The correct answer is: D. Apply a sequence of functions.
The pipe method in Pandas is a powerful tool that can be used to chain multiple data transformation steps together. This can be very useful when you need to apply a series of changes to your data, as it can save you a lot of time and effort.
To use the pipe method, you simply need to pass your data object to the pipe() function, followed by a list of functions that you want to apply to the data. The functions will be applied in the order that they are listed, and the output of each function will be passed to the next function in the chain.
For example, if you have a DataFrame called df and you want to first filter the data by a certain column, then sort the data by another column, you could use the following code:
df = df.pipe(lambda df: df[df['column1'] == 'value1']).pipe(lambda df: df.sort_values('column2'))
This code would first filter the DataFrame to only include rows where the value in the column1 column is equal to 'value1'. It would then sort the DataFrame by the values in the column2 column.
The pipe method is a very flexible tool that can be used to perform a variety of data transformation tasks. It is a great way to save time and effort when you need to apply multiple changes to your data.
Here is a brief explanation of each option:
- Option A: Filtering data. This is not the primary purpose of the “pipe” method in Pandas. The “pipe” method is designed to apply a sequence of functions, not to filter data.
- Option B: Sorting data. This is not the primary purpose of the “pipe” method in Pandas. The “pipe” method is designed to apply a sequence of functions, not to sort data.
- Option C: Grouping and aggregating data. This is not the primary purpose of the “pipe” method in Pandas. The “pipe” method is designed to apply a sequence of functions, not to group and aggregate data.
- Option D: Apply a sequence of functions. This is the primary purpose of the “pipe” method in Pandas. The “pipe” method can be used to chain multiple data transformation steps together, which can be very useful when you need to apply a series of changes to your data.