Concatenation simply means combining or putting together entities. Concatenation, in the context of pandas, refers to the process of combining two or more DataFrames along either the rows or columns axis. <\/strong>It allows us to merge datasets with similar or different structures, creating a unified DataFrame that can be easily analyzed and manipulated.<\/p>\n\n\n\n Let us now explore the various techniques we can use to concat two or more DataFrames.<\/p>\n\n\n\n We can concat the DataFrames along the rows. One way to concatenate DataFrames is by stacking them vertically along the rows axis. <\/strong>We can do this by using the pd.concat()<\/strong> function in pandas. We set the axis to 0.<\/p>\n\n\n\n Let’s see how to concatenate along rows with an example:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n We can also concatenate our DataFrames along the columns, just like we did with the rows.<\/p>\n\n\n\n We can concatenate DataFrames horizontally along the columns axis. This can be useful when we have DataFrames with different columns but the same index values. We set the axis to 1. The missing values will be replaced by Nan values. To learn how to handle the missing values refer to pandas-fillna<\/strong><\/a>.<\/p>\n\n\n\n Here is the Python code to do it:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n In addition to the pd.concat() function, pandas provide an easy shortcut for concatenating DataFrames using the append() method. The append() method can be used to append one or more DataFrames to another DataFrame.<\/p>\n\n\n\n Let us see an example:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n Another method to cancatenate DataFrames is by using the joins. When joining DataFrames, we have a lot of types of joins available to us. The type of join determines how the rows from the original DataFrames will be combined in the resulting DataFrame. <\/strong><\/p>\n\n\n\n The common types of joins are:<\/p>\n\n\n\n Let us see how to implement it in Python:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n In this article, we have discovered various methods to concat or join two or more Pandas DataFrames. Remember to experiment with different concatenation techniques, so that you will have the flexibility and power to merge and analyze datasets with ease. You can now move in to learn how to iterate over rows in Pandas<\/a>, which is also important to learn for beginners.<\/p>\n","protected":false},"excerpt":{"rendered":" Learn how to concatenate two or more dataframes in pandas along rows and columns with the code to implement it.<\/p>\n","protected":false},"author":10,"featured_media":717,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":null,"jnews_primary_category":{"id":"","hide":""},"footnotes":""},"categories":[35],"tags":[37,54],"class_list":["post-716","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-pandas","tag-pandas-dataframe"],"_links":{"self":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/716","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/comments?post=716"}],"version-history":[{"count":2,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/716\/revisions"}],"predecessor-version":[{"id":822,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/posts\/716\/revisions\/822"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media\/717"}],"wp:attachment":[{"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/media?parent=716"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/categories?post=716"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/favtutor.com\/articles\/wp-json\/wp\/v2\/tags?post=716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Concatenate along Rows<\/strong><\/h3>\n\n\n\n
