Correlated Economic Time Series and DataFrame Manipulation

2 min read

📅 2023-10-24 — Session: Correlated Economic Time Series and DataFrame Manipulation

🕒 00:20–02:50
🏷️ Labels: Dataframe Manipulation, Time Series Analysis, Economic Correlation, Python Programming
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to analyze correlations between economic time series, specifically focusing on the relationship between the BCRA’s international reserves and national law public securities, and to manipulate DataFrames for data analysis and visualization.

Key Activities

  • Economic Analysis: Reflected on the negative correlation between BCRA’s reserves and public securities, suggesting deeper analysis.
  • DataFrame Manipulation: Provided instructions for selecting columns using serie_id and defining the equivalent_series DataFrame.
  • Function Definition: Proposed defining the find_equivalent_series function for further data manipulation.
  • Troubleshooting: Addressed missing DataFrames and undefined variables, offering solutions for context restoration and variable redefinition.
  • [[Data Visualization]]: Enhanced plot aesthetics using Matplotlib and Seaborn, and generated plots for financial variables.
  • Time Series Alignment: Developed methods for aligning time series data with different units using resampling and proportionality factors.

Achievements

  • Clarified the correlation between economic indicators.
  • Provided comprehensive guides and code snippets for DataFrame manipulation and visualization.
  • Developed strategies for handling MultiIndex DataFrames and aligning time series data.

Pending Tasks

  • Define the find_equivalent_series function based on previous discussions.
  • Upload or generate necessary DataFrames (data_m, consultas, serie) for analysis.
  • Continue deeper analysis on the economic correlation insights identified.

Graph View

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