Standardized and Merged Datasets by Country and Year

• Day: 2023-09-29
• Time: 11:40 to 13:25
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Merging, Python, Pandas, Data Aggregation, Country Standardization

Description

Session Goal

The primary goal of this session was to standardize country names across multiple datasets and merge them by country and year to facilitate comprehensive data analysis.

Key Activities

• Developed Python code snippets to merge datasets using a unified ‘country’ column derived from a country_names dataframe, employing left joins for consistency.
• Implemented aggregation functions to summarize financial data, grouping by ‘country’ and ‘year’.
• Created a Python function to aggregate data in DataFrames, including a record count for each group using the agg() function.
• Standardized country names before aggregating and merging datasets, ensuring consistency across datasets.
• Merged three aggregated datasets with regime datasets, handling column name conflicts and summing total monetary columns.
• Utilized Python’s pandas library to group and sum data in regime_paper and regime dataframes by specified columns.
• Developed scripts for merging DataFrames by ‘Year’ and specific categories, saving results to CSV files.

Achievements

• Successfully standardized country names and merged datasets by country and year.
• Aggregated financial data and saved processed data into CSV files for further analysis.

Pending Tasks

• Review and optimize the aggregation scripts for performance improvements.
• Implement additional debugging techniques to ensure data accuracy and integrity.

Evidence

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• event_ids: []