Python Tutorial: Real World Example - Parsing Names From a CSV to an HTML List

In this Python Programming Tutorial, we'll be learning how to parse a CSV file and output the data to an HTML unordered list. This is a real world problem I ran into with my website. The list was becoming too large for me to maintain manually, so writing a Python script to automate this process saved me a lot of time in the future. Let's get started.

The code from this video can be found at:

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#Python

[jykw]

Dude you are the STAR. Please make more of those real world examples. Why don't you rather create a new category in your tutorial series "Real World Example" etc? You are just amazing Corey!

[sayandeepbhaskar]

I don't even like programming because I never understood the the core basics well. It's so cool now after your tutorials that I also try to practice. I can be the last man to indulge in programming, however, when I start watching your videos I couldn't stop watching it. Thank you so much for all these stuff

[vincentedepaul]

I was almost dropping out of CS class back in 2015 when i was in second Year, then i bumped into this guy...You damn good man, you made me a Python Developer...

[SaifUlIslam-dbnu]

I'm gonna complete all of your Python tutorials, and then write in my resume, "Comfortable with Python - taught by Corey Schafer".

[PainDive]

I love this channel! I learn more from you than from my college professor.

[starplatinumrqm]

You have done a great service to so many people with these videos, thank you

[JohnBillot]

Corey, these videos are excellent, so clear and I appreciate your thought processes as you go through the problem. Many thanks

[jitendraraghuwanshi]

Thanks a lot for these videos Corey 😃 I just successfully automated the process of copying and arranging photos and videos in specific date folders from my dslr to my PC 😃

[kxnyshk]

this was so cool! Using just two modules, CSV and Random, we can actually take in data online and generate our very own huge chunks of dump data sets for various testing and research purposes or even just for fun, to play with it :p
Thanks for this, really amazing!

[khaled-dz]

Best python teach ever. Thank you corey

[jasperrenaldo]

Very useful video for me who does a lot of manual stuff using CSV files. Keep up the good work Corey

[aristeidisnivorlis]

Very nice explanation with no time-loss in between, thank you for that!
One small thing for improving your efficiency is to use str.join() method instead of += (concatenation) because it's much more slower and uses more memory.
That is because strings are immutable, hence everytime a new string needs to be created by parsing all the characters from the previous one.
Would be very interesting to see some OOD tutorials in the future since you have such an amazing and clear way of explaining stuff!

[luisalexisavalos]

Great video Mr.Corey!!, Very useful in daily tasks.Greetings from Mexico.

[rotrose]

Every time, thank you very much. Best python channel across youtube

[infotakeaway]

I seriously can not thank you enough for the amount of help you are providing. This tutorial was really helpful. One thing i'm having trouble with is practicing OOP. I've watched your tutorials on OOP and they are pretty good but i don't know where to use them and how to implement them into my daily routine of practicing python as most of the code i practice is usually in functions or sometimes not in functions at all. I hope you understood what i asked as my english is not that good. Great stuff !

[smartguy]

Hi Corey, you have such a fluency in python and your teaching style is really good. I appreciate it and will contribute to your efforts once i am financially capable.

[indiansoftwareengineer]

loved all videos and subscribed, your explanation was best than others who say "we can learn it later, what that function does!"

[zeinelmokhtar]

Yes, we enjoy these tutorials. Thank you very much.

[prasoothan]

I am new to Python, your videos are awesome

[rafaelgpontes]

It's funny you posted this. I had to use this Python library recently with some data I gathered from an experiment of mine. I ended up using pandas, though. It's a nice option for data analysis/visualization and it offers functions to deal with csv files, converting them to data frames.