T16:40:24+00:00\",\"description\":\"Hi\u00e7bir etiketi bulunamad\u0131.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/pandas-concat-ornekleri\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandas concat() \u00d6rnekleri\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - 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