import pandas as pd\nimport numpy as np\n\n# Create the DataFrames\ndf1 = pd.DataFrame(np.random.randint(25, size=(4, 4)), index=["1", "2", "3", "4"], columns=["A", "B", "C", "D"])\ndf2 = pd.DataFrame(np.random.randint(25, size=(6, 4)), index=["5", "6", "7", "8", "9", "10"], columns=["A", "B", "C", "D"])\n\n# Display the original DataFrames\nprint('DataFrame 1:\\n', df1)\nprint('DataFrame 2:\\n', df2)\n\n# Concat along rows\ndf = pd.concat([df1, df2], axis=0)\n\n# Display the concated DataFrame\nprint('Concated DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nDataFrame 1:\n A B C D\n1 13 21 22 24\n2 0 8 16 8\n3 21 16 1 21\n4 10 14 19 17\n\nDataFrame 2:\n A B C D\n5 24 17 10 0\n6 0 8 5 4\n7 16 17 22 6\n8 18 21 5 10\n9 2 23 4 16\n10 15 7 0 2\n\nConcated DataFrame:\n A B C D\n1 13 21 22 24\n2 0 8 16 8\n3 21 16 1 21\n4 10 14 19 17\n5 24 17 10 0\n6 0 8 5 4\n7 16 17 22 6\n8 18 21 5 10\n9 2 23 4 16\n10 15 7 0 2\n<\/code><\/pre>\n\n\n\nConcatenate Along Columns<\/strong><\/h3>\n\n\n\n
import pandas as pd\nimport numpy as np\n\n# Create the DataFrames\ndf1 = pd.DataFrame(np.random.randint(25, size=(4, 4)), index=["1", "2", "3", "4"], columns=["A", "B", "C", "D"])\ndf2 = pd.DataFrame(np.random.randint(25, size=(6, 4)), index=["5", "6", "7", "8", "9", "10"], columns=["A", "B", "C", "D"])\n\n# Display the original DataFrames\nprint('DataFrame 1:\\n', df1)\nprint('DataFrame 2:\\n', df2)\n\n# Concat along collumns\ndf = pd.concat([df1, df2], axis=1)\n\n# Display the concated DataFrame\nprint('Concated DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nDataFrame 1:\n A B C D\n1 5 15 1 22\n2 7 10 0 2\n3 11 4 8 13\n4 15 18 18 4\n\nDataFrame 2:\n A B C D\n5 15 1 10 19\n6 2 12 16 10\n7 15 20 8 2\n8 17 13 1 10\n9 7 5 9 16\n10 2 20 13 9\n\nConcated DataFrame:\n A B C D A B C D\n1 5.0 15.0 1.0 22.0 NaN NaN NaN NaN\n2 7.0 10.0 0.0 2.0 NaN NaN NaN NaN\n3 11.0 4.0 8.0 13.0 NaN NaN NaN NaN\n4 15.0 18.0 18.0 4.0 NaN NaN NaN NaN\n5 NaN NaN NaN NaN 15.0 1.0 10.0 19.0\n6 NaN NaN NaN NaN 2.0 12.0 16.0 10.0\n7 NaN NaN NaN NaN 15.0 20.0 8.0 2.0\n8 NaN NaN NaN NaN 17.0 13.0 1.0 10.0\n9 NaN NaN NaN NaN 7.0 5.0 9.0 16.0\n10 NaN NaN NaN NaN 2.0 20.0 13.0 9.0\n<\/code><\/pre>\n\n\n\nConcatenating with Append<\/strong><\/h3>\n\n\n\n
import pandas as pd\nimport numpy as np\n\n# Create the DataFrames\ndf1 = pd.DataFrame(np.random.randint(25, size=(4, 4)), index=["1", "2", "3", "4"], columns=["A", "B", "C", "D"])\ndf2 = pd.DataFrame(np.random.randint(25, size=(6, 4)), index=["5", "6", "7", "8", "9", "10"], columns=["A", "B", "C", "D"])\n\n# Display the original DataFrames\nprint('DataFrame 1:\\n', df1)\nprint('DataFrame 2:\\n', df2)\n\n# Concat using append()\ndf = df1.append(df2)\n\n# Display the concated DataFrame\nprint('Concated DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nDataFrame 1:\n A B C D\n1 12 10 12 3\n2 3 12 9 19\n3 14 13 17 20\n4 19 20 2 14\n\nDataFrame 2:\n A B C D\n5 16 3 21 22\n6 19 12 21 23\n7 7 14 24 23\n8 0 11 16 23\n9 9 23 2 8\n10 21 10 21 18\n\nConcated DataFrame:\n A B C D\n1 12 10 12 3\n2 3 12 9 19\n3 14 13 17 20\n4 19 20 2 14\n5 16 3 21 22\n6 19 12 21 23\n7 7 14 24 23\n8 0 11 16 23\n9 9 23 2 8\n10 21 10 21 18\n<\/code><\/pre>\n\n\n\nUsing Various Types of Joins<\/strong><\/h3>\n\n\n\n
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<\/figure>\n<\/div>\n\n\nimport pandas as pd\nimport numpy as np\n\n# Create the DataFrames\ndf1 = pd.DataFrame(np.random.randint(25, size=(4, 4)), index=["1", "2", "3", "4"], columns=["A", "B", "C", "D"])\ndf2 = pd.DataFrame(np.random.randint(25, size=(6, 4)), index=["5", "6", "7", "8", "9", "10"], columns=["A", "B", "C", "D"])\n\n# Display the original DataFrames\nprint('DataFrame 1:\\n', df1)\nprint('DataFrame 2:\\n', df2)\n\n# Concat using outer join on 'B'\ndf = pd.merge(df1, df2, on='B', how='outer')\n\n# Display the concated DataFrame\nprint('Concated DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nDataFrame 1:\n A B C D\n1 7 13 21 0\n2 0 6 19 5\n3 4 1 2 14\n4 9 20 16 1\n\nDataFrame 2:\n A B C D\n5 10 23 0 3\n6 4 11 10 24\n7 2 13 13 23\n8 7 12 9 10\n9 19 23 4 15\n10 0 21 9 2\n\nConcated DataFrame:\n A_x B C_x D_x A_y C_y D_y\n0 7.0 13 21.0 0.0 2.0 13.0 23.0\n1 0.0 6 19.0 5.0 NaN NaN NaN\n2 4.0 1 2.0 14.0 NaN NaN NaN\n3 9.0 20 16.0 1.0 NaN NaN NaN\n4 NaN 23 NaN NaN 10.0 0.0 3.0\n5 NaN 23 NaN NaN 19.0 4.0 15.0\n6 NaN 11 NaN NaN 4.0 10.0 24.0\n7 NaN 12 NaN NaN 7.0 9.0 10.0\n8 NaN 21 NaN NaN 0.0 9.0 2.0\n<\/code><\/pre>\n\n\n\nConclusion<\/strong><\/h2>\n\n\n\n