Integrated function imports and data manipulation in Jupyter

2 min read

📅 2023-03-27 — Session: Integrated function imports and data manipulation in Jupyter

🕒 06:45–07:10
🏷️ Labels: Jupyter, Python, Data Processing, Pandas, Function Import
📂 Project: Dev
⭐ Priority: MEDIUM

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.

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