📅 2023-03-31 — Session: Enhanced Data Processing and Visualization Techniques
🕒 18:35–18:55
🏷️ Labels: Pandas, Matplotlib, Data Visualization, Data Cleaning, Geopandas
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to enhance data processing and visualization capabilities using Python libraries such as Pandas and Matplotlib.
Key Activities:
- Replaced commas and converted data types in DataFrame columns using Pandas.
- Converted DataFrame sums to billions and formatted them to one decimal place.
- Created histograms for multiple DataFrames using Matplotlib, including normalization of bins.
- Developed a unified function to compare DataFrames, handling NaN values and providing a lower triangular matrix of common IDs.
- Analyzed project ID overlaps and date distributions across datasets.
- Loaded GeoJSON files into GeoDataFrames using GeoPandas for geospatial analysis.
Achievements:
- Successfully implemented data cleaning and conversion techniques in Pandas.
- Enhanced data visualization methods with Matplotlib for better insights.
- Improved DataFrame comparison functions to handle NaN values effectively.
- Gained insights into project ID overlaps and date distributions through data analysis.
Pending Tasks:
- Further explore advanced data visualization techniques for geospatial data.
- Investigate additional methods for handling missing data in DataFrame comparisons.