Data aggregation and cleaning for financial datasets

• Day: 2023-09-28
• Time: 16:00 to 16:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Aggregation, Python, Pandas, Data Cleaning, Csv Export

Description

Session Goal

The primary goal of this session was to plan and execute data aggregation and cleaning processes for multiple financial datasets, focusing on money-related columns and ensuring data consistency.

Key Activities

• Developed a structured plan for aggregating datasets by characteristics and year, considering unique value constraints.
• Identified key columns of interest for datasets df_wb, df_aiddata_china, and df_aiddata_wb.
• Implemented a Python function for data aggregation using pandas, addressing common DataFrame issues such as SettingWithCopyWarning and aggregation duplication.
• Created a loop to print money column values for data review, and provided code for parsing numeric columns by cleaning and converting string-formatted numbers.
• Developed a function to identify and handle duplicate entries in DataFrames, ensuring accurate data aggregation.
• Ensured consistent datetime formatting across DataFrames for further analysis.
• Exported aggregated data to CSV files for external review.
• Notified stakeholders, Eric and Raolin, about the availability of cross-section datasets for review.

Achievements

• Successfully aggregated and cleaned multiple datasets, addressing key data processing challenges.
• Prepared datasets for stakeholder review, facilitating further analysis and feedback.

Pending Tasks

• Await feedback from Eric and Raolin regarding the cross-section datasets to make any necessary adjustments.

Evidence

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• event_ids: []