Enhanced Data Processing and Visualization Techniques

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📅 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 objective of this session was to enhance data processing and visualization capabilities using Python libraries such as Pandas and Matplotlib.

Key Activities

  • Data Cleaning and Conversion: Implemented methods to replace commas in DataFrame columns and convert them to float data types, facilitating better data manipulation.
  • Data Aggregation: Developed a technique to convert DataFrame column sums to billions, formatted to one decimal place.
  • [[Data Visualization]]: Created histograms for multiple DataFrames using Matplotlib, with improved binning and transparency.
  • Data Comparison: Developed a compare_dfs function to compare common IDs between DataFrames, including handling NaN values by replacing them with zeros.
  • Geospatial Data Handling: Loaded GeoJSON files into GeoDataFrames using GeoPandas for specific datasets.

Achievements

  • Successfully implemented data cleaning and conversion techniques, improving data processing efficiency.
  • Enhanced data visualization by creating detailed histograms and normalizing histogram bins.
  • Improved data comparison functions to handle NaN values effectively.
  • Loaded and processed geospatial data using GeoPandas.

Pending Tasks

  • Further exploration of project ID overlaps and date distributions across datasets for comprehensive analysis.

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