6.4. Challenge: Screen Scraping the CIA

The country data that we have been using was compiled and turned into a csv file in 2006. Much of that data comes from the CIA World Factbook. (not Facebook - as autocorrect so often thinks) However, the CIA does not provide the factbook in the form of a nice convenient CSV file, so we need to convert the data from its human readable format (as a webpage) to a Pandas friendly format – as csv file.

The goal of this exercise is to create an up to date version of our country data (at least 2017 as I write this) This will be challenging and fun as each column of the data is on its own page. But you can do it, and you will see how powerful you are when you have the right tools!

However – Think Generally! At the end of this we would like to be able to scrape the CIA data for any year not just 2017. So keep that in mind.

You can download each year of the factbook going back to the year 2000 from the CIA. Start with the year 2017. The nice thing about this is that you can unzip the file on your local computer but still use requests.

The Challenge of this project is that each variable is on its own page. So we are going to have to combine many pages into a single coherent data frame. Then when we have gathered all of the columns we can pull them together into one nice data frame and we’ll learn how to save that to a CSV file.

Again, think generally. If you design a good function for finding and scraping one piece of information make it work for all pieces of information and at the end you will have a little code that does a LOT of work.

Lets take a look at the file structure of the downloaded data from 2017

ls factbook/2017
appendix/          fonts/             index.html         rankorder/
css/               geos/              js/                scripts/
docs/              graphics/          print/             styles/
fields/            images/            print_Contact.pdf  wfbExt/

The folder that may jump out at you is called fields, so lets look at that in more detail.

import os
files = os.listdir('factbook/2017/fields')
['2001.html', '2002.html', '2003.html', '2004.html', '2006.html', '2007.html', '2008.html', '2010.html', '2011.html', '2012.html']

6.4.1. Getting a list of all fields

That may not look terribly useful but each of the numbered files contains one field that we can add to our data frame. Examine one of them closely and see if you can figure out a good marker we can use to find the field contained in each.

In fact now that you are investigating and if you stop and think for a minute you may conclude that there must be some kind of nice human readable table of contents. In fact there is take a look at the file notesanddefs.html

In the spirit of starting small and working our way up to a larger project lets write some code to scrape all of the fields and the file they are in from the notesanddefs.html file.

The webpage for that file looks like this:


Part of the Definitions and Notes page for the World Factbook 2017.

There are a couple of important things on this page that we will want to get. The feature name, like Administrative divisions or Airports and the link to the page that has all of the data for this feature for each country. That is the little icon on the right hand side.

When we screen scrape a webpage we take advantage of the fact that we can get that webpage using the requests module we learned about in the previous chapter and treat the web page as a simple text file! Lets look at part of the text for this page.


        <a name="2053"></a>
                <div id="2053" name="2053">
                        <li style="list-style-type: none; line-height: 20px; padding-bottom: 3px;" >
                        <span style="padding: 2px; display:block; background-color:#F8f8e7;" class="category">
                                <table width="100%" border="0" cellpadding="0" cellspacing="0" >
                                                <td style="width: 90%;" >Airports</td>
<td align="right" valign="middle">

                                                                        <a href="../fields/2053.html#6" title="Field info displayed for all countries in alpha order."> <img src="../graphics/field_listing_on.gif" border="0" style="padding:0px;" > </a>

                        <div id="data" class="category_data" style="width: 98%; font-weight: normal; background-color: #fff; padding: 5px; margin-left: 0px; border-top: 1px solid #ccc;" >
                        <div class="category_data" style="text-transform:none">

                                This entry gives the total number of airports or airfields recognizable from the air. The runway(s) may be paved (concrete or asphalt surfaces) or unpaved (grass, earth, sand, or gravel surfaces) and may include closed or abandoned installations.  Airports or airfields that are no longer recognizable (overgrown, no facilities, etc.) are not included. Note that not all airports have accommodations for refueling, maintenance, or air traffic control.</div>

If you have not seen HTML before, this may look a bit confusing. One of the skills you will develop as a data scientist is learning what to zero in on and what to ignore. This takes practice and experience so don’t be frustrated if it seems a bit overwhelming at the beginning.

