G\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda, karma\u015f\u0131k bilgi y\u0131\u011f\u0131nlar\u0131n\u0131 anlaml\u0131 ve anla\u015f\u0131l\u0131r hale getirmek kritik \u00f6neme sahiptir. Veri g\u00f6rselle\u015ftirme, bu karma\u015fay\u0131 a\u015fman\u0131n en g\u00fc\u00e7l\u00fc yollar\u0131ndan biridir. Bu makale, \u00f6zellikle kategorik verilerin bile\u015fenlerini ve toplamlar\u0131n\u0131 ayn\u0131 anda g\u00f6steren y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafiklerinin g\u00fcc\u00fcn\u00fc ve kullan\u0131m\u0131n\u0131 derinlemesine inceleyecektir. Do\u011fru kullan\u0131ld\u0131\u011f\u0131nda, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, veri setlerinizdeki gizli kal\u0131plar\u0131 ve e\u011filimleri ortaya \u00e7\u0131kararak daha bilin\u00e7li kararlar alman\u0131za yard\u0131mc\u0131 olabilir.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, birden fazla veri serisini tek bir s\u00fctunda \u00fcst \u00fcste g\u00f6steren bir grafik t\u00fcr\u00fcd\u00fcr. Her s\u00fctun, bir ana kategoriye kar\u015f\u0131l\u0131k gelir ve bu s\u00fctunun i\u00e7indeki her renkli b\u00f6l\u00fcm (y\u0131\u011f\u0131n), o kategorinin bir alt bile\u015fenini temsil eder. Bu g\u00f6rselle\u015ftirme, hem her kategorinin toplam de\u011ferini hem de bu toplam\u0131n hangi bile\u015fenlerden olu\u015ftu\u011funu ayn\u0131 anda g\u00f6rmemizi sa\u011flar.<\/p>\n
Bir y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011finde, yatay eksen (x-ekseni) genellikle kategorik verileri (\u00f6rn. aylar, \u00fcr\u00fcnler, b\u00f6lgeler) temsil ederken, dikey eksen (y-ekseni) say\u0131sal bir de\u011feri (\u00f6rn. sat\u0131\u015f miktar\u0131, n\u00fcfus, harcama) g\u00f6sterir. Her kategori i\u00e7in bir s\u00fctun bulunur ve bu s\u00fctun, farkl\u0131 renklerle ayr\u0131lm\u0131\u015f b\u00f6l\u00fcmlerden olu\u015fur. Her b\u00f6l\u00fcm, ilgili alt kategorinin de\u011ferini g\u00f6sterir ve t\u00fcm b\u00f6l\u00fcmlerin toplam\u0131, ana kategorinin toplam de\u011ferini verir.<\/p>\n
Bu grafik t\u00fcr\u00fcn\u00fcn temel g\u00fcc\u00fc, bir b\u00fct\u00fcn\u00fcn par\u00e7alar\u0131n\u0131 ve ayn\u0131 zamanda b\u00fct\u00fcn\u00fc bir arada sunabilmesidir. \u00d6rne\u011fin, bir \u015firketin farkl\u0131 \u00fcr\u00fcn kategorilerindeki ayl\u0131k sat\u0131\u015flar\u0131n\u0131 incelerken, her ay\u0131n toplam sat\u0131\u015f\u0131n\u0131 ve bu toplam\u0131n hangi \u00fcr\u00fcnlerden geldi\u011fini tek bir bak\u0131\u015fta g\u00f6rebilirsiniz. Bu, hem genel e\u011filimleri hem de detayl\u0131 katk\u0131lar\u0131 anlamak i\u00e7in idealdir.<\/p>\n
