Data formats & standards

Stefan Sobernig


October 9, 2018


Data formats

Data

Question.

What is data?

Possible views on data

Data formats

Question.

What is a data format?

What data formats do you know?

What differences between data formats did you encounter?

Data formats

Data formats

Representation of data to encode and to store these data in a computer, and to transfer data between computers

character-encoded data ("text")

binary-encoded data

Character-encoded, unstructured data

Unstructured, textual data:

Some useful Python libraries:

Character-encoded, (semi-)structured data

Question.

What is structured data? What is semi-structured data?

Character-encoded, (semi-)structured data

CSV

This is what the RFC 4180 says: Unfortunately, these rules are not always followed "in the wild":

Johann Mitlöhner, Sebastian Neumaier, Jürgen Umbrich, and Axel Polleres. Characteristics of open data CSV files. In 2nd International Conference on Open and Big Data, August 2016.

CSV





CSV

You find a CSV version of this data here: http://www.zamg.ac.at/ogd/

"Station";"Name";"Höhe m";"Datum";"Zeit";"T °C";"TP °C";"RF %";"WR °";"WG km/h";"WSR °";"WSG km/h";"N l/m²";"LDred hPa";"LDstat hPa";"SO %"

11010;"Linz/Hörsching";298;"13-10-2016";"01:00";5,8;5,3;97;230;3,6;;5,4;0;1019,4;981,3;0

11012;"Kremsmünster";383;"13-10-2016";"01:00";5,2;4;94;226;10,8;220;13,3;0;1019,6;972,3;0

11022;"Retz";320;"13-10-2016";"01:00";7;5,3;89;323;14,8;323;28,1;0;1017,7;979;0

11035;"Wien/Hohe Warte";203;"13-10-2016";"01:00";8,1;5,4;83;294;15,1;299;33,1;0;1017,4;992,2;0

11036;"Wien/Schwechat";183;"13-10-2016";"01:00";8,2;5,2;81;300;25,9;;38,9;0;1017,3;995,1;0

CSV

Question: What's NOT conformant to RFC 4180 here?

Potential issues:

XML

Various "companion standards", e.g. schema languages:

XML

Example from the entry tutorial:





XML

Potential issues: e.g.

JSON

JSON

Example: 

{  "id": 10,
    "firstname": "Alice",
    "lastname": "Doe",
    "active": true,
    "shipping_addresses":
    [ { "street": "Wonderland 1", "zip": 4711, "city": "Vienna", "country": "Austria", "home": true },
        { "street": "Welthandelsplatz 1", "zip": 1020, "city": "Vienna", "country": "Austria" },
        { "street": "MickeyMouseStreet10", "zip": 12345, "city": "Entenhausen", "country": "Germany" } ]
}

JSON

Example (vs. XML): 

<customer id="10" active="true">
    <firstname>Alice</firstname>
    <lastname>Doe</lastname>

    <shipping_addresses>
      <address home = "true"><street>Wonderland 1</street><zip>4711</zip><city>Vienna</city><country>Austria</country></address>
      <address><street>Welthandelsplatz 1</street><zip>1020</zip><city>Vienna</city><country>Austria</country></address>
      <address><street>MickeyMouseStreet10</street><zip>12345</zip><city>Entenhausen</city><country>Germany</country></address>
</customer>

JSON

Jupyter notebooks are represented as JSON documents: 

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Assignment 1\n",
    "\n",
    "This assignment is due on mm-dd-YYY-hh:mm by uploading the completed notebook at Learn@WU.\n",
    "\n",
    "### Task 1\n",
    "\n"

Summary: Character-encoded, (semi-)structured data

Excursus: Binary encoding of structured data

Data Access

Ways to access and get data

Question.

Which access methods can be used to retrieve/download a dataset?

Ways to access and get data

Downloading data

Datasets which have an URL (Web address) can be in general directly downloaded

Downloading data

Question.

Can all URLs be easily downloaded? If no, why?

Downloading data

Things to consider when downloading files.

