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Jupyter for Data Science
Jupyter for Data Science

Jupyter for Data Science: Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Jupyter for Data Science

Working with Analytical Data on Jupyter

Jupyter does none of the heavy lifting for analyzing data: all the work is done by programs written in a selected language. Jupyter provides the framework to run a variety of programming language modules. So, we have a choice how we analyze data in Jupyter.

A popular choice for data analysis programming is Python. Jupyter does have complete support for Python programming. We will look at a variety of programming solutions that might tax such a support system and see how Jupyter fairs.

Data scraping with a Python notebook

A common tool for data analysis is gathering the data from a public source such as a website. Python is adept at scraping websites for data. Here, we look at an example that loads stock price information from Google Finance data.

In particular, given a stock symbol, we want to retrieve the last year of price ranges for that symbol.

One of the pages on the Google Finance site will give the last years' worth of price data for a security company. For example, if we were interested in the price points for Advanced Micro Devices (AMD), we would enter the following URL:

https://www.google.com/finance/historical?q=NASDAQ:AMD

Here, NASDAQ is the stock exchange that carries the AMD security. On the resultant Google page, there is a table of data points of interest, as seen in the following partial screenshot.

Like many sites that you will be attempting...

Using heavy-duty data processing functions in Jupyter

Python has several groups of processing functions that can tax computer system power. Let us use some of these in Jupyter and determine if the functionality performs as expected.

Using NumPy functions in Jupyter

NumPy is a package in Python providing multidimensional arrays and routines for array processing. We bring in the NumPy package using import * from numpy statement. In particular, the NumPy package defines the array keyword, referencing a NumPy object with extensive functionality.

The NumPy array processing functions run from the mundane, such as min() and max() functions (which provide the minimum and maximum values over the array dimensions provided), to...

Using SciPy in Jupyter

SciPy is an open source library for mathematics, science and, engineering. With such a wide scope, there are many areas we can explore using SciPy:

  • Integration
  • Optimization
  • Interpolation
  • Fourier transforms
  • Linear algebra
  • There are several other intense sets of functionality as well, such as signal processing

Using SciPy integration in Jupyter

A standard mathematical process is integrating an equation. SciPy accomplishes this using a callback function to iteratively calculate out the integration of your function. For example, suppose that we wanted to determine the integral of the following equation:

We would use a script like the following. We are using the definition of pi from the standard math...

Expanding on panda data frames in Jupyter

There are more functions built-in for working with data frames than we have used so far. If we were to take one of the data frames from a prior example in this chapter, the Titanic dataset from an Excel file, we could use additional functions to help portray and work with the dataset.

As a repeat, we load the dataset using the script:

import pandas as pd
df = pd.read_excel('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.xls')

We can then inspect the data frame using the info function, which displays the characteristics of the data frame:

df.info()  

Some of the interesting points are as follows:

  • 1309 entries
  • 14 columns
  • Not many fields with valid data in the body column—most were lost
  • Does give a good overview of the types of data involved

We can also use the describe function, which gives us a statistical...

Summary

In this chapter, we looked at some of the more compute intensive tasks that might be performed in Jupyter. We used Python to scrape a website to gather data for analysis. We used Python NumPy, pandas, and SciPy functions for in-depth computation of results. We went further into pandas and explored manipulating data frames. Lastly, we saw examples of sorting and filtering data frames.

In the next chapter, we will make some predictions and use visualization to validate our predictions.

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Key benefits

  • Get the most out of your Jupyter notebook to complete the trickiest of tasks in Data Science
  • Learn all the tasks in the data science pipeline—from data acquisition to visualization—and implement them using Jupyter
  • Get ahead of the curve by mastering all the applications of Jupyter for data science with this unique and intuitive guide

Description

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

Who is this book for?

This book targets students and professionals who wish to master the use of Jupyter to perform a variety of data science tasks. Some programming experience with R or Python, and some basic understanding of Jupyter, is all you need to get started with this book.

What you will learn

  • Understand why Jupyter notebooks are a perfect fit for your data science tasks
  • Perform scientific computing and data analysis tasks with Jupyter
  • Interpret and explore different kinds of data visually with charts, histograms, and more
  • Extend SQL s capabilities with Jupyter notebooks
  • Combine the power of R and Python 3 with Jupyter to create dynamic notebooks
  • Create interactive dashboards and dynamic presentations
  • Master the best coding practices and deploy your Jupyter notebooks efficiently
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 20, 2017
Length: 242 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880070
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Length: 242 pages
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Language : English
ISBN-13 : 9781785880070
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Concepts :
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Table of Contents

10 Chapters
Jupyter and Data Science Chevron down icon Chevron up icon
Working with Analytical Data on Jupyter Chevron down icon Chevron up icon
Data Visualization and Prediction Chevron down icon Chevron up icon
Data Mining and SQL Queries Chevron down icon Chevron up icon
R with Jupyter Chevron down icon Chevron up icon
Data Wrangling Chevron down icon Chevron up icon
Jupyter Dashboards Chevron down icon Chevron up icon
Statistical Modeling Chevron down icon Chevron up icon
Machine Learning Using Jupyter Chevron down icon Chevron up icon
Optimizing Jupyter Notebooks Chevron down icon Chevron up icon

Customer reviews

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1 star 100%
Steve Gailey Nov 18, 2017
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I don't think I have ever read a book so devoid of actual content. The book starts with an overly simplistic introduction to Jupiter which consists of nothing more than a brief explanation of each menu option. No discussion of distributions, installation or configuration options. No discussion of loading alternate kernels etc. So it is clearly for beginners. Except the book then launches into and example of using the Black Scholes model for option call pricing. No real explanation of what that is or how it works. So this is a finance book for experts? No because two pages further on and we are doing gamblin analysis in R. Listing after listing on apparently random subjects with no introduction, no explanation of either the code or the concepts. They are irrelevant to Jupiter for the most part and I can't see what they are trying to teach you. Truly a dreadful book - It is the first and only time I wish I had never waster my money on a book. I hope I never meet the author at a party - He must be real hard work to talk to!
Amazon Verified review Amazon
Jesse Lethe Dec 07, 2017
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Text mixes Python and R on the examples. This makes it hard for those knowing only one of the two languages. Furthermore, author jumps into different subjects, expanding too much time on topics that are irrelevant to either Jupyter or Data Sciences.
Amazon Verified review Amazon
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