Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
Let me know when you have any questions!
In this video we stroll by means of most of the elementary ideas to make use of the Python Pandas Data Science Library. We begin off by putting in pandas and loading in an instance csv. We then have a look at alternative ways to learn the information. Read a column, rows, particular cell, and so on. Also methods to learn information primarily based on conditioning. We then transfer into some extra superior methods to kind & filter information. We have a look at making conditional modifications to our information. We additionally begin doing mixture stats utilizing the groupby perform. We completed the video speaking about how you’d work with a really giant dataset (many gigabytes)
I spotted as I add this video there are some extra issues I wish to discuss in a later video. The very first thing that involves thoughts instantly is utilizing the apply() perform on a dataframe to change the information utilizing a customized or lambda perform. If you could have questions on this or the rest earlier than I get round to creating a component 2, be happy to put in writing me a observe within the feedback.
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Thanks for watching pals! Happy coding! 🙂
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0:00 – Why Pandas?
1:46 – Installing Pandas
2:03 – Getting the information used on this video
3:50 – Loading the information into Pandas (CSVs, Excel, TXTs, and so on.)
8:49 – Reading Data (Getting Rows, Columns, Cells, Headers, and so on.)
13:10 – Iterate by means of every Row
14:11 – Getting rows primarily based on a selected situation
15:47 – High Level description of your information (min, max, imply, std dev, and so on.)
16:24 – Sorting Values (Alphabetically, Numerically)
18:19 – Making Changes to the DataBody
18:56 – Adding a column
21:22 – Deleting a column
22:14 – Summing Multiple Columns to Create new Column.
24:14 – Rearranging columns
28:06 – Saving our Data (CSV, Excel, TXT, and so on.)
31:47 – Filtering Data (primarily based on a number of circumstances)
35:40 – Reset Index
37:41 – Regex Filtering (filter primarily based on textual patterns)
43:08 – Conditional Changes
47:57 – Aggregate Statistics utilizing Groupby (Sum, Mean, Counting)
54:53 – Working with giant quantities of knowledge (setting chunksize)
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