NOTE: If you want to respect the robots.txt file, you need to first access the file (if available), inspect the allow/disallow rules and apply them to the URL you want to download.

Robots.txt

Robots.txt: Example

http://data.wu.ac.at/robots.txt 

User-agent: *
Disallow: /portalwatch/api/
Disallow: /portalwatch/portal/
Disallow: /portal/dataset/rate/
Disallow: /portal/revision/
Disallow: /portal/dataset/*/history
Disallow: /portal/api/

User-Agent: *
Crawl-Delay: 10

In this example, any robot is not allowed to access the specified sub-directories and any robot should wait 10 seconds between two requests

Accessing data via API

Some data sources can be only retrieved via Application Programming Interfaces (APIs).

Question.

Any reasons a data publisher would provide data access via an API rather than providing the data as files?

Accessing data via API

The reason for providing data access via an API:

Accessing data via API: Examples

Last.fm

The Last.fm API allows anyone to build their own programs using Last.fm data, whether they're on the Web, the desktop or mobile devices. Find out more about how you can start exploring the social music playground or just browse the list of methods below.

RottenTomates

The Rotten Tomatoes API provides access to Rotten Tomatoes' ratings and reviews, allowing approved companies and individuals to enrich their applications and widgets with Rotten Tomatoes data.

Twitter

The REST APIs provide programmatic access to read and write Twitter data. Author a new Tweet, read author profile and follower data, and more

ProgrammableWeb - an API directory for over 15K Web accessible APIs

Accessing data via a Distributed System API

Accessing data via API: WU BACH API

The WU BACH API provide machine-readable data of WU's digital ecosystem in line with many OGD [1] initiatives.

e.g https://bach.wu.ac.at/z/BachAPI/courses/search?query=data+science 

[
   [
      "18W",
      "1629",
      "Data Processing 1",
      [
         [
            6947,
            "Sobernig S."
         ],
         [
            12154,
            "Polleres A."
         ],
         [
            16682,
            "Hannak A."
         ]
      ]
   ]
   /* ... */
]

1: Open Government Data

Scraping Web data

Web scraping is the act of taking content from a Web site with the intent of using it for purposes outside the direct control of the site owner. [source]

Typical scenarios for Web scraping: Collecting data on

Some examples: Web scraping also requires to parse a HTML file using dedicated libraries.

WARNING: The legal ground for Web scraping is still not clear and we do not encourage or suggest to do Web scraping before checking if the homapage allows it . The legal topic of Web scraping will be covered in course III.

Accessing Data: the Python Way

Notebook for Accessing Data: the Python Way

download notebook

The following is the slide version of the notebook

Accessing data sources: Some Python ways

In this part, we cover two data-access methods

  1. Loading data from disk
  2. Loading data from a Web resource (URL)
We will also learn how to guess the file format by inspecting the metadata and the content of the retrieved data.

Python 3: Opening and closing data streams

The typical steps involved in consuming data are:

  1. Open a stream to read the data ( either from file or HTTP)
  2. Consume the content (e.g. loading the whole content or parts of it)
  3. Closing the stream to free up resources:

Python 3: Automatically closing data streams

The with statement is used to wrap the execution of a block with methods defined by a context manager. This allows common try / catch / finally blocks to be encapsulated for convenient reuse.

Typical use-case : automatically ensure that streams are closed.

Other use cases: timing of functions, printing of logs at the end of a call 

with COMMAND as C:
  #work with C

Loading files from disk

Given that a file is stored on the local machine, we can access the file and inspect or load its content.

There are typically two ways to read the content of a file:

  1. Load the whole content of the file and store it in a variable for further processing
  2. Read the file line by line (e.g., if files are large)
See also Chapter 7.2 in the Python 3 tutorial

File location

We need the location of the file on disk to load its content.

An absolute file path points to the same location in a file system, regardless of the current working directory. To do that, it must include the root directory. 

