Using Streamlit and Python, I developed an application with multiple stages for data cleaning. These stages mirror the steps I typically follow when wrangling a new dataset to extract insights. The application effectively automates and streamlines the data cleaning process, making it easier to handle various datasets efficiently.

Although I had some prior experience with Streamlit, building this application introduced me to the critical concept of state management for manipulating uploaded data. This aspect presented a significant learning curve and required extensive troubleshooting to implement correctly. While the individual features themselves were not overly complex, ensuring the application maintained state effectively was a challenging yet rewarding process.

From this project, I gained several valuable insights. Firstly, I realized the substantial effort required to thoroughly test even a small application of this size. Proper testing is crucial to ensure the application's reliability and functionality. Secondly, working with a cutting-edge library like Streamlit comes with its own set of challenges. The novelty of the library means there is a smaller community and fewer available resources, which can make troubleshooting and feature implementation more difficult. Despite these setbacks, the experience was immensely educational and highlighted the importance of adaptability and perseverance in the face of new technological challenges.