📅 2023-03-09 — Session: Enhanced Data Visualization and Cleaning Techniques
🕒 17:40–18:10
🏷️ Labels: Python, Data Visualization, Data Cleaning, Geospatial Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization and cleaning techniques using Python, focusing on geospatial analysis, data cleaning, and conditional formatting.
Key Activities
- Mapping Violence Data in Africa: Utilized matplotlib and geopandas to plot a map of Africa with violence data points.
- Identifying Non-Integer Values: Employed pandas to detect and handle non-integer values in a DataFrame.
- Replacing NaN Values: Used numpy to replace NaN values in arrays and convert data types.
- Highlighting DataFrame Cells: Implemented conditional formatting in pandas to highlight cells based on a threshold.
Achievements
- Successfully plotted geospatial data on a map of Africa.
- Enhanced data cleaning processes by identifying and handling non-integer and NaN values.
- Improved data visualization by applying conditional formatting to DataFrames.
Pending Tasks
- Further exploration of resources for researching violent conflicts in Algeria, including databases like UCDP and ACLED.