Another mistake to avoid when working with data is not updating your dataset properly. In this video, learn how to identify if your dataset has not been updated and successfully update your dataset as needed.
- [Narrator] Another mistake to avoid, … when working with data, … is not updating your dataset properly. … For example, let's say that I have a variable named Grades, … containing a pandas DataFrame, … and the DataFrame consists of students' grades … across five exams. … I've displayed it here. … Now, say that the student ID column, … is not relevant for my task at hand. … So, I want to drop that column from the dataset. … Say, I call the pandas drop function on grades … and passing the columns argument, a student ID like this. … As you can see, when I ran this cell, … I did indeed get a pandas DataFrame containing the data … from the grades DataFrame, … with the student ID column dropped. … However, the grades DataFrame was not updated, … which you will see when I run this cell here. … As you can see, the grades DataFrame, … still has a student ID column. … So, I did not actually update it. … To update it, I would reassign grades, … to the DataFrame that is returned, … from the drop function call. …
Skill Level Intermediate
1. Avoid Mistakes in Coding Practices
2. Avoid Mistakes in Structuring Code
3. Avoid Mistakes in Handling Data
4. Avoid Mistakes in Machine Learning
Using redundant features1m 45s
Get started with Python1m 7s
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