Debugged and Enhanced Data Visualization Pipeline

• Day: 2023-11-02
• Time: 22:45 to 23:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Matplotlib, Data Visualization, Debugging, Dataframe, Resampling

Description

Session Goal

The session aimed to debug and enhance a [[data visualization]] pipeline using Matplotlib in Python, focusing on custom y-axis formatting and resolving errors in data handling and plotting functions.

Key Activities

• Implemented a custom y-axis formatter for Matplotlib to improve tick label readability by using abbreviations for thousands and millions.
• Addressed an error in the plot_data function related to an unsupported parameter ylims, and revised the function call to correct the argument mismatch.
• Fixed sample data generation by ensuring the inclusion of a ‘grouper’ column to match expected keys in the plot_data function.
• Troubleshot a KeyError in data grouping by verifying the presence of required columns in the DataFrame and adjusting the data preparation process.
• Resolved a resampling error by correctly applying the resample method to a DataFrame, ensuring the ‘Q’ column’s presence and adjusting the plotting code accordingly.

Achievements

• Successfully implemented a custom y-axis formatter for better [[data visualization]].
• Corrected errors in the plotting functions, including argument mismatches and data preparation issues.
• Applied the correct resampling method to the DataFrame, enabling accurate plotting of yearly average values.

Pending Tasks

• Further testing of the updated plotting functions with diverse datasets to ensure robustness.
• Optimization of the [[data visualization]] pipeline for performance improvements.

Evidence

• source_file=2023-11-02.sessions.jsonl, line_number=5, event_count=0, session_id=3ff4225a057ecc104b0cb777dc7b34784cb96ece47c282828697cdf72ee61eb9
• event_ids: []