{"id":8634,"date":"2021-06-12T10:55:41","date_gmt":"2021-06-12T05:25:41","guid":{"rendered":"https:\/\/python-programs.com\/?p=8634"},"modified":"2021-11-22T18:40:39","modified_gmt":"2021-11-22T13:10:39","slug":"pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1","status":"publish","type":"post","link":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/","title":{"rendered":"Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1"},"content":{"rendered":"<h2>Merging Dataframes using Dataframe.merge() in Python<\/h2>\n<p>In this article, we will learn to merge two different DataFrames into a single one using function <code>Dataframe.merge()<\/code>.<\/p>\n<h3>Dataframe.merge() :<\/h3>\n<p>Dataframe class of Python&#8217;s Pandas library provide a function i.e. <code>merge()<\/code> which helps in merging of two DataFrames.<\/p>\n<pre><u>Syntax<\/u>:- DataFrame.merge(right, how='inner', on=None, leftOn=None, rightOn=None, left_index=False, right_index=False, sort=False, suffix=('_x', '_y'), copy=True, indicate=False, validate=None)<\/pre>\n<p><strong>Arguments:-<\/strong><\/p>\n<ul>\n<li><strong>right :<\/strong> A dataframe that is to be merged with the calling dataframe.<\/li>\n<li><strong>how :<\/strong> (Merge type). Some values are : left, right, outer, inner. It&#8217;s default value is \u2018inner\u2019. If the two dataframes contains different columns, then based <em>how<\/em> value, columns will be considered accordingly for merging.<\/li>\n<li><strong>on :<\/strong> It is the column name on which merge will be done. If not provided then merged done on basis of indexes.<\/li>\n<li><strong>left_on :<\/strong> Column in left dataframe where merging is to be done.<\/li>\n<li><strong>right_on :<\/strong> Column in right datframe, where merging is to be done.<\/li>\n<li><strong>left_index :<\/strong> (bool), default is False (If found True index index from left dataframe selected as join key)<\/li>\n<li><strong>right_index :<\/strong> (bool), default is False (If found True index index from right dataframe selecte as join key)<\/li>\n<li><strong>suffixes :<\/strong> tuple of (str, str), default (\u2018_x\u2019, \u2018_y\u2019)<\/li>\n<li>Suffix that is to be applied on overlapping columns in left and right dataframes respectively.<\/li>\n<\/ul>\n<p>Let&#8217;s see one by one<\/p>\n<ul>\n<li><a href=\"#Merge_DataFrames_on_common_columns_(Default_Inner_Join)\">Merge DataFrames on common columns (Default Inner Join)<\/a><\/li>\n<li><a href=\"#Merge_Dataframes_using_Left_Join\">Merge Dataframes using Left Join<\/a><\/li>\n<li><a href=\"#Merge_DataFrames_using_Right_Join\">Merge DataFrames using Right Join<\/a><\/li>\n<li><a href=\"#Merge_DataFrames_using_Outer_Join\">Merge DataFrames using Outer Join<\/a><\/li>\n<\/ul>\n<h3><a id=\"Merge_DataFrames_on_common_columns_(Default_Inner_Join)\"><\/a>Merge DataFrames on common columns (Default Inner Join) :<\/h3>\n<p>If we have two DataFrames of two common columns, by directly calling <code>merge()<\/code> \u00a0function the two columns will be merged considering common columns as join keys and the dissimilar columns would just be copied from one dataframe to another dataframe.<\/p>\n<p>Let&#8217;s see the below program to understand it clearly.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as sc\r\n# List of Tuples\r\nplayers = [(15,'Smith','Pune', 17,12000),\r\n            (99,'Rana', 'Mumbai', 20,2000),\r\n            (51,'Jaydev','Kolkata', 22,25640),\r\n            (31,'Shikhar','Hyderabad', 28,85410),\r\n            (12,'Sanju','Rajasthan', 21,63180),\r\n            (35,'Raina','Gujarat', 18,62790)\r\n            ]\r\n# Creation of DataFrame object\r\nplayDFObj = sc.DataFrame(players, columns=['JersyN','Name', 'Team', 'Age','Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 1 : ')\r\nprint(playDFObj)\r\nmoreInfo = [(15, 13, 180000, 12000) ,\r\n           (99, 2, 195200, 2000) ,\r\n           (51, 7, 15499, 25640) ,\r\n           (31, 17, 654000, 85410) ,\r\n           (12, 5, 201000, 63180) ,\r\n           (35, 14, 741000, 62790)\r\n            ]\r\n# Creation of DataFrame object\r\nmoreinfoObj = sc.DataFrame(moreInfo, columns=['JersyN', 'PLayingSince' , 'Salary', 'Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 2 : ')\r\nprint(moreinfoObj)\r\n# Merge two Dataframes on basis of common column by default INNER