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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Producing histograms

Histograms can help you inspect the distribution of any variable quickly. Histograms bin the observations into groups and present the count of observations in each group on a bar chart. It is a powerful and easy way to examine issues with your data as well as inspect visually if the data follows some known distribution.

In this recipe, we will produce histograms of all the prices from our dataset.

Getting ready

To run this recipe, you will need pandas and SQLAlchemy to retrieve the data. Matplotlib and Seaborn handle the presentation layer. Matplotlib is a 2D library for scientific data presentation. Seaborn builds on Matplotlib and provides an easier way to produce statistical charts (such as histograms, among others).

How to do it…

We assume that the data can be accessed from the PostgreSQL database. Check the Storing and retrieving from a relational database recipe from Chapter 1, Preparing the Data. The following code will produce a histogram of the prices and...

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