{"id":5609,"date":"2023-10-27T09:30:43","date_gmt":"2023-10-27T04:00:43","guid":{"rendered":"https:\/\/python-programs.com\/?p=5609"},"modified":"2023-11-10T12:02:27","modified_gmt":"2023-11-10T06:32:27","slug":"python-pandas-drop-columns-in-dataframe-by-label-names-or-by-index-positions","status":"publish","type":"post","link":"https:\/\/python-programs.com\/python-pandas-drop-columns-in-dataframe-by-label-names-or-by-index-positions\/","title":{"rendered":"Python Pandas : Drop columns in DataFrame by label Names or by Index Positions"},"content":{"rendered":"<h2>How to drop columns in DataFrame by label Names or by Index Positions in Python ?<\/h2>\n<p>In this article, we are going to demonstrate how to drop columns in a dataframe by their labels or index. So, let&#8217;s start exploring the topic in detail.<\/p>\n<p>In dataframe there is a function <code>drop()<\/code> which can be used to drop columns.<\/p>\n<p><em>Syntax &#8211; DataFrame.drop<\/em><\/p>\n<pre>(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')<\/pre>\n<p>Where,<\/p>\n<ul>\n<li><strong>labels :<\/strong> The label or the labels we want to delete are passed here.<\/li>\n<li><strong>index :<\/strong> The index of the position to be deleted<\/li>\n<li><strong>columns :<\/strong> If it is <code>True<\/code> it deletes the columns, else the rows<\/li>\n<li><strong>axis :<\/strong> If axis is 1 it considers the columns like the columns. If 0, it works on rows.<\/li>\n<li><strong>inplace :<\/strong> If it is <code>False<\/code> then it doesn\u2019t modify the dataframe, it returns a new one. If it is set to <code>True<\/code> it modifies the dataframe.<\/li>\n<\/ul>\n<p>We will be using the following dataset as example :<\/p>\n<pre> \u00a0   Regd\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0      Age\u00a0\u00a0\u00a0\u00a0\u00a0 City\u00a0          Exp\r\na\u00a0\u00a0\u00a0 10\u00a0\u00a0\u00a0\u00a0     Jill\u00a0            16.0\u00a0\u00a0\u00a0\u00a0 Tokyo\u00a0\u00a0       10\r\nb\u00a0\u00a0\u00a0 11\u00a0\u00a0       Rachel\u00a0     38.0\u00a0\u00a0\u00a0\u00a0 Texas\u00a0\u00a0\u00a0        5\r\nc\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0      Kirti\u00a0         39.0\u00a0     New York\u00a0\u00a0\u00a0 7\r\nd\u00a0\u00a0\u00a0 13\u00a0\u00a0\u00a0     Veena\u00a0      40.0\u00a0\u00a0\u00a0\u00a0 Texas\u00a0\u00a0         21\r\ne\u00a0\u00a0\u00a0 14\u00a0       Lucifer\u00a0\u00a0    NaN\u00a0\u00a0\u00a0\u00a0 Texas\u00a0\u00a0         30\r\nf\u00a0\u00a0\u00a0  15\u00a0\u00a0\u00a0     Pablo\u00a0       30.0\u00a0    New York\u00a0\u00a0\u00a0  7\r\ng\u00a0\u00a0\u00a0 16\u00a0\u00a0     Lionel\u00a0       45.0\u00a0    Colombia\u00a0\u00a0  11<\/pre>\n<h3>Delete a Single column in DataFrame by Column Name :<\/h3>\n<p>We can delete a single column just by passing its name into the function. Let\u2019s try deleting \u2018<code>Age<\/code>\u2019 column from the Dataframe.<\/p>\n<p>Let&#8217;s see the program how to implement this.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#Program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Example data\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\n#Creating a dataframe object\r\ndfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'], index=['a', 'b', 'c' , 'd' , 'e' , 'f', 'g']) \r\n#Modifying the dataframe and storing it into a new object\r\nmodDfObj = dfObj.drop('Age' , axis='columns')\r\nprint(modDfObj)\r\n<\/pre>\n<pre>Output :\r\n  \u00a0 Regd\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0 City\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Exp\r\na\u00a0\u00a0\u00a0 10\u00a0 \u00a0 \u00a0 \u00a0 Jill\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Tokyo\u00a0 \u00a0 \u00a0  10\r\nb\u00a0\u00a0\u00a0 11\u00a0 \u00a0 \u00a0 Rachel\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 5\r\nc\u00a0\u00a0\u00a0 12\u00a0 \u00a0 \u00a0 \u00a0Kirti\u00a0 \u00a0 \u00a0 \u00a0New York\u00a0\u00a0\u00a0 7\r\nd\u00a0\u00a0\u00a0 13\u00a0 \u00a0 \u00a0 Veena\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 21\r\ne\u00a0\u00a0\u00a0 14\u00a0 \u00a0 \u00a0 Lucifer\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a030\r\nf\u00a0\u00a0\u00a0 15\u00a0 \u00a0 \u00a0 \u00a0Pablo\u00a0 \u00a0 \u00a0New York\u00a0\u00a0\u00a0 7\r\ng\u00a0\u00a0\u00a0 16\u00a0 \u00a0 \u00a0 Lionel\u00a0 \u00a0 Colombia\u00a0 \u00a0 11<\/pre>\n<h3>Drop Multiple Columns by Label Names in DataFrame :<\/h3>\n<p>To delete multiple columns by name we just have to pass all the names as a list into the function. Let\u2019s try deleting \u2018Age\u2019 and \u2018Exp\u2019<\/p>\n<p>Let&#8217;s see the program how to implement this.