{"id":56817,"date":"2019-05-25T12:46:10","date_gmt":"2019-05-25T07:16:10","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=56817"},"modified":"2019-05-30T16:47:25","modified_gmt":"2019-05-30T11:17:25","slug":"pandas-options-and-customizations","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pandas-options-and-customizations\/","title":{"rendered":"Pandas\u00a0Options &#8211; Explore 5 Core Options to Customize Your Data"},"content":{"rendered":"<p>Pandas are the basic library for data science, data analytics, and data processing. Pandas options allow a user to customize the data according. In this article, we are going to cover 5 different types of Pandas Options and\u00a0Customizations with examples.<\/p>\n<h2>Pandas\u00a0Options and Customizations<\/h2>\n<p>Pandas have some default factors which restrict the analysis of data. Therefore to have a stronghold over the library and to make the most out of its uses, it is important to know the various methods to change the default pandas values.<\/p>\n<p>Common default values-<\/p>\n<ul>\n<li><strong>display.max_rows<\/strong> and <strong>display.max_columns<\/strong> which shows the default number of rows and columns.<\/li>\n<li><strong>display.max_colwidth<\/strong> which gives us the maximum width of the column<\/li>\n<li><strong>display.expand_frame_repr<\/strong> which gives us DataFrames that is spread across numerous pages.<\/li>\n<li><strong>display.precision<\/strong> gives us the precision of the decimal numbers<\/li>\n<\/ul>\n<p><em><strong>Do you know<\/strong> &#8211;<strong><a href=\"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/\"> How to use function applications of Pandas?<\/a><\/strong><\/em><\/p>\n<h2>Types of Pandas Options and Customization<\/h2>\n<p>Before we start Pandas options and customization, we need to import the pandas library:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt;import pandas as pd<\/pre>\n<p>There are 5 types of Pandas options to customize the data &#8211;<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56824\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations.jpg\" alt=\"Pandas Options and\u00a0Customizations\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations.jpg 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Options-and-Customizations-520x272.jpg 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<h3>1. Pandas.get_option()<\/h3>\n<p>With the help of .get_option in pandas, we can define parameter which will give us a particular detail about the default values in pandas.<\/p>\n<p>Using \u201cdisplay.max_rows&#8221; and &#8220;display.max_columns&#8221; as parameters we get a maximum number of rows and columns that can display by default.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair= pd.get_option(\"display.max_rows\")\r\n&gt;&gt;&gt; print(dataflair)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>60<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-56821 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option.png\" alt=\"display max rows in get option\" width=\"1366\" height=\"603\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option-150x66.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option-300x132.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option-768x339.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option-1024x452.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Get-option-520x230.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair2= pd.get_option(\"display.max_columns\")\r\n&gt;&gt;&gt; print(dataflair2)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>0<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-56822 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn.png\" alt=\"display max coloums in get option\" width=\"1366\" height=\"653\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn-150x72.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn-300x143.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn-768x367.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn-1024x490.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-get-option-for-max-coloumn-520x249.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<h3>2. Pandas.set_option()<\/h3>\n<p>The .set_option() function allows us to change a default value to something of our choice. In the example given below, we see that we can change the \u201cdisplay.max_rows\u201d from 60 to 90.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.set_option(\"display.max_rows\",90)\r\n&gt;&gt;&gt; dataflair3= pd.get_option(\"display.max_rows\")\r\n&gt;&gt;&gt; print(dataflair3)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>90<\/p>\n<p><em><strong>Take the easy steps to<a href=\"https:\/\/data-flair.training\/blogs\/sort-pandas-dataframes-series-array\/\"> sort pandas dataframes and series<\/a><\/strong><\/em><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.set_option(\"display.max_columns\",10)\r\n&gt;&gt;&gt; dataflair4= pd.get_option(\"display.max_columns\")\r\n&gt;&gt;&gt; print(dataflair4)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>10<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56832\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option.jpg\" alt=\"Pandas Set Option Example\" width=\"1359\" height=\"737\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option.jpg 1359w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option-150x81.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option-300x163.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option-768x416.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option-1024x555.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Set-Option-520x282.jpg 520w\" sizes=\"auto, (max-width: 1359px) 100vw, 1359px\" \/><\/a><\/p>\n<h3>3. Pandas.reset_option<\/h3>\n<p>With the help of the <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.reset_option.html\">.reset_option<\/a> in Pandas, you can get back the default values which may change previously.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.reset_option(\"display.max_rows\")\r\n&gt;&gt;&gt; dataflair5= pd.get_option(\"display.max_rows\")\r\n&gt;&gt;&gt; print(dataflair5)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>60<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.reset_option(\"display.max_columns\")\r\n&gt;&gt;&gt; dataflair6= pd.get_option(\"display.max_columns\")\r\n&gt;&gt;&gt; print(dataflair6)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>0<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56833\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option.jpg\" alt=\"Pandas reset Option with Example\" width=\"1364\" height=\"738\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option.jpg 1364w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option-150x81.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option-300x162.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option-768x416.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option-1024x554.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Example-of-Pandas-Reset-Option-520x281.jpg 520w\" sizes=\"auto, (max-width: 1364px) 100vw, 1364px\" \/><\/a><\/p>\n<h3>4. Pandas.describe_option<\/h3>\n<p>The .describe_option in Pandas describes the parameter. For example .describe_option(&#8220;display.max_rows&#8221;) would give the details about \u201cdisplay.max_rows\u201d .