Explored Data Manipulation and Visualization Techniques

• Day: 2023-07-13
• Time: 23:05 to 23:45
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Pandas, Data Visualization, Clustering, Data Manipulation

Description

Session Goal

The goal of this session was to explore various data manipulation and visualization techniques using Python, specifically focusing on pandas and [[data visualization]] libraries like Seaborn and Matplotlib.

Key Activities

• DataFrame Conversion: Learned how to convert a MultiIndex DataFrame to a regular DataFrame using reset_index and to_frame methods.
• Data Reshaping: Utilized pivot_table() and unstack() methods for reshaping data from long to wide formats.
• Correlation Matrix Visualization: Created correlation matrix heatmaps using Seaborn and Matplotlib, and explored transposing matrices for visualization.
• Hierarchical Clustering: Implemented hierarchical clustering on a correlation matrix, including visualization with dendrograms and PCA.
• Data Concatenation: Demonstrated concatenating DataFrame columns into a single string using pandas apply function.

Achievements

• Successfully implemented various data manipulation techniques to reshape and visualize data.
• Enhanced understanding of hierarchical clustering and PCA for exploratory data analysis.

Pending Tasks

• Further exploration of dimensionality reduction techniques and their applications in different datasets.
• Investigate additional [[data visualization]] methods for complex datasets.

Evidence

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