Enhanced Python Data Serialization and Processing Techniques

📅 2023-02-13 — Session: Enhanced Python Data Serialization and Processing Techniques

🕒 16:00–18:50
🏷️ Labels: Python, Data Serialization, Pandas, Performance Optimization
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore and refine techniques for data serialization and processing in Python, focusing on modules like pickle, json, and pandas.

Key Activities

• Demonstrated the use of the pickle module for saving and loading dictionaries in Python, emphasizing the protocol argument.
• Provided code snippets for handling JSON serialization, noting data type limitations.
• Merged data processing code for CSV files using pandas, optimizing by eliminating unnecessary loops and directly reading data into a DataFrame.
• Simplified DataFrame aggregation using pandas.agg, applying multiple aggregation functions efficiently.
• Addressed NaN errors in DataFrame indexing with str.contains by filtering with pd.notnull.
• Measured CSV read times using Python’s time library and visualized results with matplotlib.
• Created a Python decorator for measuring function execution time, demonstrating its application.
• Optimized chunksize parameter in pd.read_csv for better memory and processing time balance.

Achievements

• Successfully demonstrated and documented techniques for efficient data serialization and processing in Python.
• Developed strategies for error handling and performance optimization in data manipulation tasks.

Pending Tasks

• Further exploration of performance measurement tools and techniques in Python, particularly in different environments and with varying data sizes.