{"id":8116,"date":"2021-02-17T22:21:12","date_gmt":"2021-02-17T16:51:12","guid":{"rendered":"https:\/\/pynative.com\/?p=8116"},"modified":"2023-03-09T11:52:55","modified_gmt":"2023-03-09T06:22:55","slug":"pandas-drop-columns","status":"publish","type":"post","link":"https:\/\/pynative.com\/pandas-drop-columns\/","title":{"rendered":"Drop columns in pandas DataFrame"},"content":{"rendered":"\n<p>Datasets could be in any shape and form. To optimize the data analysis, we need to remove some data that is redundant or not required. This article aims to discuss all the cases of dropping single or multiple columns from a <a href=\"https:\/\/pynative.com\/python-pandas-dataframe\/\">pandas DataFrame<\/a>.<\/p>\n\n\n\n<p>The following functions are discussed in this article in detail:<\/p>\n\n\n\n<ul>\n<li><code>df.drop(columns = ['col1','col2'...])<\/code><\/li>\n\n\n\n<li><code>df.pop('col_name')<\/code><\/li>\n\n\n\n<li><code>del df['col_name']<\/code><\/li>\n<\/ul>\n\n\n\n<p>In the last section, we have shown the comparison of these functions. So stay tuned&#8230;<\/p>\n\n\n\n<p><strong>Also, See:<\/strong><\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/pynative.com\/pandas-drop-duplicates\/\">Drop duplicates in pandas DataFrame<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/pynative.com\/pandas-drop-na-columns-from-dataframe\/\">Drop columns with NA in pandas DataFrame<\/a><\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Table of contents<\/h2><ul><li><a href=\"#h-the-dataframe-drop-function\" data-level=\"2\">The DataFrame.drop() function<\/a><\/li><li><a href=\"#h-drop-single-column\" data-level=\"2\">Drop single column<\/a><\/li><li><a href=\"#h-drop-multiple-columns\" data-level=\"2\">Drop multiple columns<\/a><ul><li><a href=\"#h-using-drop-with-axis-columns-or-axis-1\" data-level=\"3\">Using drop with axis=&#8217;columns&#8217; or axis=1<\/a><\/li><\/ul><\/li><li><a href=\"#h-drop-column-in-place\" data-level=\"2\">Drop column in place<\/a><\/li><li><a href=\"#h-drop-column-by-suppressing-errors\" data-level=\"2\">Drop column by suppressing errors<\/a><\/li><li><a href=\"#h-drop-column-by-index-position\" data-level=\"2\">Drop column by index position<\/a><ul><li><a href=\"#h-drop-the-last-column\" data-level=\"3\">Drop the last column<\/a><\/li><li><a href=\"#h-drop-range-of-columns-using-iloc\" data-level=\"3\">Drop range of columns using iloc<\/a><\/li><li><a href=\"#h-drop-first-n-columns\" data-level=\"3\">Drop first n columns<\/a><\/li><\/ul><\/li><li><a href=\"#h-drop-column-from-multi-index-dataframe\" data-level=\"2\">Drop column from multi-index DataFrame<\/a><\/li><li><a href=\"#h-drop-column-using-a-function\" data-level=\"2\">Drop column using a function<\/a><ul><li><a href=\"#h-drop-all-the-columns-using-loc\" data-level=\"3\">Drop all the columns using loc<\/a><\/li><\/ul><\/li><li><a href=\"#h-drop-column-using-pandas-dataframe-pop-function\" data-level=\"2\">Drop column using pandas DataFrame.pop() function<\/a><\/li><li><a href=\"#h-drop-column-using-pandas-dataframe-delete\" data-level=\"2\">Drop column using pandas DataFrame delete<\/a><\/li><li><a href=\"#h-compare-dataframe-drop-vs-pop-vs-del\" data-level=\"2\">Compare DataFrame drop() vs. pop() vs. del<\/a><\/li><\/ul><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-the-dataframe-drop-function\">The <code>DataFrame.drop()<\/code> function<\/h2>\n\n\n\n<p>We can use this pandas function to remove the columns or rows from simple as well as multi-index DataFrame.<\/p>\n\n\n<pre id=\"syntax\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">DataFrame.drop(labels=<span class=\"hljs-literal\">None<\/span>, axis=<span class=\"hljs-number\">1<\/span>, columns=<span class=\"hljs-literal\">None<\/span>, level=<span class=\"hljs-literal\">None<\/span>, inplace=<span class=\"hljs-literal\">False<\/span>, errors=<span class=\"hljs-string\">'raise'<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong>Parameters:<\/strong><\/p>\n\n\n\n<ol>\n<li><strong><code>labels<\/code><\/strong>: It takes a list of column labels to drop.<\/li>\n\n\n\n<li><strong><code>axis<\/code><\/strong>: It specifies to drop columns or rows. set a <code>axis<\/code> to <strong>1<\/strong> or \u2018<strong>columns<\/strong>\u2019 to drop columns. By default, it drops the rows from DataFrame.<\/li>\n\n\n\n<li><strong><code>columns<\/code><\/strong>: It is an alternative to <code>axis='columns'<\/code>. It takes a single column label or list of column labels as input.<\/li>\n\n\n\n<li><strong><code>level<\/code><\/strong>: It is used in the case of a MultiIndex DataFrame to specify the level from which the labels should be removed. It takes a level position or level name as input.