Method chaining
When applying multiple operations on the same column (like in the previous exercises), you made the changes in several steps, assigning the results back in each step. However, when applying multiple successive operations on the same column, you can "chain" these operations together for clarity and ease of management. This can be achieved by calling multiple methods sequentially:
# Method chaining
df['column'] = df['column'].method1().method2().method3()
# Same as
df['column'] = df['column'].method1()
df['column'] = df['column'].method2()
df['column'] = df['column'].method3()
In this exercise you will repeat the steps you performed in the last two exercises, but do so using method chaining.
This exercise is part of the course
Feature Engineering for Machine Learning in Python
Exercise instructions
- Remove the commas (
,
) from theRawSalary
column ofso_survey_df
. - Remove the dollar (
$
) signs from theRawSalary
column. - Remove the pound (
£
) signs from theRawSalary
column. - Convert the
RawSalary
column to float.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Use method chaining
so_survey_df['RawSalary'] = so_survey_df['RawSalary']\
.____\
.____\
.____\
.____
# Print the RawSalary column
print(so_survey_df['RawSalary'])