Working With CSVs in Python Part 3 – Adding Track Information
In this tutorial, we continue with the problem setting described in Part 1. In Part 2 of this series, we corrected inconsistencies in one of the columns of the dataset – the name column. The next thing we need to do is as follows: The CSV file we download from the learning platform is such that it contains the names of all the people on the platform whose emails match our company’s domain. This means, whether a person is enrolled in a course or not, as long as their emails match that of our company’s account, their names will appear on the progress report for any course we download. This is not desirable. We wish to use some means to eliminate the names of all people who according to our records are not taking the course. This is the objective of today’s tutorial.
In order to arrive at our final goal of filtering the data, we need to add some new columns that will be used to specify which records get filtered. In this video we deal with the intermediate steps of adding some new columns to our dataset, namely
1. Adding track information
2. Adding enrolment information
In solving this problem, we will make use the Pandas library, Python list compression, dictionary comprehension as well as json. Being comfortable with these skills is very essential for python data analysis.
Stay tuned and watch as I demonstrate how to use the above techniques in Python to get our CSVs in shape for analysis.
If you haven’t seen the previous videos, you can watch them bellow to get a fuller understanding.
Part 1: • Working with CSV in Python Part 1 - Initia...
Part 2: • Working with CSV in Python Part 2 - Normal...
Source Code
Visit the following GitHub repository to follow along with the code used in this tutorial
https://github.com/quizem/data-skills...
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