Using ChatGPT to generate Python code for data manipulation

The video tutorial explains how to use ChatGPT to generate Python code for transforming a dataset into prompts and completions. It covers the process of prompt engineering, specifying dataset features, and generating code that creates a new dataframe with added columns for prompts and completions. The tutorial also discusses saving the modified dataframe for further use.

What is the primary purpose of using ChatGPT in the context of this tutorial?

• To create a new programming language

• To replace human data scientists

• To analyze movie data for trends

• To generate Python code for transforming datasets

What is the first step in prompt engineering as described in the tutorial?

• Describing the features and requirements of the project

• Writing the entire Python code manually

• Loading the dataset into a database

• Running the code without any input

Which package is imported to handle data and CSV files in the generated Python code?

• NumPy

• Pandas

• Bundles

• Matplotlib

What is the role of the OpenAI API key in the generated code?

• To encrypt the dataset

• To visualize data in graphs

• To communicate and complete the fine-tuning process

• To fetch data from a CSV file

What does the code do with the first value in each row of the dataset?

• Uses it as a completion

• Deletes it

• Ignores it

• Includes it in a prompt

How are the new prompts and completions added to the dataframe?

• By creating a new CSV file

• By appending them to existing columns

• By replacing the original data

• By adding them as new columns

What is the final step after generating the new dataframe with prompts and completions?

• Deleting the original dataset

• Saving the modified dataframe to a new CSV file

• Printing the data to the console

• Sending the data to a cloud server