{"id":15846,"date":"2024-01-17T22:03:20","date_gmt":"2024-01-17T18:33:20","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/"},"modified":"2024-01-17T22:03:20","modified_gmt":"2024-01-17T18:33:20","slug":"%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/","title":{"rendered":"\u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u067e\u0627\u0646\u062f\u0627\u0647\u0627"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%81%d8%a7%db%8c%d9%84_csv_%da%86%db%8c%d8%b3%d8%aa%d8%9f\" >\u0641\u0627\u06cc\u0644 CSV \u0686\u06cc\u0633\u062a\u061f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_%d9%88_%d9%86%d9%88%d8%b4%d8%aa%d9%86_%d9%81%d8%a7%db%8c%d9%84%e2%80%8c%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87_%d8%a7%d8%b2_%d9%be%d8%a7%d9%86%d8%af%d8%a7\" >\u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%86%d8%b5%d8%a8_%d9%be%d8%a7%d9%86%d8%af%d8%a7%d9%87%d8%a7\" >\u0646\u0635\u0628 \u067e\u0627\u0646\u062f\u0627\u0647\u0627<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_%d9%81%d8%a7%db%8c%d9%84%e2%80%8c%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_read_csv\" >\u062e\u0648\u0627\u0646\u062f\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0628\u0627 read_csv()<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%be%d8%b1%d8%b4_%d8%a7%d8%b2_%d8%b1%d8%af%db%8c%d9%81_%d9%87%d8%a7_%d9%87%d9%86%da%af%d8%a7%d9%85_%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_csv\" >\u067e\u0631\u0634 \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0647\u0627 \u0647\u0646\u06af\u0627\u0645 \u062e\u0648\u0627\u0646\u062f\u0646 CSV<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d8%aa%d8%b9%db%8c%db%8c%d9%86_%d8%ac%d8%af%d8%a7%da%a9%d9%86%d9%86%d8%af%d9%87_%d9%87%d8%a7\" >\u062a\u0639\u06cc\u06cc\u0646 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0647\u0627<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%86%d9%88%d8%b4%d8%aa%d9%86_%d9%81%d8%a7%db%8c%d9%84_%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_to_csv\" >\u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV \u0628\u0627 to_csv()<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d8%b3%d9%81%d8%a7%d8%b1%d8%b4%db%8c_%d8%b3%d8%a7%d8%b2%db%8c_%d8%ac%d8%af%d8%a7%da%a9%d9%86%d9%86%d8%af%d9%87\" >\u0633\u0641\u0627\u0631\u0634\u06cc \u0633\u0627\u0632\u06cc \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%85%d8%af%db%8c%d8%b1%db%8c%d8%aa_%d8%a7%d8%b1%d8%b2%d8%b4_%d9%87%d8%a7%db%8c_%da%af%d9%85%d8%b4%d8%af%d9%87\" >\u0645\u062f\u06cc\u0631\u06cc\u062a \u0627\u0631\u0632\u0634 \u0647\u0627\u06cc \u06af\u0645\u0634\u062f\u0647<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86-%d9%88-%d9%86%d9%88%d8%b4%d8%aa%d9%86-%d9%81%d8%a7%db%8c%d9%84%d9%87%d8%a7%db%8c-csv-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%a7-%d9%be\/#%d9%86%d8%aa%db%8c%d8%ac%d9%87\" >\u0646\u062a\u06cc\u062c\u0647<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 7<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<p>\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV \u0631\u0627 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062e\u0644\u06cc \u0628\u062e\u0648\u0627\u0646\u06cc\u062f \u0648 \u0628\u0646\u0648\u06cc\u0633\u06cc\u062f <code>open()<\/code> \u062a\u0627\u0628\u0639\u060c \u06cc\u0627 \u0627\u062e\u062a\u0635\u0627\u0635 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/docs.python.org\/3\/library\/csv.html\">csv<\/a> \u0645\u0627\u0698\u0648\u0644 &#8211; \u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/pandas.pydata.org\/\">\u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u067e\u0627\u0646\u062f\u0627\u0647\u0627<\/a> \u0628\u0631\u0627\u06cc \u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV.<\/p>\n<h2 id=\"whatisacsvfile\"><span class=\"ez-toc-section\" id=\"%d9%81%d8%a7%db%8c%d9%84_csv_%da%86%db%8c%d8%b3%d8%aa%d8%9f\"><\/span>\u0641\u0627\u06cc\u0644 CSV \u0686\u06cc\u0633\u062a\u061f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0647 \u0633\u0631\u0639\u062a \u062e\u0644\u0627\u0635\u0647 \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0686\u06cc\u0633\u062a &#8211; \u0686\u06cc\u0632\u06cc \u0628\u06cc\u0634 \u0627\u0632 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u0645\u062a\u0646\u06cc \u0633\u0627\u062f\u0647\u060c \u0628\u0627 \u067e\u06cc\u0631\u0648\u06cc \u0627\u0632 \u0686\u0646\u062f \u0642\u0631\u0627\u0631\u062f\u0627\u062f \u0642\u0627\u0644\u0628 \u0628\u0646\u062f\u06cc.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0631\u0627\u06cc\u062c \u062a\u0631\u06cc\u0646\u060c \u0633\u0627\u062f\u0647 \u062a\u0631\u06cc\u0646 \u0648 \u0633\u0627\u062f\u0647 \u062a\u0631\u06cc\u0646 \u0631\u0648\u0634 \u0628\u0631\u0627\u06cc \u0630\u062e\u06cc\u0631\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u0648\u0644\u06cc \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u0641\u0631\u0645\u062a \u062c\u062f\u0627\u0648\u0644 \u0631\u0627 \u0628\u0627 \u067e\u06cc\u0631\u0648\u06cc \u0627\u0632 \u06cc\u06a9 \u0633\u0627\u062e\u062a\u0627\u0631 \u062e\u0627\u0635 \u06a9\u0647 \u0628\u0647 \u0633\u0637\u0631\u0647\u0627 \u0648 \u0633\u062a\u0648\u0646 \u0647\u0627 \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u0634\u0648\u062f \u0645\u0631\u062a\u0628 \u0645\u06cc \u06a9\u0646\u062f.  \u0627\u06cc\u0646 \u0633\u0637\u0631\u0647\u0627 \u0648 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u062d\u0627\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0634\u0645\u0627 \u0647\u0633\u062a\u0646\u062f.<\/p>\n<p>\u06cc\u06a9 \u062e\u0637 \u062c\u062f\u06cc\u062f \u0647\u0631 \u0631\u062f\u06cc\u0641 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0634\u0631\u0648\u0639 \u0631\u062f\u06cc\u0641 \u0628\u0639\u062f\u06cc \u062e\u0627\u062a\u0645\u0647 \u0645\u06cc \u062f\u0647\u062f.  \u0628\u0647 \u0637\u0648\u0631 \u0645\u0634\u0627\u0628\u0647\u060c \u06cc\u06a9 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647\u060c \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u06cc\u06a9 \u06a9\u0627\u0645\u0627\u060c \u0633\u062a\u0648\u0646\u200c\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0647\u0631 \u0633\u0637\u0631 \u062c\u062f\u0627 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u062c\u062f\u0648\u0644\u06cc \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">| City         | State        | Capital | Population    |\n| ------------ | ------------ | ------- | ------------- |\n| Philadelphia | Pennsylvania | No      | 1.581 Million |\n| Sacramento   | California   | Yes     | 0.5 Million   |\n| New York     | New York     | No      | 8.623 Million |\n| Austin       | Texas        | Yes     | 0.95 Million  |\n| Miami        | Florida      | No      | 0.463 Million |\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0641\u0631\u0645\u062a CSV \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645\u060c \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">City,State,Capital,Population\nPhiladelphia,Pennsylvania,No,1.581 Million\nSacramento,California,Yes,0.5 Million\nNew York,New York,No,8.623 Million\nAustin,Texas,Yes,0.95 Million\nMiami,Florida,No,0.463 Million\n<\/code><\/pre>\n<p>\u0627\u06af\u0631\u0686\u0647 \u0646\u0627\u0645 (\u0645\u0642\u0627\u062f\u06cc\u0631 \u062c\u062f\u0627 \u0634\u062f\u0647 \u0628\u0627 \u06a9\u0627\u0645\u0627) \u0630\u0627\u062a\u0627\u064b \u0627\u0632 \u06a9\u0627\u0645\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0647\u0627\u06cc \u062f\u06cc\u06af\u0631 (\u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647) \u0646\u06cc\u0632 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f\u060c \u0645\u0627\u0646\u0646\u062f \u0646\u0642\u0637\u0647 \u0648\u06cc\u0631\u06af\u0648\u0644 (<code>;<\/code>).  \u0647\u0631 \u0631\u062f\u06cc\u0641 \u0627\u0632 \u062c\u062f\u0648\u0644 \u06cc\u06a9 \u062e\u0637 \u062c\u062f\u06cc\u062f \u0627\u0632 \u0641\u0627\u06cc\u0644 CSV \u0627\u0633\u062a \u0648 \u0631\u0648\u0634\u06cc \u0628\u0633\u06cc\u0627\u0631 \u0641\u0634\u0631\u062f\u0647 \u0648 \u0645\u062e\u062a\u0635\u0631 \u0628\u0631\u0627\u06cc \u0646\u0645\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062c\u062f\u0648\u0644\u06cc \u0627\u0633\u062a.<\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0622\u0646 \u0628\u06cc\u0627\u0646\u062f\u0627\u0632\u06cc\u0645 <code>read_csv()<\/code> \u062a\u0627\u0628\u0639.<\/p>\n<h2 id=\"readingandwritingcsvfilesusingpandas\"><span class=\"ez-toc-section\" id=\"%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_%d9%88_%d9%86%d9%88%d8%b4%d8%aa%d9%86_%d9%81%d8%a7%db%8c%d9%84%e2%80%8c%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87_%d8%a7%d8%b2_%d9%be%d8%a7%d9%86%d8%af%d8%a7\"><\/span>\u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u0646\u062f\u0627<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Pandas \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0628\u0633\u06cc\u0627\u0631 \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0648 \u0645\u062d\u0628\u0648\u0628 \u0628\u0631\u0627\u06cc \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0648 \u062f\u0633\u062a\u06a9\u0627\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0633\u062a.  \u06cc\u06a9\u06cc \u0627\u0632 \u0628\u0627\u0631\u0632\u062a\u0631\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0622\u0646 \u062f\u0631 \u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0627\u0646\u0648\u0627\u0639 \u0641\u0627\u06cc\u0644 \u0647\u0627 \u0627\u0632 \u062c\u0645\u0644\u0647 CSV \u0648 Excel \u0627\u0633\u062a.  \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u0637\u0648\u0631 \u0645\u0648\u062b\u0631 \u0648 \u0622\u0633\u0627\u0646 \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV \u0631\u0627 \u062f\u0631 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0628\u0639\u06cc \u0645\u0627\u0646\u0646\u062f \u062f\u0633\u062a\u06a9\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f <code>read_csv()<\/code> \u0648 <code>to_csv()<\/code>.<\/p>\n<h3 id=\"installingpandas\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%b5%d8%a8_%d9%be%d8%a7%d9%86%d8%af%d8%a7%d9%87%d8%a7\"><\/span>\u0646\u0635\u0628 \u067e\u0627\u0646\u062f\u0627\u0647\u0627<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0645\u0627 \u0628\u0627\u06cc\u062f Pandas \u0631\u0627 \u0642\u0628\u0644 \u0627\u0632 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646 \u0646\u0635\u0628 \u06a9\u0646\u06cc\u0645.  \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <code>pip<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">$<\/span><span class=\"bash\"> pip install pandas<\/span>\n<\/code><\/pre>\n<h2 id=\"readingcsvfileswithread_csv\"><span class=\"ez-toc-section\" id=\"%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_%d9%81%d8%a7%db%8c%d9%84%e2%80%8c%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_read_csv\"><\/span>\u062e\u0648\u0627\u0646\u062f\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0628\u0627 <em>read_csv()<\/em><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f import \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062a\u0627\u06cc\u062a\u0627\u0646\u06cc\u06a9\u060c \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f \u0631\u0648\u06cc <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/raw.githubusercontent.com\/datasciencedojo\/datasets\/master\/titanic.csv\">GitHub<\/a>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ntitanic_data = pd.read_csv(<span class=\"hljs-string\">'titanic.csv'<\/span>)\n<\/code><\/pre>\n<p>\u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0627\u06cc\u0646 \u0641\u0627\u06cc\u0644 \u0631\u0627 \u062f\u0631 \u062f\u0627\u06cc\u0631\u06a9\u062a\u0648\u0631\u06cc \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u062c\u0633\u062a\u062c\u0648 \u0645\u06cc \u06a9\u0646\u0646\u062f\u060c \u0637\u0628\u06cc\u0639\u062a\u0627\u060c \u0648 \u0645\u0627 \u0641\u0642\u0637 \u0645\u0633\u06cc\u0631 \u0641\u0627\u06cc\u0644 \u0631\u0627 \u0628\u0647 \u0641\u0627\u06cc\u0644\u06cc \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062a\u062c\u0632\u06cc\u0647 \u06a9\u0646\u06cc\u0645\u060c \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062a\u0646\u0647\u0627 \u0648 \u062a\u0646\u0647\u0627 \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648\u0634 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 <code>head()<\/code> \u0627\u0632 \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">titanic_data.head()\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n0            1         0       3  ...   7.2500   NaN         S\n1            2         1       1  ...  71.2833   C85         C\n2            3         1       3  ...   7.9250   NaN         S\n3            4         1       1  ...  53.1000  C123         S\n4            5         0       3  ...   8.0500   NaN         S\n<\/code><\/pre>\n<p>\u0647\u0645\u0686\u0646\u06cc\u0646\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0631\u0627 \u0627\u0632 \u0645\u0646\u0627\u0628\u0639 \u0622\u0646\u0644\u0627\u06cc\u0646\u060c \u0645\u0627\u0646\u0646\u062f GitHub\u060c \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0628\u0627 \u0627\u0631\u0633\u0627\u0644 URL \u0645\u0646\u0628\u0639 \u0628\u0647 <code>read_csv()<\/code> \u062a\u0627\u0628\u0639.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0647\u0645\u06cc\u0646 \u0641\u0627\u06cc\u0644 CSV \u0631\u0627 \u0627\u0632 \u0645\u062e\u0632\u0646 GitHub \u0628\u062e\u0648\u0627\u0646\u06cc\u0645\u060c \u0628\u062f\u0648\u0646 \u0627\u06cc\u0646\u06a9\u0647 \u0627\u0628\u062a\u062f\u0627 \u0622\u0646 \u0631\u0627 \u062f\u0631 \u062f\u0633\u062a\u06af\u0627\u0647 \u0645\u062d\u0644\u06cc \u062e\u0648\u062f \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\ntitanic_data = pd.read_csv(<span class=\"hljs-string\">r'https:\/\/raw.githubusercontent.com\/datasciencedojo\/datasets\/master\/titanic.csv'<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(titanic_data.head())\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u06cc\u0632 \u0645\u0646\u062c\u0631 \u0628\u0647:<\/p>\n<pre><code class=\"hljs\">   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n0            1         0       3  ...   7.2500   NaN         S\n1            2         1       1  ...  71.2833   C85         C\n2            3         1       3  ...   7.9250   NaN         S\n3            4         1       1  ...  53.1000  C123         S\n4            5         0       3  ...   8.0500   NaN         S\n\n(5 rows x 12 columns)\n<\/code><\/pre>\n<p>\u0628\u0647 \u0637\u0648\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636\u060c <code>read_csv()<\/code> \u0631\u0648\u0634 \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0627\u0648\u0644 \u0641\u0627\u06cc\u0644 CSV \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0633\u0631\u0628\u0631\u06af \u0633\u062a\u0648\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.  \u06af\u0627\u0647\u06cc \u0627\u0648\u0642\u0627\u062a\u060c \u0627\u06cc\u0646 \u0633\u0631\u0635\u0641\u062d\u0647 \u0647\u0627 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0646\u0627\u0645 \u0647\u0627\u06cc \u0639\u062c\u06cc\u0628 \u0648 \u063a\u0631\u06cc\u0628 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u0646\u062f\u060c \u0648 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062e\u0648\u0627\u0647\u06cc\u062f \u0627\u0632 \u0633\u0631\u0635\u0641\u062d\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.  \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0647\u062f\u0631\u0647\u0627 \u0631\u0627 \u067e\u0633 \u0627\u0632 \u062e\u0648\u0627\u0646\u062f\u0646 \u0641\u0627\u06cc\u0644 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0628\u0627 \u0627\u062e\u062a\u0635\u0627\u0635 \u062f\u0627\u062f\u0646 \u0622\u0646 \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f <code>columns<\/code> \u0632\u0645\u06cc\u0646\u0647 \u0627\u0632 <code>DataFrame<\/code> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0644\u06cc\u0633\u062a \u062f\u06cc\u06af\u0631\u06cc\u060c \u06cc\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062f\u0631 \u0648\u0647\u0644\u0647 \u0627\u0648\u0644 \u0647\u0646\u06af\u0627\u0645 \u062e\u0648\u0627\u0646\u062f\u0646 CSV \u0633\u0631\u0635\u0641\u062d\u0647 \u0647\u0627 \u0631\u0627 \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0644\u06cc\u0633\u062a\u06cc \u0627\u0632 \u0646\u0627\u0645 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645 \u0648 \u0627\u0632 \u0622\u0646 \u0646\u0627\u0645 \u0647\u0627 \u0628\u0647 \u062c\u0627\u06cc \u0646\u0627\u0645 \u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u0641\u0627\u06cc\u0644 CSV \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\ncol_names = (<span class=\"hljs-string\">'Id'<\/span>,\n             <span class=\"hljs-string\">'Survived'<\/span>,\n             <span class=\"hljs-string\">'Passenger Class'<\/span>,\n             <span class=\"hljs-string\">'Full Name'<\/span>,\n             <span class=\"hljs-string\">'Gender'<\/span>,\n             <span class=\"hljs-string\">'Age'<\/span>,\n             <span class=\"hljs-string\">'SibSp'<\/span>,\n             <span class=\"hljs-string\">'Parch'<\/span>,\n             <span class=\"hljs-string\">'Ticket Number'<\/span>,\n             <span class=\"hljs-string\">'Price'<\/span>, <span class=\"hljs-string\">'Cabin'<\/span>,\n             <span class=\"hljs-string\">'Station'<\/span>)\n\ntitanic_data = pd.read_csv(<span class=\"hljs-string\">r'E:\\Datasets\\titanic.csv'<\/span>, names=col_names)\n<span class=\"hljs-built_in\">print<\/span>(titanic_data.head())\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06cc\u0646 \u06a9\u062f \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">            Id  Survived Passenger Class  ...    