The two things to focus on here are:

  • <td style=”width: 90%;” >Airports</td><td align=”right” valign=”middle”>
  • <a href=”../fields/2053.html#6” title=”Field info displayed for all countries in alpha order.”> <img src=”../graphics/field_listing_on.gif” border=”0” style=”padding:0px;” > </a>

The <td> is a tag that defines a cell in a table. The page you see in the figure is composed of many small tables, each table has one row, and two columns. The first column contains the feature we are interested in and the second contains the icon. This would not be considered good page design by many web developers today, but you have to learn to work with what you’ve got. The icon is embedded in an <a> tag. This is the tag that is used to link one web page to another. You click on things defined by <a> tags all the time. The part href=”../fields/2053.html#6” is a hyper-ref, that contains the URL of where the link should take you. For example This Link takes you to the Runestone homepage and looks like this in html <a href=”https://runestone.academy”>This Link</a>

The indentation of the above code not accidental, the indentation shows the hierarchical structure of an html document. things that are indented to the same level are siblings, things that are nested inside other things have a parent and child relationship. We can draw a diagram that illustrates these relationships as follows:


So, what we need to do is look at the page as a whole and see if we can find a pattern that will allow us to find the two items we are interested in. In newer web pages this can be a bit easier as designers will use classes and more descriptive attributes to set off parts of the web page. But we can still accomplish the goal.

In this case if we look carefully we see that the each table we want is contained in a span and the span has the attribute class=”category”.

Now that we know the pattern we are looking for, the big question is how do we go about finding and working with each instance of what we are looking for in our web page? We could just treat each page like a big long string and use Python’s string searching facilities. But that would be painful for sure. Instead we will turn to another of Python’s packages that will make the job fun and very manageable. That package is called BeautifulSoup The name Beautiful Soup comes from Alice in Wonderland, it is the title of a song sung by the Mock Turtle. (Yes, its turtles everywhere). Using BeautifulSoup we can get the web page into a form that we can use some real power search tools. Lets see how.

First lets import the module, and read the entire webpage as a string.

from bs4 import BeautifulSoup
page = open('../Data/factbook/2017/docs/notesanddefs.html').read()
'<!doctype html>n<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7" lang="en"> <![endif]-->n<!--[if IE 7]>    <html class="no-js lt-ie9 lt-ie8" lang="en"> <![endif]-->n<!--[if IE 8]>    <html c'

Now lets have BeautifulSoup take control

page = BeautifulSoup(page)
<!DOCTYPE html>
<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7" lang="en"> <![endif]-->
<!--[if IE 7]>    <html class="no-js lt-ie9 lt-ie8" lang="en"> <![endif]-->
<!--[if IE 8]>    <html class="no-js lt-ie9" lang="en"> <![endif]-->
<!--[if gt IE 8]><!-->
<html class="no-js" lang="en">
 <!-- InstanceBegin template="/Templates/wfbext_template.dwt.cfm" codeOutsideHTMLIsLocked="false" -->
  <meta charset="utf-8"/>
  <meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/>
  <!-- InstanceBeginEditable name="doctitle" -->
   The World Factbook
  <!-- InstanceEndEditable -->
  <meta content="" name="description"/>
  <meta content="width=device-width" name="viewport"/>
  <link href="../css/fullscreen-external.css" rel="stylesheet" type="text/css"/>
  <script src="../js/modernizr-latest.js">
  <!--developers version - switch to specific production http://modernizr.com/download/-->
  <script src="../js/jquery-1.8.3.min.

So far this doesn’t seem like much help, but lets see how we can use the search capabilities of Beautiful Soup to find all of the span tags with the class “category”. To do this we will use a search syntax that is commonly used in the web development community. It is the same syntax that is used to write the rules for the Cascading Style Sheets (CSS) that are used to make our web pages look nice.

The search syntax allows us to:

  • search for all matching tags
  • search for all matching tags with a particular class (Bingo!)
  • search for some tag that has the given id
  • search for all matching tags that are the children of some other tag
  • And other variations on this theme

The search syntax is uses a couple of special characters to indicate relationships or to identify classes and ids

  • . is used to specify a class so .category finds all tags that have the attribute class=category, tag.class makes that more specific and limits the results to just the particular tags that have that class, for example span.category will only select span tags with class=category.
  • # is used to specify an id so div#2053 would only match a div tag with id=2053. #2053 would find any tag with id=2053. Note ids are meant to be unique within a web page so #2053 should ony find a single tag.
  • ` ` indicates parent child relationship, so span table would find all of the table tags that are children of a span. Or div span table would find all the tables that are children of a span that are children of a div.

