Pandas is an open-source Python Library used for high-performance data manipulation and data analysis using its powerful data structures. Python with pandas is in use in a variety of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. 

Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data - load, organize, manipulate, model, and analyze the data. 

Data Handling with Pandas:

• Importing and exploring data: Read various data formats (CSV, Excel, JSON) and examine basic properties.

• Data cleaning: Handle missing values, outliers, and inconsistencies.

• Data manipulation: Filter, sort, and group data based on specific conditions.

• Data transformation: Create new variables or modify existing ones.

Data Analysis with Pandas:

• Descriptive statistics: Calculate measures like mean, median, standard deviation, etc.

• Exploratory data analysis (EDA): Discover patterns, trends, and relationships within the data.

• Visualization: Create various charts and plots to visually represent the data.

• Statistical analysis: Perform hypothesis testing, correlation analysis, regression analysis, etc.