Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today.

           Pandas is a free, open source, originally written by Wes McKinney in 2008.

There are several advantages of Python Pandas library:

1. Data representation :- It can present data in a manner suitable for data analysis through its data structures, Series and DataFrame.

2. Less writing and more work done :- It will only be achieved through 1-2 lines with the utilization of Pandas.

3. An extensive set of features :-  Pandas provide you with an enormous collection of significant instructions and characteristics to analyze your information readily. 

4. Efficiently handles large data :- Importing big quantities of information very quickly, pandas help save a lot of time.

5. Makes data flexible and customizable :- Pandas provide an enormous number of features to apply to your information so you can customize, edit and pivot it according to your own will and willingness.

6. Convenient data filtering :- The package contains multiple methods for convenient data filtering.

Key Features of Pandas

• The Pandas library provides a really fast and efficient way to manage and explore data.
• Pandas provide a wide array of built-in tools for the purpose of reading and writing data. 
• Data these days can be found in so many different file formats, that it becomes crucial that libraries used for data analysis can read various file formats.
• Data alignment and integrated handling of missing data.
• Reshaping and pivoting of date sets.
• High performance merging and joining of data.
• Time Series functionality.

Following Applications use python Pandas Library

• Economics

• Recommendation Systems

• Stock Prediction

• Neuroscience

• Statistics

• Advertising

• Analytics

• Natural Language Processing

• Big Data

• Data Science