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#Program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Example data\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\n#Creating a dataframe object\r\ndfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'], index=['a', 'b', 'c' , 'd' , 'e' , 'f', 'g']) \r\n#Modifying the dataframe without the columns and storing it into a new object\r\nmodDfObj = dfObj.drop(['Age' , 'Exp'] , axis='columns')\r\nprint(modDfObj)\r\n<\/pre>\n<pre>Output :\r\n   Regd\u00a0\u00a0\u00a0Name\u00a0\u00a0\u00a0\u00a0\u00a0City\r\na\u00a0\u00a0\u00a0 10\u00a0\u00a0\u00a0\u00a0 Jill\u00a0\u00a0\u00a0\u00a0      Tokyo\r\nb\u00a0\u00a0\u00a0 11\u00a0\u00a0 Rachel\u00a0\u00a0\u00a0\u00a0 Texas\r\nc\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Kirti\u00a0       New York\r\nd\u00a0\u00a0\u00a0 13\u00a0\u00a0\u00a0 Veena\u00a0\u00a0\u00a0\u00a0 Texas\r\ne\u00a0\u00a0\u00a0 14\u00a0  Lucifer\u00a0\u00a0\u00a0\u00a0 Texas\r\nf\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Pablo\u00a0     New York\r\ng\u00a0\u00a0\u00a0 16\u00a0\u00a0 Lionel\u00a0    Colombia<\/pre>\n<h3>Drop Columns by Index Position in DataFrame :<\/h3>\n<p>In case we know the index position of the columns we want to drop, we can pass them into the function. Let\u2019s try deleting the same two columns as above but with their index position.<\/p>\n<p>Let&#8217;s see the program how to implement this.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#Program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Example data\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\n#Creating a dataframe object\r\ndfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'], index=['a', 'b', 'c' , 'd' , 'e' , 'f', 'g']) \r\n#Modifying the dataframe without the columns and storing it into a new object by passsing the index of the columns\r\nmodDfObj = dfObj.drop([dfObj.columns[2] , dfObj.columns[4]] ,  axis='columns')\r\nprint(modDfObj)\r\n<\/pre>\n<pre>Output :\r\n \u00a0  Regd\u00a0\u00a0\u00a0Name\u00a0\u00a0\u00a0\u00a0\u00a0 City\r\na\u00a0\u00a0\u00a0 10\u00a0\u00a0\u00a0\u00a0 Jill\u00a0\u00a0\u00a0\u00a0       Tokyo\r\nb\u00a0\u00a0\u00a0 11\u00a0\u00a0  Rachel\u00a0\u00a0\u00a0\u00a0 Texas\r\nc\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0  Kirti\u00a0        New York\r\nd\u00a0\u00a0\u00a0 13\u00a0\u00a0\u00a0 Veena\u00a0\u00a0\u00a0\u00a0  Texas\r\ne\u00a0\u00a0\u00a0 14\u00a0   Lucifer\u00a0\u00a0\u00a0\u00a0 Texas\r\nf\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0  Pablo\u00a0      New York\r\ng\u00a0\u00a0\u00a0 16\u00a0\u00a0  Lionel\u00a0     Colombia<\/pre>\n<h3>Drop Columns in Place :<\/h3>\n<p>In case we don&#8217;t want a new dataframe object to hold the modified values, but want to store it in the same object, we can do it by passing <code>inplace= True<\/code><em>. <\/em>Let\u2019s use the previous example for this.<\/p>\n<p>Let&#8217;s see the program how to implement this.