<\/li>\n\n\n\n<li><strong><code>inplace<\/code><\/strong>: It is used to specify whether to return a new DataFrame or update an existing one. It is a boolean flag with default <strong>False<\/strong>.<\/li>\n\n\n\n<li><strong><code>errors<\/code><\/strong>: It is used to suppress <strong><code>KeyError<\/code><\/strong> error if a column is not present. It takes the following inputs:<br>&#8216;<strong>ignore<\/strong>&#8216;: It suppresses the error and drops only existing labels.<br>&#8216;<strong>raise<\/strong>&#8216;: Throws the errors if the column does not exist. It is the default case.<\/li>\n<\/ol>\n\n\n\n<p><strong>Returns:<\/strong><\/p>\n\n\n\n<ul>\n<li>It returns the DataFrame with dropped columns or None if <code>inplace=True<\/code><\/li>\n\n\n\n<li>It also raises <code>KeyError<\/code> if labels are not found.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-single-column\">Drop single column<\/h2>\n\n\n\n<p>We may need to delete a single or specific column from a DataFrame.<\/p>\n\n\n\n<p>In the below example we drop the &#8216;<strong>age<\/strong>&#8216; column from the DataFrame using <code>df.drop(columns = 'col_name')<\/code><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"243\" height=\"169\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\" alt=\"Drop Single Column\" class=\"wp-image-8157\"\/><\/figure><\/div>\n\n<div class=\"hljstoolbar\"><pre id=\"code1\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)\n\n<span class=\"hljs-comment\"># drop column<\/span>\nstudent_df = student_df.drop(columns=<span class=\"hljs-string\">'age'<\/span>)\n\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code1', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code1');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping column:\n  name  age  marks\n0  Joe   20   85.1\n1  Nat   21   77.8\n\nAfter dropping column:\n  name  marks\n0  Joe   85.1\n1  Nat   77.8<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-multiple-columns\">Drop multiple columns<\/h2>\n\n\n\n<p>Use any of the following two parameters of <code>DataFrame.drop()<\/code> to delete multiple columns of DataFrame at once.<\/p>\n\n\n\n<ul>\n<li>Use the <code>column<\/code> parameter and pass the list of column names you want to remove.<\/li>\n\n\n\n<li>Set the <code>axis=1<\/code> and pass the list of column names.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong><\/p>\n\n\n\n<p>Let&#8217;s see how to drop multiple columns from the DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code2\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-3\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># drop 2 columns at a time<\/span>\nstudent_df = student_df.drop(columns=&#91;<span class=\"hljs-string\">'age'<\/span>, <span class=\"hljs-string\">'marks'<\/span>])\n\nprint(student_df.columns.values)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-3\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code2', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code2');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping columns:\n['name' 'age' 'marks']\n\nAfter dropping columns:\n['name']<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-using-drop-with-axis-columns-or-axis-1\">Using drop with <code>axis='columns'<\/code> or <code>axis=1<\/code><\/h3>\n\n\n\n<p>Let\u2019s see how to drop using the axis-style convention. This is a new approach. ( This approach makes this method match the rest of the pandas API) .<\/p>\n\n\n\n<p>Use the axis parameter of a <code>DataFrame.drop()<\/code> to delete columns. The axis can be a row or column. The column axis represented as 1 or &#8216;columns&#8217;.<\/p>\n\n\n\n<p>Set <code>axis=1<\/code> or <code>axis='columns'<\/code> and pass the list of column names you want to remove.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Let\u2019s see how to drop &#8216;<strong>age<\/strong>&#8216; and &#8216;<strong>marks<\/strong>&#8216; columns.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code3\"  id=\"norun\" class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-4\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\">student_df = student_df.drop(&#91;<span class=\"hljs-string\">'age'<\/span>, <span class=\"hljs-string\">'marks'<\/span>], axis=<span class=\"hljs-string\">'columns'<\/span>)\n<span class=\"hljs-comment\"># alternative both produces same result<\/span>\nstudent_df = student_df.drop(&#91;<span class=\"hljs-string\">'age'<\/span>, <span class=\"hljs-string\">'marks'<\/span>], axis=<span class=\"hljs-number\">1<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-4\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code3', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-in-place\">Drop column in place<\/h2>\n\n\n\n<p>In the above examples, whenever we executed drop operations, pandas created a new copy of DataFrame because the modification is not in place.