You can definitely get more complicated than that, but knowing only those 3 concepts is a really good start. To make use of the search capability we will use the select method of a BeautifulSoup object. In our case we have created a BeautifulSoup object called page. select will always return a list, so you can iterate over the list or index into the list. Lets try an example:

links = page.select('a')
<a class="go-top" href="#">GO TOP</a>

So, this tells us that there are 625 a tags on the page and the last one takes us to the top of the page.

Q-1: How many div tags are on the page?

Q-2: What kind of tag is the last tag to have the class of “cfclose” ?

Now lets put this all together and see if we can make a list of the columns and the paths to the files that contain the data. We will do this by creating a list of all of the span tags with the class category. As we iterate over each of them, we can use select to find the td tags inside the span. There should be two of them in each. The first will give us the name of the column and the second will have the path to the file contained in the href attribute.

Starting small, lets print the column names

cols = page.select("span.category")
for col in cols:
    cells = col.select('td')
    col_name cells[0].text
Administrative divisions
Age structure
Agriculture - products
Airports - with paved runways
Airports - with unpaved runways
Area - comparative
Birth rate
Broadcast media

Next lets expand on this example to get the path to the file.

cols = page.select("span.category")
for col in cols:
    cells = col.select('td')
    colname = cells[0].text
    links = cells[1].select('a')
    if len(links) > 0:
        fpath = links[0]['href']
        print(colname, fpath)
Administrative divisions ../fields/2051.html#3
Age structure ../fields/2010.html#4
Agriculture - products ../fields/2052.html#5
Airports ../fields/2053.html#6
Airports - with paved runways ../fields/2030.html#7
Airports - with unpaved runways ../fields/2031.html#8
Area ../fields/2147.html#10
Area - comparative ../fields/2023.html#11
Background ../fields/2028.html#12
Birth rate ../fields/2054.html#13
Broadcast media ../fields/2213.html#14
Budget ../fields/2056.html#15
Budget surplus (+) or deficit (-) ../fields/2222.html#16


Q-3: What is the path and filename for the file containing the data for “Internet users”? note the #xxx number that comes after .html is not part of the filename.

So, now we have the means to get the names and paths so we can populate a DataFrame with columns and data for each country. Your task is now to create a DataFrame with as many of the same columns as you can from our world_countries.csv file. You’ll have to do your own investigation into the structure of the file to find a way to scrape the information.

6.4.2. Loading all the data in rough form

Your next task is to start to construct

One more thing to note. You might assume that the country names will all be consistent from field to field but that probably isn’t the case. What is consistent is the two letter country code used in the URL to the detail information about each country as well as the id of the tr tag in the large table that contains the data you want. So, what you are are going to have to do is build a data structure for each field. you will want a name for the field, then a dictionary that maps from the two digit country code to the value of the field.

all_data = {'field name' : {coutry_code : value} ...}

It may be that the data for the field and the country is more than we want, but it will be easiest for now to just get the data in rough form then we can clean it up once we have it in a DataFrame

There are 177 different fields in the 2017 data. Loading all of them would be a huge amount of work and more data than we need. Lets start with a list that is close to our original data above

  • Country – name
  • Code2
  • Code3
  • CodeNum
  • Population
  • Area
  • Coastline
  • Climate
  • Net migration
  • Birth rate
  • Death rate
  • Infant mortality rate
  • Literacy
  • GDP
  • Government type
  • Inflation rate
  • Health expenditures
  • GDP - composition, by sector of origin
  • Land use
  • Internet users

Feel free to add others if they interest you.

If you use the structure given above you can just pass that to the DataFrame constructor and you should have something that looks like this:

