{"id":476745,"date":"2023-08-09T07:35:16","date_gmt":"2023-08-09T07:35:16","guid":{"rendered":""},"modified":"2023-09-05T11:13:20","modified_gmt":"2023-09-05T11:13:20","slug":"dataframes","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/dataframes\/","title":{"rendered":"Khung d\u1eef li\u1ec7u"},"content":{"rendered":"<p>DataFrames l\u00e0 c\u1ea5u tr\u00fac d\u1eef li\u1ec7u c\u01a1 b\u1ea3n trong khoa h\u1ecdc d\u1eef li\u1ec7u, thao t\u00e1c d\u1eef li\u1ec7u v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u. C\u1ea5u tr\u00fac linh ho\u1ea1t v\u00e0 m\u1ea1nh m\u1ebd n\u00e0y cho ph\u00e9p th\u1ef1c hi\u1ec7n c\u00e1c ho\u1ea1t \u0111\u1ed9ng h\u1ee3p l\u00fd tr\u00ean d\u1eef li\u1ec7u c\u00f3 c\u1ea5u tr\u00fac, ch\u1eb3ng h\u1ea1n nh\u01b0 l\u1ecdc, tr\u1ef1c quan h\u00f3a v\u00e0 ph\u00e2n t\u00edch th\u1ed1ng k\u00ea. N\u00f3 l\u00e0 m\u1ed9t c\u1ea5u tr\u00fac d\u1eef li\u1ec7u hai chi\u1ec1u, c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c coi l\u00e0 m\u1ed9t b\u1ea3ng bao g\u1ed3m c\u00e1c h\u00e0ng v\u00e0 c\u1ed9t, t\u01b0\u01a1ng t\u1ef1 nh\u01b0 b\u1ea3ng t\u00ednh ho\u1eb7c b\u1ea3ng SQL.<\/p>\n<h2>S\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a DataFrames<\/h2>\n<p>Kh\u00e1i ni\u1ec7m DataFrames b\u1eaft ngu\u1ed3n t\u1eeb th\u1ebf gi\u1edbi l\u1eadp tr\u00ecnh th\u1ed1ng k\u00ea, v\u1edbi ng\u00f4n ng\u1eef l\u1eadp tr\u00ecnh R \u0111\u00f3ng vai tr\u00f2 then ch\u1ed1t. Trong R, DataFrame \u0111\u00e3 v\u00e0 v\u1eabn l\u00e0 c\u1ea5u tr\u00fac d\u1eef li\u1ec7u ch\u00ednh \u0111\u1ec3 thao t\u00e1c v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u. L\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn c\u1ea5u tr\u00fac gi\u1ed1ng DataFrame c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb \u0111\u1ea7u nh\u1eefng n\u0103m 2000, khi R b\u1eaft \u0111\u1ea7u tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn trong l\u0129nh v\u1ef1c ph\u00e2n t\u00edch d\u1eef li\u1ec7u v\u00e0 th\u1ed1ng k\u00ea.<\/p>\n<p>Tuy nhi\u00ean, vi\u1ec7c s\u1eed d\u1ee5ng v\u00e0 hi\u1ec3u bi\u1ebft r\u1ed9ng r\u00e3i v\u1ec1 DataFrames h\u1ea7u h\u1ebft \u0111\u00e3 \u0111\u01b0\u1ee3c ph\u1ed5 bi\u1ebfn r\u1ed9ng r\u00e3i nh\u1edd s\u1ef1 ra \u0111\u1eddi c\u1ee7a th\u01b0 vi\u1ec7n Pandas trong Python. \u0110\u01b0\u1ee3c ph\u00e1t tri\u1ec3n b\u1edfi Wes McKinney v\u00e0o n\u0103m 2008, Pandas \u0111\u00e3 \u0111\u01b0a c\u1ea5u tr\u00fac DataFrame v\u00e0o th\u1ebf gi\u1edbi Python, n\u00e2ng cao \u0111\u00e1ng k\u1ec3 s\u1ef1 d\u1ec5 d\u00e0ng v\u00e0 hi\u1ec7u qu\u1ea3 c\u1ee7a vi\u1ec7c thao t\u00e1c v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u b\u1eb1ng ng\u00f4n ng\u1eef.