📅 2023-12-22 — Session: Organized Jupyter notebooks for data processing
🕒 14:45–15:15
🏷️ Labels: Jupyter Notebooks, Data Processing, Data Analysis, Visualization, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to establish a structured approach for organizing Jupyter notebooks, focusing on data processing, analysis, and visualization.
Key Activities:
- Developed templates for organizing Jupyter notebooks, emphasizing clarity and modularity in data processing and visualization.
- Provided detailed instructions for creating structured notebooks using Python libraries such as Dask, Pandas, Matplotlib, and Seaborn.
- Created specific notebook structures for tasks like analyzing degree distributions and performing advanced data analysis, including assortativity and quantile plots.
Achievements:
- Successfully outlined structured templates for various data analysis tasks, enhancing the organization and readability of Jupyter notebooks.
- Defined a function to calculate the assortativity coefficient using NetworkX, integrating it into the notebook structure for network analysis.
Pending Tasks:
- Further refinement of notebook templates to incorporate additional best practices and optimization techniques.