What is Pandas in Python | Python Tutorial for Beginner | Python Course [Updated 2026] - igmGuru
To Know More, Visit: https://www.igmguru.com/data-science-...
Pandas is an open-source library in Python that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on top of the NumPy library of Python. Pandas is fast and it has high performance & productivity for users.
History of Pandas Library
Pandas was created by Wes McKinney in 2008 while at AQR Capital Management and was later open-sourced in 2009. Chang She joined as a major contributor in 2012, playing a key role in its development. Over time, Pandas has evolved with numerous updates.
The latest stable version of Pandas as of May 2025 is likely to be newer than version 2.2.3, which was released on September 20, 2024. For the most up-to-date version, you can check the official Pandas documentation or PyPI.
To install the latest version, use the command:
pip install pandas
Stay up to date with the power of Pandas for your data analysis needs.
Why Use Pandas?
1. Fast and efficient for manipulating and analyzing data.
2. Data from different file objects can be easily loaded.
3. Flexible reshaping and pivoting of data sets
4. Provides time-series functionality.
What can you do using Pandas?
Pandas are generally used for data science but have you wondered why? This is because pandas are used in conjunction with other libraries that are used for data science. 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. The data produced by Pandas are often used as input for plotting functions of Matplotlib, statistical analysis in SciPy, and machine learning algorithms in Scikit-learn. Here is a list of things that we can do using Pandas.
1. Data set cleaning, merging, and joining.
2. Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data.
3. Columns can be inserted and deleted from DataFrame and higher dimensional objects.
4. Powerful group by functionality for performing split-apply-combine operations on data sets.
5. Data Visulaization