Price  Cabin   Station\n0  PassengerId  Survived          Pclass  ...     Fare  Cabin  Embarked\n1            1         0               3  ...     7.25    NaN         S\n2            2         1               1  ...  71.2833    C85         C\n3            3         1               3  ...    7.925    NaN         S\n4            4         1               1  ...     53.1   C123         S\n<\/code><\/pre>\n<p>\u0647\u0648\u0645!  \u0627\u06a9\u0646\u0648\u0646 \u0645\u0627 \u0633\u0631\u0635\u0641\u062d\u0647 \u0647\u0627\u06cc \u0633\u0641\u0627\u0631\u0634\u06cc \u062e\u0648\u062f \u0631\u0627 \u062f\u0627\u0631\u06cc\u0645\u060c \u0627\u0645\u0627 <em>\u0627\u0648\u0644\u06cc\u0646<\/em> \u0631\u062f\u06cc\u0641 \u0641\u0627\u06cc\u0644 CSV\u060c \u06a9\u0647 \u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0628\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0646\u0627\u0645 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u062f\u060c \u0646\u06cc\u0632 \u062f\u0631 \u0622\u0646 \u06af\u0646\u062c\u0627\u0646\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <code>DataFrame<\/code>.  \u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u0627\u0632 \u0627\u06cc\u0646 \u062e\u0637 \u0628\u06af\u0630\u0631\u06cc\u0645\u060c \u0632\u06cc\u0631\u0627 \u062f\u06cc\u06af\u0631 \u0628\u0631\u0627\u06cc \u0645\u0627 \u0627\u0631\u0632\u0634\u06cc \u0646\u062f\u0627\u0631\u062f.<\/p>\n<h3 id=\"skippingrowswhilereadingcsv\"><span class=\"ez-toc-section\" id=\"%d9%be%d8%b1%d8%b4_%d8%a7%d8%b2_%d8%b1%d8%af%db%8c%d9%81_%d9%87%d8%a7_%d9%87%d9%86%da%af%d8%a7%d9%85_%d8%ae%d9%88%d8%a7%d9%86%d8%af%d9%86_csv\"><\/span>\u067e\u0631\u0634 \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0647\u0627 \u0647\u0646\u06af\u0627\u0645 \u062e\u0648\u0627\u0646\u062f\u0646 CSV<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0634\u06a9\u0644 \u0631\u0627 \u062d\u0644 \u06a9\u0646\u06cc\u0645 <code>skiprows<\/code> \u0628\u062d\u062b \u0648 \u062c\u062f\u0644:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\ncol_names = (<span class=\"hljs-string\">'Id'<\/span>,\n             <span class=\"hljs-string\">'Survived'<\/span>,\n             <span class=\"hljs-string\">'Passenger Class'<\/span>,\n             <span class=\"hljs-string\">'Full Name'<\/span>,\n             <span class=\"hljs-string\">'Gender'<\/span>,\n             <span class=\"hljs-string\">'Age'<\/span>,\n             <span class=\"hljs-string\">'SibSp'<\/span>,\n             <span class=\"hljs-string\">'Parch'<\/span>,\n             <span class=\"hljs-string\">'Ticket Number'<\/span>,\n             <span class=\"hljs-string\">'Price'<\/span>, <span class=\"hljs-string\">'Cabin'<\/span>,\n             <span class=\"hljs-string\">'Station'<\/span>)\n\ntitanic_data = pd.read_csv(<span class=\"hljs-string\">r'E:\\Datasets\\titanic.csv'<\/span>, names=col_names, skiprows=(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(titanic_data.head())\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06cc\u0646 \u06a9\u062f \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">   Id  Survived  Passenger Class  ...    Price Cabin  Station\n0   1         0                3  ...   7.2500   NaN        S\n1   2         1                1  ...  71.2833   C85        C\n2   3         1                3  ...   7.9250   NaN        S\n3   4         1                1  ...  53.1000  C123        S\n4   5         0                3  ...   8.0500   NaN        S\n<\/code><\/pre>\n<p>\u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0637\u0644\u0633\u0645 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f!  \u0627\u06cc\u0646 <code>skiprows<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0644\u06cc\u0633\u062a\u06cc \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0647\u0627\u06cc\u06cc \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0631\u062f \u0634\u0648\u06cc\u062f.  \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0631\u062f \u0634\u0648\u06cc\u062f <code>0, 4, 7<\/code> \u0627\u06af\u0631 \u0634\u0645\u0627 \u0647\u0645 \u062f\u0648\u0633\u062a \u062f\u0627\u0631\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">titanic_data = pd.read_csv(<span class=\"hljs-string\">r'E:\\Datasets\\titanic.csv'<\/span>, names=col_names, skiprows=(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-number\">7<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(titanic_data.head(<span class=\"hljs-number\">10<\/span>))\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0645\u0646\u062c\u0631 \u0628\u0647 \u06cc\u06a9 <code>DataFrame<\/code> \u06a9\u0647 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0631\u062f\u06cc\u0641\u200c\u0647\u0627\u06cc\u06cc \u0631\u0627 \u06a9\u0647 \u0642\u0628\u0644\u0627\u064b \u062f\u06cc\u062f\u0647\u200c\u0627\u06cc\u0645 \u0646\u062f\u0627\u0631\u062f:<\/p>\n<pre><code class=\"hljs\">   Id  Survived  Passenger Class  ...    