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Home > Data > Data Analysis > Learning Jupyter
Learning Jupyter
Learning Jupyter

Learning Jupyter: Select, Share, Interact and Integrate with Jupyter Notebook

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Learning Jupyter

Chapter 2. Jupyter Python Scripting

Jupyter was originally IPython-an interactive version of Python to be used as a development environment. As such, most of the features of Python are available to you when developing your notebook.

In this chapter, we will cover the following topics:

  • Basic Python scripting
  • Python dataset access (from a library)
  • Python pandas
  • Python graphics
  • Python random numbers

Basic Python in Jupyter

In this chapter, we will be using Python scripts in a Jupyter Notebook. Jupyter does not interact with your scripts as much as it executes your script and records results. I think this is how Jupyter Notebooks have been extended to use other languages besides Python-the notebook just takes a script, runs it against a language engine, and records the output from the engine-all the while not really knowing what kind of script is being executed.

Similarly, I have not noticed any particular limitations when using Python in Jupyter. Some of the scripts I have run have taken a lot of time to run, used a lot of memory, opened new windows, and so on, all without failing. There are known issues running Python scripts that contain a __main__ execution loop and multithreaded applications.

We must open a Python section to our notebook to use Python coding. So, start your notebook, then, in the upper-right menu, select Python 2.

Note

I installed Jupyter in the Spring of 2016 on...

Python data access in Jupyter

Now that we have seen how Python works in Jupyter, including the underlying encoding, then how does Python accessing a large dataset work in Jupyter?

I started another view for pandas using Python Data Access as the name. From here, we will read in a large dataset and compute some standard statistics on the data. We are interested in seeing how we use pandas in Jupyter, how well the script performs, and what information is stored in the metadata (especially if it is a larger dataset).

Our script accesses the iris dataset that's built into one of the Python packages. All we are looking to do is to read in a slightly large number of items and calculate some basic operations on the dataset. We are really interested to see how much of the data is cached in the IPYNB file

The Python code is as follows:

# import the datasets package
from sklearn import datasets
# pull in the iris data
iris_dataset = datasets.load_iris()
# grab the first two columns of data
X = iris_dataset...

Python pandas in Jupyter

One of the most widely used features of Python is pandas. It is a third-party library of data analysis packages that can be used freely. In this example, we will develop a Python script that uses pandas to see if there is any effect to using it in Jupyter.

I am using the Titanic dataset from http://www.kaggle.com/c/titanic-gettingStarted/download/train.csv. I am sure the same data is available from a variety of sources.

Here is the Python script that we want to run in Jupyter:

from pandas import *
training_set = read_csv('train.csv')
training_set.head()
male = training_set[training_set.sex == 'male']
female = training_set[training_set.sex =='female']
womens_survival_rate = float(sum(female.survived))/len(female)
mens_survival_rate = float(sum(male.survived))/len(male)

The result is we calculate the survival rates of the Titanic's passengers based on their sex.

We create a new notebook, enter the script into appropriate cells, include...

Python graphics in Jupyter

How does Python graphics work in Jupyter?

I started another view for this named Python Graphics so as to distinguish the work from the previous work.

If we were to build a sample dataset of baby names and the number of births in a year of that name, we could then plot the data.

The Python coding is simple:

import pandas
import matplotlib
%matplotlib inline
baby_name = ['Alice','Charles','Diane','Edward']
number_births = [96, 155, 66, 272]
dataset = list(zip(baby_name,number_births))
df = pandas.DataFrame(data = dataset, columns=['Name', 'Number'])
df['Number'].plot()

The steps of the script are as follows:

  1. Import the graphics library (and data library) that we need.
  2. Define our data.
  3. Convert the data into a format that allows easy graphical display.
  4. Plot the data.

We would expect a graph of the number of births by baby name.

If we take the preceding script and place it into cells of our Jupyter ...

Basic Python in Jupyter

In this chapter, we will be using Python scripts in a Jupyter Notebook. Jupyter does not interact with your scripts as much as it executes your script and records results. I think this is how Jupyter Notebooks have been extended to use other languages besides Python-the notebook just takes a script, runs it against a language engine, and records the output from the engine-all the while not really knowing what kind of script is being executed.

Similarly, I have not noticed any particular limitations when using Python in Jupyter. Some of the scripts I have run have taken a lot of time to run, used a lot of memory, opened new windows, and so on, all without failing. There are known issues running Python scripts that contain a __main__ execution loop and multithreaded applications.

We must open a Python section to our notebook to use Python coding. So, start your notebook, then, in the upper-right menu, select Python 2.

Note

I installed Jupyter in the Spring of 2016 on a...

Python data access in Jupyter

Now that we have seen how Python works in Jupyter, including the underlying encoding, then how does Python accessing a large dataset work in Jupyter?

I started another view for pandas using Python Data Access as the name. From here, we will read in a large dataset and compute some standard statistics on the data. We are interested in seeing how we use pandas in Jupyter, how well the script performs, and what information is stored in the metadata (especially if it is a larger dataset).

Our script accesses the iris dataset that's built into one of the Python packages. All we are looking to do is to read in a slightly large number of items and calculate some basic operations on the dataset. We are really interested to see how much of the data is cached in the IPYNB file

The Python code is as follows:

# import the datasets package
from sklearn import datasets
# pull in the iris data
iris_dataset = datasets.load_iris()
# grab the first two columns of data
X = iris_dataset...
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Key benefits

  • Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide
  • This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease
  • This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc

Description

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.