<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.describe_option(\"display.max_rows\")<\/pre>\n<p><em><strong>Struggling with the<a href=\"https:\/\/data-flair.training\/blogs\/install-pandas-on-windows\/\"> installation of Pandas<\/a><\/strong><\/em><\/p>\n<p><strong>Output:<\/strong><\/p>\n<p>display.max_rows : int<br \/>\nIf max_rows is exceeded, switch to truncate view. Depending on<br \/>\n`large_repr`, objects are either centrally truncated or printed as<br \/>\na summary view. &#8216;None&#8217; value means unlimited.<\/p>\n<p>In case python\/IPython is running in a terminal and `large_repr`<br \/>\nequals &#8216;truncate&#8217; this can be set to 0 and pandas will auto-detect<br \/>\nthe height of the terminal and print a truncated object which fits<br \/>\nthe screen height. The IPython notebook, IPython qtconsole, or<br \/>\nIDLE do not run in a terminal and hence it is not possible to do<br \/>\ncorrect auto-detection.<br \/>\n[default: 60] [currently: 60]<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; pd.describe_option(\"display.max_columns\")<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>display.max_columns : int<br \/>\nIf max_cols is exceeded, switch to truncate view. Depending on<br \/>\n`large_repr`, objects are either centrally truncated or printed as<br \/>\na summary view. &#8216;None&#8217; value means unlimited.<\/p>\n<p>In case python\/IPython is running in a terminal and `large_repr`<br \/>\nequals &#8216;truncate&#8217; this can be set to 0 and pandas will auto-detect<br \/>\nthe width of the terminal and print a truncated object which fits<br \/>\nthe screen width. The IPython notebook, IPython qtconsole, or IDLE<br \/>\ndo not run in a terminal and hence it is not possible to do<br \/>\ncorrect auto-detection.<br \/>\n[default: 0] [currently: 0]<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56836\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option.jpg\" alt=\"Example of Pandas Describe Option\" width=\"1364\" height=\"738\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option.jpg 1364w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option-150x81.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option-300x162.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option-768x416.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option-1024x554.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Describe-Option-520x281.jpg 520w\" sizes=\"auto, (max-width: 1364px) 100vw, 1364px\" \/><\/a><\/p>\n<h3>5. Pandas.option_context<\/h3>\n<p>Using .opton_context function we can invoke a pandas option function which will be only active within the scope of the function.<\/p>\n<p>In the\u00a0below example, display.max_rows is set to 30 only inside the .option-context scope. Outside the function scope, it returns back to being 60.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; with pd.option_context(\"display.max_rows\",30):\r\n\u00a0 \u00a0 \u00a0 print(pd.get_option(\"display.max_rows\"))<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>30<\/p>\n<p><em><strong>Don&#8217;t forget to check out<\/strong> &#8211;<strong><a href=\"https:\/\/data-flair.training\/blogs\/basic-functionality-in-pandas\/\"> Pandas Basic Functionality<\/a><\/strong><\/em><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; print(pd.get_option(\"display.max_rows\"))<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>60<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56834\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context.jpg\" alt=\"Example of pandas Option Context\" width=\"1364\" height=\"738\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context.jpg 1364w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context-150x81.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context-300x162.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context-768x416.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context-1024x554.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-option-Context-520x281.jpg 520w\" sizes=\"auto, (max-width: 1364px) 100vw, 1364px\" \/><\/a><\/p>\n<h2>Summary<\/h2>\n<p>We have studied Pandas options and customization functions, now you are ready to use and customize pandas accordingly. This will allow you to have a better grasp over Pandas and derive the most out of the library.<\/p>\n<p>For queries and suggestions, you can approach our comment section.<\/p>\n<p><strong>Excited for our next Pandas Tutorial?<\/strong><em> Check out <strong><a href=\"https:\/\/data-flair.training\/blogs\/pandas-panel\/\">how to create panels in Pandas<\/a>?<\/strong><\/em><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1550,&quot;href&quot;:&quot;https:\\\/\\\/pandas.pydata.org\\\/pandas-docs\\\/stable\\\/reference\\\/api\\\/pandas.reset_option.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205045645\\\/https:\\\/\\\/pandas.pydata.org\\\/pandas-docs\\\/stable\\\/reference\\\/api\\\/pandas.reset_option.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 10:47:27&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-13 11:56:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-21 15:22:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-29 06:15:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-06 17:46:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-13 12:22:15&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-04 16:46:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-14 06:20:10&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-22 15:03:51&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-29 11:26:55&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-12 19:16:01&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-20 06:50:32&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-12 19:21:39&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-20 05:09:17&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-30 10:42:06&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-30 10:42:06&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pandas are the basic library for data science, data analytics, and data processing. Pandas options allow a user to customize the data according. In this article, we are going to cover 5 different types&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":56824,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19475],"tags":[19861,19866,19863,19867,19860,19862,19865,19864],"class_list":["post-56817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pandas","tag-pandas-customizations","tag-pandas-describe-option","tag-pandas-get-option","tag-pandas-option-context","tag-pandas-options","tag-pandas-options-and-customizations","tag-pandas-reset-option","tag-pandas-set-option"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pandas\u00a0Options - Explore 5 Core Options to Customize Your Data - DataFlair<\/title>\n<meta name=\"description\" content=\"Pandas Options and\u00a0Customizations allows a user to customize the data. 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