Price Cabin  Station\n<span class=\"hljs-number\">0<\/span>   <span class=\"hljs-number\">1<\/span>         <span class=\"hljs-number\">0<\/span>                <span class=\"hljs-number\">3<\/span>  ...   <span class=\"hljs-number\">7.2500<\/span>   NaN        S\n<span class=\"hljs-number\">1<\/span>   <span class=\"hljs-number\">2<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">1<\/span>  ...  <span class=\"hljs-number\">71.2833<\/span>   C85        C\n<span class=\"hljs-number\">2<\/span>   <span class=\"hljs-number\">3<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">3<\/span>  ...   <span class=\"hljs-number\">7.9250<\/span>   NaN        S\n<span class=\"hljs-number\">3<\/span>   <span class=\"hljs-number\">5<\/span>         <span class=\"hljs-number\">0<\/span>                <span class=\"hljs-number\">3<\/span>  ...   <span class=\"hljs-number\">8.0500<\/span>   NaN        S\n<span class=\"hljs-number\">4<\/span>   <span class=\"hljs-number\">6<\/span>         <span class=\"hljs-number\">0<\/span>                <span class=\"hljs-number\">3<\/span>  ...   <span class=\"hljs-number\">8.4583<\/span>   NaN        Q\n<span class=\"hljs-number\">5<\/span>   <span class=\"hljs-number\">8<\/span>         <span class=\"hljs-number\">0<\/span>                <span class=\"hljs-number\">3<\/span>  ...  <span class=\"hljs-number\">21.0750<\/span>   NaN        S\n<span class=\"hljs-number\">6<\/span>   <span class=\"hljs-number\">9<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">3<\/span>  ...  <span class=\"hljs-number\">11.1333<\/span>   NaN        S\n<span class=\"hljs-number\">7<\/span>  <span class=\"hljs-number\">10<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">2<\/span>  ...  <span class=\"hljs-number\">30.0708<\/span>   NaN        C\n<span class=\"hljs-number\">8<\/span>  <span class=\"hljs-number\">11<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">3<\/span>  ...  <span class=\"hljs-number\">16.7000<\/span>    G6        S\n<span class=\"hljs-number\">9<\/span>  <span class=\"hljs-number\">12<\/span>         <span class=\"hljs-number\">1<\/span>                <span class=\"hljs-number\">1<\/span>  ...  <span class=\"hljs-number\">26.5500<\/span>  C103        S\n<\/code><\/pre>\n<p>\u0628\u0647 \u062e\u0627\u0637\u0631 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f \u06a9\u0647 \u067e\u0631\u0634 \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0647\u0627 \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f <em>\u0642\u0628\u0644 \u0627\u0632<\/em> \u0631\u0627 <code>DataFrame<\/code> \u0628\u0647 \u0637\u0648\u0631 \u06a9\u0627\u0645\u0644 \u062a\u0634\u06a9\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0647\u06cc\u0686 \u0634\u0627\u062e\u0635\u06cc \u0627\u0632 \u0622\u0646 \u0631\u0627 \u0627\u0632 \u062f\u0633\u062a \u0646\u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u0627\u062f <code>DataFrame<\/code> \u062e\u0648\u062f\u060c \u0647\u0631 \u0686\u0646\u062f\u060c \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0648\u0631\u062f\u060c \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 <code>Id<\/code> \u0641\u06cc\u0644\u062f (\u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0632 \u0641\u0627\u06cc\u0644 CSV) \u0641\u0627\u0642\u062f \u0634\u0646\u0627\u0633\u0647 \u0627\u0633\u062a <code>4<\/code> \u0648 <code>7<\/code>.<\/p>\n<p>\u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0635\u0645\u06cc\u0645 \u0628\u06af\u06cc\u0631\u06cc\u062f \u06a9\u0647 \u0647\u062f\u0631 \u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u06a9\u0627\u0645\u0644 \u062d\u0630\u0641 \u06a9\u0646\u06cc\u062f\u060c \u06a9\u0647 \u0645\u0646\u062c\u0631 \u0628\u0647 a <code>DataFrame<\/code> \u06a9\u0647 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u062f\u0627\u0631\u062f <code>0...n<\/code> \u0633\u062a\u0648\u0646 \u0647\u0627\u06cc \u0633\u0631\u0635\u0641\u062d\u0647\u060c \u0628\u0627 \u062a\u0646\u0638\u06cc\u0645 <code>header<\/code> \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0628\u0647 <code>None<\/code>:<\/p>\n<pre><code class=\"hljs\">titanic_data = pd.read_csv(<span class=\"hljs-string\">r'E:\\Datasets\\titanic.csv'<\/span>, header=<span class=\"hljs-literal\">None<\/span>, skiprows=(<span class=\"hljs-number\">0<\/span>))\n<\/code><\/pre>\n<p>\u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0627\u0632 \u0631\u062f\u06cc\u0641 \u0627\u0648\u0644 \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0631\u062f \u0634\u0648\u06cc\u062f\u060c \u0632\u06cc\u0631\u0627 \u0627\u06af\u0631 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0646\u06a9\u0646\u06cc\u062f\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u0631\u062f\u06cc\u0641 \u0627\u0648\u0644 \u062f\u0631 \u0648\u0627\u0642\u0639 \u062f\u0631 \u0631\u062f\u06cc\u0641 \u0627\u0648\u0644 \u0642\u0631\u0627\u0631 \u0645\u06cc \u06af\u06cc\u0631\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">   0   1   2                                                  3       4   ...  7                 8        9 \n0   1   0   3                            Braund, Mr. Owen Harris    male  ...   0         A\/5 21171   7.2500\n1   2   1   1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...   0          PC 17599  71.2833\n2   3   1   3                             Heikkinen, Miss. Laina  female  ...   0  STON\/O2. 3101282   7.9250\n3   4   1   1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...   0            113803  53.1000\n4   5   0   3                           Allen, Mr. William Henry    male  ...   