📅 2023-05-02 — Session: Implemented Data Filtering and Aggregation Techniques
🕒 19:50–20:00
🏷️ Labels: Data Manipulation, Pandas, Audio Processing, Data Analysis, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session focused on exploring and implementing various data manipulation techniques using Python libraries such as Pandas, NumPy, and SciPy. The goal was to enhance skills in data filtering, aggregation, and audio signal processing.
Key Activities
- Data Manipulation with Pandas: Techniques for filtering, classification, and merging datasets were explored with practical code examples.
- Audio Signal Processing: Exercises on filtering noise from audio signals using Fourier transforms and low-pass filters in Python.
- Data Filtering Exercises: Implemented data filtering for automotive and sales datasets using Pandas, focusing on specific conditions like price and sales dates.
- Data Aggregation and Summary: Practical exercises on aggregating and summarizing sales data using Pandas, including calculating averages and identifying trends.
Achievements
- Successfully implemented data filtering techniques in various contexts, including automotive datasets and audio signals.
- Achieved a comprehensive understanding of data aggregation and summary methods using Pandas and NumPy.
Pending Tasks
- Further exploration of advanced audio processing techniques and their applications in different domains.
- Investigation of more complex data aggregation scenarios and their impact on data analysis outcomes.