<\/p>\n<h2>Kh\u00e1m ph\u00e1 kh\u00e1i ni\u1ec7m v\u1ec1 DataFrames<\/h2>\n<p>DataFrames th\u01b0\u1eddng \u0111\u01b0\u1ee3c \u0111\u1eb7c tr\u01b0ng b\u1edfi c\u1ea5u tr\u00fac hai chi\u1ec1u c\u1ee7a ch\u00fang, bao g\u1ed3m c\u00e1c h\u00e0ng v\u00e0 c\u1ed9t, trong \u0111\u00f3 m\u1ed7i c\u1ed9t c\u00f3 th\u1ec3 thu\u1ed9c m\u1ed9t ki\u1ec3u d\u1eef li\u1ec7u kh\u00e1c nhau (s\u1ed1 nguy\u00ean, chu\u1ed7i, s\u1ed1 float, v.v.). H\u1ecd cung c\u1ea5p m\u1ed9t c\u00e1ch tr\u1ef1c quan \u0111\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u c\u00f3 c\u1ea5u tr\u00fac. Ch\u00fang c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u1ea1o t\u1eeb nhi\u1ec1u ngu\u1ed3n d\u1eef li\u1ec7u kh\u00e1c nhau nh\u01b0 t\u1ec7p CSV, t\u1ec7p Excel, truy v\u1ea5n SQL tr\u00ean c\u01a1 s\u1edf d\u1eef li\u1ec7u ho\u1eb7c th\u1eadm ch\u00ed t\u1eeb \u0111i\u1ec3n v\u00e0 danh s\u00e1ch Python.<\/p>\n<p>L\u1ee3i \u00edch ch\u00ednh c\u1ee7a vi\u1ec7c s\u1eed d\u1ee5ng DataFrames n\u1eb1m \u1edf kh\u1ea3 n\u0103ng x\u1eed l\u00fd kh\u1ed1i l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3. DataFrames cung c\u1ea5p m\u1ed9t lo\u1ea1t c\u00e1c h\u00e0m t\u00edch h\u1ee3p cho c\u00e1c t\u00e1c v\u1ee5 thao t\u00e1c d\u1eef li\u1ec7u nh\u01b0 nh\u00f3m, h\u1ee3p nh\u1ea5t, \u0111\u1ecbnh h\u00ecnh l\u1ea1i v\u00e0 t\u1ed5ng h\u1ee3p d\u1eef li\u1ec7u, do \u0111\u00f3 \u0111\u01a1n gi\u1ea3n h\u00f3a qu\u00e1 tr\u00ecnh ph\u00e2n t\u00edch d\u1eef li\u1ec7u.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong v\u00e0 ch\u1ee9c n\u0103ng c\u1ee7a DataFrames<\/h2>\n<p>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a DataFrame ch\u1ee7 y\u1ebfu \u0111\u01b0\u1ee3c x\u00e1c \u0111\u1ecbnh b\u1edfi Ch\u1ec9 m\u1ee5c, C\u1ed9t v\u00e0 D\u1eef li\u1ec7u c\u1ee7a n\u00f3.<\/p>\n<ul>\n<li>\n<p>Ch\u1ec9 m\u1ee5c gi\u1ed1ng nh\u01b0 m\u1ed9t \u0111\u1ecba ch\u1ec9, \u0111\u00f3 l\u00e0 c\u00e1ch c\u00f3 th\u1ec3 truy c\u1eadp b\u1ea5t k\u1ef3 \u0111i\u1ec3m d\u1eef li\u1ec7u n\u00e0o tr\u00ean Khung d\u1eef li\u1ec7u ho\u1eb7c Chu\u1ed7i. C\u1ea3 h\u00e0ng v\u00e0 c\u1ed9t \u0111\u1ec1u c\u00f3 ch\u1ec9 m\u1ee5c, ch\u1ec9 m\u1ee5c h\u00e0ng \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 \u201cch\u1ec9 m\u1ee5c\u201d v\u00e0 \u0111\u1ed1i v\u1edbi c\u1ed9t th\u00ec \u0111\u00f3 l\u00e0 t\u00ean c\u1ed9t.<\/p>\n<\/li>\n<li>\n<p>C\u00e1c c\u1ed9t bi\u1ec3u th\u1ecb c\u00e1c bi\u1ebfn ho\u1eb7c t\u00ednh n\u0103ng c\u1ee7a t\u1eadp d\u1eef li\u1ec7u. M\u1ed7i c\u1ed9t trong DataFrame c\u00f3 m\u1ed9t ki\u1ec3u d\u1eef li\u1ec7u ho\u1eb7c dtype, c\u00f3 th\u1ec3 l\u00e0 s\u1ed1 (int, float), chu\u1ed7i (\u0111\u1ed1i t\u01b0\u1ee3ng) ho\u1eb7c datetime.