Implemented data merging and database transition strategies

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Implemented data merging and database transition strategies

  • Day: 2023-08-17
  • Time: 21:10 to 22:45
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data Merging, Python, Pandas, Database, API

Description

Session Goal

The session aimed to explore and implement strategies for merging datasets in Python using pandas, transitioning data processing from CSV files to a database, and generating a tree view of Google Drive using Python.

Key Activities

  • Developed a structured approach to merge multiple datasets in Python using pandas, including specific merge operations and code snippets.
  • Provided Python code for merging two DataFrames and adding specific columns from external datasets.
  • Outlined steps to modify a script to read data from a database instead of a CSV, including establishing a database connection and understanding the database structure.
  • Offered guidance on connecting to a relational database, querying specific data columns, and processing results using Python and pandas.
  • Described a process for loading CSV files into DataFrames and joining them according to DBML relationships.
  • Provided a code snippet for adapting data loading procedures to new CSV file paths, including merging DataFrames and filtering columns.
  • Guided the generation of a tree view of Google Drive using the Google Drive API and Python.

Achievements

  • Successfully implemented data merging strategies in Python using pandas.
  • Transitioned data processing from CSV to database, establishing a connection and querying data.
  • Generated a tree view of Google Drive using Python and the Google Drive API.

Pending Tasks

  • Further refine database querying and data processing scripts for specific use cases.
  • Explore additional automation opportunities in data loading and merging processes.

Evidence

  • source_file=2023-08-17.sessions.jsonl, line_number=6, event_count=0, session_id=9c84195cd8b9ce70653a13b5e41dd02a78f685da70a6580d8eb9e89992aae492
  • event_ids: []

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