<\/p>\n
HTML tablolar\u0131n\u0131 pandas DataFrame’e okumak i\u00e7in pandas read_html()<\/a> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu Vikipedi sayfas\u0131ndan<\/a> NBA tak\u0131m isimleri tablosunu okumak i\u00e7in bu i\u015flevin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Read_html()<\/strong> i\u015flevini kullanmadan \u00f6nce muhtemelen lxml’i y\u00fcklemeniz gerekecektir:<\/span><\/p>\n Not<\/strong> : E\u011fer Jupyter notebook kullan\u0131yorsan\u0131z bu kurulumu ger\u00e7ekle\u015ftirdikten sonra kerneli yeniden ba\u015flatman\u0131z gerekmektedir.<\/span><\/p>\n Daha sonra, bu Wikipedia<\/a> sayfas\u0131ndaki her HTML tablosunu okumak i\u00e7in read_html()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Bu sayfada toplam 44 HTML tablosunun bulundu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n \u0130lgilendi\u011fim tablonun “B\u00f6lme” s\u00f6zc\u00fc\u011f\u00fcn\u00fc i\u00e7erdi\u011fini biliyorum, dolay\u0131s\u0131yla yaln\u0131zca bu s\u00f6zc\u00fc\u011f\u00fc i\u00e7eren HTML tablolar\u0131n\u0131 almak i\u00e7in match<\/strong> arg\u00fcman\u0131n\u0131 kullanabilirim:<\/span><\/p>\n Daha sonra tablo s\u00fctunlar\u0131n\u0131n adlar\u0131n\u0131 listeleyebilirim<\/a> :<\/span><\/p>\n Yaln\u0131zca ilk iki s\u00fctunla ilgileniyorum, bu nedenle DataFrame’i yaln\u0131zca bu s\u00fctunlar\u0131 i\u00e7erecek \u015fekilde filtreleyebilirim<\/a> :<\/span><\/p>\n Final tablosu yaln\u0131zca \u201cB\u00f6l\u00fcm\u201d ve \u201cTak\u0131m\u201d s\u00fctunlar\u0131n\u0131 i\u00e7erir.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler pandalarda di\u011fer dosya t\u00fcrlerinin nas\u0131l okunaca\u011f\u0131n\u0131 a\u00e7\u0131klamaktad\u0131r:<\/span><\/p>\n Pandalar ile bir metin dosyas\u0131 nas\u0131l okunur<\/a> HTML tablolar\u0131n\u0131 pandas DataFrame’e okumak i\u00e7in pandas read_html() i\u015flevini kullanabilirsiniz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: df = pd. read_html (‘ https:\/\/en.wikipedia.org\/wiki\/National_Basketball_Association ‘) A\u015fa\u011f\u0131daki \u00f6rnek, bu Vikipedi sayfas\u0131ndan NBA tak\u0131m isimleri tablosunu okumak i\u00e7in bu i\u015flevin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: Pandalarla bir HTML tablosunu okuyun Read_html() i\u015flevini kullanmadan \u00f6nce muhtemelen lxml’i y\u00fcklemeniz gerekecektir: pip install […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-2525","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df = pd. read_html<\/span> (' https:\/\/en.wikipedia.org\/wiki\/National_Basketball_Association<\/span> ')\n<\/strong><\/pre>\n \u00d6rnek: Pandalarla bir HTML tablosunu okuyun<\/strong><\/span><\/h3>\n
pip install<\/span> lxml<\/strong><\/span><\/pre>\n import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> unicodedata import<\/span> normalize\n\n#read all HTML tables from specific URL\n<\/span>tabs = pd. read_html<\/span> (' https:\/\/en.wikipedia.org\/wiki\/National_Basketball_Association<\/span> ')\n\n#display total number of tables read\n<\/span>len<\/span> (tabs)\n\n44<\/span><\/span><\/strong><\/pre>\n #read HTML tables from specific URL with the word \"Division\" in them\n<\/span>tabs = pd. read_html<\/span> (' https:\/\/en.wikipedia.org\/wiki\/National_Basketball_Association<\/span> ',\n match=' Division<\/span> ')\n\n#display total number of tables read\n<\/span>len<\/span> (tabs)\n\n1<\/span><\/span><\/strong><\/pre>\n #define table\ndf = tabs[0]<\/span>\n\n#list all column names of table\nlist<\/span> (df)\n\n[('Division', 'Eastern Conference'),\n ('Team', 'Eastern Conference'),\n ('Location', 'Eastern Conference'),\n ('Arena', 'Eastern Conference'),\n ('Capacity', 'Eastern Conference'),\n ('Coordinates', 'Eastern Conference'),\n ('Founded', 'Eastern Conference'),\n ('Joined', 'Eastern Conference'),\n ('Unnamed: 8_level_0', 'Eastern Conference')]<\/span><\/span><\/strong><\/pre>\n #filter DataFrame to only contain first two columns\n<\/span>df_final = df. iloc<\/span> [:, 0:2]\n\n#rename columns\n<\/span>df_final. columns<\/span> = [' Division<\/span> ', ' Team<\/span> ']\n\n#view first few rows of final DataFrame\n<\/span>print<\/span> ( df_final.head<\/span> ())\n\n Division Team\n0 Atlantic Boston Celtics\n1 Atlantic Brooklyn Nets\n2 Atlantic New York Knicks\n3 Atlantic Philadelphia 76ers\n4 Atlantic Toronto Raptors\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandalar ile Excel dosyalar\u0131 nas\u0131l okunur<\/a>
Pandalar ile CSV dosyalar\u0131 nas\u0131l okunur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"