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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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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Toc

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

Measuring central tendency

Central tendency is the trend of values clustered around the averages such as the mean, mode, and median values of data. The main objective of central tendency is to compute the center-leading value of observations. Central tendency determines the descriptive summary and provides quantitative information about a group of observations. It has the capability to represent a whole set of observations. Let's see each type of central tendency measure in detail in the coming sections.

Mean

The mean value is the arithmetic mean or average, which is computed by the sum of observations divided by the number of observations. It is sensitive to outliers and noise, with the result that whenever uncommon or unusual values are added to a group, its mean gets deviated from the typical central value. Assume x1, x2, . . . , x N is N observations. The formula for the mean of these values is shown here:

Let's compute the mean value of the communication skill score column...

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