Handling Missing Data in Pandas Part - 1| #38 of 53: The Complete Pandas Course
Of course, you will definitely come across missing data whenever you are working on data analysis, or simply data wrangling.
Let's explore how to work with and handle missing data using pandas.
🔹 How to Handle Missing Data in Python Pandas with Easy Tricks.
In Python, the original way of representing missing data is using the null character. But the problem with numbness it can be used to represent anything that is missing, not just data.
So to represent missing data, the most popular means of representing missing is NP dot Nan, this is provided by the NumPy package. Now, more recently, pandas has introduced pd.na as well as pd.na t. here NP dot Nan means not a number Nan for not a number, whereas na is just any missing value. And a T is not a time.
So this is specific to time data. Now, one thing you should be very careful when working with missing data is you should never do any sort of direct comparison with missing data. That is you should never use this double equal to sign, you should never use this double equal to sign whenever you're comparing missing data.
Now these are the different representations and being dot Nan gives just Nan na surrounded by these greater than and less than sign this is pd.na and pd.na. d is not a time, right, this is the representation.
Now, if you do a comparison of Nan against Nan using this w equal to sign, it is going to give you false. Something similar happens when you try to do pd.na as well This gives you any not even false. Whereas if we want to check these kinds of checks, this should work using a saw operator or in operator this will work fine.
Let me know in the comments section if you have any questions!
If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos!
Thanks for watching!
#machinelearningplus #python #pandas #datascience