Windows: C:\Users\userName\data\course-syllabus.txt
Linux/Mac: /home/userName/data/course-syllabus.txt
A relative path points to the relative location of a file based on the given/current working directory. 
Windows:
Linux: ~/data/course-syllabus.txt #starting from home directory
Linux: ../data/course-syllabus.txt #go one folder back, then into data

Function: open() 

help( open )

Help on built-in function open in module __builtin__:
open(...)
    open(name[, mode[, buffering]]) -> file object

    Open a file using the file() type, returns a file object.
    This is the preferred way to open a file.
    See file.__doc__ for further information.

Read content of file into memory

the function read consumes the entire contents of the file will be read and returned 

filePath="./data/course-syllabus.txt"
#open file in read mode
f = open(filePath) # or open(filePath, 'r')

print("Full Output of content:")
content= f.read() # read the whole content and store it in variable content
print(content)
f.close() # do not forget to close the file

#better
with open(filePath) as f: # Carefully with indention and tabs
    content = f.read()
    print(content)
see also: ./src/openFile.py

Read content of file into memory 

Terminal> python3 ./src/openFile.py
Full Output of content:
This fast-paced class is intended for getting students interested in data science up to speed:
We start with an introduction to the field of "Data Science" and into the overall Data Science Process.
The primary focus of the rest of the course is on gaining fundamental knowledge for Data processing, ...
We will learn how to deal with different data formats and how to use methods and tools to integrate data from various sources, ...

This fast-paced class is intended for getting students interested in data science up to speed:
We start with an introduction to the field of "Data Science" and into the overall Data Science Process.
The primary focus of the rest of the course is on gaining fundamental knowledge for Data processing, ...
We will learn how to deal with different data formats and how to use methods and tools to integrate data from various sources, ...

Read a single line from a file

The function readline consumes a single line from the file; a newline character (\n) is left at the end of the string 

filePath="./data/course-syllabus.txt"
#open file in read mode
with open(filePath) as f:# or open(filePath, 'r')
    print("first line: "+f.readline())
    print("second line: "+f.readline())
    print("third line: "+f.readline())

Read a single line from a file: Output 

Terminal> python3 ./src/openFileReadLine.py
first line: This fast-paced class is intended for getting students interested in data science up to speed:

second line: We start with an introduction to the field of "Data Science" and into the overall Data Science Process.

third line: The primary focus of the rest of the course is on gaining fundamental knowledge for Data processing, ...

see also: ./src/openFileReadLine.py

Read lines from a file using a loop 

filePath="./data/course-syllabus.txt"
#open file in read mode
with open(filePath)  as f:# or open(filePath, 'r')
    for line in f: # loop over every line in the file (separated by newline)
        print(line)

Read lines from a file using a loop: Output 

Terminal> python3 ./src/openFileLoopLines.py
This fast-paced class is intended for getting students interested in data science up to speed:

We start with an introduction to the field of "Data Science" and into the overall Data Science Process.

The primary focus of the rest of the course is on gaining fundamental knowledge for Data processing, ...

We will learn how to deal with different data formats and how to use methods and tools to integrate data from various sources, ...

see also: ./src/openFileLoopLines.py

Resource-saving way to guess the format of a file

Question.

How can we guess the format of a file using as few resources as possible?

Resource-saving way to guess the format of a file

Getting the file size of a local file 

filePath="./data/course-syllabus.txt"

import os
fSize = os.path.getsize(filePath)

print('File size of'+filePath+' is: '+str(fSize) + ' Bytes') # typcasting of an int to str for str concatination

fileSize.py: Output 

Terminal> python3 ./src/fileSize.py
File size of./data/course-syllabus.txt is: 435 Bytes

see also: ./src/fileSize.py

Loading data from a Web resource (URL)

There exists many libaries in Python 3 to interact with Web resources using the HTTP protocol.

HTTP protocol operations

The HTTP protocol is the foundation of data communication for the World Wide Web

The current version of the protocol is HTTP1.1.

A client (browser or library) typically uses the HTTP GET operation to retrieve information about and the content of a HTTP URL.

