Exploring Data Manipulation with Python Pandas

What is the primary reason to use pandas over Excel?

• Pandas is easier to learn than Excel.

• Pandas has a better user interface.

• Pandas offers more flexibility and can handle larger datasets than Excel.

• Pandas can only handle smaller datasets than Excel.

How do you load data into pandas?

• Using the 'import_data' function.

• By manually entering data.

• Using the 'read_csv' function.

• Using the 'load_data' function.

Which function is used to sort values in a DataFrame?

• sort_data()

• arrange()

• order_by()

• sort_values()

How can you add a new column to a DataFrame in pandas?

• Using the 'add_column' method.

• By assigning values to a new column name.

• Using the 'insert_column' function.

• By calling the 'new_column' method.

What is the correct way to save a DataFrame to a CSV file?

• df.write_csv('filename.csv')

• df.export_csv('filename.csv')

• df.to_csv('filename.csv')

• df.save('filename.csv')

How can you save a DataFrame to a CSV file without including the index?

• Use the to_csv() function with index=False

• Use the save_csv() function without specifying the index

• Use the to_csv() function with index=True

• DataFrames cannot be saved to CSV without the index

What is required to filter a DataFrame based on multiple conditions?

• No special syntax is needed for multiple conditions

• Use the & operator without parentheses

• Use the | operator with parentheses

• Separate conditions with parentheses and use the & operator

How can you reset the index of a filtered DataFrame?

• Call the reset_index() method on the DataFrame

• Delete the original index and create a new one manually

• Use the reindex() method with the new index

• Indexes cannot be reset in Pandas

Which function is used to group data in a DataFrame and calculate aggregate statistics?

• groupby()

• aggregate()

• group()

• collect()

How can you handle large datasets that do not fit into memory in Pandas?

• Use the chunksize parameter in the read_csv() function

• Split the file into smaller files manually and read them one by one

• Increase the memory of the machine

• Pandas cannot handle datasets that do not fit into memory