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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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 Navlani Navlani
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Navlani
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Spearman's rank correlation coefficient

Spearman's rank correlation coefficient is Pearson's correlation coefficient on the ranks of the observations. It is a non-parametric measure for rank correlation. It assesses the strength of the association between two ranked variables. Ranked variables are ordinal numbers, arranged in order. First, we rank the observations and then compute the correlation of ranks. It can apply to both continuous and discrete ordinal variables. When the distribution of data is skewed or an outlier is affected, then Spearman's rank correlation is used instead of Pearson's correlation because it doesn't have any assumptions for data distribution.

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