Loading data from a Web resource: urllib

First things first. We need to load the library to be able to use it 

import urllib.request

Afterwards we need to open a connection to the HTTP Server and request the content of the URL 

urllib.request.urlopen( URL )

Urllib: accessing a URL 

import urllib.request
url="https://bach.wu.ac.at/z/BachAPI/courses/search?query=data+processing"
with urllib.request.urlopen(url) as f:
    print(f.read())

Urllib: Output 

Terminal> python3 ./src/urllib-load.py
b"[['18S', '5778', u'Data Processing 1', [[12631, u'Umbrich J.']]], ['18W', '1629', u'Data Processing 1', [[6947, u'Sobernig S.'], [12154, u'Polleres A.'], [16682, u'Hannak A.']]], ['18S', '5779', u'Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science', [[13928, u'Kirrane S.']]], ['18W', '1630', u'Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science', [[13928, u'Kirrane S.']]]]"

see also: ./src/urllib-load.py

Loading data from a Web resource: requests library

First things first. We need to install and then load the library to be able to use it. the requests library is installed by default on the course container and in any anaconda installation. 

import requests

Afterwards we need to open a connection to the HTTP Server and request the content of the URL 

requests.get( URL )

Requests: accessing a URL 

import requests
url="https://bach.wu.ac.at/z/BachAPI/courses/search?query=data+processing"

r = requests.get( url )
content=r.text
print(content)

Code: Output 

Terminal> python3 ./src/requests-load.py
[['18S', '5778', u'Data Processing 1', [[12631, u'Umbrich J.']]], ['18W', '1629', u'Data Processing 1', [[6947, u'Sobernig S.'], [12154, u'Polleres A.'], [16682, u'Hannak A.']]], ['18S', '5779', u'Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science', [[13928, u'Kirrane S.']]], ['18W', '1630', u'Data Processing 2: Scalable Data Processing, Legal & Ethical Foundations of Data Science', [[13928, u'Kirrane S.']]]]

see also: ./src/requests-load.py

Guessing the file format via the URL

Question.

How can we guess the format of the content of a URL?

Guessing the file format via the URL

HTTP Response Header

Every HTTP operation has a HTTP request and response header. A HTTP response header is a message from a HTTP server for a request. The header message contains:

HTTP Response Header Examples 

HTTP/1.1 200 OK
Date: Wed, 12 Oct 2016 12:39:12 GMT
Server: Apache/2.4.18 (Ubuntu) mod_wsgi/4.3.0 Python/2.7.12
Last-Modified: Wed, 12 Oct 2016 07:29:32 GMT
ETag: "1b3-53ea5f4498d97"
Accept-Ranges: bytes
Content-Length: 435
Vary: Accept-Encoding
Content-Type: text/plain

See also the corresponding RFC. Interesting header fields: Content-Type and Content-Length

Notice.

Python3 is case-sensitive, meaning that "Content-Type" != "content-type". Sometimes, header fields might be in lower-case or capitalized

HTTP Response Header with Urllib 

import urllib.request
url="https://bach.wu.ac.at/z/BachAPI/courses/search?query=data+processing"
req =  urllib.request.Request( url , method="HEAD")  # create a HTTP HEAD request
with urllib.request.urlopen(req) as resp:
    header = resp.info()
    # print the full header
    print("Header:")
    print(header)

    ## print the content-type
    print("Content-Type:")
    print(header['Content-Type'])

    ## print the content-type
    print("Content-Length in Bytes:")
    print(header['Content-Length'])

Urllib: Output 

Terminal> python3 ./src/urllib-header.py
Header:
Date: Sun, 04 Nov 2018 21:41:52 GMT
Server: Zope/(2.13.23, python 2.7.9, linux2) ZServer/1.1
Set-Cookie: BACH_PRXY_ID=W99noJUM7@7465DEOMkybQAAAAE; path=/; domain=.wu.ac.at; expires=Wed, 01-Nov-2028 21:41:52 GMT
Set-Cookie: BACH_PRXY_SN=W99noJUM7@7465DEOMkybQAAAAE; path=/; domain=.wu.ac.at
Last-Modified: Wed, 29 Sep 2010 07:54:06 GMT
Accept-Ranges: none
Content-Type: application/octet-stream
Content-Length: 381
Via: 1.1 bach.wu.ac.at
X-XSS-Protection: 1; mode=block
X-Frame-Options: SAMEORIGIN
X-Content-Type-Options: nosniff
Connection: close


