📅 2023-05-02 — Session: Executed data manipulation and filtering exercises
🕒 19:50–20:00
🏷️ Labels: Data Manipulation, Python, Pandas, Numpy, Scipy, Audio Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to explore and execute various data manipulation and filtering techniques using Python libraries such as Pandas, NumPy, and SciPy. This included exercises on data filtering in both data analysis and audio processing contexts.
Key Activities:
- Reviewed techniques for data manipulation using Pandas, including filtering, classification, and merging of datasets.
- Executed exercises on filtering data in audio signals using Fourier Transform and low-pass filters with libraries like librosa and SciPy.
- Conducted exercises on filtering datasets using Pandas, focusing on specific conditions like price and fuel consumption.
- Engaged in data aggregation and summary exercises using Pandas and NumPy, calculating statistics such as mean, median, and mode.
Achievements:
- Successfully applied various data filtering techniques across different domains, enhancing understanding of practical applications in data analysis and audio processing.
- Completed exercises on data aggregation and summarization, gaining insights into data trends and statistical calculations.
Pending Tasks:
- Further exploration of unique exercise suggestions for data manipulation to foster creativity and avoid repetition in future sessions.