Geleneksel (grupland\u0131r\u0131lm\u0131\u015f) s\u00fctun grafikleri, farkl\u0131 kategorileri yan yana s\u00fctunlar halinde g\u00f6sterirken, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri bu kategorileri tek bir s\u00fctun i\u00e7inde \u00fcst \u00fcste g\u00f6sterir. Grupland\u0131r\u0131lm\u0131\u015f s\u00fctun grafikleri, farkl\u0131 kategoriler aras\u0131ndaki do\u011frudan kar\u015f\u0131la\u015ft\u0131rmalar i\u00e7in daha uygunken, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, bir ana kategorinin toplam\u0131n\u0131 ve bu toplam\u0131n bile\u015fenlere nas\u0131l ayr\u0131ld\u0131\u011f\u0131n\u0131 vurgulamak i\u00e7in daha etkilidir.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, belirli veri analizi ihtiya\u00e7lar\u0131 i\u00e7in olduk\u00e7a g\u00fc\u00e7l\u00fc ve \u00e7ok y\u00f6nl\u00fc bir ara\u00e7t\u0131r. \u0130\u015fte bu grafik t\u00fcr\u00fcn\u00fc tercih etmeniz i\u00e7in ba\u015fl\u0131ca nedenler:<\/p>\n
Her bir ana kategori i\u00e7in toplam de\u011feri g\u00f6rsel olarak kar\u015f\u0131la\u015ft\u0131rmak olduk\u00e7a kolayd\u0131r. S\u00fctunlar\u0131n y\u00fckseklikleri, ilgili kategorinin genel performans\u0131n\u0131 veya b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc an\u0131nda g\u00f6sterir. Bu, hangi kategorinin di\u011ferlerinden daha b\u00fcy\u00fck veya daha k\u00fc\u00e7\u00fck oldu\u011funu h\u0131zl\u0131ca anlaman\u0131z\u0131 sa\u011flar.<\/p>\n
Bu grafikler, bir b\u00fct\u00fcn\u00fcn par\u00e7alar\u0131n\u0131 net bir \u015fekilde ortaya koyar. \u00d6rne\u011fin, bir web sitesinin trafi\u011fini kaynaklara (organik, do\u011frudan, sosyal medya) g\u00f6re incelerken, toplam trafi\u011fi ve her kayna\u011f\u0131n bu toplama katk\u0131s\u0131n\u0131 g\u00f6rebilirsiniz. Bu, kaynaklar\u0131n g\u00f6receli \u00f6nemini ve zaman i\u00e7indeki de\u011fi\u015fimlerini anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n
E\u011fer yatay eksen zaman periyotlar\u0131n\u0131 (aylar, \u00e7eyrekler) temsil ediyorsa, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, hem toplam de\u011ferin hem de her bir bile\u015fenin zaman i\u00e7indeki de\u011fi\u015fimini izlemek i\u00e7in harikad\u0131r. Bu sayede, mevsimsel e\u011filimleri, b\u00fcy\u00fcme oranlar\u0131n\u0131 veya d\u00fc\u015f\u00fc\u015fleri kolayca tespit edebilirsiniz.<\/p>\n
Birden fazla de\u011fi\u015fkeni i\u00e7eren karma\u015f\u0131k veri setlerini, tek bir g\u00f6rselde \u00f6zetleyerek daha anla\u015f\u0131l\u0131r hale getirir. Bu, sunumlar veya raporlar i\u00e7in idealdir, \u00e7\u00fcnk\u00fc izleyicilerin birden fazla grafi\u011fi yorumlamas\u0131na gerek kalmaz.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri genellikle iki ana t\u00fcrde kar\u015f\u0131m\u0131za \u00e7\u0131kar ve her birinin farkl\u0131 kullan\u0131m senaryolar\u0131 vard\u0131r.<\/p>\n
