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Project Overview

Developed a robust data merging tool, my_m_and_a, to merge customer data from three CSV files, ensuring standardized and unified formats.

Key Features

  • Implemented the my_m_and_a function to merge customer data from three CSV files, optimizing the merged data for seamless integration with the existing database schema.
  • Ensured compatibility with the existing database schema, facilitating efficient integration of the merged data.
  • Utilized Pandas for efficient data manipulation, cleaning, and standardization.
  • Employed various helper functions (clean, capitalize, transform_gender, split_string, remove_prefix) to transform and enhance data consistency.
  • Leveraged my_ds_babel to convert the merged CSV data into SQL format for easy integration.
  • Ensured seamless integration of the newly acquired customer data with the existing customer database.
  • Implemented a schema with fields such as gender, first name, last name, email, age, city, country, created_at, and referral.

Tools and Libraries

  • Python
  • Pandas
  • my_ds_babel

Usage

  1. Utilize the my_m_and_a function to merge customer data from three CSV files.
  2. Ensure the compatibility of the merged data with your existing database schema.
  3. Leverage Pandas and helper functions for data cleaning and standardization.
  4. Use my_ds_babel for converting the merged CSV data into SQL format.
  5. Integrate the newly acquired customer data seamlessly into your existing customer database.