Reader small image

You're reading from  The Data Wrangling Workshop - Second Edition

Product typeBook
Published inJul 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781839215001
Edition2nd Edition
Languages
Tools
Right arrow
Authors (3):
Brian Lipp
Brian Lipp
author image
Brian Lipp

Brian Lipp is a Technology Polyglot, Engineer, and Solution Architect with a wide skillset in many technology domains. His programming background has ranged from R, Python, and Scala, to Go and Rust development. He has worked on Big Data systems, Data Lakes, data warehouses, and backend software engineering. Brian earned a Master of Science, CSIS from Pace University in 2009. He is currently a Sr. Data Engineer working with large Tech firms to build Data Ecosystems.
Read more about Brian Lipp

Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
author image
Shubhadeep Roychowdhury

Shubhadeep Roychowdhury holds a master's degree in computer science from West Bengal University of Technology and certifications in machine learning from Stanford. He works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics.
Read more about Shubhadeep Roychowdhury

Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
author image
Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.
Read more about Dr. Tirthajyoti Sarkar

View More author details
Right arrow

Statistics and Visualization with NumPy and Pandas

One of the great advantages of using libraries such as NumPy and pandas is that a plethora of built-in statistical and visualization methods are available, for which we don't have to search for and write new code. Furthermore, most of these subroutines are written using C or Fortran code (and pre-compiled), making them extremely fast to execute.

Refresher on Basic Descriptive Statistics

For any data wrangling task, it is quite useful to extract basic descriptive statistics, which should describe the data in ways such as the mean, median, and mode and create some simple visualizations or plots. These plots are often the first step in identifying fundamental patterns as well as oddities (if present) in the data. In any statistical analysis, descriptive statistics is the first step, followed by inferential statistics, which tries to infer the underlying distribution or process that the data might have been generated from. You...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
The Data Wrangling Workshop - Second Edition
Published in: Jul 2020Publisher: PacktISBN-13: 9781839215001

Authors (3)

author image
Brian Lipp

Brian Lipp is a Technology Polyglot, Engineer, and Solution Architect with a wide skillset in many technology domains. His programming background has ranged from R, Python, and Scala, to Go and Rust development. He has worked on Big Data systems, Data Lakes, data warehouses, and backend software engineering. Brian earned a Master of Science, CSIS from Pace University in 2009. He is currently a Sr. Data Engineer working with large Tech firms to build Data Ecosystems.
Read more about Brian Lipp

author image
Shubhadeep Roychowdhury

Shubhadeep Roychowdhury holds a master's degree in computer science from West Bengal University of Technology and certifications in machine learning from Stanford. He works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics.
Read more about Shubhadeep Roychowdhury

author image
Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.
Read more about Dr. Tirthajyoti Sarkar