<\/p>\n<\/li>\n<li>\n<p>D\u1eef li\u1ec7u bi\u1ec3u th\u1ecb c\u00e1c gi\u00e1 tr\u1ecb ho\u1eb7c quan s\u00e1t cho c\u00e1c \u0111\u1ed1i t\u01b0\u1ee3ng \u0111\u01b0\u1ee3c bi\u1ec3u th\u1ecb b\u1eb1ng c\u00e1c c\u1ed9t. Ch\u00fang \u0111\u01b0\u1ee3c truy c\u1eadp b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c ch\u1ec9 s\u1ed1 h\u00e0ng v\u00e0 c\u1ed9t.<\/p>\n<\/li>\n<\/ul>\n<p>V\u1ec1 c\u00e1ch th\u1ee9c ho\u1ea1t \u0111\u1ed9ng c\u1ee7a DataFrames, h\u1ea7u h\u1ebft c\u00e1c thao t\u00e1c tr\u00ean ch\u00fang \u0111\u1ec1u li\u00ean quan \u0111\u1ebfn vi\u1ec7c thao t\u00e1c d\u1eef li\u1ec7u v\u00e0 ch\u1ec9 m\u1ee5c. V\u00ed d\u1ee5: s\u1eafp x\u1ebfp DataFrame s\u1eafp x\u1ebfp l\u1ea1i c\u00e1c h\u00e0ng d\u1ef1a tr\u00ean c\u00e1c gi\u00e1 tr\u1ecb trong m\u1ed9t ho\u1eb7c nhi\u1ec1u c\u1ed9t, trong khi nh\u00f3m theo thao t\u00e1c bao g\u1ed3m vi\u1ec7c k\u1ebft h\u1ee3p c\u00e1c h\u00e0ng c\u00f3 c\u00f9ng gi\u00e1 tr\u1ecb trong c\u00e1c c\u1ed9t \u0111\u01b0\u1ee3c ch\u1ec9 \u0111\u1ecbnh th\u00e0nh m\u1ed9t h\u00e0ng.<\/p>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a DataFrames<\/h2>\n<p>DataFrames cung c\u1ea5p nhi\u1ec1u t\u00ednh n\u0103ng h\u1ed7 tr\u1ee3 ph\u00e2n t\u00edch d\u1eef li\u1ec7u. M\u1ed9t s\u1ed1 t\u00ednh n\u0103ng ch\u00ednh bao g\u1ed3m:<\/p>\n<ol>\n<li>\n<p><strong>Hi\u1ec7u qu\u1ea3<\/strong>: DataFrames cho ph\u00e9p l\u01b0u tr\u1eef v\u00e0 thao t\u00e1c d\u1eef li\u1ec7u hi\u1ec7u qu\u1ea3, \u0111\u1eb7c bi\u1ec7t \u0111\u1ed1i v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn.<\/p>\n<\/li>\n<li>\n<p><strong>T\u00ednh linh ho\u1ea1t<\/strong>: H\u1ecd c\u00f3 th\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u thu\u1ed9c nhi\u1ec1u lo\u1ea1i kh\u00e1c nhau \u2013 s\u1ed1, ph\u00e2n lo\u1ea1i, v\u0103n b\u1ea3n, v.v.<\/p>\n<\/li>\n<li>\n<p><strong>Uy\u1ec3n chuy\u1ec3n<\/strong>: Ch\u00fang cung c\u1ea5p c\u00e1c c\u00e1ch linh ho\u1ea1t \u0111\u1ec3 l\u1eadp ch\u1ec9 m\u1ee5c, c\u1eaft, l\u1ecdc v\u00e0 t\u1ed5ng h\u1ee3p d\u1eef li\u1ec7u.<\/p>\n<\/li>\n<li>\n<p><strong>Ch\u1ee9c n\u0103ng<\/strong>: Ch\u00fang cung c\u1ea5p nhi\u1ec1u ch\u1ee9c n\u0103ng t\u00edch h\u1ee3p s\u1eb5n \u0111\u1ec3 thao t\u00e1c v\u00e0 chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u, ch\u1eb3ng h\u1ea1n nh\u01b0 h\u1ee3p nh\u1ea5t, \u0111\u1ecbnh h\u00ecnh l\u1ea1i, ch\u1ecdn l\u1ecdc, c\u0169ng nh\u01b0 c\u00e1c ch\u1ee9c n\u0103ng ph\u00e2n t\u00edch th\u1ed1ng k\u00ea.