Pandas is an open-source data analysis library written in python that is made to work with relational data easily and effectively. It provides various methods of data structure and algorithm to manipulate the data and perform complex operation easily.
It unleashes the power and speed of NumPy to make data analysis and processing easy for data scientists. The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional data.
In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.
Prior to Pandas, Python was majorly used for data munging and preparation. It had very little contribution to data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, prepare, manipulate, model, and analyze.
Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, statistics, analytics, etc.
Key Features of Pandas
- Fast and efficient DataFrame object with the default and customized indexing.
- Tools for loading data into in-memory data objects from different file formats.
- Data alignment and integrated handling of missing data.
- Reshaping and pivoting of data sets.
- Label-based slicing, indexing, and subsetting of large data sets.
- Columns from a data structure can be deleted or inserted.
- Group by data for aggregation and transformations.
- High-performance merging and joining of data.
- Time Series functionality.
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