Understanding the differences between seaborn and pandas
The seaborn library is a popular Python library for creating visualizations. Like pandas, it does not do any actual plotting itself and is a wrapper around matplotlib. Seaborn plotting functions work with pandas DataFrames to create aesthetically pleasing visualizations.
While seaborn and pandas both reduce the overhead of matplotlib, the way they approach data is completely different. Nearly all of the seaborn plotting functions require tidy (or long) data.
Processing tidy data during data analysis often creates aggregated or wide data. This data, in wide format, is what pandas uses to make its plots.
In this recipe, we will build similar plots with both seaborn and pandas to show the types of data (tidy versus wide) that they accept.
How to do it…
- Read in the employee dataset:
>>> employee = pd.read_csv('data/employee.csv', ... parse_dates=['HIRE_DATE...
