Common mistakes to avoid when using popular Python visualization libraries

While visualization libraries in Python, such as matplotlib and seaborn, are powerful tools for creating data visualizations, there are some common mistakes that individuals encounter when creating different visualizations. Here are a few examples of such mistakes along with example codes:

1. Not adjusting figure size:

By default, the figure size might not be optimal for displaying your visualizations, and neglecting to adjust the figure size can lead to crowded, distorted, or tiny plots which makes it difficult to view the plot. Here’s an example code that uses a function to set an appropriate size for a line plot:

2. Not formatting plot labels, title, and legend:

For better readability, it’s crucial to format plot labels and legends properly and also give an informative name to the plot as this helps the viewers understand quickly what the plot is about. Ignoring this aspect can make your visualizations confusing or less informative. In the example code below, different counts are used to label the plot and give it a title and a legend.

3. Not using correct plotting functions:

Matplotlib and Seaborn provide various plotting functions for different types of visualizations, and using the wrong function for a specific data type or visualization objective can lead to unexpected and misleading results. Here’s an example visualization of a line plot and for this particular data, a scatter plot is inappropriate as the data is continuous and there is only one feature/variable.

4. Not setting the axes limits:

When plotting data using Matplotlib or Seaborn, the default behavior is to automatically determine the axis limits based on the range of the data. However, in some cases, this can cause misrepresentation of the data and you may want to set specific limits to ensure the plot accurately represents the data or focuses on a specific range of values. Here is an example code that shows how you can set specific limits for the axes:

5. Overcomplicating Plots:

Sometimes individuals try to incorporate too many elements into a single plot which results in cluttered and confusing visualizations. To simplify your plots and improve their clarity, consider using separate figures or subplots for different elements. Here is an example that creates two different plots on different figures: