Data & code used in this Tutorial:
Python Pandas Documentation:
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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Link to original source of data from Kaggle:
0:00 – Why Pandas?
1:46 – Installing Pandas
2:03 – Getting the data used in this video
3:50 – Loading the data into Pandas (CSVs, Excel, TXTs, etc.)
8:49 – Reading Data (Getting Rows, Columns, Cells, Headers, etc.)
13:10 – Iterate through each Row
14:11 – Getting rows based on a specific condition
15:47 – High Level description of your data (min, max, mean, std dev, etc.)
16:24 – Sorting Values (Alphabetically, Numerically)
18:19 – Making Changes to the DataFrame
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, etc.)
31:47 – Filtering Data (based on multiple conditions)
35:40 – Reset Index
37:41 – Regex Filtering (filter based on textual patterns)
43:08 – Conditional Changes
47:57 – Aggregate Statistics using Groupby (Sum, Mean, Counting)
54:53 – Working with large amounts of data (setting chunksize)
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