<\/p>\n\n\n\n<p>Parameter <code>inplace<\/code> is used to indicate if drop column from the existing DataFrame or create a copy of it.<\/p>\n\n\n\n<ul>\n<li>If the&nbsp;<code>inplace=True<\/code>&nbsp;then it updates the existing DataFrame and does not return anything.<\/li>\n\n\n\n<li>If the&nbsp;<code>inplace=False<\/code>&nbsp;then it creates a new DataFrame with updated changes and returns it.<\/li>\n<\/ul>\n\n\n\n<p><strong>Note<\/strong>: Set <code>inplace=True<\/code> when we are doing function chaining to avoid assigning the result back to a variable as we are performing modifications in place.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code4\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-5\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># drop columns in place<\/span>\nstudent_df.drop(columns=&#91;<span class=\"hljs-string\">'age'<\/span>, <span class=\"hljs-string\">'marks'<\/span>], inplace=<span class=\"hljs-literal\">True<\/span>)\n\nprint(student_df.columns.values)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-5\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code4', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code4');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping columns:\n['name' 'age' 'marks']\n\nAfter dropping columns:\n['name']<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-by-suppressing-errors\">Drop column by suppressing errors<\/h2>\n\n\n\n<p>By default, The&nbsp;<code>DataFrame.drop()<\/code>&nbsp; throws <code>KeyError<\/code> if the column you are trying to delete does not exist in the dataset.<\/p>\n\n\n\n<p>If we want to drop the column only if exists then we can suppress the error by using the parameter <code>errors<\/code>.<\/p>\n\n\n\n<ul>\n<li>Set&nbsp;<code>errors='ignore'<\/code>&nbsp;to not throw any errors.<\/li>\n\n\n\n<li>Set&nbsp;<code>errors='raised'<\/code> to throw&nbsp;<code>KeyError<\/code>&nbsp;for the unknown columns<\/li>\n<\/ul>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we are trying to drop the column which does not exist in the DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code5\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-6\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)\n\n<span class=\"hljs-comment\"># supress error<\/span>\nstudent_df = student_df.drop(columns=<span class=\"hljs-string\">'salary'<\/span>, errors=<span class=\"hljs-string\">'ignore'<\/span>)  <span class=\"hljs-comment\"># No change in the student_df<\/span>\n\n<span class=\"hljs-comment\"># raise error<\/span>\nstudent_df = student_df.drop(columns=<span class=\"hljs-string\">'salary'<\/span>)  <span class=\"hljs-comment\"># KeyError: \"&#91;'salary'] not found in axis\"<\/span>\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-6\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code5', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code5');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-by-index-position\">Drop column by index position<\/h2>\n\n\n\n<p>If there is a case where we want to drop columns in the DataFrame, but we do not know the name of the columns still we can delete the column using its index position.<\/p>\n\n\n\n<p><strong>Note<\/strong>: Column index starts from 0 (zero) and it goes till the last column whose index value will be&nbsp;<code>len(df.columns)-1<\/code>&nbsp;.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-drop-the-last-column\">Drop the last column<\/h3>\n\n\n\n<p>Assume you want to drop the first column or the last column of the DataFrame without using the column name.<\/p>\n\n\n\n<p>In such cases, use the DataFrame.columns attribute to delete a column of the DataFrame based on its index position. Simply pass <code>df.columns[index]<\/code> to the columns parameter of the <code>DataFrame.drop()<\/code>.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we are dropping the last column of the DataFrame using&nbsp; <code>df.columns[last_index]<\/code>.