{"id":145178,"date":"2025-05-30T18:17:36","date_gmt":"2025-05-30T12:47:36","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145178"},"modified":"2025-05-30T18:54:50","modified_gmt":"2025-05-30T13:24:50","slug":"slicing-pandas-dataframe","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/slicing-pandas-dataframe\/","title":{"rendered":"Slicing Pandas Dataframe"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1EnKXu5OAxyPWZPfXjhCRC_R4t4XP9Iqm\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Pandas Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">#Slicing in DataFrame\r\nimport pandas as pd\r\nmyfile=\"D:\/\/mypandas\/employee.xlsx\"\r\ndf=pd.read_excel(myfile)\r\n\r\n#df[start:stop:step] \r\n#print(df[['empname','totalsalary']])\r\n#print(df[1:11:2])\r\n#print(df[['empname','totalsalary']][1:11:2])\r\n#print(df.head(3))\r\n#print(df.tail(4))\r\n# print(df.columns)\r\n# print(df[['empname','totalsalary']])\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:58,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1EnKXu5OAxyPWZPfXjhCRC_R4t4XP9Iqm\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205134022\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1EnKXu5OAxyPWZPfXjhCRC_R4t4XP9Iqm\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-11 11:40:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-27 17:34:07&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-04 08:56:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-21 22:38:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-26 10:25:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-18 11:54:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-02 12:22:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-09 17:41:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-18 03:11:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-28 18:11:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-01 23:55:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-05 23:43:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 10:40:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-01 11:49:45&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-17 10:19:45&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-21 08:38:02&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-25 04:50:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-02 14:11:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-06 06:07:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-11 23:17:49&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-07-15 11:24:14&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-15 11:24:14&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Pandas Dataset #Slicing in DataFrame import pandas as pd myfile=&#8221;D:\/\/mypandas\/employee.xlsx&#8221; df=pd.read_excel(myfile) #df[start:stop:step] #print(df[[&#8217;empname&#8217;,&#8217;totalsalary&#8217;]]) #print(df[1:11:2]) #print(df[[&#8217;empname&#8217;,&#8217;totalsalary&#8217;]][1:11:2]) #print(df.head(3)) #print(df.tail(4)) # print(df.columns) # print(df[[&#8217;empname&#8217;,&#8217;totalsalary&#8217;]]) &nbsp; &nbsp;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19475],"tags":[34286,9393,30038,34264,34287,9399,34285],"class_list":["post-145178","post","type-post","status-publish","format-standard","hentry","category-pandas","tag-how-to-slice-pandas-dataframe","tag-pandas","tag-pandas-practical","tag-pandas-program","tag-pandas-program-on-slicing","tag-pandas-tutorial","tag-slicing-pandas-dataframe"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Slicing Pandas Dataframe - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/slicing-pandas-dataframe\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Slicing Pandas Dataframe - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Pandas Dataset #Slicing in DataFrame import pandas as pd myfile=&quot;D:\/\/mypandas\/employee.xlsx&quot; df=pd.read_excel(myfile) #df[start:stop:step] #print(df[[&#039;empname&#039;,&#039;totalsalary&#039;]]) #print(df[1:11:2]) #print(df[[&#039;empname&#039;,&#039;totalsalary&#039;]][1:11:2]) #print(df.head(3)) #print(df.tail(4)) # print(df.columns) # print(df[[&#039;empname&#039;,&#039;totalsalary&#039;]]) &nbsp; &nbsp;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/slicing-pandas-dataframe\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-05-30T12:47:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-05-30T13:24:50+00:00\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Slicing Pandas Dataframe - DataFlair","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/slicing-pandas-dataframe\/","og_locale":"en_US","og_type":"article","og_title":"Slicing Pandas Dataframe - DataFlair","og_description":"Program 1 Pandas Dataset #Slicing in DataFrame import pandas as pd myfile=\"D:\/\/mypandas\/employee.xlsx\" df=pd.read_excel(myfile) #df[start:stop:step] #print(df[['empname','totalsalary']]) #print(df[1:11:2]) #print(df[['empname','totalsalary']][1:11:2]) #print(df.head(3)) #print(df.tail(4)) # print(df.columns) # print(df[['empname','totalsalary']]) &nbsp; 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