DATA ANALYTICS & INSIGHTS

–Python Pandas–

Python Introduction

Python emphasizes code readability with its notable use of significant whitespace. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python’s comprehensive standard library, along with its extensive ecosystem of third-party packages, makes it a popular choice for a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. Its ease of learning and use, combined with powerful capabilities, makes Python an excellent choice for both beginners and experienced developers.

Pandas Introduction

Pandas is a powerful and flexible open-source data manipulation and analysis library for Python. It provides data structures and functions needed to work with structured data seamlessly, offering tools for cleaning, transforming, analyzing, and visualizing data. The primary data structures in pandas are Series and DataFrame.
  • Series: One-dimensional labeled array capable of holding any data type.
  • DataFrame: Two-dimensional labeled data structure with columns of different types.
Pandas is built on top of NumPy and integrates well with other Python libraries like Matplotlib for visualization and SciPy for scientific computing.

Key Features

  • Data Alignment: Automatically aligns data in computations.
  • Handling Missing Data: Provides tools to handle missing data.
  • Reshaping Data: Includes functions for reshaping data (pivot, stack, unstack).
  • Data Merging: Offers merging and joining operations for combining datasets.
  • Time Series: Equipped with capabilities for time series data analysis.

Data Loading and Input/Output (I/O)

Common Delimiters

  1. Comma (,): Used in CSV (Comma-Separated Values) files.
  2. Tab (\t): Often used in TSV (Tab-Separated Values) files.
  3. Space ( ): Used when data elements are separated by spaces.
  4. Semicolon (;): Used in place of commas to avoid conflicts with comma-containing data.
  5. Pipe (|): Used when data elements need to be clearly separated and other delimiters are present within the data.

head() and tail()

– Are two commonly used methods to quickly examine the first and last few rows of a DataFrame, respectively. These methods are very useful for getting a quick overview of a dataset.