Python tools for data collection 

Python script tools are essential for automating data collection and processing, leveraging the vast libraries and frameworks available in Python. These tools effectively extract data from various sources, such as APIs and online databases, ensuring timely and accurate data collection, which is crucial for maintaining up-to-date datasets. By automating the extraction process, Python scripts minimize the risk of human error while saving considerable time and effort.

Once the data is collected, Python script tools can standardize, format, and clean the raw data, preparing it for upload to ECAstats. Python scripts streamline the data-handling process from collection to preparation, making them essential for data-driven projects.

• Sustainable Development Goal (SDG)

• Gross Domestic Product (GDP)

• Balance of Payment (BOP)

• Agriculture (FAOSTAT)

• Education

• Government Finance

• Human Development

• Labour

• Mo Ibrahim Governance

• UN Comtrade

• World Bank

• Automated Data Retrieval:

  Extract data from various APIs and online databases with minimal manual intervention.

• Data Cleaning & Processing:

  Standardizes, formats, and cleans raw data to align with UNECA’s ECAstats.

• Multi-Format Support:

  Exports collected data to CSV and Excel for easy access.

• Flexible Data Export:

  Export processed data into CSV and Excel files in pivoted and non-pivoted formats.

• Data Upload:

  Prepares Excel files for uploading to ECAstats.

• Open Jupyter Notebook:

  Navigate to your script.

• Open the Script File:

  Open the script file in Jupyter Notebook.

• Run the Script:

  The script will collect and process data.

• Check the Files:

  The script will generate and save Excel files. Check them for accuracy and completeness.

• Upload the Files:

  Log in to ECAstats and upload the Excel files.