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Learning Jupyter

You're reading from   Learning Jupyter Select, Share, Interact and Integrate with Jupyter Notebook

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884870
Length 238 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Jupyter FREE CHAPTER 2. Jupyter Python Scripting 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter JavaScript Coding 6. Interactive Widgets 7. Sharing and Converting Jupyter Notebooks 8. Multiuser Jupyter Notebooks 9. Jupyter Scala 10. Jupyter and Big Data

Log file examination


I downloaded one of the access_log files from http://www.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 timestamp, type of HTTP access, URL referenced, HTTP type, resultant 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(),"Other")...
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