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#Program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Example data\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\n#Creating a dataframe object\r\ndfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'], index=['a', 'b', 'c' , 'd' , 'e' , 'f', 'g']) \r\n#Modifying the dataframe without the columns and storing it into the same object \r\ndfObj.drop([dfObj.columns[2] , dfObj.columns[4]] ,  axis='columns',inplace = True)\r\nprint(dfObj)\r\n<\/pre>\n<pre>Output :\r\n  \u00a0 \u00a0Regd\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0 City\r\na\u00a0\u00a0\u00a0 10\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Jill\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Tokyo\r\nb\u00a0\u00a0\u00a0 11\u00a0 \u00a0 \u00a0 \u00a0Rachel\u00a0\u00a0\u00a0\u00a0 Texas\r\nc\u00a0\u00a0\u00a0 12\u00a0 \u00a0 \u00a0 \u00a0 Kirti\u00a0 \u00a0 \u00a0 \u00a0New York\r\nd\u00a0\u00a0\u00a0 13\u00a0 \u00a0 \u00a0 \u00a0Veena\u00a0\u00a0\u00a0\u00a0 Texas\r\ne\u00a0\u00a0\u00a0 14\u00a0 \u00a0 \u00a0 Lucifer\u00a0\u00a0\u00a0\u00a0 Texas\r\nf\u00a0\u00a0\u00a0 15\u00a0 \u00a0 \u00a0 \u00a0Pablo\u00a0 \u00a0 New York\r\ng\u00a0\u00a0\u00a0 16\u00a0 \u00a0 \u00a0Lionel\u00a0 \u00a0 \u00a0Colombia<\/pre>\n<h3>Drop Column If Exists :<\/h3>\n<p>In case the column\/row does not exist we can do a check beforehand to avoid further bugs in the program. We can do so by using the same function <code>drop( )<\/code> , it checks for the columns and if it is not found it returns <code>KeyError<\/code> which we can handle by an if-else condition.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#program :\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Example data\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\n#Creating a dataframe object\r\ndfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'], index=['a', 'b', 'c' , 'd' , 'e' , 'f', 'g']) \r\n#Checking for a non-existent column\r\nif 'Last Name' in dfObj.columns :\r\n    dfObj.drop('Last Name' ,  axis='columns')\r\n    print(dfObj)\r\nelse :\r\n    print(\"The column was not found\")\r\n<\/pre>\n<pre>Output :\r\nThe column was not found<\/pre>\n<p>Want to expert in the python programming language? Exploring\u00a0<a href=\"https:\/\/python-programs.com\/python-data-analysis-using-pandas\/\">Python Data Analysis using Pandas<\/a>\u00a0tutorial changes your knowledge from basic to advance level in python concepts.<\/p>\n<p><strong>Read more Articles on Python Data Analysis Using Padas \u2013 Remove Contents from a Dataframe<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/python-programs.com\/python-pandas-how-to-drop-rows-in-dataframe-by-index-labels\/\">Drop rows in DataFrame by index labels<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/python-pandas-how-to-drop-rows-in-dataframe-by-conditions-on-column-values\/\">Drop rows in DataFrame by conditions on column values<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/pandas-drop-rows-with-nan-missing-values-in-any-or-selected-columns-of-dataframe\/\">Drop rows from a DataFrame with missing values or NaN in columns<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>How to drop columns in DataFrame by label Names or by Index Positions in Python ? In this article, we are going to demonstrate how to drop columns in a dataframe by their labels or index. So, let&#8217;s start exploring the topic in detail. In dataframe there is a function drop() which can be used &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/python-pandas-drop-columns-in-dataframe-by-label-names-or-by-index-positions\/\"> <span class=\"screen-reader-text\">Python Pandas : Drop columns in DataFrame by label Names or by Index Positions<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[5],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python Pandas : Drop columns in DataFrame by label Names or by Index Positions - Python Programs<\/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:\/\/python-programs.com\/python-pandas-drop-columns-in-dataframe-by-label-names-or-by-index-positions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Pandas : Drop columns in DataFrame by label Names or by Index Positions - Python Programs\" \/>\n<meta property=\"og:description\" content=\"How to drop columns in DataFrame by label Names or by Index Positions in Python ? In this article, we are going to demonstrate how to drop columns in a dataframe by their labels or index. So, let&#8217;s start exploring the topic in detail. 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