The six most important read_csv arguments in Pandas

Do you work with CSV files? Of course you do! It's by far the most common format. And the read_csv function supports dozens of arguments -- so many that it's often hard to know where to start reading the documentation.
In this video, I show you the six arguments for read_csv that you're most likely to need. I then demonstrate them with real data files, in Jupyter. (You can download my notebook, plus the data files, from github.com/reu... .)

[@gdvissch]

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[@ReuvenLerner]

So delighted that you found me here! Thanks for the very kinds words; I really miss the LJ days.

[@gdvissch]

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[@munivoltarc]

I am seeing your python educational videos every video is amazingly simple and easy to learn to novice, thanks for your service to many across the world

[@ReuvenLerner]

I'm delighted that you're enjoying the videos! Thanks for your kind words.

[@Scooterboy_and_others109]

Fantastic Demo, To the point with good simple examples

[@ReuvenLerner]

Glad to hear; thanks!

[@The.WorldVentures]

Thank you so much, you have saved my a***

[@ReuvenLerner]

Fantastic; I'm delighted to hear it!

[@John-pb7gb]

How can we a csv file if we have an uneven number of columns? Let's say the header row has Name, Phone number, and Address separated by comma, but only some of the data in the Address column has more commas(ex: St.Vincent road, Dallas, TX) something like this. How should I read the file

[@ReuvenLerner]

CSV files need to have the same number of columns in each row. You can sometimes get away with null values, if there are commas next to each other, but I don't believe that you can ever have variable-length lows.