Bu t\u00fcr, her s\u00fctunun toplam de\u011ferini ve bu toplam\u0131n alt bile\u015fenlerini do\u011frudan say\u0131sal de\u011ferleriyle g\u00f6sterir. S\u00fctunlar\u0131n y\u00fckseklikleri, ana kategorinin toplam de\u011ferini temsil eder. \u00d6rne\u011fin, ayl\u0131k toplam sat\u0131\u015flar\u0131 ve bu sat\u0131\u015flar\u0131n \u00fcr\u00fcn baz\u0131nda da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rmek istedi\u011finizde bu t\u00fcr\u00fc kullan\u0131rs\u0131n\u0131z. Bu, hem mutlak de\u011ferleri hem de bile\u015fenlerin toplamdaki pay\u0131n\u0131 anlamak i\u00e7in faydal\u0131d\u0131r.<\/p>\n
%100 y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, her s\u00fctunun toplam\u0131n\u0131 %100 olarak normalize eder. Bu, her bir bile\u015fenin ana kategorinin toplam\u0131na olan y\u00fczdesel katk\u0131s\u0131n\u0131 g\u00f6sterir. S\u00fctunlar\u0131n y\u00fckseklikleri her zaman ayn\u0131d\u0131r (yani %100’e kadar uzan\u0131r). Bu t\u00fcr grafikler, bile\u015fenlerin g\u00f6receli paylar\u0131n\u0131n zaman i\u00e7inde veya kategoriler aras\u0131nda nas\u0131l de\u011fi\u015fti\u011fini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in m\u00fckemmeldir. \u00d6rne\u011fin, farkl\u0131 b\u00f6lgelerdeki pazar pay\u0131 da\u011f\u0131l\u0131m\u0131n\u0131 veya bir \u00fcr\u00fcn\u00fcn toplam sat\u0131\u015flar i\u00e7indeki y\u00fczdesini izlemek i\u00e7in idealdir.<\/p>\n
Bir y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011finin etkili olabilmesi i\u00e7in baz\u0131 tasar\u0131m ilkelerine dikkat etmek gerekir. Do\u011fru tasar\u0131m, verinin daha h\u0131zl\u0131 ve do\u011fru anla\u015f\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n
Farkl\u0131 bile\u015fenleri temsil eden renklerin kolayca ay\u0131rt edilebilir olmas\u0131 \u00e7ok \u00f6nemlidir. Birbirine yak\u0131n tonlardan ka\u00e7\u0131n\u0131n ve y\u00fcksek kontrastl\u0131 renkler kullan\u0131n. Ayr\u0131ca, renk k\u00f6r\u00fc dostu paletleri tercih etmek, grafi\u011finizin daha geni\u015f bir kitle taraf\u0131ndan eri\u015filebilir olmas\u0131n\u0131 sa\u011flar. Renkleri tutarl\u0131 bir \u015fekilde kullan\u0131n; ayn\u0131 bile\u015fen, farkl\u0131 s\u00fctunlarda her zaman ayn\u0131 renkte olmal\u0131d\u0131r.<\/p>\n
Yatay eksendeki kategorileri mant\u0131kl\u0131 bir s\u0131raya g\u00f6re d\u00fczenleyin (\u00f6rn. zamana g\u00f6re, alfabetik olarak veya de\u011fere g\u00f6re). S\u00fctun i\u00e7indeki bile\u015fenleri de belirli bir mant\u0131\u011fa g\u00f6re s\u0131ralamak (\u00f6rn. en b\u00fcy\u00fckten k\u00fc\u00e7\u00fc\u011fe veya belirli bir hiyerar\u015fiye g\u00f6re) okumay\u0131 kolayla\u015ft\u0131r\u0131r. Her y\u0131\u011f\u0131n par\u00e7as\u0131n\u0131n veya en az\u0131ndan ana bile\u015fenlerin etiketlenmesi, veriyi daha anla\u015f\u0131l\u0131r hale getirir. Toplam de\u011ferleri s\u00fctunlar\u0131n \u00fczerine eklemek de faydal\u0131 olabilir.<\/p>\n
