Developed advanced correlation functions in Pandas

• Day: 2023-10-23
• Time: 22:10 to 23:45
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Pandas, Data Analysis, Correlation, Time Series

Description

Session Goal

The goal of this session was to enhance data analysis capabilities by developing advanced correlation functions using the Pandas library in Python.

Key Activities

• Developed a function to compute correlations of a specified column with all other columns in a Pandas DataFrame.
• Implemented a MultiIndex approach to organize correlation results more effectively.
• Explored the use of the min_periods parameter to ensure correlations are calculated only with a minimum number of valid observations.
• Addressed handling of missing values in correlation calculations, ensuring accurate results by considering only non-missing data.
• Created a function to calculate correlations between a focal time series and other time series, incorporating thresholds for filtering results.

Achievements

• Successfully implemented multiple Python functions to calculate correlations in various contexts, including handling missing values and using MultiIndex for better result organization.

Pending Tasks

• Further testing and validation of the developed functions with larger datasets to ensure performance and accuracy.
• Integration of these functions into larger data analysis workflows for practical application.

Evidence

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• event_ids: []