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code6\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-7\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># find position of the last column and drop<\/span>\npos = len(student_df.columns) - <span class=\"hljs-number\">1<\/span>\nstudent_df = student_df.drop(columns=student_df.columns&#91;pos])\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># delete column present at index 1<\/span>\n<span class=\"hljs-comment\"># student_df.drop(columns = student_df.columns&#91;1])<\/span><\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-7\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code6', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code6');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping column:\n['name' 'age' 'marks']\n\nAfter dropping column:\n['name' 'age']<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-drop-range-of-columns-using-iloc\">Drop range of columns using <code>iloc<\/code><\/h3>\n\n\n\n<p>There could be a case when we need to delete the fourth column from the dataset or need to delete a range of columns. We can use <code>DataFrame.iloc<\/code> to select single or multiple columns from the DataFrame.<\/p>\n\n\n\n<p>We can use <code>DataFrame.iloc<\/code> in the <code>columns<\/code> parameter to specify the index position of the columns which need to drop.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Let&#8217;s see how we can drop the range of the columns based on the index position. In the below example, we are dropping columns from index position 1 to 3 (exclusive).<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code7\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-8\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: <span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># drop column from 1 to 3<\/span>\nstudent_df = student_df.drop(columns=student_df.iloc&#91;:, <span class=\"hljs-number\">1<\/span>:<span class=\"hljs-number\">3<\/span>])\n\nprint(student_df.columns.values)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-8\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code7', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code7');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping columns:\n['name' 'age' 'marks']\n\nAfter dropping columns:\n['name']<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-drop-first-n-columns\">Drop first n columns<\/h3>\n\n\n\n<p>If we need to delete the first &#8216;n&#8217; columns from a DataFrame, we can use <code>DataFrame.iloc<\/code> and the <a href=\"https:\/\/pynative.com\/python-range-function\/\">Python <code>range()<\/code><\/a> function to specify the columns&#8217; range to be deleted.<\/p>\n\n\n\n<p>We need to use the built-in function range() with <code>columns <\/code>parameter of <code>DataFrame.drop()<\/code>.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we are dropping the first two columns from a DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code8\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-9\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>], <span class=\"hljs-string\">\"class\"<\/span>: &#91;<span class=\"hljs-string\">\"A\"<\/span>, <span class=\"hljs-string\">\"B\"<\/span>],\n                <span class=\"hljs-string\">\"city\"<\/span>: &#91;<span class=\"hljs-string\">\"London\"<\/span>, <span class=\"hljs-string\">\"Zurich\"<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># drop column 1 and 2<\/span>\nstudent_df = student_df.drop(columns=student_df.iloc&#91;:, range(<span class=\"hljs-number\">2<\/span>)])\n\n<span class=\"hljs-comment\"># print only columns<\/span>\nprint(student_df.columns.values)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-9\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code8', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code8');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong> Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping:\n['name' 'age' 'marks' 'class' 'city']\n\nAfter dropping:\n['marks' 'class' 'city']<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-from-multi-index-dataframe\">Drop column from multi-index DataFrame<\/h2>\n\n\n\n<p>DataFrame can have multiple column headers, such DataFrame is called a <strong>multi-index<\/strong> DataFrame. Such headers are divided into the levels where the first header is at level 0, the second header is at level 1, and so on.<\/p>\n\n\n\n<p>We can drop a column from any level of multi-index DataFrame. By default, it drops columns from all the levels, but we can use a parameter <code>level<\/code> to drop from a particular level only.<\/p>\n\n\n\n<p>We need to pass a level name or level index as <code class=\"language-python\">level=level_index<\/code>.<\/p>\n\n\n\n<p>Below is the multi-index DataFrame with two column headers.