Grafi\u011fin ba\u015fl\u0131\u011f\u0131, neyi temsil etti\u011fini a\u00e7\u0131k\u00e7a belirtmelidir. Yatay ve dikey eksen etiketleri, \u00f6l\u00e7\u00fclen birimleri ve kategorileri net bir \u015fekilde a\u00e7\u0131klamal\u0131d\u0131r. Okuyucunun grafi\u011fi yorumlamak i\u00e7in herhangi bir varsay\u0131mda bulunmas\u0131na gerek kalmamal\u0131d\u0131r.<\/p>\n
\u00c7ok fazla kategori veya \u00e7ok fazla bile\u015fen i\u00e7eren y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri karma\u015f\u0131k hale gelebilir. E\u011fer grafi\u011finiz \u00e7ok kalabal\u0131k g\u00f6r\u00fcn\u00fcyorsa, baz\u0131 bile\u015fenleri “Di\u011fer” kategorisi alt\u0131nda grupland\u0131rmay\u0131 veya farkl\u0131 bir g\u00f6rselle\u015ftirme t\u00fcr\u00fcn\u00fc (\u00f6rn. treemap) d\u00fc\u015f\u00fcnmeyi de\u011ferlendirin.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri g\u00fc\u00e7l\u00fc ara\u00e7lar olsa da, her senaryo i\u00e7in uygun de\u011fildir. Yanl\u0131\u015f kullan\u0131ld\u0131\u011f\u0131nda, veriyi yanl\u0131\u015f yorumlamaya yol a\u00e7abilir.<\/p>\n
E\u011fer yatay eksende \u00e7ok fazla kategori veya her s\u00fctunda \u00e7ok fazla bile\u015fen varsa, grafik karma\u015f\u0131k ve okunmas\u0131 zor hale gelir. \u00c7ok say\u0131da renk ve k\u00fc\u00e7\u00fck y\u0131\u011f\u0131nlar, grafi\u011fin ana mesaj\u0131n\u0131 kaybetmesine neden olabilir. Genellikle 5-7 bile\u015fenden fazlas\u0131 i\u00e7in bu grafik t\u00fcr\u00fc uygun olmayabilir.<\/p>\n
E\u011fer baz\u0131 bile\u015fenlerin de\u011ferleri di\u011ferlerine g\u00f6re \u00e7ok k\u00fc\u00e7\u00fckse, bu k\u00fc\u00e7\u00fck y\u0131\u011f\u0131nlar grafikte neredeyse g\u00f6r\u00fcnmez hale gelebilir. Bu durumda, \u00f6nemli olabilecek k\u00fc\u00e7\u00fck katk\u0131lar g\u00f6zden ka\u00e7abilir. Bu t\u00fcr veriler i\u00e7in pasta grafi\u011fi veya a\u011fa\u00e7 haritas\u0131 gibi alternatifler daha iyi olabilir.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafiklerinde, bir s\u00fctunun taban\u0131ndan ba\u015flamayan bile\u015fenleri do\u011frudan kar\u015f\u0131la\u015ft\u0131rmak zordur. \u00d6rne\u011fin, bir s\u00fctundaki ikinci bile\u015fenin, ba\u015fka bir s\u00fctundaki ikinci bile\u015fenle b\u00fcy\u00fckl\u00fck olarak kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 g\u00f6z yan\u0131lg\u0131lar\u0131na neden olabilir. E\u011fer ana amac\u0131n\u0131z bile\u015fenler aras\u0131 do\u011frudan kar\u015f\u0131la\u015ft\u0131rmalar yapmaksa, grupland\u0131r\u0131lm\u0131\u015f s\u00fctun grafi\u011fi veya \u00e7izgi grafi\u011fi daha uygun olabilir.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, g\u00fcn\u00fcm\u00fczde bir\u00e7ok veri g\u00f6rselle\u015ftirme arac\u0131yla kolayca olu\u015fturulabilir. \u0130\u015fte baz\u0131 pop\u00fcler ara\u00e7lar ve bir Python \u00f6rne\u011fi:<\/p>\n
