Data cleaning and manipulating
13:33
Data cleaning with ChatGPT's code interpreter is an important part of better analyzing your data.
When preparing data for analysis, it is often necessary to tidy up the dataset by modifying, fixing, and organizing the information so that it is consistent and ready for computational review. 
This can involve eliminating any flawed, unnecessary, or improperly formatted data as well as converting the data into a standardized structure. The goal is to transform raw data into a clean uniform dataset using code interpreter so that ChatGPT can efficiently analyze your newly cleaned data in the future.
Here are 10 example prompts to get your mind in the right direction when it comes to data cleaning:
1.	"Make sure this data has no blank cells."
2.	"Check that all the dates are formatted the same way."
3.	"Remove any incomplete records."
4.	"Fix any typos or misspellings."
5.	"Make the headers consistent across all columns."
6.	"Remove any duplicates I don't need."
7.	"Put all the text data in lowercase."
8.	"Separate this data into different tables if needed."
9.	"Make sure the numerical data only includes numbers."
10.	"Delete rows missing key information."
Resources

Free Practice Datasets
https://www.kaggle.com/datasets