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Numerical Computing with Python

You're reading from   Numerical Computing with Python Harness the power of Python to analyze and find hidden patterns in the data

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789953633
Length 682 pages
Edition 1st Edition
Languages
Concepts
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Authors (5):
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Pratap Dangeti Pratap Dangeti
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Pratap Dangeti
Theodore Petrou Theodore Petrou
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Theodore Petrou
Allen Yu Allen Yu
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Allen Yu
Aldrin Yim Aldrin Yim
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Aldrin Yim
Claire Chung Claire Chung
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Claire Chung
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Table of Contents (21) Chapters Close

Title Page
Contributors
About Packt
Preface
1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Tree-Based Machine Learning Models 3. K-Nearest Neighbors and Naive Bayes 4. Unsupervised Learning 5. Reinforcement Learning 6. Hello Plotting World! 7. Visualizing Online Data 8. Visualizing Multivariate Data 9. Adding Interactivity and Animating Plots 10. Selecting Subsets of Data 11. Boolean Indexing 12. Index Alignment 13. Grouping for Aggregation, Filtration, and Transformation 14. Restructuring Data into a Tidy Form 15. Combining Pandas Objects 1. Other Books You May Enjoy Index

Transforming through a weight loss bet


One method to increase motivation to lose weight is to make a bet with someone else. The scenario in this recipe will track weight loss from two individuals over the course of a four-month period and determine a winner.

Getting ready

In this recipe, we use simulated data from two individuals to track the percentage of weight loss over the course of four months. At the end of each month, a winner will be declared based on the individual who lost the highest percentage of body weight for that month. To track weight loss, we group our data by month and person, then call the transform method to find the percentage weight loss at each week from the start of the month.

How to do it...

  1. Read in the raw weight_loss dataset, and examine the first month of data from the two people, Amy and Bob. There are a total of four weigh-ins per month:
>>> weight_loss = pd.read_csv('data/weight_loss.csv')
>>> weight_loss.query('Month == "Jan"')
  1. To determine the...
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