Microsoft Excel ve Google Sheets gibi elektronik tablo programlar\u0131, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri olu\u015fturmak i\u00e7in en yayg\u0131n ve eri\u015filebilir ara\u00e7lard\u0131r. Verilerinizi d\u00fczenledikten sonra, “Ekle” men\u00fcs\u00fcnden “S\u00fctun Grafi\u011fi” se\u00e7ene\u011fini bulup “Y\u0131\u011f\u0131lm\u0131\u015f S\u00fctun” veya “%100 Y\u0131\u011f\u0131lm\u0131\u015f S\u00fctun”u se\u00e7erek kolayca olu\u015fturabilirsiniz.<\/p>\n
Python’un Matplotlib ve Seaborn k\u00fct\u00fcphaneleri, daha \u00f6zelle\u015ftirilebilir ve programatik grafikler olu\u015fturmak i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lard\u0131r. \u0130\u015fte basit bir Matplotlib \u00f6rne\u011fi:<\/p>\n
\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# \u00d6rnek veri\ndata = {\n 'Ay': ['Ocak', '\u015eubat', 'Mart', 'Nisan'],\n '\u00dcr\u00fcn A': [100, 120, 150, 130],\n '\u00dcr\u00fcn B': [80, 90, 110, 100],\n '\u00dcr\u00fcn C': [50, 60, 70, 80]\n}\ndf = pd.DataFrame(data)\n\n# Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011fi olu\u015fturma\nfig, ax = plt.subplots(figsize=(10, 6))\n\nax.bar(df['Ay'], df['\u00dcr\u00fcn A'], label='\u00dcr\u00fcn A', color='#4CAF50') # Ye\u015fil\nax.bar(df['Ay'], df['\u00dcr\u00fcn B'], bottom=df['\u00dcr\u00fcn A'], label='\u00dcr\u00fcn B', color='#FFC107') # Sar\u0131\nax.bar(df['Ay'], df['\u00dcr\u00fcn C'], bottom=df['\u00dcr\u00fcn A'] + df['\u00dcr\u00fcn B'], label='\u00dcr\u00fcn C', color='#2196F3') # Mavi\n\nax.set_xlabel('Ay', fontsize=12)\nax.set_ylabel('Sat\u0131\u015f Miktar\u0131', fontsize=12)\nax.set_title('Ayl\u0131k \u00dcr\u00fcn Sat\u0131\u015f Da\u011f\u0131l\u0131m\u0131', fontsize=14, fontweight='bold')\nax.legend(fontsize=10)\nplt.xticks(rotation=45, ha='right') # Etiketleri d\u00f6nd\u00fcrme\nplt.grid(axis='y', linestyle='--', alpha=0.7) # Yatay \u0131zgara \u00e7izgileri\nplt.tight_layout() # D\u00fczeni otomatik ayarla\nplt.show()\n<\/pre>\n<\/code><\/p>\n
\u0130\u015f Zekas\u0131 Ara\u00e7lar\u0131 (Power BI, Tableau)<\/h4>\n
Power BI, Tableau, Qlik Sense gibi modern i\u015f zekas\u0131 (BI) ara\u00e7lar\u0131, s\u00fcr\u00fckle-b\u0131rak aray\u00fczleri sayesinde y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri de dahil olmak \u00fczere karma\u015f\u0131k g\u00f6rselle\u015ftirmeleri h\u0131zl\u0131ca olu\u015fturman\u0131za olanak tan\u0131r. Bu ara\u00e7lar, b\u00fcy\u00fck veri setleriyle \u00e7al\u0131\u015fmak ve etkile\u015fimli panolar olu\u015fturmak i\u00e7in idealdir.<\/p>\n
Ger\u00e7ek D\u00fcnya Uygulamalar\u0131 ve \u00d6rnek Senaryolar<\/h3>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, bir\u00e7ok farkl\u0131 sekt\u00f6rde ve veri analizi senaryosunda yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n
Sat\u0131\u015f Performans\u0131 Analizi<\/h4>\n
