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

Product typeBook
Published inJul 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781839215001
Edition2nd Edition
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Authors (3):
Brian Lipp
Brian Lipp
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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
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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
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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

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Data Formatting

In this section, we will format a given dataset. The main motivations behind formatting data properly are as follows:

  • It helps all the downstream systems have a single and pre-agreed form of data for each data point, thus avoiding surprises and, in effect, there is no risk which might break the system.
  • To produce a human-readable report from lower-level data that is, most of the time, created for machine consumption.
  • To find errors in data.

There are a few ways to perform data formatting in Python. We will begin with the modulus % operator.

The % operator

Python gives us the modulus % operator to apply basic formatting on data. To demonstrate this, we will load the data by reading the combined_data.csv file, and then we will apply some basic formatting to it.

Note

The combined_data.csv file contains some sample medical data for four individuals. The file can be found here: https://packt.live/310179U.

We can load the data from the...

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The Data Wrangling Workshop - Second Edition
Published in: Jul 2020Publisher: PacktISBN-13: 9781839215001

Authors (3)

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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