{"id":56618,"date":"2019-05-22T15:17:56","date_gmt":"2019-05-22T09:47:56","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=56618"},"modified":"2019-05-25T11:36:03","modified_gmt":"2019-05-25T06:06:03","slug":"pandas-function-applications","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/","title":{"rendered":"Pandas Function Applications &#8211; How to use pipe(), apply(), applymap()"},"content":{"rendered":"<p>While coding, one has to apply functions to Pandas objects. To apply these pandas function applications &#8211; pipe(), apply(), and applymap(), you should know these three important methods. The knowledge of these methods helps us to choose the method of application wisely while coding. The appropriate method for applying the functions depends on whether your function expects to operate element-wise, row wise, or column wise.<\/p>\n<ul>\n<li><strong>pipe():<\/strong>\u00a0Table wise function applications in Pandas<\/li>\n<li><strong>apply()<\/strong>: Row or column wise function operation<\/li>\n<li><strong>applymap()<\/strong>: Element-wise function applications in Pandas<\/li>\n<\/ul>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56638\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications.jpg\" alt=\"How to use function applications in Pandas\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications.jpg 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications-520x272.jpg 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<h2>Pandas Function Applications<\/h2>\n<p>Before we explore the pandas function applications, we need to import pandas and numpy-<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; import pandas as pd\r\n&gt;&gt;&gt; import numpy as np<\/pre>\n<h3>1. Table Wise Function Application: pipe()<\/h3>\n<p><em>The custom operations performed by passing a function and an appropriate number of parameters<\/em>. <em>These are known as pipe arguments<\/em>. Hence, the operation is performed on the entire DataFrame or Series. When we want to apply one function to a series or DataFrame, then apply another, then another, and so on, the notation can become messy. It can also makes the program more prone to error. Here, pipe() becomes useful.<\/p>\n<p>Define the pipe() function application in Python Pandas<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; def adder(ele1,ele2):\r\n         return ele1+ele2<\/pre>\n<p><strong>It&#8217;s the right time to enhance your skills for<a href=\"https:\/\/data-flair.training\/blogs\/basic-functionality-in-pandas\/\"> Pandas Basic Functionality<\/a><\/strong>.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56635\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy.jpg\" alt=\"import pandas and numpy\" width=\"1366\" height=\"746\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy.jpg 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy-150x82.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy-300x164.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy-768x419.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy-1024x559.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/import-pandas-and-numpy-520x284.jpg 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p>Let&#8217;s see how pipe() function application works in Pandas Series and DataFrame-<\/p>\n<h4>1.1. Using pipe() Functions Application on Pandas Series<\/h4>\n<p>First, we create a Pandas Series-<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_s1 = pd.Series([11,21,31,41,51])\r\n&gt;&gt;&gt; dataflair_s1<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p>1\u00a0 \u00a011<br \/>\n2\u00a0 \u00a021<br \/>\n3\u00a0 \u00a031<br \/>\n4\u00a0 \u00a041<br \/>\n5\u00a0 \u00a051<br \/>\ndtype: int64<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56636\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series.jpg\" alt=\"How to generate a Series in Pandas\" width=\"1366\" height=\"747\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series.jpg 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series-150x82.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series-300x164.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series-768x420.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series-1024x560.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/creating-a-Pandas-Series-520x284.jpg 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p>Now, using <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.pipe.html\">pipe() function<\/a> application on Pandas series-<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_s1.pipe(adder,3)<\/pre>\n<p><strong>Output<\/strong>:<\/p>\n<p>1\u00a0 \u00a014<br \/>\n2\u00a0 \u00a024<br \/>\n3\u00a0 \u00a034<br \/>\n4\u00a0 \u00a044<br \/>\n5\u00a0 \u00a054<br \/>\ndtype: int64<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56626\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series.png\" alt=\"How to use pipe function on Pandas Series\" width=\"1366\" height=\"745\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series-150x82.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series-300x164.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series-768x419.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series-1024x558.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/using-pipe-function-on-Pandas-series-520x284.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p><strong>Grab these easy steps to<\/strong> <strong><a href=\"https:\/\/data-flair.training\/blogs\/sort-pandas-dataframes-series-array\/\">Sort Pandas Series and DataFrames<\/a><\/strong><\/p>\n<h4>1.2.\u00a0Using pipe() Function Application on Pandas\u00a0DataFrame<\/h4>\n<p>Creating a DataFrame in Pandas<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_df1=pd.DataFrame(6*np.random.randn(6,3),columns=['c1','c2','c3'])\r\n&gt;&gt;&gt; dataflair_df1<\/pre>\n<p><strong>Output-<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56637\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas.jpg\" alt=\"How to Create Pandas Dataframes\" width=\"1366\" height=\"744\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas.jpg 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas-150x82.