0            373450   8.0500\n<\/code><\/pre>\n<h3 id=\"specifyingdelimiters\"><span class=\"ez-toc-section\" id=\"%d8%aa%d8%b9%db%8c%db%8c%d9%86_%d8%ac%d8%af%d8%a7%da%a9%d9%86%d9%86%d8%af%d9%87_%d9%87%d8%a7\"><\/span>\u062a\u0639\u06cc\u06cc\u0646 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0647\u0627<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0642\u0628\u0644\u0627\u064b \u06af\u0641\u062a\u0647 \u0634\u062f\u060c \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u064b \u0628\u0627 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0631\u0648\u0628\u0631\u0648 \u062e\u0648\u0627\u0647\u06cc\u062f \u0634\u062f \u06a9\u0647 \u062f\u0631 \u0648\u0627\u0642\u0639 \u0627\u0632 \u06a9\u0627\u0645\u0627 \u0628\u0631\u0627\u06cc \u062c\u062f\u0627\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0646\u0645\u06cc \u06a9\u0646\u062f.  \u062f\u0631 \u0686\u0646\u06cc\u0646 \u0645\u0648\u0627\u0642\u0639\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 <code>sep<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0628\u0631\u0627\u06cc \u062a\u0639\u06cc\u06cc\u0646 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0647\u0627\u06cc \u062f\u06cc\u06af\u0631:<\/p>\n<pre><code class=\"hljs\">titanic_data = pd.read_csv(<span class=\"hljs-string\">r'E:\\Datasets\\titanic.csv'<\/span>, sep=<span class=\"hljs-string\">';'<\/span>)\n<\/code><\/pre>\n<h2 id=\"writingcsvfileswithto_csv\"><span class=\"ez-toc-section\" id=\"%d9%86%d9%88%d8%b4%d8%aa%d9%86_%d9%81%d8%a7%db%8c%d9%84_%d9%87%d8%a7%db%8c_csv_%d8%a8%d8%a7_to_csv\"><\/span>\u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV \u0628\u0627 <em>to_csv()<\/em><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0632 \u0646\u0648\u060c <code>DataFrame<\/code>s \u062c\u062f\u0648\u0644\u06cc \u0647\u0633\u062a\u0646\u062f.  \u0686\u0631\u062e\u0627\u0646\u062f\u0646 a <code>DataFrame<\/code> \u062a\u0628\u062f\u06cc\u0644 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0628\u0647 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0633\u0627\u062f\u0647 \u0627\u0633\u062a <code>DataFrame<\/code> &#8211; \u0645\u0627 \u0628\u0647 <code>write_csv()<\/code> \u062a\u0627\u0628\u0639 \u0631\u0648\u06cc \u0631\u0627 <code>DataFrame<\/code> \u0646\u0645\u0648\u0646\u0647\u060c \u0645\u062b\u0627\u0644.<\/p>\n<p>\u0647\u0646\u06af\u0627\u0645 \u0646\u0648\u0634\u062a\u0646 \u0627\u0644\u0641 <code>DataFrame<\/code> \u0628\u0647 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0646\u0627\u0645 \u0633\u062a\u0648\u0646\u200c\u0647\u0627 \u0631\u0627 \u0646\u06cc\u0632 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>columns<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u060c \u06cc\u0627 \u062a\u0639\u06cc\u06cc\u0646 \u06cc\u06a9 \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 <code>sep<\/code> \u0628\u062d\u062b \u0648 \u062c\u062f\u0644.  \u0627\u06af\u0631 \u0647\u06cc\u0686\u06a9\u062f\u0627\u0645 \u0627\u0632 \u0627\u06cc\u0646\u0647\u0627 \u0631\u0627 \u0645\u0634\u062e\u0635 \u0646\u06a9\u0646\u06cc\u062f\u060c \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0628\u0627 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0627\u0631\u0632\u0634 \u062c\u062f\u0627 \u0634\u062f\u0647 \u0628\u0627 \u06a9\u0627\u0645\u0627 \u0645\u0648\u0627\u062c\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u0634\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0627 \u0627\u06cc\u0646 \u0628\u0627\u0632\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncities = pd.DataFrame(((<span class=\"hljs-string\">'Sacramento'<\/span>, <span class=\"hljs-string\">'California'<\/span>), (<span class=\"hljs-string\">'Miami'<\/span>, <span class=\"hljs-string\">'Florida'<\/span>)), columns=(<span class=\"hljs-string\">'City'<\/span>, <span class=\"hljs-string\">'State'<\/span>))\ncities.to_csv(<span class=\"hljs-string\">'cities.csv'<\/span>)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627\u060c \u0645\u0627 \u06cc\u06a9 \u0633\u0627\u062f\u0647 \u0633\u0627\u062e\u062a\u0647 \u0627\u06cc\u0645 <code>DataFrame<\/code> \u0628\u0627 \u062f\u0648 \u0634\u0647\u0631 \u0648 \u0627\u06cc\u0627\u0644\u062a \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0622\u0646\u0647\u0627.  \u0633\u067e\u0633\u060c \u0645\u0627 \u062c\u0644\u0648 \u0631\u0641\u062a\u06cc\u0645 \u0648 \u0622\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0630\u062e\u06cc\u0631\u0647 \u06a9\u0631\u062f\u06cc\u0645 <code>to_csv()<\/code> \u0648 \u0646\u0627\u0645 \u0641\u0627\u06cc\u0644 \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u06a9\u0631\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u0645\u0646\u062c\u0631 \u0628\u0647 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u062c\u062f\u06cc\u062f \u062f\u0631 \u062f\u0627\u06cc\u0631\u06a9\u062a\u0648\u0631\u06cc \u06a9\u0627\u0631\u06cc \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a\u06cc \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u062f\u0631 \u062d\u0627\u0644 \u0627\u062c\u0631\u0627 \u0647\u0633\u062a\u06cc\u062f\u060c \u06a9\u0647 \u0634\u0627\u0645\u0644:<\/p>\n<pre><code class=\"hljs\">,City,State\n0,Sacramento,California\n1,Miami,Florida\n<\/code><\/pre>\n<p>\u0627\u06af\u0631\u0686\u0647\u060c \u0627\u06cc\u0646 \u0648\u0627\u0642\u0639\u0627\u064b \u0628\u0647 \u062e\u0648\u0628\u06cc \u0642\u0627\u0644\u0628 \u0628\u0646\u062f\u06cc \u0646\u0634\u062f\u0647 \u0627\u0633\u062a.  \u0645\u0627 \u0647\u0646\u0648\u0632 \u0634\u0627\u062e\u0635 \u0647\u0627 \u0631\u0627 \u0627\u0632 <code>DataFrame<\/code>\u060c \u06a9\u0647 \u0647\u0645\u0686\u0646\u06cc\u0646 \u06cc\u06a9 \u0646\u0642\u0637\u0647 \u06af\u0645\u0634\u062f\u0647 \u0639\u062c\u06cc\u0628 \u0631\u0627 \u0642\u0628\u0644 \u0627\u0632 \u0646\u0627\u0645 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0642\u0631\u0627\u0631 \u0645\u06cc \u062f\u0647\u062f.  \u0627\u06af\u0631 \u0627\u06cc\u0646 CSV \u0631\u0627 \u062f\u0648\u0628\u0627\u0631\u0647 \u0628\u0647 a \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u0645 <code>DataFrame<\/code>\u060c \u062e\u0631\u0627\u0628 \u0645\u06cc \u0634\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">df = pd.read_csv(<span class=\"hljs-string\">'cities.csv'<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(df)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">   Unnamed: <span class=\"hljs-number\">0<\/span>        City       State\n<span class=\"hljs-number\">0<\/span>           <span class=\"hljs-number\">0<\/span>  Sacramento  California\n<span class=\"hljs-number\">1<\/span>           <span class=\"hljs-number\">1<\/span>       Miami     Florida\n<\/code><\/pre>\n<p>\u0634\u0627\u062e\u0635 \u0647\u0627 \u0627\u0632 <code>DataFrame<\/code> \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u062a\u0628\u062f\u06cc\u0644 \u0628\u0647 \u06cc\u06a9 \u0633\u062a\u0648\u0646 \u062c\u062f\u06cc\u062f \u0634\u062f\u060c \u06a9\u0647 \u0627\u06a9\u0646\u0648\u0646 \u0627\u0633\u062a <code>Unnamed<\/code>.<\/p>\n<p>\u0647\u0646\u06af\u0627\u0645 \u0630\u062e\u06cc\u0631\u0647 \u0641\u0627\u06cc\u0644\u060c \u0645\u0637\u0645\u0626\u0646 \u0634\u0648\u06cc\u0645 <em>\u0631\u0647\u0627 \u06a9\u0631\u062f\u0646<\/em> \u0634\u0627\u062e\u0635 \u0627\u0632 <code>DataFrame<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncities = pd.DataFrame(((<span class=\"hljs-string\">'Sacramento'<\/span>, <span class=\"hljs-string\">'California'<\/span>), (<span class=\"hljs-string\">'Miami'<\/span>, <span class=\"hljs-string\">'Florida'<\/span>)), columns=(<span class=\"hljs-string\">'City'<\/span>, <span class=\"hljs-string\">'State'<\/span>))\ncities.to_csv(<span class=\"hljs-string\">'cities.csv'<\/span>, index=<span class=\"hljs-literal\">False<\/span>)\n<\/code><\/pre>\n<p>\u0627\u06a9\u0646\u0648\u0646\u060c \u0627\u06cc\u0646 \u0645\u0646\u062c\u0631 \u0628\u0647 \u0641\u0627\u06cc\u0644\u06cc \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u062d\u0627\u0648\u06cc:<\/p>\n<pre><code class=\"hljs\">City,State\nSacramento,California\nMiami,Florida\n<\/code><\/pre>\n<p>\u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0637\u0644\u0633\u0645 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f!  \u0627\u06af\u0631 \u062f\u0648\u0628\u0627\u0631\u0647import \u0622\u0646 \u0648 print \u0645\u062d\u062a\u0648\u06cc\u0627\u062a\u060c <code>DataFrame<\/code> \u0628\u0647 \u062e\u0648\u0628\u06cc \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">df = pd.read_csv(<span class=\"hljs-string\">'cities.csv'<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(df)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">         City       State\n0  Sacramento  California\n1       Miami     Florida\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0647\u062f\u0631 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0631\u0627 \u0627\u0632 \u0633\u0631\u0635\u0641\u062d\u0647 \u0647\u0627\u06cc \u067e\u06cc\u0634 \u0641\u0631\u0636 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncities = pd.DataFrame(((<span class=\"hljs-string\">'Sacramento'<\/span>, <span class=\"hljs-string\">'California'<\/span>), (<span class=\"hljs-string\">'Miami'<\/span>, <span class=\"hljs-string\">'Florida'<\/span>)), columns=(<span class=\"hljs-string\">'City'<\/span>, <span class=\"hljs-string\">'State'<\/span>))\nnew_column_names = (<span class=\"hljs-string\">'City_Name'<\/span>, <span class=\"hljs-string\">'State_Name'<\/span>)\ncities.to_csv(<span class=\"hljs-string\">'cities.csv'<\/span>, index=<span class=\"hljs-literal\">False<\/span>, header=new_column_names)\n<\/code><\/pre>\n<p>\u0645\u0627 \u0633\u0627\u062e\u062a\u0647 \u0627\u06cc\u0645 <code>new_header<\/code> \u0644\u06cc\u0633\u062a\u06cc \u06a9\u0647 \u062d\u0627\u0648\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u062e\u062a\u0644\u0641 \u0628\u0631\u0627\u06cc \u0633\u062a\u0648\u0646 \u0647\u0627\u06cc \u0645\u0627 \u0627\u0633\u062a.  \u0633\u067e\u0633 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>header<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u060c \u0645\u0627 \u0627\u06cc\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 \u062c\u0627\u06cc \u0646\u0627\u0645 \u0633\u062a\u0648\u0646 \u0627\u0635\u0644\u06cc \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645.  \u0627\u06cc\u0646 \u062a\u0648\u0644\u06cc\u062f \u06cc\u06a9 <code>cities.csv<\/code> \u0628\u0627 \u0627\u06cc\u0646 \u0645\u0637\u0627\u0644\u0628:<\/p>\n<pre><code class=\"hljs\">City_Name,State_Name\nSacramento,California\nMiami,Florida\nWashington DC,Unknown\n<\/code><\/pre>\n<h3 id=\"customizingdelimiter\"><span class=\"ez-toc-section\" id=\"%d8%b3%d9%81%d8%a7%d8%b1%d8%b4%db%8c_%d8%b3%d8%a7%d8%b2%db%8c_%d8%ac%d8%af%d8%a7%da%a9%d9%86%d9%86%d8%af%d9%87\"><\/span>\u0633\u0641\u0627\u0631\u0634\u06cc \u0633\u0627\u0632\u06cc \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062c\u062f\u0627\u06a9\u0646\u0646\u062f\u0647 \u0631\u0627 \u0627\u0632 \u067e\u06cc\u0634 \u0641\u0631\u0636 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u0645 (<code>,<\/code>) \u0627\u0631\u0632\u0634 \u0628\u0647 \u06cc\u06a9 \u062c\u062f\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncities = pd.DataFrame(((<span class=\"hljs-string\">'Sacramento'<\/span>, <span class=\"hljs-string\">'California'<\/span>), (<span class=\"hljs-string\">'Miami'<\/span>, <span class=\"hljs-string\">'Florida'<\/span>)), columns=(<span class=\"hljs-string\">'City'<\/span>, <span class=\"hljs-string\">'State'<\/span>))\ncities.to_csv(<span