Area Birth rate Climate Coastline Death rate GDP (purchasing power parity) GDP - composition, by sector of origin Government type Health expenditures Infant mortality rate Internet users Land use Literacy Population Country
aa total: 180 sq km\nland: 180 sq km\nwater: 0 sq km 12.4 births/1,000 population (2017 est.) tropical marine; little seasonal temperature v... 68.5 km 8.4 deaths/1,000 population (2017 est.) $2.516 billion (2009 est.)\n$2.258 billion (20... agriculture: 0.4%\nindustry: 33.3%\nservices: ... parliamentary democracy (Legislature); part of... NaN total: 10.7 deaths/1,000 live births\nmale: 14... total: 106,309\npercent of population: 93.5% (... agricultural land: 11.1%\narable land 11.1%; p... definition: age 15 and over can read and write... 115,120 (July 2017 est.) Aruba
ac total: 442.6 sq km (Antigua 280 sq km; Barbuda... 15.7 births/1,000 population (2017 est.) tropical maritime; little seasonal temperature... 153 km 5.7 deaths/1,000 population (2017 est.) $2.288 billion (2016 est.)\n$2.145 billion (20... agriculture: 2.3%\nindustry: 20.2%\nservices: ... parliamentary democracy (Parliament) under a c... 5.5% of GDP (2014) total: 12.1 deaths/1,000 live births\nmale: 13... total: 60,000\npercent of population: 65.2% (J... agricultural land: 20.5%\narable land 9.1%; pe... definition: age 15 and over has completed five... 94,731 (July 2017 est.) Antigua and Barbuda
ae total: 83,600 sq km\nland: 83,600 sq km\nwater... 15.1 births/1,000 population (2017 est.) desert; cooler in eastern mountains 1,318 km 1.9 deaths/1,000 population (2017 est.) $671.1 billion (2016 est.)\n$643.1 billion (20... agriculture: 0.8%\nindustry: 39.5%\nservices: ... federation of monarchies 3.6% of GDP (2014) total: 10 deaths/1,000 live births\nmale: 11.6... total: 5,370,299\npercent of population: 90.6%... agricultural land: 4.6%\narable land 0.5%; per... definition: age 15 and over can read and write... 6,072,475 (July 2017 est.)\nnote: the UN estim... United Arab Emirates
af total: 652,230 sq km\nland: 652,230 sq km\nwat... 37.9 births/1,000 population (2017 est.) arid to semiarid; cold winters and hot summers 0 km (landlocked) 13.4 deaths/1,000 population (2017 est.) $66.65 billion (2016 est.)\n$64.29 billion (20... agriculture: 22%\nindustry: 22%\nservices: 56%... presidential Islamic republic 8.2% of GDP (2014) total: 110.6 deaths/1,000 live births\nmale: 1... total: 3,531,770\npercent of population: 10.6%... agricultural land: 58.07%\narable land 20.5%; ... definition: age 15 and over can read and write... 34,124,811 (July 2017 est.) Afghanistan
ag total: 2,381,741 sq km\nland: 2,381,741 sq km\... 22.2 births/1,000 population (2017 est.) arid to semiarid; mild, wet winters with hot, ... 998 km 4.3 deaths/1,000 population (2017 est.) $609.6 billion (2016 est.)\n$582.7 billion (20... agriculture: 12.9%\nindustry: 36.2%\nservices:... presidential republic 7.2% of GDP (2014) total: 19.6 deaths/1,000 live births\nmale: 21... total: 17,291,463\npercent of population: 42.9... agricultural land: 17.4%\narable land 18.02%; ... definition: age 15 and over can read and write... 40,969,443 (July 2017 est.) Algeria

So, we have made lot of progress but we have a lot of cleanup to do! You will have noticed that many of the fields that we wanted to be numeric are definitely not. Many of them are in a more human readable format than computer digestible. You should consult the documentation on the extract method in pandas as it will help you get want you want from the strings you currently have.

6.4.3. Cleaning the data

With the data now in a DataFrame we can begin the hard work of cleaning. it up. We can do this nicely and tackle one column at a time. This is a lot of string processing and type conversion work. A lot of this can be made easier by using regular expression pattern matching. Which is a very big skill to add to your arsenal. If you haven’t used them before or are out of practice Go through this tutorial

Instructors Note: This would work well as a class project where each team gets a column to transform and then everyone can share their solution with everyone else. Or if you don’t have enough students then each team can take one or more columns.

Q-4: What is the average value for the column Infant mortality rate? Two significant digits.

6.4.4. Saving the data

We can save the data using to_csv

6.4.5. Rinse Repeat

If you try to repeat the exercise above for 2016 it works great! What about 2015? Earlier? How far back can you go before your code breaks?

What you will find when you go back illustrates one of the real ugly parts of screen scraping. Which is that you are at the mercy of the web site designer. All they have to do is make one little change to a CSS class or the id of an element and boom your whole strategy goes away

6.4.6. Comparing across the years.

If you or you and your classmates can scrape all 17 years of world factbook data you will really have achieved something. And are destined for internet fame if you make your notebooks public. You will likely have noticed that lots of people want this data in a more convenient format.

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Next Section - 6.5. Comparing forms of Government