JOIN\r\nmergedDataf = playDFObj.merge(moreinfoObj)\r\nprint(mergedDataf)\r\n<\/pre>\n<pre>Output :\r\nDataFrame 1 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0\u00a0 12000\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2000\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0 25640\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0 85410\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0 63180\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0\u00a0 62790\r\nDataFrame 2 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0 PLayingSince\u00a0 Salary\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\u00a0\u00a0\u00a0\u00a0\u00a0 12000\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00a0 195200\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2000\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\u00a0\u00a0\u00a0\u00a0\u00a0 25640\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000\u00a0\u00a0\u00a0\u00a0\u00a0 85410\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\u00a0\u00a0\u00a0\u00a0\u00a0 63180\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000\u00a0\u00a0\u00a0\u00a0\u00a0 62790\r\n \u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age\u00a0 Sponsered\u00a0 PLayingSince\u00a0 Salary\r\n0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0\u00a0 12000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\r\n1\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00a0 195200\r\n2\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0 25640\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\r\n3\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0 85410\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000\r\n4\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0 63180\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\r\n5\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0\u00a0 62790\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000<\/pre>\n<h3>What is Inner Join ?<\/h3>\n<p>In above case, inner join occured for key columns i.e. &#8216;JersyN&#8217; &amp; &#8216;Sponsered&#8217;. During inner join the common columns of two dataframes are picked and merged. We can also explicitly do inner join by passing <code>how<\/code> argument with values as <code>inner<\/code>. After implementing both the cases will have same result.<\/p>\n<h3><a id=\"Merge_Dataframes_using_Left_Join\"><\/a>Merge Dataframes using Left Join :<\/h3>\n<h4>What is left join ?<\/h4>\n<p>While merging columns we can include all rows from left DataFrame and NaN from which values are missing in right DataFrame.<\/p>\n<p>Let&#8217;s see the below program to understand it clearly.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as sc\r\n# List of Tuples\r\nplayers = [(15,'Smith','Pune', 17,12000),\r\n            (99,'Rana', 'Mumbai', 20,2000),\r\n            (51,'Jaydev','Kolkata', 22,25640),\r\n            (31,'Shikhar','Hyderabad', 28,85410),\r\n            (12,'Sanju','Rajasthan', 21,63180),\r\n            (35,'Raina','Gujarat', 18,62790)\r\n            ]\r\n# Creation of DataFrame object\r\nplayDFObj = sc.DataFrame(players, columns=['JersyN','Name', 'Team', 'Age','Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 1 : ')\r\nprint(playDFObj)\r\nmoreInfo = [(15, 13, 180000, 12000) ,\r\n           (99, 2, 2000) ,\r\n           (51, 7, 15499, 25640) ,\r\n           (31, 17, 654000) ,\r\n           (12, 5, 201000, 63180) ,\r\n           (35, 14, 741000, 62790)\r\n            ]\r\n# Creation of DataFrame object\r\nmoreinfoObj = sc.DataFrame(moreInfo, columns=['JersyN', 'PLayingSince' , 'Salary', 'Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 2 : ')\r\nprint(moreinfoObj)\r\n# Merge two Dataframes on basis of common column by default INNER JOIN\r\nmergedDataf = playDFObj.merge(moreinfoObj, how='left')\r\nprint('After merging: ')\r\nprint(mergedDataf)\r\n<\/pre>\n<pre>Output :\r\nDataFrame 1 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0\u00a0 12000\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2000\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0 25640\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0 85410\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0 63180\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0\u00a0 62790\r\nDataFrame 2 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0 