Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.join.html
pandas.DataFrame.join — pandas 3.0.2 documentation
Join columns of another DataFrame.
Pandas
pandas.pydata.org › docs › user_guide › merging.html
Merge, join, concatenate and compare — pandas 3.0.2 documentation
When concatenating DataFrame with named axes, pandas will attempt to preserve these index/column names whenever possible. In the case where all inputs share a common name, this name will be assigned to the result. When the input names do not all agree, the result will be unnamed. The same is true for MultiIndex, but the logic is applied separately on a level-by-level basis. The join keyword specifies how to handle axis values that don’t exist in the first DataFrame.
Videos
19:55
Why Your DataFrame Join Is Failing – And How to Fix It (Pandas ...
08:57
How To Combine Data in Python || Pandas Merge (Left Join, Right ...
10:07
Pandas Merge Vs. Join: Which One Should You Use? | Python Data ...
Merging DataFrames in Pandas | Python Pandas Tutorials
29:27
Python Pandas Tutorial: Joining and Merging Pandas DataFrame #13 ...
10:28
Pandas Inner Join Outer Left Right Cross Join - YouTube
GeeksforGeeks
geeksforgeeks.org › pandas › pandas-join-dataframes
Pandas Join Dataframes - GeeksforGeeks
July 23, 2025 - In this article, we will explore how to join DataFrames using methods like merge(), join(), and concat() in Pandas.
W3Schools
w3schools.com › python › pandas › ref_df_join.asp
Pandas DataFrame join() Method
import pandas as pd data1 = { "name": ... pd.DataFrame(data2) newdf = df1.join(df2) Try it Yourself » · The join() method inserts column(s) from another DataFrame, or Series....
DataCamp
datacamp.com › tutorial › joining-dataframes-pandas
pandas DataFrame Joins: merge(), concat(), join() Guide | DataCamp
February 5, 2026 - To simply concatenate the DataFrames along the row you can use the concat() function in pandas. You will have to pass the names of the DataFrames in a list as the argument to the concat() function: ... You can notice that the two DataFrames df1 and df2 are now concatenated into a single DataFrame ...
Medium
medium.com › @whyamit101 › understanding-pandas-join-fc4906d75316
Understanding pandas join()
February 26, 2025 - Simply put, join() in pandas helps you combine two DataFrames based on their index or a common column.
Analytics Vidhya
analyticsvidhya.com › home › python joins: a guide to merge pandas and learn different join methods
Python Joins: A Guide to Merge Pandas and Learn Different Join Methods
May 1, 2025 - Learn about the different python joins like inner, left, right, and full outer join, and how they work around various data frames in pandas.
Real Python
realpython.com › pandas-merge-join-and-concat
Combining Data in pandas With merge(), .join(), and concat() – Real Python
February 7, 2023 - In this step-by-step tutorial, you'll learn three techniques for combining data in pandas: merge(), .join(), and concat(). Combining Series and DataFrame objects in pandas is a powerful way to gain new insights into your data.
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas join explained with examples
Pandas Join Explained With Examples - Spark By {Examples}
June 25, 2025 - Pandas join() is similar to SQL join where it combines columns from multiple DataFrames based on row indices. In pandas join can be done only on indexes but not on columns. By default, it uses the left join on the row index.
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.merge.html
pandas.DataFrame.merge — pandas 3.0.2 documentation
Merge DataFrame or named Series objects with a database-style join.
Pandas
pandas.pydata.org › docs › reference › api › pandas.merge.html
pandas.merge — pandas 3.0.1 documentation
Second pandas object to merge. how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’, ‘left_anti’, ‘right_anti}, default ‘inner’ Type of merge to be performed. left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
Note.nkmk.me
note.nkmk.me › home › python › pandas
pandas: Merge DataFrame with merge(), join() (INNER, OUTER JOIN) | note.nkmk.me
August 1, 2023 - The pandas.merge() function requires the two DataFrame objects as its first left and second right arguments.
StrataScratch
stratascratch.com › blog › types-of-pandas-joins-and-how-to-use-them-in-python
Types of Pandas Joins and How to use them in Python - StrataScratch
September 27, 2023 - Here are different types of pandas joins and how to use them in Python. Inner, Outer, Right, and Left joins are explained with examples from Amazon and Meta.
