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The Pandas' Arsenal: 20 Powerful Functions for Data Science Warriors

T. Aditya Sai Srinivas, B. Thulasi Thanmai, A. David Donald, G. Thippanna, I. Venkat Sai, I. V. Dwaraka Srihith

Abstract


This paper explores the use of Pandas, a popular Python library, to tackle a wide range of data science tasks efficiently. By leveraging just 20 key Pandas functions, it is possible to accomplish approximately 80% of the common data manipulation and analysis tasks encountered in data science projects. The paper provides an overview of these functions, highlighting their capabilities and how they can be applied in various scenarios. By understanding and mastering these functions, data scientists can significantly enhance their productivity and streamline their workflow.


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References


McKinney, W. (2012). Python for Data Analysis. OReilly Media. Inc., Sebastopol.

Ghoniem, A, Mohamed F,(2015). Data Cleaning and Preprocessing Techniques: A Review. International Journal of Computer Applications.120(1).

Pandas Documentation: The official documentation for Pandas is available at https://pandas.pydata.org/docs/. It provides detailed information about Pandas functions, methods, and usage examples.

Kaggle Kernels: Kaggle Kernels are accessible on the Kaggle platform at https://www.kaggle.com/kernels. You can search for specific kernels related to data cleaning, preprocessing, and analysis using Pandas.


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