PLayingSince\u00a0 Salary\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\u00a0\u00a0\u00a0 12000.0\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00a0\u00a0\u00a0 2000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\u00a0\u00a0\u00a0 25640.0\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\u00a0\u00a0\u00a0 63180.0\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000\u00a0\u00a0\u00a0 62790.0\r\nAfter merging:\r\n \u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age Sponsered\u00a0 PLayingSince\u00a0\u00a0\u00a0 Salary\r\n0 \u00a0\u00a0\u00a0\u00a0\u00a015\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0 12000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13.0\u00a0 180000.0\r\n1\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0 2000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\n2\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0 25640\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7.0\u00a0\u00a0 15499.0\r\n3\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0 85410\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\n4\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0 63180\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5.0\u00a0 201000.0\r\n5\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0 62790\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14.0\u00a0 741000.0<\/pre>\n<h3><a id=\"Merge_DataFrames_using_Right_Join\"><\/a>Merge DataFrames using Right Join :<\/h3>\n<h4>What is Right join ?<\/h4>\n<p>While merging columns we can include all rows from right DataFrame and NaN from which values are missing in left DataFrame.<\/p>\n<p>Let&#8217;s see the below program to understand it clearly.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as sc\r\n# List of Tuples\r\nplayers = [(15,'Smith','Pune', 17,12000),\r\n            (99,'Rana', 'Mumbai', 20,2000),\r\n            (51,'Jaydev','Kolkata', 22,25640),\r\n            (31,'Shikhar','Hyderabad', 28,85410),\r\n            (12,'Sanju','Rajasthan', 21,63180),\r\n            (35,'Raina','Gujarat', 18,62790)\r\n            ]\r\n# Creation of DataFrame object\r\nplayDFObj = sc.DataFrame(players, columns=['JersyN','Name', 'Team', 'Age','Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 1 : ')\r\nprint(playDFObj)\r\nmoreInfo = [(15, 13, 180000, 12000) ,\r\n           (99, 2, 2000) ,\r\n           (51, 7, 15499, 25640) ,\r\n           (31, 17, 654000) ,\r\n           (12, 5, 201000, 63180) ,\r\n           (35, 14, 741000, 62790)\r\n            ]\r\n# Creation of DataFrame object\r\nmoreinfoObj = sc.DataFrame(moreInfo, columns=['JersyN', 'PLayingSince' , 'Salary', 'Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 2 : ')\r\nprint(moreinfoObj)\r\n# Merge two Dataframes on basis of common column by default INNER JOIN\r\nmergedDataf = playDFObj.merge(moreinfoObj, how='right')\r\nprint('After merging: ')\r\nprint(mergedDataf)\r\n<\/pre>\n<pre>Output :\r\nDataFrame 1 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0\u00a0 12000\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a02000\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0 25640\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0 85410\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0 63180\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0\u00a0 62790\r\nDataFrame 2 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0 PLayingSince\u00a0 Salary\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\u00a0\u00a0\u00a0 12000.0\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00a0\u00a0\u00a0 2000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\u00a0\u00a0\u00a0 25640.0\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\u00a0\u00a0\u00a0 63180.0\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000\u00a0\u00a0\u00a0 62790.0\r\nAfter merging:\r\n \u00a0 JersyN\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0\u00a0 Age\u00a0 Sponsered\u00a0 PLayingSince\u00a0 Salary\r\n0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0 17.0\u00a0\u00a0\u00a0 12000.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\r\n1\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0 22.0\u00a0\u00a0\u00a0 25640.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\r\n2\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0 21.0\u00a0\u00a0\u00a0 63180.