{"id":145181,"date":"2025-05-30T18:36:50","date_gmt":"2025-05-30T13:06:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145181"},"modified":"2025-05-30T18:55:24","modified_gmt":"2025-05-30T13:25:24","slug":"how-to-apply-filter-in-pandas","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/how-to-apply-filter-in-pandas\/","title":{"rendered":"How to Apply Filter in Pandas"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:63,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1ViNQqfFyNBkJq04ZtIBs8p-o9FdClzrm\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205135009\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1ViNQqfFyNBkJq04ZtIBs8p-o9FdClzrm\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-19 18:56:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-10 18:29:34&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-21 23:32:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-07 08:03:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-28 00:47:48&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-04-28 00:47:48&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1ViNQqfFyNBkJq04ZtIBs8p-o9FdClzrm\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Pandas Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># Filter in Python\r\nimport pandas as pd\r\nmyfile=\"D:\/\/mypandas\/employee2.xlsx\"\r\ndf=pd.read_excel(myfile)\r\n#print(df.loc[(df['totalsalary']&gt;10000)])\r\n# and  &amp;   or |   not ~\r\n#print(df.loc[(df['gender']=='male') &amp; (df['totalsalary']&gt;10000)])\r\n#print(df.loc[(df['empdept']=='CS') &amp; (df['gender']=='female')])\r\n#print(df.loc[(df['empname'].str.contains('A')) | (df['empname'].str.contains('a'))])\r\n#print(df.loc[(df['empname'].str.startswith('Raj'))])\r\n#print(df.loc[~(df['totalsalary']&gt;10000)])\r\n<\/pre>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Pandas Dataset # Filter in Python import pandas as pd myfile=&#8221;D:\/\/mypandas\/employee2.xlsx&#8221; df=pd.read_excel(myfile) #print(df.loc[(df[&#8216;totalsalary&#8217;]&gt;10000)]) # and &amp; or | not ~ #print(df.loc[(df[&#8216;gender&#8217;]==&#8217;male&#8217;) &amp; (df[&#8216;totalsalary&#8217;]&gt;10000)]) #print(df.loc[(df[&#8217;empdept&#8217;]==&#8217;CS&#8217;) &amp; (df[&#8216;gender&#8217;]==&#8217;female&#8217;)]) #print(df.loc[(df[&#8217;empname&#8217;].str.contains(&#8216;A&#8217;)) | (df[&#8217;empname&#8217;].str.contains(&#8216;a&#8217;))]) #print(df.loc[(df[&#8217;empname&#8217;].str.startswith(&#8216;Raj&#8217;))]) #print(df.loc[~(df[&#8216;totalsalary&#8217;]&gt;10000)]) &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":[34295,34297,34294,9393,30038,34264,34296,9399],"class_list":["post-145181","post","type-post","status-publish","format-standard","hentry","category-pandas","tag-apply-filter-in-pandas","tag-filter-in-pandas","tag-how-to-apply-filter-in-pandas","tag-pandas","tag-pandas-practical","tag-pandas-program","tag-pandas-program-on-how-to-apply-filter","tag-pandas-tutorial"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to Apply Filter in Pandas - 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\/how-to-apply-filter-in-pandas\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Apply Filter in Pandas - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Pandas Dataset # Filter in Python import pandas as pd myfile=&quot;D:\/\/mypandas\/employee2.xlsx&quot; df=pd.read_excel(myfile) #print(df.loc[(df[&#039;totalsalary&#039;]&gt;10000)]) # and &amp; or | not ~ #print(df.loc[(df[&#039;gender&#039;]==&#039;male&#039;) &amp; (df[&#039;totalsalary&#039;]&gt;10000)]) #print(df.loc[(df[&#039;empdept&#039;]==&#039;CS&#039;) &amp; (df[&#039;gender&#039;]==&#039;female&#039;)]) #print(df.loc[(df[&#039;empname&#039;].str.contains(&#039;A&#039;)) | (df[&#039;empname&#039;].str.contains(&#039;a&#039;))]) #print(df.loc[(df[&#039;empname&#039;].str.startswith(&#039;Raj&#039;))]) #print(df.loc[~(df[&#039;totalsalary&#039;]&gt;10000)]) &nbsp;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/how-to-apply-filter-in-pandas\/\" \/>\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=\"2025-05-30T13:06:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-05-30T13:25:24+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":"How to Apply Filter in Pandas - 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\/how-to-apply-filter-in-pandas\/","og_locale":"en_US","og_type":"article","og_title":"How to Apply Filter in Pandas - DataFlair","og_description":"Program 1 Pandas Dataset # Filter in Python import pandas as pd myfile=\"D:\/\/mypandas\/employee2.xlsx\" df=pd.read_excel(myfile) #print(df.loc[(df['totalsalary']&gt;10000)]) # and &amp; 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