<\/p>\n<\/li>\n<li>\n<p><strong>H\u1ed9i nh\u1eadp<\/strong>: H\u1ecd c\u00f3 th\u1ec3 d\u1ec5 d\u00e0ng t\u00edch h\u1ee3p v\u1edbi c\u00e1c th\u01b0 vi\u1ec7n kh\u00e1c \u0111\u1ec3 tr\u1ef1c quan h\u00f3a (nh\u01b0 Matplotlib, Seaborn) v\u00e0 h\u1ecdc m\u00e1y (nh\u01b0 Scikit-learn).<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1c lo\u1ea1i DataFrame<\/h2>\n<p>M\u1eb7c d\u00f9 c\u1ea5u tr\u00fac c\u01a1 b\u1ea3n c\u1ee7a DataFrame v\u1eabn gi\u1eef nguy\u00ean nh\u01b0ng ch\u00fang c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i d\u1ef1a tr\u00ean lo\u1ea1i d\u1eef li\u1ec7u ch\u00fang ch\u1ee9a v\u00e0 ngu\u1ed3n d\u1eef li\u1ec7u. \u0110\u00e2y l\u00e0 c\u00e1ch ph\u00e2n lo\u1ea1i chung:<\/p>\n<table>\n<thead>\n<tr>\n<th>Lo\u1ea1i khung d\u1eef li\u1ec7u<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Khung d\u1eef li\u1ec7u s\u1ed1<\/td>\n<td>Ch\u1ec9 bao g\u1ed3m d\u1eef li\u1ec7u s\u1ed1.<\/td>\n<\/tr>\n<tr>\n<td>Khung d\u1eef li\u1ec7u ph\u00e2n lo\u1ea1i<\/td>\n<td>Bao g\u1ed3m d\u1eef li\u1ec7u ph\u00e2n lo\u1ea1i ho\u1eb7c chu\u1ed7i.<\/td>\n<\/tr>\n<tr>\n<td>Khung d\u1eef li\u1ec7u h\u1ed7n h\u1ee3p<\/td>\n<td>Ch\u1ee9a c\u1ea3 d\u1eef li\u1ec7u s\u1ed1 v\u00e0 ph\u00e2n lo\u1ea1i.<\/td>\n<\/tr>\n<tr>\n<td>Khung d\u1eef li\u1ec7u chu\u1ed7i th\u1eddi gian<\/td>\n<td>Ch\u1ec9 m\u1ee5c l\u00e0 d\u1ea5u th\u1eddi gian, bi\u1ec3u th\u1ecb d\u1eef li\u1ec7u chu\u1ed7i th\u1eddi gian.<\/td>\n<\/tr>\n<tr>\n<td>Khung d\u1eef li\u1ec7u kh\u00f4ng gian<\/td>\n<td>Ch\u1ee9a d\u1eef li\u1ec7u kh\u00f4ng gian ho\u1eb7c \u0111\u1ecba l\u00fd, th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong c\u00e1c ho\u1ea1t \u0111\u1ed9ng c\u1ee7a GIS.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng DataFrames v\u00e0 c\u00e1c th\u00e1ch th\u1ee9c li\u00ean quan<\/h2>\n<p>DataFrames \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong nhi\u1ec1u \u1ee9ng d\u1ee5ng:<\/p>\n<ol>\n<li><strong>L\u00e0m s\u1ea1ch d\u1eef li\u1ec7u<\/strong>: X\u00e1c \u0111\u1ecbnh v\u00e0 x\u1eed l\u00fd c\u00e1c gi\u00e1 tr\u1ecb b\u1ecb thi\u1ebfu, c\u00e1c gi\u00e1 tr\u1ecb ngo\u1ea1i l\u1ec7, v.v.<\/li>\n<li><strong>Chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u<\/strong>: Thay \u0111\u1ed5i thang \u0111o c\u1ee7a c\u00e1c bi\u1ebfn, m\u00e3 h\u00f3a c\u00e1c bi\u1ebfn ph\u00e2n lo\u1ea1i, v.v.<\/li>\n<li><strong>T\u1ed5ng h\u1ee3p d\u1eef li\u1ec7u<\/strong>: Nh\u00f3m d\u1eef li\u1ec7u v\u00e0 t\u00ednh to\u00e1n th\u1ed1ng k\u00ea t\u00f3m t\u1eaft.<\/li>\n<li><strong>Ph\u00e2n t\u00edch d\u1eef li\u1ec7u<\/strong>: Ti\u1ebfn h\u00e0nh ph\u00e2n t\u00edch th\u1ed1ng k\u00ea, x\u00e2y d\u1ef1ng m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n, v.v.