Content-Type:
application/octet-stream
Content-Length in Bytes:
381

see also: ./src/urllib-header.py

HTTP Response Header with Requests 

import requests
url="https://bach.wu.ac.at/z/BachAPI/courses/search?query=data+processing"

r = requests.head( url ) # would also work with a HTTP Get
headerDict=r.headers
print(headerDict)

Requests: Output 

Terminal> python3 ./src/requests-header.py
{'Date': 'Sun, 04 Nov 2018 21:41:52 GMT', 'Server': 'Zope/(2.13.23, python 2.7.9, linux2) ZServer/1.1', 'Set-Cookie': 'BACH_PRXY_ID=W99noAmYkAL4tUwUhkXWIwAAACk; path=/; domain=.wu.ac.at; expires=Wed, 01-Nov-2028 21:41:52 GMT, BACH_PRXY_SN=W99noAmYkAL4tUwUhkXWIwAAACk; path=/; domain=.wu.ac.at', 'Last-Modified': 'Wed, 29 Sep 2010 07:54:06 GMT', 'Accept-Ranges': 'none', 'Content-Type': 'application/octet-stream', 'Content-Length': '381', 'Via': '1.1 bach.wu.ac.at', 'X-XSS-Protection': '1; mode=block', 'X-Frame-Options': 'SAMEORIGIN', 'X-Content-Type-Options': 'nosniff', 'Connection': 'close'}

see also: ./src/requests-header.py

Inspect Request library HTTP Response Headers 

import requests
url="http://datascience.ai.wu.ac.at/ss17_datascience/data/course-syllabus.txt"

r = requests.head( url ) # would also work with a HTTP Get
headerDict=r.headers

#>
#print all available response header keys
print("Header")
print(headerDict)

#access content-type header
if "Content-Type:" in headerDict:
    print("Content-Type:")
    print( headerDict['Content-Type'] )

Requests: Output 

Terminal> python3 ./src/requests-header-inspect.py
Header
{'Date': 'Sun, 04 Nov 2018 21:41:53 GMT', 'Server': 'Apache/2.4.29 (Ubuntu)', 'Location': 'https://datascience.ai.wu.ac.at/ss17_datascience/data/course-syllabus.txt', 'Keep-Alive': 'timeout=5, max=100', 'Connection': 'Keep-Alive', 'Content-Type': 'text/html; charset=iso-8859-1'}

see also: ./src/requests-header-inspect.py

Handling data formats and character encodings

Character Encodings

Question.

Why is that occurring? "J�rgen"

Character Encodings

Further notebooks

Excursus: How and where to find data?

Question

Question.

How can you find interesting datasets for your project?

Possible ways to find data

Google, Bing, Yahoo

Many datasets can be found using a Web Search engine such as Google, Bing or Yahoo

Combine your keyword search with tokens such as "csv", ".csv".

Such search engines offer also more advanced search features to filter for particular data formats

Fileformat search on Bing and Yahoo does not return very good results

Follow questions/search on Quora, Stackoverflow, Reddit

Quora and Stackoverflow are question-and-answer sites where people can pose any question and receive community answers

Some direct links:

Notice.

The popular useful platform change over time. Hint: Follow Metcalfe's Law

Blogs about datascience

Some general datascience blogs regularly have posts about datasets

Lists of datascience blogs:

Curated lists of datasets

Many people also provide a curated lists of public datasets or APIs to datasets. These lists can be typically found via a Google/Bing/Yahoo Search

Some examples:

Open Data Portals >

So called (Open Data) portals are catalogs for datasets.

Further links:

Question

Question.

What should you consider if you use "public" datasets

Consuming "public" datasets

Public does not necessarily mean free

Many public datasets come with certain restrictions of what one is allowed to do with the data.

The data license ( if available) typically specifies the following questions:

Notice.

More about licenses of datasets in SBWL3