Pandas (software)

Pandas (software)
Marc Garcia · BSD · source
Name	Pandas
Developer	Wes McKinney
Initial release	2008
Latest release version	1.4.3
Latest release date	2022
Operating system	Cross-platform
Platform	Python (programming language)
Genre	Data analysis
License	BSD licenses

Pandas (software) is a popular Python (programming language) library used for Data analysis and Data manipulation. It was created by Wes McKinney and is widely used in the Data science community, including by companies like Google, Microsoft, and Facebook. The library is built on top of other popular Python (programming language) libraries, including NumPy and SciPy. It is also closely related to other Data analysis libraries, such as Matplotlib and Scikit-learn.

Introduction

Pandas is a powerful library that provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables. It is widely used in various fields, including Finance with companies like Goldman Sachs and JPMorgan Chase, Scientific computing with organizations like NASA and CERN, and Data journalism with news outlets like The New York Times and The Guardian. The library is also used in Academia by researchers at universities like Harvard University and Stanford University. Pandas is often used in conjunction with other popular Data analysis libraries, including R (programming language) and Julia (programming language).

Features

Pandas provides a wide range of features, including data structures like Series (pandas) and DataFrame (pandas), which are similar to R (programming language)'s Vector (mathematics and physics) and Data frame objects. It also provides functions for data manipulation, such as Merging (pandas) and Reshaping (pandas), which are similar to SQL's Join (SQL) and Pivot table operations. Additionally, Pandas provides tools for data analysis, including GroupBy (pandas) and PivotTable (pandas), which are similar to Microsoft Excel's PivotTable and Google Data Studio's Data visualization tools. Pandas is also closely integrated with other popular Data analysis libraries, including Statsmodels and Seaborn (Python library).

Data Structures

Pandas provides two primary data structures: Series (pandas) and DataFrame (pandas). A Series (pandas) is a one-dimensional labeled array, similar to R (programming language)'s Vector (mathematics and physics) object. A DataFrame (pandas) is a two-dimensional labeled data structure with columns of potentially different types, similar to Microsoft Excel's Spreadsheet or SQL's Table (database) object. These data structures are designed to efficiently handle large datasets and provide a wide range of functions for data manipulation and analysis, including Data cleaning and Data transformation with libraries like OpenRefine and Trifacta. Pandas data structures are also compatible with other popular Data analysis libraries, including Apache Spark and Hadoop.

History

Pandas was created by Wes McKinney in 2008 while working at AQR Capital Management. The library was initially designed to provide a more efficient and flexible way to handle structured data in Python (programming language). The first version of Pandas was released in 2008 and was widely adopted by the Data science community, including by companies like Palantir Technologies and Airbnb. Since then, Pandas has become one of the most popular Data analysis libraries in Python (programming language), with a large and active community of users and contributors, including NumFOCUS and Python Software Foundation. Pandas has also been widely adopted in Academia by researchers at universities like Massachusetts Institute of Technology and University of California, Berkeley.

Applications

Pandas has a wide range of applications in various fields, including Finance with companies like Bloomberg L.P. and Thomson Reuters, Scientific computing with organizations like Los Alamos National Laboratory and European Organization for Nuclear Research, and Data journalism with news outlets like The Washington Post and ProPublica. It is also widely used in Academia by researchers at universities like University of Oxford and University of Cambridge. Pandas is often used in conjunction with other popular Data analysis libraries, including Scikit-learn and TensorFlow. Additionally, Pandas is used in various industries, including Healthcare with companies like UnitedHealth Group and Pfizer, Marketing with companies like Amazon and Facebook, and Government with agencies like National Institutes of Health and United States Census Bureau.

Development

Pandas is an open-source library, and its development is managed by NumFOCUS, a non-profit organization that supports the development of open-source scientific computing libraries. The library is maintained by a team of contributors, including Wes McKinney and other experienced developers, who work together to add new features, fix bugs, and improve performance. Pandas is also widely used in Industry by companies like IBM and SAP SE, and in Academia by researchers at universities like Carnegie Mellon University and University of Texas at Austin. The library is compatible with other popular Data analysis libraries, including R (programming language) and Julia (programming language), and is widely adopted in various fields, including Finance and Scientific computing. Pandas is also closely integrated with other popular Data analysis tools, including Jupyter Notebook and Apache Zeppelin.

Category:Python libraries