Integrated function imports and data manipulation in Jupyter

• Day: 2023-03-27
• Time: 06:45 to 07:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Jupyter, Python, Data Processing, Pandas, Function Import

Description

Session Goal

The session aimed to streamline the process of importing functions across Jupyter notebooks and enhance data manipulation techniques using Pandas.

Key Activities

• Explored methods for using functions defined in one Jupyter notebook in another, emphasizing the use of the PYTHONPATH environment variable to facilitate imports.
• Implemented the aggregate_csv function from the data_process.py module in a Python script, providing a step-by-step guide and example code.
• Configured the environment by adding the ./../functions directory to the PYTHONPATH in Jupyter Notebooks to enable seamless function imports.
• Addressed a DataFrame column not found error by suggesting checks for typos and ensuring proper DataFrame loading.
• Enhanced data manipulation in Pandas by converting ‘date’ and ‘datetime’ columns to datetime objects, creating a ‘period’ column, and optimizing DataFrame handling by dropping rows with missing datetime values.

Achievements

• Successfully integrated function imports across Jupyter notebooks using the PYTHONPATH configuration.
• Improved data processing workflows by implementing efficient data manipulation techniques in Pandas.

Pending Tasks

• Further testing and validation of the aggregate_csv function implementation in various data processing scenarios.
• Continued refinement of error handling strategies for DataFrame operations.

Evidence

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• event_ids: []