<\/li>\n<li><strong>Tr\u1ef1c quan h\u00f3a d\u1eef li\u1ec7u<\/strong>: T\u1ea1o s\u01a1 \u0111\u1ed3 v\u00e0 \u0111\u1ed3 th\u1ecb \u0111\u1ec3 hi\u1ec3u d\u1eef li\u1ec7u t\u1ed1t h\u01a1n.<\/li>\n<\/ol>\n<p>M\u1eb7c d\u00f9 DataFrame r\u1ea5t linh ho\u1ea1t v\u00e0 m\u1ea1nh m\u1ebd nh\u01b0ng ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 g\u1eb7p ph\u1ea3i nh\u1eefng th\u00e1ch th\u1ee9c nh\u01b0 x\u1eed l\u00fd d\u1eef li\u1ec7u b\u1ecb thi\u1ebfu, x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn kh\u00f4ng v\u1eeba v\u1edbi b\u1ed9 nh\u1edb ho\u1eb7c th\u1ef1c hi\u1ec7n c\u00e1c thao t\u00e1c d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p. Tuy nhi\u00ean, h\u1ea7u h\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 n\u00e0y c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c gi\u1ea3i quy\u1ebft b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u00e1c ch\u1ee9c n\u0103ng m\u1edf r\u1ed9ng \u0111\u01b0\u1ee3c cung c\u1ea5p b\u1edfi c\u00e1c th\u01b0 vi\u1ec7n h\u1ed7 tr\u1ee3 DataFrame nh\u01b0 Pandas v\u00e0 Dask.<\/p>\n<h2>So s\u00e1nh DataFrame v\u1edbi c\u00e1c c\u1ea5u tr\u00fac d\u1eef li\u1ec7u t\u01b0\u01a1ng t\u1ef1<\/h2>\n<p>D\u01b0\u1edbi \u0111\u00e2y l\u00e0 so s\u00e1nh DataFrame v\u1edbi hai c\u1ea5u tr\u00fac d\u1eef li\u1ec7u kh\u00e1c l\u00e0 Chu\u1ed7i v\u00e0 M\u1ea3ng:<\/p>\n<table>\n<thead>\n<tr>\n<th>Tham s\u1ed1<\/th>\n<th>Khung d\u1eef li\u1ec7u<\/th>\n<th>Lo\u1ea1t<\/th>\n<th>M\u1ea3ng<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>K\u00edch th\u01b0\u1edbc<\/td>\n<td>hai chi\u1ec1u<\/td>\n<td>M\u1ed9t chi\u1ec1u<\/td>\n<td>C\u00f3 th\u1ec3 \u0111a chi\u1ec1u<\/td>\n<\/tr>\n<tr>\n<td>Lo\u1ea1i d\u1eef li\u1ec7u<\/td>\n<td>C\u00f3 th\u1ec3 kh\u00f4ng \u0111\u1ed3ng nh\u1ea5t<\/td>\n<td>\u0111\u1ed3ng nh\u1ea5t<\/td>\n<td>\u0111\u1ed3ng nh\u1ea5t<\/td>\n<\/tr>\n<tr>\n<td>Kh\u1ea3 n\u0103ng thay \u0111\u1ed5i<\/td>\n<td>C\u00f3 th\u1ec3 thay \u0111\u1ed5i<\/td>\n<td>C\u00f3 th\u1ec3 thay \u0111\u1ed5i<\/td>\n<td>Ph\u1ee5 thu\u1ed9c v\u00e0o lo\u1ea1i m\u1ea3ng<\/td>\n<\/tr>\n<tr>\n<td>Ch\u1ee9c n\u0103ng<\/td>\n<td>C\u00e1c ch\u1ee9c n\u0103ng t\u00edch h\u1ee3p m\u1edf r\u1ed9ng \u0111\u1ec3 thao t\u00e1c v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u<\/td>\n<td>Ch\u1ee9c n\u0103ng h\u1ea1n ch\u1ebf so v\u1edbi DataFrame<\/td>\n<td>C\u00e1c ph\u00e9p to\u00e1n c\u01a1 b\u1ea3n nh\u01b0 s\u1ed1 h\u1ecdc v\u00e0 l\u1eadp ch\u1ec9 m\u1ee5c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn DataFrames<\/h2>\n<p>DataFrames, v\u1edbi t\u01b0 c\u00e1ch l\u00e0 m\u1ed9t c\u1ea5u tr\u00fac d\u1eef li\u1ec7u, \u0111\u01b0\u1ee3c thi\u1ebft l\u1eadp