Pandas is a powerful and versatile library that simplifies tasks of data manipulation in Python. It is built on the top of the NumPy library which means that a lot of structures of NumPy are used or replicated in Pandas. It is well-suited for working with tabular data, such as spreadsheets or SQL tables.

Pandas is a powerful and open-source library Python library for data manipulation and analysis, providing data structures and functions for efficient operations.

Pre-requisites :

• Python
• Pip or Conda (depending on user)

Installation :

Install Pandas with pip :

  pip install pandas

Install Pandas with conda :

  conda create -c conda-forge -n name_of_my_env python pandas

  # This will create a minimal environment with only Python and pandas installed. To put your self inside this environment run :
  source activate name_of_my_env
  # On Windows :
  activate name_of_my_env

Why use Pandas ?

• Pandas allows us to analyze big data and make conclusions based on statistical theories.

• Pandas can clean messy data sets, and make them readable and relevant.

• Relevant data is very important in data science.

Why Pandas over Excel ?

• Programmatic Data Manipulation :- Pandas allows for automation and complex data transformations through Python code, enabling efficient handling of large datasets and repetitive tasks.

• Efficiency with Large Datasets :- Pandas is optimized for working with large volumes of data, leveraging high-performance libraries like NumPy, ensuring faster processing compared to Excel, which may struggle with large datasets.

• Integration with Python Ecosystem :- Pandas seamlessly integrates with other Python libraries for data visualization, machine learning, and statistical analysis, enabling users to leverage the rich capabilities of Python for advanced analytics tasks, which may not be readily available within Excel.

Video (Pandas Playlist) & Blog Tutorial :