Beatrik -- performed the steps of creating a Category DataFrame and Subcategory DataFrame. The output of these operations were exported as category.csv and subcategory..csv.
Jorge Torres -- performing several operations on a DataFrame to process contact information from an Excel file and then exporting it to a CSV file. Reads data from an Excel file into a Pandas DataFrame. Parses JSON-like strings in one column of the DataFrame, extracting contact information. Splits the 'name' column into 'first_name' and 'last_name' columns. Reorders the columns in the DataFrame. Check the datatypes one before exporting as CSV file. Exports the DataFrame to a CSV file.
Parris -- Steps Taken
Creation of Campaign DataFrame: The DataFrame campaign_df is created as a copy of the existing DataFrame crowdfunding_info_df. The desired columns are renamed, converted to appropriate data types, and formatted: The "blurb" column is renamed to "description". The "goal" column is converted to float data type. The "pledged" column is converted to float data type. The "launch_date" column is converted to datetime format. The "end_date" column is converted to datetime format. The "category_id" and "subcategory_id" columns are merged with respective unique numbers from category and subcategory DataFrames. A new column containing the unique four-digit contact ID number from the contact.xlsx file is created. (Contributed by Parris) Exporting DataFrame: The cleaned DataFrame is exported to a CSV file named campaign.csv. Reading Contact Information: Contact information is read from the Excel file contacts.xlsx into a DataFrame named contact_info_df. The header is specified at row 3. Nicole -- Created crowdfunding database using steps in Project 2 - Create the Crowdfunding Database
Sources: Chat GPT, Ask BCS Learning Assistant Dependencies -Python 3.x -Pandas -NumPy
Collaborators P. Burton N. Cambron J. Torres B. Arimbi