Merging DataFrames in Pandas | Python Pandas Tutorials

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In this series we will be walking through everything you need to know to get started in Pandas! In this video, we learn about Merging, Joining, and Concatenating Dataframes in Pandas.
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[@SamiMouloudMerkouche]

Im datascience learner I use datacamp as my learning platform and your video has helped a lot with that.Thanks for the amazing explanation and keep going we need more people like you.

[@Daniel-me6mu]

Love this explanation. It will help me to go through my exam :))

[@sj1795]

Really appreciate how you explain these concepts so clearly. As always, THANK YOU ALEX! You are the BEST!

[@bhavikakapadia2497]

Thanks Alex for the amazing Explanation. I learn a lot by watching your video. Are we expecting more pandas tutorials in the upcoming video?

[@AlexTheAnalyst]

Yep! I've got about 3 more including visualizations, data cleaning, and data exploration :)

[@casual_gaming001]

Thanks alot, i have been on youtube and Stack overflow all evening. Best explanation

[@hello-fs9bt]

Thank you very much! I don't know how to tell you how much this video help me but you just saved me a lot of time! Very well-explained, easy to understand! I wish you all the good things in life.

[@thaonguyen-xu2tw]

hi, thank you for the explanation. I liked it already but still want to comment to appreciate your effort.

[@carmelool1]

Excellent Video. Very well explained. Thanks for doing it.

[@pamm3914]

Well Explained Sir.... Thanku so much

[@midohany9]

Thanks for the amazing explanation. This is the first playlist I know you from and Your way of illustration is pretty simple and helped me get confusing pandas terms. Many thanks again ❤

[@JaimeHaddad]

Thank you Alex ! Well explained and very simple to understand ! Great as always!! 👏

[@pavithras4085]

Many Thanks for this clear explanation :)

[@БулатШарафутдинов-р6д]

Thank you!)

[@ElBasraoui]

bro I'm a data scientist student, it's the first time I understand that in such a stunningly beautiful way

[@venudunde3453]

thanks Alex, nice explanation

[@djitymik]

You nailed it! Thank u very much sir!!!

[@OliviaWu-q9z]

This is so helpful. Thank you!

[@shadrinan90]

Thank you for concise and interesting video. I ve learned everything I wanted

[@chamo7110]

You have a new subscriber here

[@neildelacruz6059]

Excellent Thanks

[@aissamboudra7995]

Many thanks, clear and useful

[@JW-pu1uk]

This is such a solid explanation on this. If someone is familiar with SQL JOINS, they should feel right at home here (with a few exceptions of course). Don't sleep on Pandas, and don't sleep on AtA videos.

[@khadijashaikh9617]

please make a video on numpy library in python

[@josemariahernandezlara4061]

The best!

[@sachinmaroky4600]

thank you

[@osiomogieasekome8799]

I don't know if its because I learnt sql first but I feel its more straight forward than python... Also doing this a month later, the append still works without any warning 😅 I wonder python would decide to remove

[@ArtyomAshigov-l8j]

I watched other videos about Pandas, they were really good. I just wrote a comment here, that was probably deleted and I am not sure why

[@AlexTheAnalyst]

Had an issue with my first video and had to reupload - thanks for watching! :D

[@angelinesworld5841]

Need notes for this. Could you please share this

[@SieanElpidama]

Qestion is there a function of merge as well in Sql specifically PostgeSQL?

[@winaraj]

merge doesnt work in vsc? says no attribute

[@anikkantisikder2179]

Great

[@مسافر-ح2ط]

Alex tell me the truth, are you a divine angel?

[@garvjain3462]

🎯 Key Takeaways for quick navigation: Merging, joining, and concatenating data frames in Pandas is crucial for combining separate data frames into one. Types of joins: inner join (default), outer join, left join, and right join. Cross join compares each value from the left data frame with every value from the right data frame. The join function is used to join data frames based on specified indexes, but it requires more manual configuration compared to the merge function. Concatenation places one data frame on top of another (vertically) or side by side (horizontally). The append function is deprecated and should be replaced with the pandas.concat function for appending rows from one data frame to another. Understanding these operations is essential for working with multiple data sources in Pandas. Made with HARPA AI

[@guocity]

how to do union in dataframe?

[@beingotaku9864]

Merge is bettter hands down.

[@epicthunder2502]

yeah... I don't want stress😂

[@the_seahawk22]

wait how did you get the file path link? Sorry I am not computer savy

[@shivamborkotoky3788]

after you download the file from the given link in description, copy the file path from your pc

[@akzork]

Dark mode when?

[@patrickmeehan6856]

Barromir= Boromir

[@mdsy2707]

1:38 😂🤣🤣

[@femikomolafe5604]

Wished I saw this video earlier....I would have saved days of wasted hours with simple concatenate function. Tnk Alex

[@shalinivijayravikumar9713]

"Hi Alex, could you please upload a video on NumPy for beginners? It would be incredibly helpful for me, and I would greatly appreciate it."

[@Niranga.555]

Dear Alex, Thanks for your nice explanation..!

[@vijayj8024]

Thank you alex. You are amazing....

[@anurasenarathna1703]

Very useful series, very well explained. Thank you very much for sharing your knowledge.

[@vintage_anime_vault]

wonder how many Data Analysts you made out there !!

[@sandeepmanjunath8004]

Just one doubt is it possible to join on any condition other than equality condition in pandas like we do in SQL for example T1.Col1 < T2.Col1

[@abdulkadirguven1173]

Nice tuto. Thank you very much.

[@_sweeteststarr]

Legolas 😂😂

[@monique1112223]

I got an error message on the cross join and it isn't visible in the menu with SHIFT + TAB either. Interesting.

[@vidyutroy2868]

thank you for sharing such tutorials

[@abdullahmirza7606]

Alex the king 👑👑

[@t_millagain5255]

Definitely what i needed 👍🏽

[@rashadkasim]

Can we do 2 dataframes side by side in pyspark? Similar to concatenate here

[@albertneil290]

Awesome ❤❤ Very well explained.....

[@jameelabduljalil25]

I thought Lord of the Rings sponsored this video.

[@stefanvilcu8260]

wonderful video

[@soundmagus]

Hey Alex, thanks for these videos they are great :) However i am getting different results from you when using df1.merge(df2), its showing IDs 1001,2,6,7,8 - and i cant figure out why, has soimething changed in the most up to date python? (also shows the same if i use df1.merge(df2, how = 'inner', on = ['FellowshipID', 'FirstName'])but with _x and y_ for Age. FellowshipID FirstName Age_x Age_y 0 1001 Frodo 50 50 1 1002 Samwise 39 39 2 1006 Legolas 2931 2931 3 1007 Elrond 6520 6520 4 1008 Barromir 51 51