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Here we can briefly introduce python generators as well. For example, consider a CSV file where entries are name, age, location. We can parse this data to a dataframe using a generator. Image location is a comma separated string field and we want to read latitude and longitude separately.
Hi, @anuradhawick
Thanks for taking time to suggest this modification. It definitely addresses an issue that many researchers will likely incur at some point. That said, what are your thoughts though on this being a good match for potentially absolute beginners. I fear that if someone is brand new to all this, there is a lot of automagical stuff introduced by the yield keyword that might be a bridge to far for some to wrap their head around. It might be a better fit for the instructor notes. Also, there is the potential for a community based re-write (https://carpentries.slack.com/archives/C03LE48AY/p1711535383742769) so I will likely table any major changes like this until that is settled one way or another. If you wanted to do a PR to put it into the instructor section prior to that, though, I would be happy to consider it.
How could the content be improved?
The following section introduce how data can be processed using loops
Automating data processing using For Loops
I believe it would also be advantageous to have a similar section in the following
Reading CSV Data Using Pandas
Here we can briefly introduce python generators as well. For example, consider a CSV file where entries are
name
,age
,location
. We can parse this data to a dataframe using a generator. Imagelocation
is a comma separated string field and we want to read latitude and longitude separately.This is specially useful in large datasets where loading large amount of data in text form is memory consuming.
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