Комментарии:
Can we call it reconciliation?
ОтветитьDid you mean on='customer_id'?
Ответитьliteraaly pandas mege is exclusively used by us in banking on everyday basis.
ОтветитьStill use sql query to join, I'll try it next, thanks!
ОтветитьIts a must know for SQL and relational databases too
ОтветитьPlease which platform are Yu running this python on?
ОтветитьHow to use SQL with python 😂
ОтветитьWhats the difference between merge and join on pandas. And could you make a video of those commands using polars?
ОтветитьCust id is abcd lol
ОтветитьPerfect loop! Very useful content!
ОтветитьMore of this content Sir! This is undeniably fun to learn. 😍
ОтветитьBeautiful Videos Man
ОтветитьYou missed Pandas join function
ОтветитьLol. Today, i spent 6h on a merge, and now you make this video. Here is an idea for a future video I'd like to see from you: Table 1 has a few columns representing different types of analysts. The actual values are not their names but ids. Table 2 has (obviously) an id column, FirstName and LastName. I don't want a simple merge. I want a mapping. I want Table 1's ids to be replaced with (corresponding FullNames). Throw in some condiments as well, like: columns in table 1 have in fact the ids stored as strings while Table 2 Id column has int64
ОтветитьMerge is great but what if you want to merge two dataframes that have slightly different string values on the column you want to merge? I am in the situation where I have one large dataframe (>60k rows) and one small (200 rows) and I want to merge a certain column of the 60k dataframe to the 200 row dataframe. I know that both dataframes have a column that share almost the same values ( Example: DF1 Column A = 'Dogs', DF2 Column A = 'Dog' ) Any tips on how to merge those? tried to clean the data with fuzzy matching, but that took to long, because my dirty dataframe has over 60k rows. Any tips are welcome!
ОтветитьThanks Rob!
Also in Tableau, we can do it easily
Shouldn't we be using concat instead?
ОтветитьI like the indicator trick!! Thank you for sharing
ОтветитьThis video is an infinite loop! 🤯 it ends with "so now you know why..." and begins with "this technique is a must know for working with dataframes in Python" 👍🏻
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