Introduction to Pandas

Importance of Pandas for Data Analysis

• Pandas is a crucial library for data analysis in Python.
• Steps involving Pandas:
  • Getting data from various sources (databases, Excel, CSV, etc.)
  • Processing data (combining, merging, analyzing)
  • Visualizing data (creating charts)
  • Creating reports
  • Performing simple statistical analysis
  • Aiding in machine learning tasks (in combination with other libraries)
• Version 1.0 Released: Indicates maturity and reliability.
• Primary library for Data Analysis in Python.

Introduction to Pandas Data Structures

Series and DataFrame

• Two main data structures:
  • Series: Similar to a list but with more functionality.
  • DataFrame: Similar to an Excel table; more familiar to most users.

Series in Detail

• Series: Ordered sequence of elements with an index.
• Looks like a Python list, but with significant differences.
  • Data type: All elements in a series have the same data type (e.g., float64).
    • For example, population data of the G7 countries in millions.
  • Underlying data structure: Uses a NumPy array to store objects.
  • Series can have a name: Helpful when part of a DataFrame column.
  • Indexing: Similar to lists but more explicit. Elements can be accessed by an index.
  • Difference from lists:
    • Lists: Sequential index implied (0, 1, 2, ...).
    • Series: Explicit and arbitrary indexing. Provides meaningful labels/indices.
  • Similarity to dictionaries: Series elements can be accessed by keys or labels, but series remains ordered unlike traditional Python dictionaries.

Creating Series

• Methods to create a series:
  • Pass the data and indices in the creation step.
  • Indexing done directly by specified indices.

Advantages of Using Series

• Ordered structure like a list.
• Labeled indices like a dictionary.
• Combines benefits of lists and dictionaries while providing more functionalities.