Published September 26, 2023 | Version v1

Exploratory Data Analysis on Autopilot: Python's Automatic Solutions

  • 1. Student, Alliance University, Bangalore
  • 2. Assistant Professor, Ashoka Women's Engineering College, Kurnool
  • 3. Associate Professor, Ashoka Women's Engineering College, Kurnool
  • 4. Professor, Ashoka Women's Engineering College, Kurnool
  • 5. Student, Ashoka Women's Engineering College, Kurnool

Description

Python has gained immense popularity in the fields of data science and machine learning due to its extensive libraries and efficient coding capabilities, enabling time-saving solutions. This article presents a comprehensive tutorial on Automatic Exploratory Data Analysis (EDA) using Python. By leveraging Python libraries, we can swiftly extract valuable insights and statistical information from datasets, reducing the manual effort involved in data exploration. The article aims to equip readers with the knowledge and tools to efficiently analyze data, revealing hidden patterns and trends, all accomplished through just a few lines of code. By the end of this article, readers will have a clear understanding of how Python's automated EDA techniques can revolutionize the data analysis process, maximizing efficiency and productivity.

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References

  • 1. Seshadri, R., Chandakkar, P. S., & Mathur, A. (2019). AutoViz: Automatic Visualization of Data. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1018-1028.
  • 2. Polfliet, J., Van den Bossche, J., & De Wachter, S. (2020). Pandas Profiling: Automatic Exploratory Data Analysis. Journal of Open Source Software, 5(54), 2591.
  • 3. Watson, A. (2021). AutoEDA: Automatic Exploratory Data Analysis with a Focus on Statistical Testing. Journal of Open Source Software, 6(57), 2892.
  • 4. Bertrand, F. (2020). Sweetviz: Automated EDA and Visualization. Journal of Open Source Software, 5(55), 2763.
  • 5. DataPrep - A Python Library for Data Preprocessing. Retrieved from: https://github.com/sfu-db/DataPrep