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\r\n3\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0 18.0\u00a0\u00a0\u00a0 62790.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000\r\n4\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a02\u00a0\u00a0\u00a0 2000\r\n5\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000<\/pre>\n<h3><a id=\"Merge_DataFrames_using_Outer_Join\"><\/a>Merge DataFrames using Outer Join :<\/h3>\n<h4>What is Outer join ?<\/h4>\n<p>While merging columns of two dataframes, we can even include all rows of two DataFrames and add NaN for the values missing in left or right DataFrame.<\/p>\n<p>Let&#8217;s see the below program to understand it clearly.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import pandas as sc\r\n# List of Tuples\r\nplayers = [(15,'Smith','Pune', 17,12000),\r\n            (99,'Rana', 'Mumbai', 20,2000),\r\n            (51,'Jaydev','Kolkata', 22,25640),\r\n            (31,'Shikhar','Hyderabad', 28,85410),\r\n            (12,'Sanju','Rajasthan', 21,63180),\r\n            (35,'Raina','Gujarat', 18,62790)\r\n            ]\r\n# Creation of DataFrame object\r\nplayDFObj = sc.DataFrame(players, columns=['JersyN','Name', 'Team', 'Age','Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 1 : ')\r\nprint(playDFObj)\r\nmoreInfo = [(15, 13, 180000, 12000) ,\r\n           (99, 2, 2000) ,\r\n           (51, 7, 15499, 25640) ,\r\n           (31, 17, 654000) ,\r\n           (12, 5, 201000, 63180) ,\r\n           (35, 14, 741000, 62790)\r\n            ]\r\n# Creation of DataFrame object\r\nmoreinfoObj = sc.DataFrame(moreInfo, columns=['JersyN', 'PLayingSince' , 'Salary', 'Sponsered'], index=['I', 'II', 'III', 'IV', 'V', 'VI'])\r\nprint('DataFrame 2 : ')\r\nprint(moreinfoObj)\r\n# Merge two Dataframes on basis of common column by default INNER JOIN\r\nmergedDataf = playDFObj.merge(moreinfoObj, how='outer')\r\nprint('After merging: ')\r\nprint(mergedDataf)\r\n<\/pre>\n<pre>Output :\r\nDataFrame 1 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0 Age\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a015\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0\u00a0 17\u00a0\u00a0\u00a0\u00a0\u00a0 12000\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2000\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0 25640\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0 85410\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0 63180\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0\u00a0 18\u00a0\u00a0\u00a0\u00a0\u00a0 62790\r\nDataFrame 2 :\r\n \u00a0\u00a0\u00a0 JersyN\u00a0 PLayingSince\u00a0 Salary\u00a0 Sponsered\r\nI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 180000\u00a0\u00a0\u00a0 12000.0\r\nII\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2\u00a0\u00a0\u00a0 2000\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\nIII\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7\u00a0\u00a0 15499\u00a0\u00a0\u00a0 25640.0\r\nIV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17\u00a0 654000\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0NaN\r\nV\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5\u00a0 201000\u00a0\u00a0\u00a0 63180.0\r\nVI\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 741000\u00a0\u00a0\u00a0 62790.0\r\nAfter merging:\r\n \u00a0 JersyN\u00a0\u00a0\u00a0\u00a0 Name\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Team\u00a0\u00a0 Age\u00a0 Sponsered\u00a0 PLayingSince\u00a0\u00a0\u00a0 Salary\r\n0\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0\u00a0\u00a0 Smith\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Pune\u00a0 17.0\u00a0\u00a0\u00a0 12000.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 13.0\u00a0 180000.0\r\n1\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0 Rana\u00a0\u00a0\u00a0\u00a0 Mumbai\u00a0 20.0\u00a0\u00a0\u00a0\u00a0 2000.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\n2\u00a0\u00a0\u00a0\u00a0\u00a0 51\u00a0\u00a0 Jaydev\u00a0\u00a0\u00a0 Kolkata\u00a0 22.0\u00a0\u00a0\u00a0 25640.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7.0\u00a0\u00a0 15499.0\r\n3\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0 Shikhar\u00a0 Hyderabad\u00a0 28.0\u00a0\u00a0\u00a0 85410.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\r\n4\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0\u00a0\u00a0 Sanju\u00a0 Rajasthan\u00a0 21.0\u00a0\u00a0\u00a0 63180.