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"616\" height=\"204\" src=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-column-from-multiindex.png\" alt=\"Drop Column from Multiindex DataFrame\" class=\"wp-image-8152\" srcset=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-column-from-multiindex.png 616w, https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-column-from-multiindex-300x99.png 300w\" sizes=\"(max-width: 616px) 100vw, 616px\" \/><figcaption class=\"wp-element-caption\">Multi-index DataFrame<\/figcaption><\/figure>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Let&#8217;s see how we can drop column &#8216;<strong>marks<\/strong>&#8216; from level 1.<\/p>\n\n\n\n<p><strong>Note:<\/strong> If we do not provide a <code>level<\/code> parameter then it will drop the column from all the levels if exist.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code9\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-10\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\n<span class=\"hljs-comment\"># create column header<\/span>\ncol = pd.MultiIndex.from_arrays(&#91;&#91;<span class=\"hljs-string\">'Class A'<\/span>, <span class=\"hljs-string\">'Class A'<\/span>, <span class=\"hljs-string\">'Class B'<\/span>, <span class=\"hljs-string\">'Class B'<\/span>],\n                                 &#91;<span class=\"hljs-string\">'Name'<\/span>, <span class=\"hljs-string\">'Marks'<\/span>, <span class=\"hljs-string\">'Name'<\/span>, <span class=\"hljs-string\">'Marks'<\/span>]])\n<span class=\"hljs-comment\"># create dataframe from 2darray<\/span>\nstudent_df = pd.DataFrame(&#91;&#91;<span class=\"hljs-string\">'Joe'<\/span>, <span class=\"hljs-string\">'85.10'<\/span>, <span class=\"hljs-string\">'Nat'<\/span>, <span class=\"hljs-string\">'77.80'<\/span>], &#91;<span class=\"hljs-string\">'Harry'<\/span>, <span class=\"hljs-string\">'91.54'<\/span>, <span class=\"hljs-string\">'Sam'<\/span>, <span class=\"hljs-string\">'68.55'<\/span>]], columns=col)\nprint(student_df)\n\n<span class=\"hljs-comment\"># drop column<\/span>\nstudent_df = student_df.drop(columns=&#91;<span class=\"hljs-string\">'Marks'<\/span>], level=<span class=\"hljs-number\">1<\/span>)\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-10\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code9', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code9');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">After dropping column:\n  Class A Class B\n     Name    Name\n0     Joe     Nat\n1   Harry     Sam<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-using-a-function\">Drop column using a function<\/h2>\n\n\n\n<p>We can also use the function to delete columns by applying some logic or based on some condition. We can use built-in as well as user-defined functions to drop columns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-drop-all-the-columns-using-loc\">Drop all the columns using <code>loc<\/code><\/h3>\n\n\n\n<p>If we want to drop all the columns from DataFrame we can easily do that using <code>DataFrame.loc<\/code> in the <code>columns<\/code> parameter of <code>DataFrame.drop()<\/code>.<\/p>\n\n\n\n<p><code>DataFrame.loc<\/code> is used to specify the column labels which need to delete.&nbsp; If we do not specify any column labels like <code class=\"language-python\">df.loc[:]<\/code> then it will drop all the columns in the DataFrame.<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>In the below example, we are dropping all the columns from the student DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code10\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-11\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df.columns.values)\n\n<span class=\"hljs-comment\"># drop column 1 and 2<\/span>\nstudent_df = student_df.drop(columns=student_df.loc&#91;:])\n\n<span class=\"hljs-comment\"># print only columns<\/span>\nprint(student_df.columns.values)\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-11\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code10', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code10');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping columns:\n['name' 'age' 'marks']\n\nAfter dropping columns:\n[]<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-using-pandas-dataframe-pop-function\">Drop column using pandas <code>DataFrame.pop()<\/code> function<\/h2>\n\n\n\n<p>If we want to delete a single column then we can also do that using <code>DataFrame.pop(col_label)<\/code> function. We need to pass a column label that needs to delete.<\/p>\n\n\n\n<p>It removes the column in-place by updating the existing DataFrame. It raises <code>KeyError<\/code> if the column is not found.<\/p>\n\n\n\n<p><strong>Note:<\/strong> It can be used to drop a column only. It cannot drop multiple columns or row(s).