📅 2023-03-09 — Session: Data Cleaning and Visualization Techniques in Python
🕒 17:40–18:10
🏷️ Labels: Python, Data Visualization, Data Cleaning, Pandas, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to explore various data cleaning and visualization techniques using Python libraries, focusing on practical implementations in data science projects.
Key Activities
- Mapping Violence Data: Utilized Matplotlib and Geopandas to plot a map of Africa with violence data points, demonstrating geospatial visualization capabilities.
- Data Cleaning with Pandas: Developed methods to identify and handle non-integer values in DataFrames, addressing conversion errors.
- Handling NaN Values: Used Numpy to replace NaN values in arrays, showcasing data manipulation techniques.
- DataFrame Styling: Implemented conditional formatting in Pandas to highlight cells based on threshold values, enhancing data presentation.
- Research Resources: Compiled a list of resources for studying violent conflicts in Algeria, supporting data-driven research efforts.
Achievements
- Successfully implemented data visualization and cleaning techniques, improving data handling and presentation skills.
- Enhanced understanding of Python libraries for data science applications.
Pending Tasks
- Further exploration of advanced data visualization techniques and integration with other data sources.
- Additional research on conflict data for comprehensive analysis.