Enhanced Places API and Data Handling Techniques

• Day: 2025-08-25
• Time: 22:35 to 22:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Places Api, Data Cleaning, Google Cloud, Csv Handling, Python

Description

Session Goal

The session aimed to refine the handling of Google Cloud’s Places API and improve data manipulation techniques for restaurant datasets.

Key Activities

• API Management: Adjusted the field mask and flatten logic for the Places API v1 by replacing places.placeId with places.name and deriving Place IDs accordingly.
• API Usage Optimization: Explored SKU tiers in Google Cloud’s Places API to understand billing impacts and optimize API usage.
• Data Loading and Cleaning: Loaded a CSV file of Buenos Aires restaurant data into a Pandas DataFrame, displaying initial data insights and performing data cleaning to address missing values.
• CSV Data Retrieval: Provided guidance on field masking for CSV data retrieval to ensure completeness of desired fields.

Achievements

• Successfully adjusted API field masks and flatten logic for improved data retrieval.
• Enhanced understanding of Google Cloud API billing and usage strategies.
• Completed initial data loading and cleaning of restaurant datasets, improving data quality.

Pending Tasks

• Further refine API usage strategies to minimize costs while maximizing data retrieval efficiency.
• Continue data analysis on the cleaned restaurant dataset to extract actionable insights.

Evidence

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• event_ids: []