Getting visualizations with Python

In this section, we are going to go over visualizations of the data in Python, analogous to the preceding R section. We will use plotnine to have visualizations similar to those created in R using ggplot2 and provide interpretations of the results.

Getting the data

Like in the earlier chapters, we will load the data using pandas. Just like before, the path to the XLSX file may be different for you from what I have, so adjust the filepath accordingly:

import pandas as pd
# Define the file path (may be different for you)
file_path = "./Chapter 12/diamonds.xlsx"
# Load the dataset into a pandas DataFrame
df = pd.read_excel(file_path)
# Display the first few rows of the DataFrame
print(df.head())

Note that we use the raw diamonds dataset without spitting it first and then recombining it, as it was done in the R part of the chapter.

Visualizing the data

Once we have our data loaded, we can use plotnine to create visualizations...