Joining and Merging Dataframes – p.6 Data Analysis with Python and Pandas Tutorial
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Welcome to Part 6 of the Data Analysis with Python and Pandas tutorial series. In this part, we’re going to talk about joining and merging dataframes, as another method of combining dataframes. In the previous tutorial, we covered concatenation and appending.
Joining/merging tutorial text and sample code: http://pythonprogramming.net/join-merge-data-analysis-python-pandas-tutorial/
http://pythonprogramming.net
https://twitter.com/sentdex
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Thank you! very helpful
I have Q can read file.csv instead of value
place halp me
Poliyeeeeee
Simple question, why you make a space after the comma?
for merging sure if you have any sql experiecei it would be much simpler .. I advice u go through some sql guys.. any ways thank you man for such great tutorial ♥ take my heart =D
need your help
Thanks for the tutorial – however, this is where jupyter based dataframe visualisation would have been helpful to understand the nuances of various joins.
Hello, Thank you so much for this video, it helped me a lot!!. However i have a doubt regarding "inplace = True", could you pls tell me the use of that?
What if I want to add a new calculated column to my merged df based on a value from one or both dfs?
So suppose I merge df1 and df2 on Year but I want my merged df to include a column ("Is HPI >x? Yes / No")
Beautiful!!! Exactly what I needed. Thanks alot.
I've been reading pandas documentation to find the difference between concat, merge and join. Your explanation is very clear, thanks! (I didn't understand the documentation entirely)
Hey,
What if I have two dataframes with different dtypes and I want to join them ? How can it be done ? As by simply using join() method all values are becoming NaN.
Good work! I sorta understand it!
Default for join is 'left ' not inner , at least in 2019
How do you merge 2 dataframes with different id's (which is used with the 'on')
Its great to know Merge on dataframes. In one of my requirement I tried merging 2 datasets from 2 different sources( sybase & Oracle) which has nearly 60k records each. While applying merge it throws memory exception. I'm using outer join to identify the records which are available in Sybase and not loaded in oracle. Would you be able to recommend something to fix this memory error?
Hi Harrison, I religiously watch your tutorials. They're very detailed and works for all levels of understanding.
I come from a SAS data engineering background. And for me SAS macros to write reusable code is very handy. I know there's ways to do that with python functions. But it would be great if you could do a tutorial to create user defined functions to work with dataframes in pandas.
Thanks again.
Thanks! 🙂
aaaawwwweeeeeeessssssoooooooommmmmmmmmmmeeeeeeeee
Could you please explain why the data replication occurs?
Great videos! Thank you for the awesome course!
I like your tutorials sentdex! But I have a problem, what is the cause of data replication?
And what are the differences between join and merge
Why does merge give a keyerror when we merge on index?
Thank you @sentdex. These are high quality, crystal clear tutorials.
Really helpful, thanks
when using join , is join used for only inner join or it can be used for other tyes of join?
Thank you so sooo much. You rock !!! –
Fantastic. Was really chuffed you covered merging (joining) on an index – with all my data being time series (i.e. 'Date' as the index), this is exactly what I need!
By the way, the plural of 'index' is 'indices' 😉
Great as always, thanks sentdex!
https://pythonprogramming.net/join-merge-data-analysis-python-pandas-tutorial/
Hi Sentdex, thanks for the videos!
I was wondering; as neither dataframe nor list objects have .encode as an attribute, how can I best .encode(utf-8) a dataframe containing non-ascii characters so that it displays properly?
Why does it duplicate data when you merge the first time? 1:41
"You sure can there little student!"
Unfortunate confusion caused by the words join and merge. Because when you think of a sql join, in pandas that is a merge.
From the docs:
pandas provides a single function, merge, as the entry point for all standard database join operations between DataFrame objects:
join just appears to be a special case of merge:
The related DataFrame.join method, uses merge internally for the index-on-index (by default) and column(s)-on-index join. If you are joining on index only, you may wish to use DataFrame.join to save yourself some typing.
Why is there even a duplication taking place? Basically, i am not able to understand merging based on 2 keys. Any help would be appreciated. I'm a newbie.
You keep asking what's going on? …. but you never listen to what I have to say 🙁
JK, amazing tutorial & fabulous approach.