Data manipulation and visualization session

• Day: 2023-10-24
• Time: 00:20 to 02:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Manipulation, Visualization, Python, Pandas, Matplotlib

Description

Session Goal: The session aimed to manipulate and visualize economic time series data using Python, focusing on data alignment, filtering, and enhancing plot aesthetics.

Key Activities:

• Analyzed the correlation between international reserves of BCRA and national law public securities, noting a negative correlation.
• Provided instructions and code snippets for selecting specific columns from DataFrames using serie_id values.
• Defined and implemented the find_equivalent_series function to facilitate data selection.
• Addressed missing DataFrames (data_m, consultas, serie) necessary for analysis, offering options for data upload or sample generation.
• Guided on filtering MultiIndex DataFrame columns and enhancing plots using Matplotlib and Seaborn.
• Troubleshot issues with undefined variables, MultiIndex column renaming, and discrepancies in column names.
• Generated plots for financial variables using Matplotlib and Seaborn.
• Aligned time series data with different units by resampling and applying proportionality factors.

Achievements:

• Successfully defined and utilized the find_equivalent_series function.
• Enhanced plot aesthetics and clarity using Matplotlib and Seaborn.
• Resolved issues related to undefined variables and column renaming in DataFrames.

Pending Tasks:

• Further analysis of the correlation between BCRA reserves and public securities.
• Complete the upload or generation of missing DataFrames for full analysis.

Evidence

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• event_ids: []