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.
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.
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 ❤
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
🎯 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
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.
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
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.
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.
It feels soothing to see your appearence after going through some newly oriented tasks...
Really appreciate how you explain these concepts so clearly. As always, THANK YOU ALEX! You are the BEST!
Right away from Ghana , every bit of your explanation is on point and you have had so much impact on my journey as a data analyst. Thank you Alex
Thanks alot, i have been on youtube and Stack overflow all evening. Best explanation
Thanks Alex for the amazing Explanation. I learn a lot by watching your video. Are we expecting more pandas tutorials in the upcoming video?
Yep! I've got about 3 more including visualizations, data cleaning, and data exploration :)
Excellent Video. Very well explained. Thanks for doing it.
This is so helpful. Thank you!
thanks Alex, nice explanation
Thank you Alex ! Well explained and very simple to understand ! Great as always!! 👏
Nice tuto. Thank you very much.
You nailed it! Thank u very much sir!!!
Thank you!)
Thank you alex. You are amazing....
Dear Alex,
Thanks for your nice explanation..!
This is set theory... union, intersection, set difference and cross product
you explain better than my professor....thank you!
Many thanks, clear and useful
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 ❤
bro I'm a data scientist student, it's the first time I understand that in such a stunningly beautiful way
Wished I saw this video earlier....I would have saved days of wasted hours with simple concatenate function. Tnk Alex
Many Thanks for this clear explanation :)
hi, thank you for the explanation. I liked it already but still want to comment to appreciate your effort.
Excellent Thanks
Very useful series, very well explained. Thank you very much for sharing your knowledge.
Well Explained Sir.... Thanku so much
You have a new subscriber here
Thank you for concise and interesting video. I ve learned everything I wanted
Love this explanation. It will help me to go through my exam :))
thank you for sharing such tutorials
Definitely what i needed 👍🏽
Awesome ❤❤
Very well explained.....
The best!
Alex the king 👑👑
please make a video on numpy library in python
wonder how many Data Analysts you made out there !!
Thanks. What do you think about capability of these compared to power query in excel?
how do you put image in the notebook?
wonderful video
Need notes for this. Could you please share this
can i also do it with json files or do they need to be csv files ?
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
Qestion is there a function of merge as well in Sql specifically PostgeSQL?
I got an error message on the cross join and it isn't visible in the menu with SHIFT + TAB either. Interesting.
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
Merge is bettter hands down.
yeah... I don't want stress😂
Am unable to import the dataset am getting the error 'str' object is not callable. what could be the issue?
i restarted the kernel and it worked
Can we do 2 dataframes side by side in pyspark? Similar to concatenate here
merge doesnt work in vsc? says no attribute
how to do union in dataframe?
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
Had an issue with my first video and had to reupload - thanks for watching! :D
thank you
"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."
wait how did you get the file path link? Sorry I am not computer savy
after you download the file from the given link in description, copy the file path from your pc
Selam. Oglun cok sansli, ona duskun bir babasi var masallah
Alex tell me the truth, are you a divine angel?
🎯 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
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.
Great
Barromir :O
Dark mode when?
1:38
😂🤣🤣
Barromir= Boromir
#append_is_no_more
I thought Lord of the Rings sponsored this video.
Legolas 😂😂
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