t\u1ed1t v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng ti\u1ebfp t\u1ee5c l\u00e0 m\u1ed9t c\u00f4ng c\u1ee5 c\u01a1 b\u1ea3n trong ph\u00e2n t\u00edch v\u00e0 thao t\u00e1c d\u1eef li\u1ec7u. Tr\u1ecdng t\u00e2m hi\u1ec7n nay t\u1eadp trung nhi\u1ec1u h\u01a1n v\u00e0o vi\u1ec7c n\u00e2ng cao kh\u1ea3 n\u0103ng c\u1ee7a c\u00e1c th\u01b0 vi\u1ec7n d\u1ef1a tr\u00ean DataFrame \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn h\u01a1n, c\u1ea3i thi\u1ec7n t\u1ed1c \u0111\u1ed9 t\u00ednh to\u00e1n v\u00e0 cung c\u1ea5p c\u00e1c ch\u1ee9c n\u0103ng n\u00e2ng cao h\u01a1n.<\/p>\n<p>V\u00ed d\u1ee5: c\u00e1c c\u00f4ng ngh\u1ec7 nh\u01b0 Dask v\u00e0 Vaex \u0111ang n\u1ed5i l\u00ean nh\u01b0 nh\u1eefng gi\u1ea3i ph\u00e1p trong t\u01b0\u01a1ng lai \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn h\u01a1n b\u1ed9 nh\u1edb b\u1eb1ng DataFrames. H\u1ecd cung c\u1ea5p API DataFrame \u0111\u1ec3 th\u1ef1c hi\u1ec7n t\u00ednh to\u00e1n song song, gi\u00fap c\u00f3 th\u1ec3 ho\u1ea1t \u0111\u1ed9ng v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn h\u01a1n.<\/p>\n<h2>Hi\u1ec7p h\u1ed9i m\u00e1y ch\u1ee7 proxy v\u1edbi DataFrames<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, \u0111\u00f3ng vai tr\u00f2 trung gian cho c\u00e1c y\u00eau c\u1ea7u t\u1eeb kh\u00e1ch h\u00e0ng \u0111ang t\u00ecm ki\u1ebfm t\u00e0i nguy\u00ean t\u1eeb c\u00e1c m\u00e1y ch\u1ee7 kh\u00e1c. M\u1eb7c d\u00f9 ch\u00fang c\u00f3 th\u1ec3 kh\u00f4ng t\u01b0\u01a1ng t\u00e1c tr\u1ef1c ti\u1ebfp v\u1edbi DataFrame nh\u01b0ng ch\u00fang \u0111\u00f3ng vai tr\u00f2 quan tr\u1ecdng trong vi\u1ec7c thu th\u1eadp d\u1eef li\u1ec7u - \u0111i\u1ec1u ki\u1ec7n ti\u00ean quy\u1ebft \u0111\u1ec3 t\u1ea1o DataFrame.<\/p>\n<p>D\u1eef li\u1ec7u \u0111\u01b0\u1ee3c thu th\u1eadp ho\u1eb7c thu th\u1eadp th\u00f4ng qua m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eafp x\u1ebfp th\u00e0nh DataFrames \u0111\u1ec3 ph\u00e2n t\u00edch th\u00eam. V\u00ed d\u1ee5: n\u1ebfu m\u1ed9t ng\u01b0\u1eddi s\u1eed d\u1ee5ng m\u00e1y ch\u1ee7 proxy \u0111\u1ec3 thu th\u1eadp d\u1eef li\u1ec7u web, d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c thu th\u1eadp c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eafp x\u1ebfp th\u00e0nh DataFrame \u0111\u1ec3 l\u00e0m s\u1ea1ch, chuy\u1ec3n \u0111\u1ed5i v\u00e0 ph\u00e2n t\u00edch.<\/p>\n<p>H\u01a1n n\u1eefa, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00fap thu th\u1eadp d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u v\u1ecb tr\u00ed \u0111\u1ecba l\u00fd kh\u00e1c nhau b\u1eb1ng c\u00e1ch che gi\u1ea5u \u0111\u1ecba ch\u1ec9 IP, sau \u0111\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c c\u1ea5u tr\u00fac th\u00e0nh DataFrame \u0111\u1ec3 ti\u1ebfn h\u00e0nh ph\u00e2n t\u00edch theo v\u00f9ng c\u1ee5 th\u1ec3.