Bir \u015firketin farkl\u0131 \u00fcr\u00fcn gruplar\u0131n\u0131n (\u00f6rn. elektronik, giyim, ev e\u015fyalar\u0131) ayl\u0131k veya \u00e7eyreklik sat\u0131\u015flar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131labilir. Bu, y\u00f6neticilerin hangi \u00fcr\u00fcn grubunun toplam sat\u0131\u015flara ne kadar katk\u0131da bulundu\u011funu ve zaman i\u00e7indeki e\u011filimleri g\u00f6rmesine yard\u0131mc\u0131 olur.<\/p>\n
Pazar Pay\u0131 Da\u011f\u0131l\u0131m\u0131<\/h4>\n
Farkl\u0131 b\u00f6lgelerdeki veya zaman dilimlerindeki bir \u00fcr\u00fcn\u00fcn pazar pay\u0131n\u0131 rakiplere g\u00f6re g\u00f6stermek i\u00e7in %100 y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri idealdir. Bu, \u015firketlerin kendi pazar paylar\u0131n\u0131n nas\u0131l de\u011fi\u015fti\u011fini ve rakiplerine k\u0131yasla konumlar\u0131n\u0131 anlamalar\u0131n\u0131 sa\u011flar.<\/p>\n
Demografik Veri Analizi<\/h4>\n
Bir b\u00f6lgedeki n\u00fcfusun ya\u015f gruplar\u0131na veya cinsiyetlere g\u00f6re da\u011f\u0131l\u0131m\u0131n\u0131 zaman i\u00e7inde izlemek i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, bir \u00fclkenin toplam n\u00fcfusunun gen\u00e7, orta ya\u015fl\u0131 ve ya\u015fl\u0131 kesimlere g\u00f6re nas\u0131l de\u011fi\u015fti\u011fini g\u00f6rmek, sosyal politikalar\u0131n olu\u015fturulmas\u0131nda \u00f6nemli bilgiler sunar.<\/p>\n
Proje Y\u00f6netimi ve Kaynak Tahsisi<\/h4>\n
Bir projenin farkl\u0131 a\u015famalar\u0131nda (planlama, geli\u015ftirme, test) harcanan zaman\u0131 veya kaynaklar\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131labilir. Bu, proje y\u00f6neticilerinin kaynaklar\u0131n nas\u0131l da\u011f\u0131t\u0131ld\u0131\u011f\u0131n\u0131 ve hangi a\u015famalar\u0131n daha fazla zaman ald\u0131\u011f\u0131n\u0131 anlamas\u0131na yard\u0131mc\u0131 olur.<\/p>\n
Sonu\u00e7<\/h3>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, kategorik verilerin bile\u015fenlerini ve toplamlar\u0131n\u0131 ayn\u0131 anda sunarak, veri hikayelerini anlatmada paha bi\u00e7ilmez bir ara\u00e7t\u0131r. Do\u011fru kullan\u0131ld\u0131\u011f\u0131nda, karma\u015f\u0131k veri setlerindeki kal\u0131plar\u0131, e\u011filimleri ve ili\u015fkileri h\u0131zla ortaya \u00e7\u0131karabilir, b\u00f6ylece daha bilin\u00e7li ve stratejik kararlar al\u0131nmas\u0131n\u0131 sa\u011flar. Ancak, bu grafik t\u00fcr\u00fcn\u00fcn s\u0131n\u0131rl\u0131l\u0131klar\u0131n\u0131 anlamak ve do\u011fru tasar\u0131m ilkelerini uygulamak, g\u00f6rselle\u015ftirmenin etkinli\u011fini en \u00fcst d\u00fczeye \u00e7\u0131karmak i\u00e7in kritik \u00f6neme sahiptir. Veri g\u00f6rselle\u015ftirme yolculu\u011funuzda, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafiklerinin g\u00fcc\u00fcn\u00fc ke\u015ffetmek, verilerinizden en iyi \u015fekilde yararlanman\u0131za yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\nY\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri ne i\u00e7in en iyidir?<\/h4>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, bir ana kategorinin toplam de\u011ferini ve bu toplam\u0131n farkl\u0131 alt bile\u015fenlere nas\u0131l ayr\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6stermek i\u00e7in en iyisidir. \u00d6zellikle zaman i\u00e7indeki de\u011fi\u015fimleri veya farkl\u0131 kategoriler aras\u0131ndaki bile\u015fen da\u011f\u0131l\u0131mlar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak istedi\u011finizde etkilidir.<\/p>\n
