{"id":5196,"date":"2021-05-10T09:32:36","date_gmt":"2021-05-10T04:02:36","guid":{"rendered":"https:\/\/python-programs.com\/?p=5196"},"modified":"2021-11-22T18:45:28","modified_gmt":"2021-11-22T13:15:28","slug":"pandas-how-to-merge-dataframes-by-index-using-dataframe-merge","status":"publish","type":"post","link":"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-by-index-using-dataframe-merge\/","title":{"rendered":"Pandas : How to merge Dataframes by index using Dataframe.merge()"},"content":{"rendered":"<h2>How to merge Dataframes by index using Dataframe.merge() in Python ?<\/h2>\n<p>In this article we are going to see how we can merge two dataframes by using index of both the dataframes or by suing index of one dataframe and some columns of the other dataframe, and how we can keep a merged dataframe with similar indices. So, let&#8217;s start the exploring the topic.<\/p>\n<h3>DataFrame.merge()<\/h3>\n<p>SYNTAX :<\/p>\n<pre>DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, index_left=False, index_right=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)<\/pre>\n<p>For this short example, we will only focus on some arguments :<\/p>\n<ul>\n<li><strong>On :<\/strong> This is the column name on which the merge is supposed to be done.<\/li>\n<li><strong>Left_on :<\/strong> The column names of the left dataframe which are to be merged<\/li>\n<li><strong>Right_on :<\/strong> The column names of the right dataframe which are to be merged<\/li>\n<li><strong>index_left :<\/strong> It takes a boolean value whose default values is false. If it is true then it will choose indices from the left dataframe as join key.<\/li>\n<li><strong>index_right :<\/strong> It takes a boolean value whose default values is false. If it is true then it will choose indices from the right dataframe as join key.<\/li>\n<\/ul>\n<p>To demonstrate we will be taking the following two dataframes :<\/p>\n<p>Left dataframe :<\/p>\n<pre>  \u00a0 \u00a0Regd\u00a0\u00a0\u00a0\u00a0 Name\u00a0 \u00a0 Age\u00a0 \u00a0 \u00a0 \u00a0 City\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Exp\r\n0\u00a0\u00a0\u00a0 10\u00a0 \u00a0 \u00a0 \u00a0 Jill\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 16.0\u00a0\u00a0\u00a0\u00a0 Tokyo\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 10\r\n1\u00a0\u00a0\u00a0 11\u00a0 \u00a0 \u00a0 \u00a0 Rachel\u00a0 \u00a0 38.0\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a05\r\n2\u00a0\u00a0\u00a0 12\u00a0 \u00a0 \u00a0 \u00a0 Kirti\u00a0 \u00a0 \u00a0 \u00a0 39.0\u00a0 \u00a0 \u00a0New York\u00a0 \u00a0 \u00a07\r\n3\u00a0\u00a0\u00a0 13\u00a0 \u00a0 \u00a0 \u00a0 Veena\u00a0 \u00a0 \u00a040.0\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 21\r\n4\u00a0\u00a0\u00a0 14\u00a0 \u00a0 \u00a0 \u00a0 Lucifer\u00a0 \u00a0 NaN\u00a0\u00a0\u00a0\u00a0Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 30\r\n5\u00a0\u00a0\u00a0 15\u00a0 \u00a0 \u00a0 \u00a0 Pablo\u00a0 \u00a0 \u00a0 30.0\u00a0 \u00a0 New York\u00a0 \u00a0 \u00a07\r\n6\u00a0\u00a0\u00a0 16\u00a0 \u00a0 \u00a0 \u00a0 Lionel\u00a0 \u00a0 \u00a045.0\u00a0 \u00a0 Colombia\u00a0 \u00a0 11<\/pre>\n<p>Right dataframe :<\/p>\n<pre>  \u00a0 \u00a0Regd\u00a0\u00a0\u00a0\u00a0 Exp\u00a0 \u00a0 \u00a0 \u00a0Wage\u00a0 \u00a0 \u00a0Bonus\r\n0\u00a0\u00a0\u00a0 10\u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a075000\u00a0 \u00a0 \u00a0 2000\r\n1\u00a0\u00a0\u00a0 11\u00a0 \u00a0 \u00a0 \u00a0 Senior\u00a0 \u00a072200\u00a0 \u00a0 \u00a01000\r\n2\u00a0\u00a0\u00a0 12\u00a0 \u00a0 \u00a0 \u00a0 Expert\u00a0 \u00a090999\u00a0 \u00a0 \u00a01100\r\n3\u00a0\u00a0\u00a0 13\u00a0 \u00a0 \u00a0 \u00a0 Expert\u00a0 \u00a090000\u00a0 \u00a0 \u00a01000\r\n4\u00a0\u00a0\u00a0 14\u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a020000\u00a0 \u00a0 \u00a0 2000\r\n5\u00a0\u00a0\u00a0 15\u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a050000\u00a0 \u00a0 \u00a0 1500\r\n6\u00a0\u00a0\u00a0 16\u00a0 \u00a0 \u00a0 \u00a0 Senior\u00a0 \u00a081000\u00a0 \u00a0 \u00a01000<\/pre>\n<h3>Merging two Dataframes by index of both the dataframes :<\/h3>\n<p>Here you might have noticed we have a common column named \u2018Regd\u2019 . So we can merge both the dataframes by passing left_index and right_index as true in the function.