<\/p>\n\n\n\n<p><strong>Example<\/strong><\/p>\n\n\n\n<p>Let&#8217;s see how we can drop the &#8216;<strong>age<\/strong>&#8216; column from a student DataFrame.<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code11\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-12\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)\n\n<span class=\"hljs-comment\"># drop column<\/span>\nstudent_df.pop(<span class=\"hljs-string\">'age'<\/span>)\n\nprint(student_df)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-12\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code11', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code11');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping column:\n    name  age  marks\n0    Joe   20  85.10\n1    Nat   21  77.80\n\nAfter dropping column:\n    name  marks\n0    Joe  85.10\n1    Nat  77.80<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-drop-column-using-pandas-dataframe-delete\">Drop column using pandas DataFrame delete<\/h2>\n\n\n\n<p>We can also use the pandas inbuilt function <code>del<\/code> to drop a single column from a DataFrame. It is a very simplified way of dropping the column from a DataFrame.<\/p>\n\n\n\n<p>We need to select the column of DataFrame which needs to be deleted and pass it as&nbsp; <code class=\"language-python\">del df[col_label]<\/code>.<\/p>\n\n\n\n<p><strong>Note:<\/strong> It can be used to drop a column only. It cannot drop multiple columns or row(s).<\/p>\n\n\n<div class=\"hljstoolbar\"><pre id=\"code12\"  class=\"wp-block-code language-python\" aria-describedby=\"shcb-language-13\" data-shcb-language-name=\"Python\" data-shcb-language-slug=\"python\"><span><code class=\"hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n\nstudent_dict = {<span class=\"hljs-string\">\"name\"<\/span>: &#91;<span class=\"hljs-string\">\"Joe\"<\/span>, <span class=\"hljs-string\">\"Nat\"<\/span>], <span class=\"hljs-string\">\"age\"<\/span>: &#91;<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">21<\/span>], <span class=\"hljs-string\">\"marks\"<\/span>: &#91;<span class=\"hljs-number\">85.10<\/span>, <span class=\"hljs-number\">77.80<\/span>]}\n\n<span class=\"hljs-comment\"># Create DataFrame from dict<\/span>\nstudent_df = pd.DataFrame(student_dict)\nprint(student_df)\n\n<span class=\"hljs-comment\"># drop column<\/span>\n<span class=\"hljs-keyword\">del<\/span> student_df&#91;<span class=\"hljs-string\">'age'<\/span>]\n\nprint(student_df)\n<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-13\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">Python<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">python<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre><button class=\"hljs-copy-button\" onclick=\"copy_code('code12', this);\"><i class=\"far fa-copy1\" aria-hidden=\"true\"><\/i><\/button><button class=\"hljs-run-button\" onclick=\"run_code('code12');\"><i class=\"far fa-play-circle\" aria-hidden=\"true\"><\/i> Run<\/button><\/div>\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Before dropping column:\n    name  age  marks\n0    Joe   20  85.10\n1    Nat   21  77.80\n\nAfter dropping column:\n    name  marks\n0    Joe  85.10\n1    Nat  77.80<\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-compare-dataframe-drop-vs-pop-vs-del\">Compare DataFrame drop() vs. pop() vs. del<\/h2>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table><thead><tr><th>Features<\/th><th>drop()<\/th><th>pop()<\/th><th>del<\/th><\/tr><\/thead><tbody><tr><td>Operates on axis<\/td><td>columns and rows<\/td><td>only column<\/td><td>only column<\/td><\/tr><tr><td>Delete multiple columns<\/td><td>Yes<\/td><td>No<\/td><td>No<\/td><\/tr><tr><td>Drop in-place or return a copy<\/td><td>Both<\/td><td>Only in-place<\/td><td>Only in-place<\/td><\/tr><tr><td>Performance<\/td><td>Fast<\/td><td>Slow<\/td><td>Slow<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Datasets could be in any shape and form. To optimize the data analysis, we need to remove some data that is redundant or not required. This article aims to discuss all the cases of dropping single or multiple columns from a pandas DataFrame. The following functions are discussed in this article in detail: In the [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[21,22],"tags":[40],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.6 (Yoast SEO v22.6) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Drop Columns in Pandas DataFrame<\/title>\n<meta name=\"description\" content=\"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/pynative.com\/pandas-drop-columns\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Drop columns in