Appendix — Python Pandas

Pandas is a Python library that provides high-performance, easy-to-use data structures and data analysis tools. It is a fundamental building block for data analysis in Python, offering flexible and efficient ways to work with structured data.

Key Features of Pandas

• Data Structures: Pandas introduces two primary data structures:
  • Series: A one-dimensional labeled array capable of holding any data type.
  • DataFrame: A two-dimensional labeled data structure with columns that can hold different data types.  
• Data Manipulation: Pandas offers a rich set of functions for data manipulation, including:
  • Selection: Selecting specific rows or columns based on labels or indices.
  • Filtering: Filtering data based on conditions.
  • Aggregation: Calculating summary statistics (e.g., mean, median, standard deviation).
  • Grouping: Grouping data by categories and performing aggregations.
  • Joining and Merging: Combining data from multiple DataFrames.
• Data Cleaning: Pandas provides tools for cleaning and preparing data, such as handling missing values, removing duplicates, and converting data types.
• Data Visualization: While Pandas itself does not have extensive visualization capabilities, it integrates well with libraries like Matplotlib and Seaborn for creating informative plots.