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<p>\u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 DataFrames, h\u00e3y xem x\u00e9t c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ul>\n<li><a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_new\" rel=\"noopener nofollow\">T\u00e0i li\u1ec7u v\u1ec1 g\u1ea5u tr\u00fac<\/a><\/li>\n<li><a href=\"https:\/\/www.rdocumentation.org\/packages\/base\/versions\/3.6.2\/topics\/data.frame\" target=\"_new\" rel=\"noopener nofollow\">T\u00e0i li\u1ec7u v\u1ec1 khung d\u1eef li\u1ec7u R<\/a><\/li>\n<li><a href=\"https:\/\/docs.dask.org\/en\/latest\/\" target=\"_new\" rel=\"noopener nofollow\">T\u00e0i li\u1ec7u Dask<\/a><\/li>\n<li><a href=\"https:\/\/docs.vaex.io\/en\/latest\/\" target=\"_new\" rel=\"noopener nofollow\">T\u00e0i li\u1ec7u Vaex<\/a><\/li>\n<\/ul>","protected":false},"featured_media":468173,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476745","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>An In-Depth Exploration of DataFrames<\/mark>","faq_items":[{"question":"What are DataFrames?","answer":"<p>DataFrames are a two-dimensional data structure, similar to a table with rows and columns, used primarily for data manipulation and analysis in programming languages such as R and Python.<\/p>"},{"question":"Where did the concept of DataFrames originate?","answer":"<p>The concept of DataFrames originated from the statistical programming language, R. However, it became widely popularized with the advent of the Pandas library in Python.<\/p>"},{"question":"How does the internal structure of DataFrames work?","answer":"<p>The internal structure of a DataFrame is primarily defined by its Index, Columns, and Data. The Index is like an address that is used to access any data point across the DataFrame or Series. Columns represent the variables or features of the dataset and can be of different data types. The Data represents the values or observations, which can be accessed using the row and column indices.<\/p>"},{"question":"What are some key features of DataFrames?","answer":"<p>Key features of DataFrames include their efficiency in handling large volumes of data, versatility in handling different data types, flexibility in indexing and aggregating data, wide range of built-in functions for data manipulation, and easy integration with other libraries for visualization and machine learning.