Tidy data
Reshaping your data has several applications. One important application is to switch from a data analytics friendly to a reporting friendly format. This concept is further expanded in the Tidy data paper by Hadley Wickham.
Data in a tidy format also allows you to perform groupby operations as seen in the previous exercise.
In this exercise you will use melt() and .pivot_table() from pandas to reshape your data from one
shape to another. Remember that when you call .pivot_table() on your data, you also need to call the .reset_index() method to get your original DataFrame back.
Before you start reshaping the airquality DataFrame, inspect it in the shell. We've imported pandas as pd.
Este ejercicio forma parte del curso
Python for R Users
Ejercicio interactivo práctico
Prueba este ejercicio completando el código de muestra.
# Melt the airquality DataFrame
airquality_melted = ____(____, id_vars=['Day', 'Month'])
print(airquality_melted)