TutorialsPoint
tutorialspoint.com › python_pandas › python_pandas_merging_joining.htm
Python Pandas - Merging/Joining
Pandas provides high-performance, in-memory join operations similar to those in SQL databases. These operations allow you to merge multiple DataFrame objects based on common keys or indexes efficiently.
GeeksforGeeks
geeksforgeeks.org › python › different-types-of-joins-in-pandas
Different Types of Joins in Pandas - GeeksforGeeks
July 23, 2025 - Inner join is the most common type of join you’ll be working with. It returns a Dataframe with only those rows that have common characteristics. This is similar to the intersection of two sets. ... # importing pandas import pandas as pd # Creating dataframe a a = pd.DataFrame() # Creating Dictionary d = {'id': [1, 2, 10, 12], 'val1': ['a', 'b', 'c', 'd']} a = pd.DataFrame(d) # Creating dataframe b b = pd.DataFrame() # Creating dictionary d = {'id': [1, 2, 9, 8], 'val1': ['p', 'q', 'r', 's']} b = pd.DataFrame(d) # inner join df = pd.merge(a, b, on='id', how='inner') # display dataframe df
Medium
medium.com › @carlacosmo › pandas-dataframe-merge-join-and-concat-2c90b3fd610d
Pandas DataFrame: Merge, Join and Concat | by Carlacosmo | Medium
April 12, 2024 - #Right join by index df_join = df.join(df_type, how='right', lsuffix='_left', rsuffix='_right') df_join ... Combination of two or more dataframes. Combination of columns (horizontally). Methods: inner, outer, left, right. Keys: Index by Index, Index by Column. Concat is a pandas function that combines two or more dataframes by index, vertically and horizontally.
Vultr Docs
docs.vultr.com › python › third-party › pandas › DataFrame › join
Python Pandas DataFrame join() - Merge DataFrames | Vultr Docs
December 24, 2024 - Left joins include all rows from the left DataFrame and the matched rows from the right DataFrame. Unmatched entries will have NaN in columns of the right DataFrame. ... import pandas as pd df1 = pd.DataFrame({'A': [1, 2, 3]}) df2 = pd.DataFrame({'B': [4, 5, 6]}, index=[1, 2, 3]) result = df1.join(df2) print(result) Explain Code
Pandas
pandas.pydata.org › docs › reference › api › pandas.Series.str.join.html
pandas.Series.str.join — pandas 3.0.2 documentation - PyData |
If the elements of a Series are lists themselves, join the content of these lists using the delimiter passed to the function.
Top answer 1 of 4
5
A proposition with merge/lreshape :
mg = pd.merge(df1, df2, on="col3", how="left")
grps = {c: [f"{c}_{s}" for s in ["x", "y"]]
for c in df1.columns.intersection(df2.columns).drop("col3")}
out = pd.lreshape(mg, grps).drop_duplicates().convert_dtypes()
NB: The loop is really optional here and can be replaced with a hardcoded mapping of the common columns (except the one to join on, i.e, col3) between both DataFrames :
grps = {'col4': ['col4_x', 'col4_y'], 'col5': ['col5_x', 'col5_y']}
Output :
print(out)
col1 col2 col3 col4 col5
0 1 2 a 3 4
1 11 22 aa 33 44
3 11 22 aa 332 442
4 111 222 aaa 333 444
[4 rows x 5 columns]
2 of 4
4
Here's another alternative using concat, filling NaNs and then dropping duplicates:
out = pd.concat([df1,df2]).sort_values(['col3'])
out[['col1', 'col2']] = out[['col1', 'col2']].ffill()
out[['col4', 'col5']] = out[['col4', 'col5']].bfill()
out = (out
.drop_duplicates(subset=['col3', 'col4', 'col5'])
.convert_dtypes()
.reset_index(drop=True)
)
Output:
col1 col2 col3 col4 col5
0 1 2 a 3 4
1 11 22 aa 33 44
2 11 22 aa 332 442
3 111 222 aaa 333 444