Missing values
It is very rare to find a dataset that doesn't contain any missing values. Missing values are represented as NaN
in pandas. You can use the isnull()
pandas function to check for missing values.
pd.isnull(df['column'])
will return True
if the value is missing,
or False
if there are no missing values.
Compared to R, missing values behave a little differently in Python.
For example, the .mean()
method automatically ignores missing values in Python.
You can also recode missing values with the .fillna()
method. This will replace all missing values in the column with the provided value.
In this exercise, we've modified the tips
dataset such that it contains some missing values.
This exercise is part of the course
Python for R Users
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the rows where total_bill is missing
print(tips.loc[____(____)])