pandas DataFrame\" \/>\n<meta property=\"og:description\" content=\"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/pynative.com\/pandas-drop-columns\/\" \/>\n<meta property=\"og:site_name\" content=\"PYnative\" \/>\n<meta property=\"article:published_time\" content=\"2021-02-17T16:51:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-03-09T06:22:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\" \/>\n<meta name=\"author\" content=\"Vishakha Patil\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@PyNative\" \/>\n<meta name=\"twitter:site\" content=\"@PyNative\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Vishakha Patil\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/\"},\"author\":{\"name\":\"Vishakha Patil\",\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/cf16379a409929b0fb28a81b0425449e\"},\"headline\":\"Drop columns in pandas DataFrame\",\"datePublished\":\"2021-02-17T16:51:12+00:00\",\"dateModified\":\"2023-03-09T06:22:55+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/\"},\"wordCount\":1417,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f\"},\"image\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\",\"keywords\":[\"pandas\"],\"articleSection\":[\"Pandas\",\"Python\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/pynative.com\/pandas-drop-columns\/#respond\"]}],\"accessibilityFeature\":[\"tableOfContents\"]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/\",\"url\":\"https:\/\/pynative.com\/pandas-drop-columns\/\",\"name\":\"Drop Columns in Pandas DataFrame\",\"isPartOf\":{\"@id\":\"https:\/\/pynative.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\",\"datePublished\":\"2021-02-17T16:51:12+00:00\",\"dateModified\":\"2023-03-09T06:22:55+00:00\",\"description\":\"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.\",\"breadcrumb\":{\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/pynative.com\/pandas-drop-columns\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage\",\"url\":\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\",\"contentUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png\",\"width\":243,\"height\":169,\"caption\":\"Drop Single Column\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/pynative.com\/pandas-drop-columns\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/pynative.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Python\",\"item\":\"https:\/\/pynative.com\/python\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Pandas\",\"item\":\"https:\/\/pynative.com\/python\/pandas\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Drop columns in pandas DataFrame\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/pynative.com\/#website\",\"url\":\"https:\/\/pynative.com\/\",\"name\":\"PYnative\",\"description\":\"Python Programming\",\"publisher\":{\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/pynative.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f\",\"name\":\"Vishal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/vishalHule.jpg\",\"contentUrl\":\"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/vishalHule.jpg\",\"width\":968,\"height\":1065,\"caption\":\"Vishal\"},\"logo\":{\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/image\/\"},\"description\":\"Founder of PYnative.com. I am a Python developer and I love to write articles to help developers. | All the best for your future Python endeavors!\",\"sameAs\":[\"https:\/\/pynative.com\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/cf16379a409929b0fb28a81b0425449e\",\"name\":\"Vishakha Patil\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/pynative.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/2c38e42c41b3cd15310924cd23ec53bc?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/2c38e42c41b3cd15310924cd23ec53bc?s=96&d=mm&r=g\",\"caption\":\"Vishakha Patil\"}}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Drop Columns in Pandas DataFrame","description":"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.","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:\/\/pynative.com\/pandas-drop-columns\/","og_locale":"en_US","og_type":"article","og_title":"Drop columns in pandas DataFrame","og_description":"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.","og_url":"https:\/\/pynative.com\/pandas-drop-columns\/","og_site_name":"PYnative","article_published_time":"2021-02-17T16:51:12+00:00","article_modified_time":"2023-03-09T06:22:55+00:00","og_image":[{"url":"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png"}],"author":"Vishakha Patil","twitter_card":"summary_large_image","twitter_creator":"@PyNative","twitter_site":"@PyNative","twitter_misc":{"Written