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Practical Business Intelligence

You're reading from   Practical Business Intelligence Optimize Business Intelligence for Efficient Data Analysis

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785885433
Length 352 pages
Edition 1st Edition
Languages
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Author (1):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
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Table of Contents (16) Chapters Close

Practical Business Intelligence
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Practical Business Intelligence FREE CHAPTER 2. Web Scraping 3. Analysis with Excel and Creating Interactive Maps and Charts with Power BI 4. Creating Bar Charts with D3.js 5. Forecasting with R 6. Creating Histograms and Normal Distribution Plots with Python 7. Creating a Sales Dashboard with Tableau 8. Creating an Inventory Dashboard with QlikSense 9. Data Analysis with Microsoft SQL Server

Visualizing normal distribution plots in Python


The plot most often accompanied by a histogram is a normal distribution plot. These plots come in handy when we are trying to identify averages, outliers, and distributions. Also, they are very easy to produce with Python. They require the following two libraries to be installed:

  • numpy

  • scipy

Note

sciPy will help us with producing the normalization parameters of the curve and NumPy, a library that is often associated with linear algebra, will help us perform several mathematical functions.

We installed scipy earlier in the chapter; however, numpy may need to be installed either through PyCharm or through the command line, as follows:

pip install numpy

We can begin by importing both of them into our project, as seen in the following script.

import numpy as np 
import scipy.stats as stats 

A normal distribution curve requires two values for its creation:

  • Mean

  • Standard deviation

Note

The mean sets the center of the curve and the standard deviation sets...

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