When creating visualizations of your data, it is easy to get caught up on the aesthetic. But it is important to identify the characteristics of the data that pertain to the problem you are addressing and ensure that those characteristics are the focus of your visualization. In this video, learn how to choose the right type of visualization.
- [Instructor] When visualizing data, … a common mistake to avoid is not choosing … the right type of visualization. … I'll be walking through some examples to demonstrate … how to select the right type of visualization for your goal. … Let's say that I have a variable named grades … containing a Canvas data frame, … and the data frame consists … of student scores across five exams. … I've displayed it here. … Now, say I want to visualize the distribution … of students' exam grades. … If I choose to use a bar plot, … I will have trouble achieving my goal, … as bar plots are meant for visualizing categorical data. … Instead, I can use a histogram, … as histograms are met for visualizing … quantitative data, specifically distributions. … I can call the hist function … from matplotlib's pyplot sub-module … and pass in the grade column from the grades data frame. … It would look something like this. … There we go. … To avoid choosing the wrong type of visualization, … think about your criteria …
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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