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5.0\u00a0 201000.0\r\n5\u00a0\u00a0\u00a0\u00a0\u00a0 35\u00a0\u00a0\u00a0 Raina\u00a0\u00a0\u00a0 Gujarat\u00a0 18.0\u00a0\u00a0\u00a0 62790.0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 14.0\u00a0 741000.0\r\n6\u00a0\u00a0\u00a0\u00a0\u00a0 99\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.0\u00a0\u00a0\u00a0 2000.0\r\n7\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 NaN\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 17.0\u00a0 654000.0<\/pre>\n<p>Want to expert in the python programming language? Exploring\u00a0<a href=\"https:\/\/python-programs.com\/python-data-analysis-using-pandas\/\">Python Data Analysis using Pandas<\/a>\u00a0tutorial changes your knowledge from basic to advance level in python concepts.<\/p>\n<p><strong>Read more Articles on Python Data Analysis Using Padas<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/python-programs.com\/pandas-merge-dataframes-on-specific-columns-or-on-index-in-python-part-2\/\">How to merge Dataframes on specific columns or on index in Python?<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-by-index-using-dataframe-merge\/\">How to merge Dataframes by index using Dataframe.merge()?<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/python-count-nan-and-missing-values-in-dataframe-using-pandas\/\">Count NaN or missing values in DataFrame<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/pandas-count-rows-in-a-dataframe-all-or-those-only-that-satisfy-a-condition\/\">Count rows in a dataframe | all or those only that satisfy a condition<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/pandas-6-different-ways-to-iterate-over-rows-in-a-dataframe-update-while-iterating-row-by-row\/\">6 Different ways to iterate over rows in a Dataframe &amp; Update while iterating row by row<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/pandas-loop-or-iterate-over-all-or-certain-columns-of-a-dataframe\/\">Loop or Iterate over all or certain columns of a DataFrame<\/a><\/li>\n<li><a href=\"https:\/\/python-programs.com\/python-pandas-how-to-display-full-dataframe-i-e-print-all-rows-columns-without-truncation\/\">How to display full Dataframe i.e. print all rows &amp; columns without truncation<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Merging Dataframes using Dataframe.merge() in Python In this article, we will learn to merge two different DataFrames into a single one using function Dataframe.merge(). Dataframe.merge() : Dataframe class of Python&#8217;s Pandas library provide a function i.e. merge() which helps in merging of two DataFrames. Syntax:- DataFrame.merge(right, how=&#8217;inner&#8217;, on=None, leftOn=None, rightOn=None, left_index=False, right_index=False, sort=False, suffix=(&#8216;_x&#8217;, &#8216;_y&#8217;), &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/\"> <span class=\"screen-reader-text\">Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":9,"featured_media":8641,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[5],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 - Python Programs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 - Python Programs\" \/>\n<meta property=\"og:description\" content=\"Merging Dataframes using Dataframe.merge() in Python In this article, we will learn to merge two different DataFrames into a single one using function Dataframe.merge(). Dataframe.merge() : Dataframe class of Python&#8217;s Pandas library provide a function i.e. merge() which helps in merging of two DataFrames. Syntax:- DataFrame.merge(right, how=&#039;inner&#039;, on=None, leftOn=None, rightOn=None, left_index=False, right_index=False, sort=False, suffix=(&#039;_x&#039;, &#039;_y&#039;), &hellip; Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 Read More &raquo;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/\" \/>\n<meta property=\"og:site_name\" content=\"Python Programs\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/btechgeeks\" \/>\n<meta property=\"article:published_time\" content=\"2021-06-12T05:25:41+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-11-22T13:10:39+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"720\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@btech_geeks\" \/>\n<meta