{"id":57204,"date":"2019-05-30T17:34:47","date_gmt":"2019-05-30T12:04:47","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=57204"},"modified":"2019-05-31T12:51:12","modified_gmt":"2019-05-31T07:21:12","slug":"pandas-concatenation","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pandas-concatenation\/","title":{"rendered":"Pandas Concatenation – Best Tutorial for Concatenating Series & DataFrames"},"content":{"rendered":"

Python pandas concatenation is a process of joining of the object along an axis, with set logic applied to other axes, if any (because a series doesn\u2019t have any other axes).\u00a0These are the main parameters involved in pandas concatenation- object, axis, handling of other axes, and keys.<\/p>\n

You have seen many articles on the internet about pandas concatenation. But, we are going to serve you the best one, in which, you will get the knowledge and practice the concatenation on pandas series and dataframes.<\/p>\n

\"Pandas<\/a><\/p>\n

Before we start\u00a0concatenation, we need to import the pandas library:<\/p>\n

>>> import pandas as pd<\/pre>\n
 <\/p>\n
1. How to concatenate pandas series?<\/h2>\n
1.1. How to create a pandas series?<\/h4>\n
>>> dataflair_a= pd.Series([1,2,3,4])\r\n>>> dataflair_a<\/pre>\n
Output-<\/strong><\/p>\n
0\u00a0 \u00a01
\n1\u00a0 \u00a02
\n2\u00a0 \u00a03
\n3\u00a0 \u00a04
\ndtype: int64<\/p>\n
Get a complete guide to master in pandas series<\/a><\/em><\/strong><\/p>\n
Create another Pandas series(b)\u00a0<\/strong><\/p>\n
>>> dataflair_b= pd.Series([5,6,7,8])\r\n>>> dataflair_b<\/pre>\n
Output-<\/strong><\/p>\n
0 5
\n1 6
\n2 7
\n3 8
\ndtype: int64<\/p>\n
\"Create<\/a><\/p>\n
1.2. How to concatenate the pandas series?<\/h4>\n
>>> pd.concat([dataflair_a,dataflair_b])<\/pre>\n
Output-<\/strong><\/p>\n
0\u00a0 \u00a01
\n1\u00a0 \u00a02
\n2\u00a0 \u00a03
\n3\u00a0 \u00a04
\n0\u00a0 \u00a05
\n1\u00a0 \u00a06
\n2\u00a0 \u00a07
\n3\u00a0 \u00a08
\ndtype: int64<\/p>\n
\"Concatenate<\/a><\/p>\n
Get the easy steps to\u00a0sort pandas dataframes and series<\/a> with example<\/strong><\/em><\/p>\n
1.3. Clear the existing index and make a new index<\/h4>\n
>>> pd.concat([dataflair_a,dataflair_b], ignore_index=True)<\/pre>\n
Output-<\/strong><\/p>\n
0\u00a0 \u00a01
\n1\u00a0 \u00a02
\n2\u00a0 \u00a03
\n3\u00a0 \u00a04
\n4\u00a0 \u00a05
\n5\u00a0 \u00a06
\n6\u00a0 \u00a07
\n7\u00a0 \u00a08
\ndtype: int64<\/p>\n
\"make<\/a><\/p>\n
1.4. How to add a hierarchical index on pandas series?<\/h4>\n
Let’s take this example to perform pandas concatenation on keys-<\/p>\n
>>> pd.concat([dataflair_a, dataflair_b], keys=['a', 'b',])<\/pre>\n
Output-<\/strong><\/p>\n
a\u00a0 \u00a0 0\u00a0 \u00a0 1
\n1\u00a0 \u00a0 \u00a02
\n2\u00a0 \u00a0 3
\n3\u00a0 \u00a0 4
\nb\u00a0 \u00a0 0\u00a0 \u00a05
\n1\u00a0 \u00a06
\n2\u00a0 \u00a07
\n3\u00a0 \u00a08
\ndtype: int64<\/p>\n
Don’t forget to check\u00a0pandas function applications<\/a><\/strong><\/em><\/p>\n
\"Pandas<\/a><\/p>\n
1.5. Label the index<\/h4>\n
>>> pd.concat([dataflair_a, dataflair_b], keys=['a', 'b'],names=['Series name', 'Row ID'])<\/pre>\n
Output-<\/strong><\/p>\n
Series name Row ID
\na\u00a0 \u00a0 0\u00a0 \u00a0 1
\n1\u00a0 \u00a0 \u00a02
\n2\u00a0 \u00a0 3
\n3\u00a0 \u00a0 4
\nb\u00a0 \u00a0 0\u00a0 \u00a05
\n1\u00a0 \u00a06
\n2\u00a0 \u00a07
\n3\u00a0 \u00a08
\ndtype: int64<\/p>\n
\"<\/a><\/p>\n
2. How to concatenate pandas dataframes?<\/h2>\n
2.1. How to create pandas dataframes?<\/h4>\n
Print the first pandas dataframe<\/p>\n
>>> dataflair_A = pd.DataFrame([['a', 1], ['b', 2]], columns=['letter', 'number']) \r\n>>> dataflair_B = pd.DataFrame([['c', 3], ['d', 4]], columns=['letter', 'number'])\r\n>>> dataflair_A<\/pre>\n
Print the second pandas dataframe<\/p>\n
>>> dataflair_B<\/pre>\n
Don’t miss the opportunity to grab the details about pandas dataframes<\/a><\/strong><\/em><\/p>\n
Output-<\/strong><\/p>\n
\"The<\/a><\/p>\n
2.2 How to concatenate pandas dataframes?<\/h4>\n
>>> pd.concat([dataflair_A,dataflair_B])<\/pre>\n
