Python Pandas Tutorial: Joining and Merging Pandas DataFrame #13

In this lecture you will learn about Joining and Merging Pandas DataFrames in elegant ways.
In any real world data science and analysis situation with Python, you’ll need to merge or join Pandas Dataframes together to form your analysis dataset. Merging and joining dataframes is a core process that any aspiring data analyst will need to master and Pandas is its high-performance, in-memory join and merge operations.
This lecture will cover following in greater details:
1.) Categories of Joining Pandas DataFrame
2.) Inner Join or Common Join
2.) Left Join | Left Outer Join
3.) Right Join | Right Outer Join
4.) Full Join | Outer Join
5.) Cross Join
6.) How to verify the records are coming from which dataset?
7.) What does indicator parameter in Pandas merger operations?
8.) How to set the suffixes to identify the dataset's columns
9.) How to apply joining operations based on index and primary key?
10.) How to joining multiple DataFrame using Pandas Merge?
11.) How to get all joins result from full join and why full join is useful in Pandas?
12.) How to verify the expected output from joining operations.
RawData: github.com/Abh...
Jupyter File: github.com/Abh...
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[@Feroz88]

You done an excellent job bro ♥

[@dss1871]

All concepts of merge operation in very less time...much appreciated 👍👍👍👍... Thanks for the sharing with us.👍

[@ProgrammingIsFunn]

I am glad, its helpful for you.

[@testsample1005]

elegant way pandas merge operations.

[@user-pg8tk9ze9g]

Best explanation

[@ProgrammingIsFunn]

Thanks 🙏

[@zahedinima732]

Great video, lots of concepts with intuitive explanations. It would be great if you could create another video to cover antijoin and semijoin as well. Thanks!

[@arunbabai]

Very nice video

[@pilliarun6337]

Great explanation.... keep making videos like this... you will be more successful 👍

[@selvamsabarish1998]

One more suggestion, please show how to use Selfjoin for substituting for the futncion of windows function.

[@eswar9423]

Very Clear Explanation

[@MITHUNKUMAR-nx8rd]

Thanks for upload bro!

[@subha1818]

Great explanation 👍 Lots of doubts cleared

[@vivekkumarsingh5664]

Useful.

[@R_Misar]

👌🏻Thank you so much sir for such a great explanation😇

[@easydatascience2508]

Hei, bro, you can watch mine too.

[@Chiraggarg011]

👍👍👍👍👍👍👍👍

[@sukritiguin5637]

Great Video...❤❤❤❤❤❤❤❤❤❤❤❤

[@Shriramkrishnhari]

👍🗽

[@mosama22]

Thank you 😊

[@ProgrammingIsFunn]

Your welcome 🙏

[@sonal008]

But it is not making permanent changes. Every time if we refresh the changes are gone

[@ProgrammingIsFunn]

can you elaborate your concern in details? Permanent changes will be store in DataFrame, once the DataFrame has been joined successfully then you can export the result if you want.

[@jqts6490]

Thanks for the video. I was able to successfully merge and find some errors from IDs I did not find using VBA vlookups. I was curious. Is there a way to highlight difference between columns in this merged database. example: Number of Vehicles_SS: 7 vs Number of Vehicles_SA: 2 and it would highlight the row, or even just those values, base on the ID it was merged on? I am having a hard time find this. Trying to get rid of VBA, which i have doing this, But it is SUPER slow with the data I have to process.

[@Social_Brat]

How do you get suggestions while typing in Jupyter Notebook?

[@ProgrammingIsFunn]

Simple type any variable with dot and hit the tab

[@JayJay-ki4mi]

No need to use print statement. Just type the variable and run.