class=\"hljs-string\">'cities.csv'<\/span>, index=<span class=\"hljs-literal\">False<\/span>, sep=<span class=\"hljs-string\">';'<\/span>)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0645\u0646\u062c\u0631 \u0628\u0647 \u06cc\u06a9 <code>cities.csv<\/code> \u0641\u0627\u06cc\u0644\u06cc \u06a9\u0647 \u0634\u0627\u0645\u0644:<\/p>\n<pre><code class=\"hljs\">City;State\nSacramento;California\nMiami;Florida\n<\/code><\/pre>\n<h3 id=\"handlingmissingvalues\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%af%db%8c%d8%b1%db%8c%d8%aa_%d8%a7%d8%b1%d8%b2%d8%b4_%d9%87%d8%a7%db%8c_%da%af%d9%85%d8%b4%d8%af%d9%87\"><\/span>\u0645\u062f\u06cc\u0631\u06cc\u062a \u0627\u0631\u0632\u0634 \u0647\u0627\u06cc \u06af\u0645\u0634\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u06af\u0627\u0647\u06cc\u060c <code>DataFrame<\/code>\u0645\u0642\u0627\u062f\u06cc\u0631 \u06af\u0645 \u0634\u062f\u0647 \u0627\u06cc \u062f\u0627\u0631\u0646\u062f \u06a9\u0647 \u0645\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062a\u0631\u06a9 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645 <code>NaN<\/code> \u06cc\u0627 <code>NA<\/code>.  \u062f\u0631 \u0686\u0646\u06cc\u0646 \u0645\u0648\u0627\u0631\u062f\u06cc\u060c \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062e\u0648\u0627\u0647\u06cc\u062f \u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0645\u06cc \u0646\u0648\u06cc\u0633\u06cc\u062f\u060c \u0622\u0646\u0647\u0627 \u0631\u0627 \u0642\u0627\u0644\u0628 \u0628\u0646\u062f\u06cc \u06a9\u0646\u06cc\u062f.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>na_rep<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0648 \u0645\u0642\u062f\u0627\u0631\u06cc \u0631\u0627 \u06a9\u0647 \u0628\u0627\u06cc\u062f \u0628\u0647 \u062c\u0627\u06cc \u06cc\u06a9 \u0645\u0642\u062f\u0627\u0631 \u06af\u0645\u0634\u062f\u0647 \u0642\u0631\u0627\u0631 \u062f\u0627\u062f\u0647 \u0634\u0648\u062f \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\ncities = pd.DataFrame(((<span class=\"hljs-string\">'Sacramento'<\/span>, <span class=\"hljs-string\">'California'<\/span>), (<span class=\"hljs-string\">'Miami'<\/span>, <span class=\"hljs-string\">'Florida'<\/span>), (<span class=\"hljs-string\">'Washington DC'<\/span>, pd.NA)), columns=(<span class=\"hljs-string\">'City'<\/span>, <span class=\"hljs-string\">'State'<\/span>))\ncities.to_csv(<span class=\"hljs-string\">'cities.csv'<\/span>, index=<span class=\"hljs-literal\">False<\/span>, na_rep=<span class=\"hljs-string\">'Unknown'<\/span>)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627\u060c \u0645\u0627 \u062f\u0648 \u062c\u0641\u062a \u0634\u0647\u0631-\u0627\u06cc\u0627\u0644\u062a \u0645\u0639\u062a\u0628\u0631 \u062f\u0627\u0631\u06cc\u0645\u060c \u0627\u0645\u0627 <code>Washington DC<\/code> \u062d\u0627\u0644\u062a \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0647 \u0627\u0633\u062a  \u0627\u06af\u0631 \u0627\u06cc\u0646 \u06a9\u062f \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u0645\u060c \u0646\u062a\u06cc\u062c\u0647 \u0622\u0646 a <code>cities.csv<\/code> \u0628\u0627 \u0645\u0637\u0627\u0644\u0628 \u0632\u06cc\u0631:<\/p>\n<pre><code class=\"hljs\">City,State\nSacramento,California\nMiami,Florida\nWashington DC,Unknown\n<\/code><\/pre>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87\"><\/span>\u0646\u062a\u06cc\u062c\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Pandas \u067e\u0627\u06cc\u062a\u0648\u0646 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f.  \u0628\u0631\u0627\u06cc \u062e\u0648\u0627\u0646\u062f\u0646 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV\u060c <code>read_csv()<\/code> \u0627\u0632 \u0631\u0648\u0634 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0646\u0627\u0645\u200c\u0647\u0627\u06cc \u0647\u062f\u0631 \u0633\u0641\u0627\u0631\u0634\u06cc \u0631\u0627 \u0647\u0646\u06af\u0627\u0645 \u062e\u0648\u0627\u0646\u062f\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u0627\u0632 \u0637\u0631\u06cc\u0642 <code>names<\/code> \u0648\u06cc\u0698\u06af\u06cc \u0627\u0632 <code>read_csv()<\/code> \u0631\u0648\u0634.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0628\u0631\u0627\u06cc \u0646\u0648\u0634\u062a\u0646 \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Pandas\u060c \u0627\u0628\u062a\u062f\u0627 \u0628\u0627\u06cc\u062f \u06cc\u06a9 \u0634\u06cc Pandas DataFrame \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u062f \u0648 \u0633\u067e\u0633 \u0622\u0646 \u0631\u0627 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u06a9\u0646\u06cc\u062f. <code>to_csv<\/code> \u0631\u0648\u0634 \u0631\u0648\u06cc DataFrame<\/p>\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-17 22:03:10<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;15846&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644\u200c\u0647\u0627\u06cc CSV \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u067e\u0627\u0646\u062f\u0627\u0647\u0627&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 7<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV \u0631\u0627 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062e\u0644\u06cc \u0628\u062e\u0648\u0627\u0646\u06cc\u062f \u0648 \u0628\u0646\u0648\u06cc\u0633\u06cc\u062f open() \u062a\u0627\u0628\u0639\u060c \u06cc\u0627 \u0627\u062e\u062a\u0635\u0627\u0635 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a csv \u0645\u0627\u0698\u0648\u0644 &#8211; \u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0631\u0627\u06cc \u062e\u0648\u0627\u0646\u062f\u0646 \u0648 \u0646\u0648\u0634\u062a\u0646 \u0641\u0627\u06cc\u0644 \u0647\u0627\u06cc CSV. 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