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas-300x163.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas-768x418.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas-1024x558.jpg 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Creating-a-DataFrame-in-Pandas-520x283.jpg 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p>Now, using pipe() function application on Pandas DataFrame-<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_df1.pipe(adder,3)<\/pre>\n<p><strong>Output<\/strong>:<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56628\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame.png\" alt=\"Using using pipe() function application on Pandas DataFrame\" width=\"1366\" height=\"739\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame-300x162.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame-768x415.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame-1024x554.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/pipe-function-on-Pandas-DataFrame-520x281.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p><strong>Note<\/strong>: Moving forward, we will be using the same DataFrame and Series to avoid any confusion.<\/p>\n<h3>2. Row or Column Wise Function Operations: apply()<\/h3>\n<p>You may apply arbitrary functions to the axes of a DataFrame or Panel by using the apply() method. It can also be applied to a Series. It takes an optional axis argument. <em>By default, the operation will be performed column-wise, taking every column as an array.<\/em> It enables the user, to pass a function and then apply it to all the values of the DataFrame or Series. It is a huge improvement for the library as it allows the segregation of data according to the given conditions, making it efficiently usable in <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>machine learning<\/strong><\/a> and data science.<\/p>\n<p>Defining the apply() function<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; def sq(ele1):\r\n      return ele1*ele1<\/pre>\n<h4>2.1 Applying apply() functions on Pandas Series<\/h4>\n<p>Using apply() functions on Pandas Series.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_s1.apply(sq)<\/pre>\n<p><strong>Output<\/strong>:<\/p>\n<p>1\u00a0 121<br \/>\n2\u00a0 \u00a0441<br \/>\n3\u00a0 \u00a0961<br \/>\n4\u00a0 \u00a01681<br \/>\n5\u00a0 \u00a02601<br \/>\ndtype: int64<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56630\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series.png\" alt=\"How to apply a function on Pandas Series\" width=\"1366\" height=\"739\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series-300x162.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series-768x415.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series-1024x554.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/apply-functions-on-pandas-Series-520x281.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p><strong>Get a Complete<\/strong> <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-vs-data-science\/\">Difference Between Machine Learning and Data Science<\/a><\/strong><\/p>\n<h4>2.2\u00a0Applying apply() functions on Pandas\u00a0DataFrame<\/h4>\n<p><strong>Applying function column-wise on Pandas DataFrames<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_df1.apply(sq, axis=0)<\/pre>\n<p><strong>Output<\/strong>:<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56631\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames.png\" alt=\"How to apply() function coloumn wise on dataframes\" width=\"1359\" height=\"739\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames.png 1359w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames-150x82.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames-300x163.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames-768x418.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames-1024x557.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-column-wise-on-Pandas-DataFrames-520x283.png 520w\" sizes=\"auto, (max-width: 1359px) 100vw, 1359px\" \/><\/a><\/p>\n<p><strong>Applying function row-wise on Pandas DataFrames<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_df1.apply(sq, axis=1)\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<h3><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56632\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames.png\" alt=\"Applying function row-wise on Pandas DataFrames\" width=\"1366\" height=\"739\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-300x162.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-768x415.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1024x554.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-520x281.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><br \/>\n3. Element-wise Function Application: applymap()<\/h3>\n<p>Every function cannot be vectorized. The method <em>applymap() on DataFrame<\/em> is capable of taking and returning a single value. This Pandas function application is used to apply a function to DataFrame, that accepts and returns only one scalar value to every element of the DataFrame. It is a Data-centric method of applying functions to DataFrames. We use the word<strong> lambda to define the functions.<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">&gt;&gt;&gt; dataflair_df1.applymap(lambda x: x**2)<\/pre>\n<p><strong>Output<\/strong>:<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-56633\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1.png\" alt=\"Applying function row-wise on Pandas DataFrames\" width=\"1366\" height=\"735\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1.png 1366w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1-150x81.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1-300x161.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1-768x413.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1-1024x551.