Analyzing missingness percentage
Before jumping into treating missing data, it is essential to analyze the various factors surrounding missing data. The elementary step in analyzing the data is to analyze the amount of missingness, that is the number of values missing for a variable. In this exercise, you'll calculate the total number of missing values per column and also find out the percentage of missing values per column.
In this exercise, you will load the 'airquality' dataset by parsing the Date column and then calculate the sum of missing values and the degree of missingness in percent on the nullity DataFrame
Este ejercicio forma parte del curso
Dealing with Missing Data in Python
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Load the air-quality.csv dataset
airquality = pd.read_csv(___, parse_dates=[___], index_col=___)