How to Find and Delete Duplicate Rows from Pandas DataFrame | Python for Data Analysis
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- Опубликовано: 1 окт 2024
- This video is going to be very interesting. we will learn how to find duplicates in a dataframe based on one or multiple columns. we will also learn how to then drop or delete those duplicate rows.
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Great content. Please do post all the scenarios using Pandas with Jupyter. It is easy to understand. Thanks!
Sure. Thank you 😊
Hey Ankit, I have python data analytics interview coming up. You have less content in this section. If you have any important concepts in mind please let me know.
Hi Ankit, Please create more videos on pandas. Your way of teaching is too good and easy to understand.
Sure I will
This is great and easy to understand. Please create a playlist for data analysis using python. It will be very helpful.
Will do soon
Ankit Sir. Great Video just like other Videos. I am liking Jupyter Notebook than Spyder editor as it is clean and easily understandable
Noted
Hey Ankit, for the next pandas videos can you drop the dataset as well in the description to follow along?
Sure
how can I find duplicates with multiple columns ?
You can use 2 columns in dataframe to find duplicates on 2 columns
Very good
Grt work Sir , please make more videos on pandas 🙏
Excellent, was looking for this few days back. Jupyter is very simple to understand. Thanks
Thank you 😊
Awesome vdo
amazing and good explanation, but request you to please write every steps in separate cell. will be easier to understand and practice purpose.
Sure
Jupyter UI is excellent.Please add more content just like SQL
Sure
Awesome Content bro ❤️ Looking forward for more videos in the upcomings🎉
Thank you 😊
Atom interface i like the most.
How to find duplicates in more than one columns?
Take subset. df[[col1,col2]].duplicated
Thank you
Really appreciate your work...awesome content
Thank you 😊
Ammazing❤
Such a beautiful explanation ❤️
Thank you 😊
Yes, Jupyter is more useful than Spyder IDE because it is more oriented towards step by step exceution which is better for learning purpose.
Looking forward for more python videos...