<\/p>"},{"question":"Are there different types of DataFrames?","answer":"<p>Yes, DataFrames can be classified based on the type of data they hold. They can be Numeric, Categorical, Mixed, Time Series, or Spatial.<\/p>"},{"question":"Where are DataFrames used and what are some common challenges?","answer":"<p>DataFrames are used in various applications including data cleaning, transformation, aggregation, analysis, and visualization. Some common challenges include handling missing data, working with large data sets that do not fit into memory, and performing complex data manipulations.<\/p>"},{"question":"How do DataFrames compare with other similar data structures like Series and Arrays?","answer":"<p>DataFrames are two-dimensional and can handle heterogeneous data, with more extensive built-in functions for data manipulation and analysis compared to Series and Arrays. Series are one-dimensional and can only handle homogeneous data, with less functionality. Arrays can be multi-dimensional, also handle homogeneous data, and are mutable or immutable depending on the array type.<\/p>"},{"question":"What is the future perspective of DataFrames?","answer":"<p>DataFrames are likely to continue being a fundamental tool in data analysis and manipulation. The focus now is more on enhancing the capabilities of DataFrame-based libraries to handle larger datasets, improve computational speed, and provide more advanced functionalities.<\/p>"},{"question":"How can proxy servers be used or associated with DataFrames?","answer":"<p>While proxy servers might not directly interact with DataFrames, they play a crucial role in data gathering. Data collected through proxy servers can be organized into DataFrames for further analysis. Additionally, proxy servers can help collect data from various geo-locations, which can then be structured into a DataFrame for conducting region-specific analysis.<\/p>"},{"question":"Where can I find more resources to learn about DataFrames?","answer":"<p>You can find more resources about DataFrames in the documentation of libraries like <a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_new\">Pandas<\/a>, <a href=\"https:\/\/www.rdocumentation.org\/packages\/base\/versions\/3.6.2\/topics\/data.frame\" target=\"_new\">R<\/a>, <a href=\"https:\/\/docs.dask.org\/en\/latest\/\" target=\"_new\">Dask<\/a>, and <a href=\"https:\/\/docs.vaex.io\/en\/latest\/\" target=\"_new\">Vaex<\/a>.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476745","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/476745\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468173"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=476745"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}