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Wes McKinney, creator of pandas, discusses the power of pandas in data manipulation and analysis, evolution of pandas, Python frameworks for data science advancements, efficiency in data processing libraries like Apache Arrow, and the introduction of SQL Glott for standardizing SQL queries.
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- Pandas simplifies data cleaning and analysis tasks, handling tabular data efficiently.
- Community support and company contributions have helped maintain and expand Pandas' functionalities.
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The Rise of Pandas in Data Science
Pandas, a data manipulation and analysis toolkit for Python, has become essential in the Python community for handling tabular data efficiently. Created by Wes McKinney, Pandas simplifies data cleaning, manipulation, and analysis tasks, offering data frames to work with tabular data like spreadsheets. Initially built to address the lack of tools handling non-numeric data alongside NumPy's numerical computing focus, Pandas has grown to be used by millions of projects, benefitting from a dedicated community and user-friendly features.
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