Output-<\/strong><\/p>\n
\"Pandas<\/a><\/p>\n
2.3 Concatenating pandas dataframes having different columns<\/h4>\n
Concatenate pandas dataframes with different, overlapping columns and return everything<\/strong><\/p>\n
Create the third dataframe in Pandas<\/p>\n
>>> dataflair_C = pd.DataFrame([['c', 3, 'duck'], ['d', 4, 'hen']],columns=['letter', 'number', 'bird'])\r\n>>> dataflair_C<\/pre>\n
Concatenate it with A using:<\/p>\n
>>> pd.concat([dataflair_A,dataflair_C])<\/pre>\n
Explore the 3 unique ways to iterate over dataframes<\/a><\/strong><\/em><\/p>\n
Output-<\/strong><\/p>\n
\"Pandas<\/a><\/p>\n
Notice NaN where there are no values in dataframe A.<\/p>\n
2.4 Concatenating pandas dataframes with overlapping columns and only returning those<\/h4>\n
>>> pd.concat([dataflair_A,dataflair_C], join=\"inner\")<\/pre>\n
Output-<\/strong><\/p>\n
\"Pandas<\/a><\/p>\n
2.5 How to combine pandas dataframes horizontally?<\/h4>\n
>>> pd.concat([dataflair_A,dataflair_B], axis=1)<\/pre>\n
Output-<\/strong><\/p>\n
\"combine<\/a><\/p>\n
2.6 Concatenating pandas dataframes using .append()<\/h4>\n
.append()<\/a> makes an entire copy of the data again and again before appending. Therefore reusing it continuously can lower your program\u2019s performance significantly.<\/p>\n
>>> dataflair_A = pd.DataFrame([['a', 1], ['b', 2]], columns=['letter', 'number'])\r\n>>> dataflair_A<\/pre>\n
>>> dataflair_B = pd.DataFrame([['c', 3], ['d', 4]], columns=['letter', 'number'])\r\n>>> dataflair_B<\/pre>\n
>>> result = dataflair_A.append(dataflair_B)\r\n>>> result<\/pre>\n
Now, you can customize your data with 5 Pandas Options<\/a><\/strong><\/em><\/p>\n
Output-<\/strong><\/p>\n
\"Pandas<\/a><\/p>\n
2.7.Concatenating pandas dataframes while ignoring indexes<\/h4>\n
If you are working with two dataframes which do not have quite meaningful indexes, you can choose to concatenate them, while ignoring their overlapping indexes. For doing this, you will have to use the ignore_index argument.<\/p>\n
>>> result = pd.concat([dataflair_A,dataflair_B], ignore_index=True)\r\n>>> result<\/pre>\n
Output-<\/strong><\/p>\n
\"Concatenating<\/a><\/p>\n
Thus, you can see that the previous indexes were ignored and new indexes were created altogether.<\/p>\n
2.8. Concatenating pandas dataframes using mixed ndims<\/h4>\n
If you want to, you can concatenate a mix of dataframe and series. Going by the hierarchy, the series will be converted into a dataframe with the name of the series being the name of the column name.<\/p>\n
>>> dataflair_s = pd.Series(['S0', 'S1'], name='S')\r\n>>> result = pd.concat([dataflair_A,dataflair_s], axis=1)\r\n>>> result<\/pre>\n
\"<\/a><\/p>\n
Summary<\/h2>\n
Now, you can\u00a0concatenate dataframes and series in pandas easily with the help of the pandas.concat() and append() functions. Pandas\u00a0concatenation makes your work easy. In our next Pandas tutorial, we will discuss\u00a0how to merge and join objects in pandas?<\/strong><\/p>\n
Hope, this Pandas Concatenation helped you. Give us suggestions and feedback to serve you better.
Python pandas concatenation is a process of joining of the object along an axis, with set logic applied to other axes, if any (because a series doesn\u2019t have any other axes).\u00a0These are the main...<\/p>\n","protected":false},"author":5,"featured_media":57241,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19475],"tags":[19966,19967,19965,19963,19964],"class_list":["post-57204","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pandas","tag-concatenating-pandas-dataframes","tag-concatenating-pandas-series","tag-pandas-concat","tag-pandas-concatenate","tag-pandas-concatenating"],"yoast_head":"\nPandas Concatenation - Best Tutorial for Concatenating Series & DataFrames - DataFlair<\/title>\n<meta name=\"description\" content=\"Python Pandas Concatenation is a process of joining of the object along an axis. 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