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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.

Este ejercicio forma parte del curso

Feature Engineering for Machine Learning in Python

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Instrucciones del ejercicio

  • Remove the commas (,) from the RawSalary column of so_survey_df.
  • Remove the dollar ($) signs from the RawSalary column.
  • Remove the pound (£) signs from the RawSalary column.
  • Convert the RawSalary column to float.

Ejercicio interactivo práctico

Prueba este ejercicio completando el código de muestra.

# Use method chaining
so_survey_df['RawSalary'] = so_survey_df['RawSalary']\
                              .____\
                              .____\
                              .____\
                              .____
 
# Print the RawSalary column
print(so_survey_df['RawSalary'])
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