Enhanced Data Processing and Analysis Techniques

2 min read

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

🕒 20:40–22:30
🏷️ Labels: Python, Data Processing, Ipython, Pandas, Performance
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore and implement various data processing and analysis techniques using Python, focusing on performance measurement, error handling, file operations, and data manipulation.

Key Activities

  • Performance Measurement: Utilized %timeit and %time magic commands in IPython Notebook to measure execution time, enhancing code efficiency.
  • Error Handling in CSV Processing: Modified Python code to handle OSError by ensuring the existence of output directories, incorporating performance profiling.
  • File Operations: Demonstrated copying files and CSVs between directories using os, shutil, and glob libraries in Python.
  • Data Manipulation with Pandas: Utilized the replace method to map values in DataFrames without introducing NaN values.
  • Data Analysis: Compared DataFrames to identify and count differences, using methods like sum, sort_values, and head to analyze the most common discrepancies.

Achievements

  • Successfully implemented performance measurement and error handling in data processing tasks.
  • Efficiently managed file operations within IPython Notebook.
  • Enhanced data manipulation and analysis capabilities using pandas, improving data comparison techniques.

Pending Tasks

  • Further optimization of code for large-scale data processing.
  • Exploration of additional data analysis methods to improve accuracy and efficiency.

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