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
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.
🎯 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
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