Debugged NaN values in Afrobarometer data

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📅 2023-09-24 — Session: Debugged NaN values in Afrobarometer data

🕒 00:15–00:35
🏷️ Labels: Data_Processing, Python, R, Afrobarometer, Nan, Datetime
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to identify and resolve NaN values in the datetime column of Afrobarometer datasets and ensure proper data processing using both R and Python.

Key Activities

  • Debugging NaN Values: A systematic approach was outlined to identify and resolve NaN values in the datetime column of Afrobarometer datasets. This involved loading data and creating a comprehensive covariate data frame.
  • Python Implementation: A Python script using the pandas library was provided to read and process multiple CSV files, mirroring an R implementation. This included data frame manipulation and year extraction from datetime columns.
  • Handling Mixed Date-Time Formats: Instructions were given on resolving issues from mixed date-time formats in a pandas DataFrame using the infer_datetime_format argument in the pd.to_datetime function.
  • Fixing Date Parsing Error: A solution was provided for a persistent error in date parsing by suggesting the use of ‘format=“mixed”’ in the pandas to_datetime function.
  • Email Draft: A draft email was created to provide an update on the Afrobarometer dataset, detailing the examination process and results regarding null values.

Achievements

  • Successfully debugged and resolved NaN values in the Afrobarometer datasets.
  • Implemented a Python script for processing CSV files, enhancing data handling capabilities.
  • Addressed mixed date-time format issues and date parsing errors in pandas.

Pending Tasks

  • Further testing of the Python script on additional datasets to ensure robustness.
  • Finalize and send the email draft regarding the Afrobarometer dataset analysis.

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