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"># Program :\r\n\r\n\r\n# Importing the module\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n#Left Dataframe\r\nstudents = [(10,'Jill',    16,     'Tokyo',    10),\r\n            (11,'Rachel',  38,     'Texas',     5),\r\n            (12,'Kirti',   39,     'New York',  7),\r\n            (13,'Veena',   40,     'Texas',    21),\r\n            (14,'Lucifer', np.NaN, 'Texas',    30),\r\n            (15,'Pablo',   30,     'New York',  7),\r\n            (16,'Lionel',  45,     'Colombia', 11) ]\r\nlDfObj = pd.DataFrame(students, columns=['Regd','Name','Age','City','Exp'],index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\r\n\r\n#Right dataframe\r\nwage = [(10, 'Junior', 75000, 2000) ,\r\n        (11, 'Senior', 72200, 1000) ,\r\n        (12, 'Expert', 90999, 1100) ,\r\n        (13, 'Expert', 90000, 1000) ,\r\n        (14, 'Junior', 20000, 2000) ,\r\n        (15, 'Junior', 50000, 1500) ,\r\n        (16, 'Senior', 81000, 1000)]\r\nrDfObj = pd.DataFrame(wage, columns=['Regd','Exp','Wage','Bonus'] , index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\r\n\r\n#Merging both the dataframes\r\nnewDF = lDfObj.merge(rDfObj, left_index=True, right_index=True)\r\n\r\n#printing the merged dataframe\r\nprint(\"The merged dataframe is-\")\r\nprint(newDF)\r\n\r\n<\/pre>\n<pre>Output :\r\nThe merged dataframe is-\r\n  \u00a0 \u00a0Regd_x\u00a0 \u00a0 Name\u00a0 \u00a0 \u00a0Age\u00a0 \u00a0 \u00a0 \u00a0City\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Exp_x\u00a0  Regd_y\u00a0\u00a0  Exp_y\u00a0 \u00a0 \u00a0 Wage\u00a0 \u00a0 \u00a0Bonus\r\na\u00a0\u00a0\u00a0\u00a0\u00a0 10\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Jill\u00a0 \u00a0 \u00a0 \u00a0 \u00a016.0\u00a0 \u00a0 \u00a0 Tokyo\u00a0 \u00a0 \u00a0 \u00a0 \u00a010\u00a0\u00a0\u00a0\u00a0\u00a0   10\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a0 \u00a075000\u00a0 \u00a0 \u00a0 2000\r\nb\u00a0\u00a0\u00a0\u00a0\u00a0 11\u00a0 \u00a0 \u00a0 \u00a0 Rachel\u00a0 \u00a038.0\u00a0 \u00a0 \u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a05\u00a0 \u00a0 \u00a0 \u00a0  11\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Senior\u00a0 \u00a0 \u00a072200\u00a0 \u00a0 \u00a0 1000\r\nc\u00a0\u00a0\u00a0\u00a0\u00a0 12\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Kirti\u00a0 \u00a0 \u00a0 \u00a039.0\u00a0 \u00a0 \u00a0 New York\u00a0 \u00a0 7\u00a0 \u00a0 \u00a0 \u00a0  12\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Expert\u00a0 \u00a0 90999\u00a0 \u00a0 \u00a01100\r\nd\u00a0\u00a0\u00a0\u00a0\u00a0 13\u00a0 \u00a0 \u00a0 \u00a0 Veena\u00a0 \u00a0 40.0\u00a0 \u00a0 \u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a021\u00a0\u00a0\u00a0\u00a0\u00a0  13\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Expert\u00a0 \u00a0 \u00a090000\u00a0 \u00a0 \u00a0 1000\r\ne\u00a0\u00a0\u00a0\u00a0\u00a0 14\u00a0 \u00a0 \u00a0 \u00a0 Lucifer\u00a0 \u00a0 NaN\u00a0\u00a0\u00a0\u00a0 Texas\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a030\u00a0\u00a0\u00a0\u00a0\u00a0  14\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a0 \u00a020000\u00a0 \u00a0 \u00a0 2000\r\nf\u00a0\u00a0\u00a0\u00a0\u00a0 15\u00a0 \u00a0 \u00a0 \u00a0 \u00a0Pablo\u00a0 \u00a0 \u00a0 30.0\u00a0 \u00a0 \u00a0 New York\u00a0 \u00a0 7\u00a0 \u00a0 \u00a0 \u00a0  15\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Junior\u00a0 \u00a0 \u00a050000\u00a0 \u00a0 \u00a0 1500\r\ng\u00a0\u00a0\u00a0\u00a0\u00a0 16\u00a0 \u00a0 \u00a0 \u00a0Lionel\u00a0 \u00a0 \u00a0 45.0\u00a0 \u00a0 \u00a0 Colombia\u00a0 \u00a0 11\u00a0\u00a0\u00a0\u00a0\u00a0  16\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Senior\u00a0 \u00a0 \u00a081000\u00a0 \u00a0 \u00a0 1000<\/pre>\n<p>Finally, if we want to merge a dataframe by the index of the first dataframe with some column from the other dataframe, we can also do that.<\/p>\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-how-to-merge-dataframes-using-dataframe-merge-in-python-part-1\/\">How to merge Dataframes using Dataframe.merge() in Python?<\/a><\/li>\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\/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>How to merge Dataframes by index using Dataframe.merge() in Python ? In this article we are going to see how we can merge two dataframes by using index of both the dataframes or by suing index of one dataframe and some columns of the other dataframe, and how we can keep a merged dataframe with &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/pandas-how-to-merge-dataframes-by-index-using-dataframe-merge\/\"> <span class=\"screen-reader-text\">Pandas : How to merge Dataframes by index using Dataframe.merge()<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[5],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas : How to merge Dataframes by index using Dataframe.merge() - 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-by-index-using-dataframe-merge\/\" \/>\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 by index using Dataframe.merge() - Python Programs\" \/>\n<meta property=\"og:description\" content=\"How to merge Dataframes by index using Dataframe.merge() in Python ? 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