Who is this book for?

This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser. Data science professionals will also find this book very useful to perform technical and scientific computing in a graphical, agile manner.

What you will learn

  • Install and run the Jupyter Notebook system on your machine
  • Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook
  • Use interactive widgets to manipulate and visualize data in real time
  • Start sharing your Notebook with colleagues
  • Invite your colleagues to work with you in the same Notebook
  • Organize your Notebook using Jupyter namespaces
  • Access big data in Jupyter

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 30, 2016
Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889455
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Jupyter

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Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
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Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Nov 30, 2016
Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889455
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Jupyter

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Table of Contents

10 Chapters
1. Introduction to Jupyter Chevron down icon Chevron up icon
1. Introduction to Jupyter
First look at Jupyter
Installing Jupyter on Windows
Installing Jupyter on Mac
Notebook structure
Notebook workflow
Basic notebook operations
Security in Jupyter
Configuration options for Jupyter
Summary
2. Jupyter Python Scripting Chevron down icon Chevron up icon
2. Jupyter Python Scripting
Basic Python in Jupyter
Python data access in Jupyter
Python pandas in Jupyter
Python graphics in Jupyter
Python random numbers in Jupyter
Summary
3. Jupyter R Scripting Chevron down icon Chevron up icon
3. Jupyter R Scripting
Adding R scripting to your installation
Basic R in Jupyter
R dataset access
R visualizations in Jupyter
R cluster analysis
R forecasting
Summary
4. Jupyter Julia Scripting Chevron down icon Chevron up icon
4. Jupyter Julia Scripting
Adding Julia scripting to your installation
Basic Julia in Jupyter
Julia limitations in Jupyter
Standard Julia capabilities
Julia visualizations in Jupyter
Julia Vega plotting
Julia parallel processing
Julia control flow
Julia regular expressions
Julia unit testing
Summary
5. Jupyter JavaScript Coding Chevron down icon Chevron up icon
5. Jupyter JavaScript Coding
Adding JavaScript scripting to your installation
JavaScript Hello World Jupyter Notebook
Basic JavaScript in Jupyter
JavaScript limitations in Jupyter
Node.js d3 package
Node.js stats-analysis package
Node.js JSON handling
Node.js canvas package
Node.js plotly package
Node.js asynchronous threads
Node.js decision-tree package
Summary
6. Interactive Widgets Chevron down icon Chevron up icon
6. Interactive Widgets
Installing widgets
Widget basics
Interact widget
Interactive widget
Widgets
Summary
7. Sharing and Converting Jupyter Notebooks Chevron down icon Chevron up icon
7. Sharing and Converting Jupyter Notebooks
Sharing notebooks
Converting notebooks
Summary
8. Multiuser Jupyter Notebooks Chevron down icon Chevron up icon
8. Multiuser Jupyter Notebooks
Sample interactive notebook
JupyterHub
Docker
Summary
9. Jupyter Scala Chevron down icon Chevron up icon
9. Jupyter Scala
Installing the Scala kernel
Scala data access in Jupyter
Scala array operations
Scala random numbers in Jupyter
Scala closures
Scala higher-order functions
Scala pattern matching
Scala case classes
Scala immutability
Scala collections
Named arguments
Scala traits
Summary
10. Jupyter and Big Data Chevron down icon Chevron up icon
10. Jupyter and Big Data
Apache Spark
Our first Spark script
Spark word count
Sorted word count
Estimate Pi
Log file examination
Spark primes
Spark text file analysis
Spark - evaluating history data
Summary

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Customer reviews

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(3 Ratings)
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1 star 100%
Drew Dec 04, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
There are things that could be useful theoretically. Starting with my first gripe - The book states that it is using Python 2. It is November, 2016 - and Python 2 is dead. I don't have a problem with using Python2, but I do have a problem with not identifying that you use Python 2. It just comes across as false advertising.Don't worry, that isn't my only gripe. There are a few things, like commands on how to use Jupyter that you may find useful, but a quick internet search may prove more useful. The word vomit continually shows little understanding of what is going on. That means this isn't even a language barrier, it is just plain bad. I included to screenshots. It shows commentary that conveys that he doesn't understand what is going on, while using built in datasets. I was using those datasets on day 1 of my exposure to the material. 'I understand that he doesn't understand why numbers don't add up to 100%. But he needs to follow it up with details on whether there is data actually missing (which represents an unaddressed category), whether there is an error in the math (like floating point error), or what is going on. As it is, I assume that this is his first exposure, and he just tells the readers that sometimes there are problems. Not that the writer will take any action to resolve them.The statistics one is a great expression of incompetence. If you are going to use a statistical methodology, at least understand statistics. The Standard Deviation won't drift a lot when rolling two six-sided dice. The theoretical odds of the sum are always between 2 and 12. The small domain limits the statistical data quite nicely. There are specific patterns which we don't need to go into, but needless to say, it makes the writer appear incompetent about his chosen content. I could read more, but why?
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Matt Mar 28, 2017
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Only Windows and Mac OS. No Linux. Not helpful.
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Axel Willy Jan 28, 2017
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Selbst für Einsteiger nicht zu gebrauchen. Ein paar Seiten Text kopiert aus Manpages, dazu sehr viele übergroße Screenshots. Informationsgehalt fast nicht vorhanden. Buch zurück geschickt.
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About the author

Profile icon Toomey
Toomey
Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.
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For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.