name=\"twitter:site\" content=\"@btech_geeks\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Satyabrata Jena\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Organization\",\"@id\":\"https:\/\/python-programs.com\/#organization\",\"name\":\"BTech Geeks\",\"url\":\"https:\/\/python-programs.com\/\",\"sameAs\":[\"https:\/\/www.instagram.com\/btechgeeks\/\",\"https:\/\/www.linkedin.com\/in\/btechgeeks\",\"https:\/\/in.pinterest.com\/btechgeek\/\",\"https:\/\/www.youtube.com\/channel\/UC9MlCqdJ3lKqz2p5114SDIg\",\"https:\/\/www.facebook.com\/btechgeeks\",\"https:\/\/twitter.com\/btech_geeks\"],\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/python-programs.com\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/python-programs.com\/wp-content\/uploads\/2020\/11\/BTechGeeks.png\",\"contentUrl\":\"https:\/\/python-programs.com\/wp-content\/uploads\/2020\/11\/BTechGeeks.png\",\"width\":350,\"height\":70,\"caption\":\"BTech Geeks\"},\"image\":{\"@id\":\"https:\/\/python-programs.com\/#\/schema\/logo\/image\/\"}},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/python-programs.com\/#website\",\"url\":\"https:\/\/python-programs.com\/\",\"name\":\"Python Programs\",\"description\":\"Python Programs with Examples, How To Guides on Python\",\"publisher\":{\"@id\":\"https:\/\/python-programs.com\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/python-programs.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/#primaryimage\",\"url\":\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png\",\"contentUrl\":\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png\",\"width\":1280,\"height\":720},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/#webpage\",\"url\":\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/\",\"name\":\"Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 - 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Syntax:- DataFrame.merge(right, how='inner', on=None, leftOn=None, rightOn=None, left_index=False, right_index=False, sort=False, suffix=('_x', '_y'), &hellip; Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 Read More &raquo;","og_url":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/","og_site_name":"Python Programs","article_publisher":"https:\/\/www.facebook.com\/btechgeeks","article_published_time":"2021-06-12T05:25:41+00:00","article_modified_time":"2021-11-22T13:10:39+00:00","og_image":[{"width":1280,"height":720,"url":"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_creator":"@btech_geeks","twitter_site":"@btech_geeks","twitter_misc":{"Written by":"Satyabrata Jena","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Organization","@id":"https:\/\/python-programs.com\/#organization","name":"BTech Geeks","url":"https:\/\/python-programs.com\/","sameAs":["https:\/\/www.instagram.com\/btechgeeks\/","https:\/\/www.linkedin.com\/in\/btechgeeks","https:\/\/in.pinterest.com\/btechgeek\/","https:\/\/www.youtube.com\/channel\/UC9MlCqdJ3lKqz2p5114SDIg","https:\/\/www.facebook.com\/btechgeeks","https:\/\/twitter.com\/btech_geeks"],"logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/python-programs.com\/#\/schema\/logo\/image\/","url":"https:\/\/python-programs.com\/wp-content\/uploads\/2020\/11\/BTechGeeks.png","contentUrl":"https:\/\/python-programs.com\/wp-content\/uploads\/2020\/11\/BTechGeeks.png","width":350,"height":70,"caption":"BTech Geeks"},"image":{"@id":"https:\/\/python-programs.com\/#\/schema\/logo\/image\/"}},{"@type":"WebSite","@id":"https:\/\/python-programs.com\/#website","url":"https:\/\/python-programs.com\/","name":"Python Programs","description":"Python Programs with Examples, How To Guides on Python","publisher":{"@id":"https:\/\/python-programs.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/python-programs.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/#primaryimage","url":"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png","contentUrl":"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/06\/Pandas-How-to-Merge-Dataframes-using-Dataframe.merge-in-Python-\u2013-Part-1.png","width":1280,"height":720},{"@type":"WebPage","@id":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/#webpage","url":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/","name":"Pandas : How to Merge Dataframes using Dataframe.merge() in Python \u2013 Part 1 - 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