Enhanced Data Processing and Visualization with Python

• Day: 2023-07-30
• Time: 17:55 to 18:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Pandas, Seaborn, Data Processing, Data Visualization

Description

Session Goal

The session aimed to enhance data processing and visualization capabilities using Python libraries, specifically Seaborn and Pandas.

Key Activities

• Color Palette Generation: Utilized Seaborn’s color_palette() function to interpolate and generate a color palette based on three given colors. This involved executing practical code examples to demonstrate the functionality.
• CSV to Dictionary Conversion: Demonstrated how to read a CSV file using Pandas and convert a specific column from hexadecimal format to an RGB tuple in a dictionary format.
• Quantile Calculation: Provided code snippets for calculating the 0.1 and 0.9 quantiles of a DataFrame using Pandas’ quantile method and .agg() method.
• Conditional Value Modification: Showcased Python code for modifying a dictionary value conditionally, based on data read from a CSV file.

Achievements

• Successfully generated color palettes using Seaborn, enhancing visualization techniques.
• Converted CSV data to dictionary format, facilitating easier data manipulation.
• Calculated multiple quantiles in a DataFrame, providing insights into data distribution.
• Implemented conditional logic to modify dictionary values based on CSV data.

Pending Tasks

• Further exploration of advanced [[data visualization]] techniques using Seaborn.
• Optimization of data processing workflows in Pandas for larger datasets.

Evidence

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• event_ids: []