Handle missing values
In order to replace missing values in your time series data, you can use the command:
df = df.fillna(method="ffill")
where the argument specifies the type of method you want to use. For example, specifying bfill (i.e backfilling) will ensure that missing values are replaced using the next valid observation, while ffill (i.e. forward-filling) ensures that missing values are replaced using the last valid observation.
Recall from the previous exercise that co2_levels has 59 missing values.
This exercise is part of the course
Visualizing Time Series Data in Python
Exercise instructions
- Impute these missing values in
co2_levelsby using backfilling. - Print the total number of missing values.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Impute missing values with the next valid observation
co2_levels = co2_levels.____(method=____)
# Print out the number of missing values
____(____.____())