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Applying-function-row-wise-on-Pandas-DataFrames-1-520x280.png 520w\" sizes=\"auto, (max-width: 1366px) 100vw, 1366px\" \/><\/a><\/p>\n<p><strong>Note<\/strong>: This method does not work on a Series.<\/p>\n<h2>Summary<\/h2>\n<p>We have learned how to apply functions to pandas and the various situations in which each method is beneficial. Using them in specific cases will not only make your program elegant, presentable, and easy to use, but also remove any possibilities of errors or bugs. Knowing all of these is key to becoming an efficient programmer.<\/p>\n<p><strong>Take the next tutorial on &#8211; Panel in Pandas and build your<a href=\"https:\/\/data-flair.training\/blogs\/skills-needed-to-become-a-data-scientist\/\"> skills for Data Scientist<\/a>.<\/strong><\/p>\n<p>If you face any problems while using Pandas Function Applications, feel free to ask in the comments.<\/p>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1557,&quot;href&quot;:&quot;https:\\\/\\\/pandas.pydata.org\\\/pandas-docs\\\/stable\\\/reference\\\/api\\\/pandas.DataFrame.pipe.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251006091246\\\/https:\\\/\\\/pandas.pydata.org\\\/pandas-docs\\\/stable\\\/reference\\\/api\\\/pandas.DataFrame.pipe.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 11:09:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-16 17:58:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-22 07:43:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-11 17:54:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-16 01:50:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-06 10:56:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-13 12:12:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-18 04:05:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 03:15:15&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-26 09:20:13&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-04 16:40:20&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-12 12:18:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-20 10:43:33&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-26 08:05:29&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-30 11:52:01&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-09 12:27:43&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-13 15:59:47&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-21 03:56:03&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-28 14:18:28&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-04 18:49:49&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-20 13:39:33&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-27 13:49:30&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-12 19:03:45&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-18 04:42:04&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-22 19:15:23&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-27 01:47:35&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-27 01:47:35&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>While coding, one has to apply functions to Pandas objects. To apply these pandas function applications &#8211; pipe(), apply(), and applymap(), you should know these three important methods. The knowledge of these methods helps&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":56638,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19475],"tags":[19809,19807,19808,19806],"class_list":["post-56618","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pandas","tag-function-applications-in-pandas","tag-pandas-apply-function","tag-pandas-applymap-functions","tag-pandas-pipe-function"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pandas Function Applications - How to use pipe(), apply(), applymap() - DataFlair<\/title>\n<meta name=\"description\" content=\"Knowledge of Python Pandas Function Applications helps us to choose the method of application wisely while coding choose among apply(), pipe(), applymap() functions with examples\" \/>\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\/pandas-function-applications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas Function Applications - How to use pipe(), apply(), applymap() - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Knowledge of Python Pandas Function Applications helps us to choose the method of application wisely while coding choose among apply(), pipe(), applymap() functions with examples\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/\" \/>\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=\"2019-05-22T09:47:56+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-05-25T06:06:03+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"802\" \/>\n\t<meta property=\"og:image:height\" content=\"420\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\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=\"4 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Pandas Function Applications - How to use pipe(), apply(), applymap() - DataFlair","description":"Knowledge of Python Pandas Function Applications helps us to choose the method of application wisely while coding choose among apply(), pipe(), applymap() functions with examples","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\/pandas-function-applications\/","og_locale":"en_US","og_type":"article","og_title":"Pandas Function Applications - How to use pipe(), apply(), applymap() - DataFlair","og_description":"Knowledge of Python Pandas Function Applications helps us to choose the method of application wisely while coding choose among apply(), pipe(), applymap() functions with examples","og_url":"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2019-05-22T09:47:56+00:00","article_modified_time":"2019-05-25T06:06:03+00:00","og_image":[{"width":802,"height":420,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/05\/Pandas-Function-Applications.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/pandas-function-applications\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/7f83c342f5d1632d6f7b4b0b0f447823"},"headline":"Pandas Function Applications &#8211; 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