by":"Vishakha Patil","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/pynative.com\/pandas-drop-columns\/#article","isPartOf":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/"},"author":{"name":"Vishakha Patil","@id":"https:\/\/pynative.com\/#\/schema\/person\/cf16379a409929b0fb28a81b0425449e"},"headline":"Drop columns in pandas DataFrame","datePublished":"2021-02-17T16:51:12+00:00","dateModified":"2023-03-09T06:22:55+00:00","mainEntityOfPage":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/"},"wordCount":1417,"commentCount":0,"publisher":{"@id":"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f"},"image":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage"},"thumbnailUrl":"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png","keywords":["pandas"],"articleSection":["Pandas","Python"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/pynative.com\/pandas-drop-columns\/#respond"]}],"accessibilityFeature":["tableOfContents"]},{"@type":"WebPage","@id":"https:\/\/pynative.com\/pandas-drop-columns\/","url":"https:\/\/pynative.com\/pandas-drop-columns\/","name":"Drop Columns in Pandas DataFrame","isPartOf":{"@id":"https:\/\/pynative.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage"},"image":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage"},"thumbnailUrl":"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png","datePublished":"2021-02-17T16:51:12+00:00","dateModified":"2023-03-09T06:22:55+00:00","description":"Drop single or multiple columns from pandas DataFrame. Covers functions to delete the columns from simple to multi-index DataFrame.","breadcrumb":{"@id":"https:\/\/pynative.com\/pandas-drop-columns\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/pynative.com\/pandas-drop-columns\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pynative.com\/pandas-drop-columns\/#primaryimage","url":"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png","contentUrl":"https:\/\/pynative.com\/wp-content\/uploads\/2021\/02\/drop-single-column.png","width":243,"height":169,"caption":"Drop Single Column"},{"@type":"BreadcrumbList","@id":"https:\/\/pynative.com\/pandas-drop-columns\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/pynative.com\/"},{"@type":"ListItem","position":2,"name":"Python","item":"https:\/\/pynative.com\/python\/"},{"@type":"ListItem","position":3,"name":"Pandas","item":"https:\/\/pynative.com\/python\/pandas\/"},{"@type":"ListItem","position":4,"name":"Drop columns in pandas DataFrame"}]},{"@type":"WebSite","@id":"https:\/\/pynative.com\/#website","url":"https:\/\/pynative.com\/","name":"PYnative","description":"Python Programming","publisher":{"@id":"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/pynative.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":["Person","Organization"],"@id":"https:\/\/pynative.com\/#\/schema\/person\/64b55d5bde2265918c5a9931de4de71f","name":"Vishal","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pynative.com\/#\/schema\/person\/image\/","url":"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/vishalHule.jpg","contentUrl":"https:\/\/pynative.com\/wp-content\/uploads\/2019\/01\/vishalHule.jpg","width":968,"height":1065,"caption":"Vishal"},"logo":{"@id":"https:\/\/pynative.com\/#\/schema\/person\/image\/"},"description":"Founder of PYnative.com. I am a Python developer and I love to write articles to help developers. | All the best for your future Python endeavors!","sameAs":["https:\/\/pynative.com"]},{"@type":"Person","@id":"https:\/\/pynative.com\/#\/schema\/person\/cf16379a409929b0fb28a81b0425449e","name":"Vishakha Patil","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pynative.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/2c38e42c41b3cd15310924cd23ec53bc?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/2c38e42c41b3cd15310924cd23ec53bc?s=96&d=mm&r=g","caption":"Vishakha Patil"}}]}},"featured_image_src":null,"featured_image_src_square":null,"author_info":{"display_name":"Vishakha Patil","author_link":"https:\/\/pynative.com\/author\/pvishakha\/"},"_links":{"self":[{"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/posts\/8116"}],"collection":[{"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/comments?post=8116"}],"version-history":[{"count":0,"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/posts\/8116\/revisions"}],"wp:attachment":[{"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/media?parent=8116"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/categories?post=8116"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pynative.com\/wp-json\/wp\/v2\/tags?post=8116"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}