Debugged NaN values in Afrobarometer data

• Day: 2023-09-24
• Time: 00:15 to 00:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data_Processing, Nan, Afrobarometer, Python, Pandas

Description

Session Goal

The primary goal of this session was to identify and resolve NaN values in the datetime column of Afrobarometer datasets, and to implement a robust data processing pipeline using both R and Python.

Key Activities

• Debugged NaN values in the Afrobarometer dataset, focusing on the datetime column.
• Developed a systematic approach to load data and create a comprehensive covariate data frame.
• Implemented a Python script using pandas to read and process multiple CSV files, mirroring an R implementation.
• Addressed mixed date-time formats in pandas DataFrame using infer_datetime_format in pd.to_datetime.
• Fixed date parsing errors in pandas by using ‘format=“mixed”‘.
• Drafted an email update on the Afrobarometer dataset’s null values.

Achievements

• Successfully identified and proposed solutions for handling NaN values and mixed date-time formats in the dataset.
• Created a dual implementation in R and Python for data processing tasks.

Pending Tasks

• Finalize and send the email update regarding the Afrobarometer dataset’s null values.

Evidence

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• event_ids: []