Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith
Data & code used in this Tutorial: https://github.com/KeithGalli/pandas
Python Pandas Documentation: http://pandas.pydata.org/pandas-docs/...
Let me know if you have any questions!
In this video we walk through many of the fundamental concepts to use the Python Pandas Data Science Library. We start off by installing pandas and loading in an example csv. We then look at different ways to read the data. Read a column, rows, specific cell, etc. Also ways to read data based on conditioning. We then move into some more advanced ways to sort & filter data. We look at making conditional changes to our data. We also start doing aggregate stats using the groupby function. We finished the video talking about how you would work with a very large dataset (many gigabytes)
I realized as I upload this video there are some additional things I want to talk about in a later video. The first thing that comes to mind immediately is using the apply() function on a dataframe to alter the data using a custom or lambda function. If you have questions on this or anything else before I get around to making  a part 2, feel free to write me a note in the comments.
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Thanks for watching friends! Happy coding! :)
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Link to original source of data from Kaggle: https://www.kaggle.com/abcsds/pokemon