Ne zaman y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011fi kullanmaktan ka\u00e7\u0131nmal\u0131y\u0131m?<\/h4>\n
\u00c7ok fazla kategori veya \u00e7ok fazla bile\u015fen oldu\u011funda, k\u00fc\u00e7\u00fck de\u011ferlerin g\u00f6r\u00fcnmez hale geldi\u011fi durumlarda veya bile\u015fenler aras\u0131nda do\u011frudan, hassas kar\u015f\u0131la\u015ft\u0131rmalar yapman\u0131z gerekti\u011finde y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri kullanmaktan ka\u00e7\u0131nmal\u0131s\u0131n\u0131z.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri ile normal s\u00fctun grafikleri aras\u0131ndaki temel fark nedir?<\/h4>\n
Normal (grupland\u0131r\u0131lm\u0131\u015f) s\u00fctun grafikleri, farkl\u0131 veri serilerini yan yana ayr\u0131 s\u00fctunlar halinde g\u00f6sterirken, y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri bu serileri tek bir s\u00fctun i\u00e7inde \u00fcst \u00fcste y\u0131\u011far. Y\u0131\u011f\u0131lm\u0131\u015f grafikler, toplam\u0131n bile\u015fenlere ayr\u0131lmas\u0131n\u0131 vurgular.<\/p>\n
%100 y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011fi ne zaman tercih edilmelidir?<\/h4>\n
%100 y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011fi, bile\u015fenlerin mutlak de\u011ferlerinden ziyade, bir b\u00fct\u00fcn i\u00e7indeki y\u00fczdesel paylar\u0131n\u0131n veya oranlar\u0131n\u0131n zaman i\u00e7inde ya da kategoriler aras\u0131nda nas\u0131l de\u011fi\u015fti\u011fini kar\u015f\u0131la\u015ft\u0131rmak istedi\u011finizde tercih edilmelidir.<\/p>\n
Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafi\u011fi tasarlarken en \u00f6nemli ipucu nedir?<\/h4>\n
En \u00f6nemli ipucu, renkleri ak\u0131ll\u0131ca se\u00e7mek ve tutarl\u0131 kullanmakt\u0131r. Farkl\u0131 bile\u015fenler i\u00e7in kolayca ay\u0131rt edilebilir, y\u00fcksek kontrastl\u0131 renkler se\u00e7in ve bu renkleri grafi\u011fin her yerinde ayn\u0131 bile\u015fen i\u00e7in kullan\u0131n. Ayr\u0131ca, net ba\u015fl\u0131klar ve eksen etiketleri eklemeyi unutmay\u0131n.<\/p>\n","protected":false},"excerpt":{"rendered":"Y\u0131\u011f\u0131lm\u0131\u015f s\u00fctun grafikleri, birden fazla veri serisini tek bir s\u00fctunda \u00fcst \u00fcste g\u00f6steren bir grafik t\u00fcr\u00fcd\u00fcr. Her s\u00fctun, bir ana kategoriye kar\u015f…","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-36532","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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