Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The Jupyter Notebook 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, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Log file examination


I downloaded one of the access_log files from monitorware.com. Like any other web access log, we have one line per entry, like this:

64.242.88.10 - - [07/Mar/2004:16:05:49 -0800] "GET /twiki/bin/edit/Main/Double_bounce_sender?topicparent=Main.ConfigurationVariables HTTP/1.1" 401 12846 

The first part is the IP address of the caller, followed by a timestamp, the type of HTTP access, the URL referenced, the HTTP type, the resulting HTTP response code, and finally the number of bytes in the response.

We can use Spark to load in and parse out some statistics of the log entries, as in this script:

import pyspark
if not 'sc' in globals():
    sc = pyspark.SparkContext()

textFile = sc.textFile("access_log")
print(textFile.count(), "access records")

gets = textFile.filter(lambda line: "GET" in line)
print(gets.count(), "GETs")

posts = textFile.filter(lambda line: "POST" in line)
print(posts.count(), "POSTs")

other = textFile.subtract(gets).subtract(posts)
print(other.count(...