While many of the examples in this book have focused on reading files and looking for data in those files, there are many different sources of information when one considers the Internet.
In this chapter we will pretend to be a web browser and retrieve web pages using the HyperText Transport Protocol (HTTP). Then we will read through the web page data and parse it.
The network protocol that powers the web is actually quite simple and there is built-in support in Python called sockets which makes it very easy to make network connections and retrieve data over those sockets in a Python program.
A socket is much like a file, except that a single socket provides a two-way connection between two programs. You can both read from and write to the same socket. If you write something to a socket, it is sent to the application at the other end of the socket. If you read from the socket, you are given the data which the other application has sent.
But if you try to read a socket when the program on the other end of the socket has not sent any data—you just sit and wait. If the programs on both ends of the socket simply wait for some data without sending anything, they will wait for a very long time.
So an important part of programs that communicate over the Internet is to have some sort of protocol. A protocol is a set of precise rules that determine who is to go first, what they are to do, and then what the responses are to that message, and who sends next, and so on. In a sense the two applications at either end of the socket are doing a dance and making sure not to step on each other’s toes.
There are many documents which describe these network protocols. The HyperText Transport Protocol is described in the following document:
This is a long and complex 176-page document with a lot of detail. If you find it interesting, feel free to read it all. But if you take a look around page 36 of RFC2616 you will find the syntax for the GET request. To request a document from a web server, we make a connection to the data.py4e.org server on port 80, and then send a line of the form
GET http://data.py4e.org/romeo.txt HTTP/1.0
where the second parameter is the web page we are requesting, and then we also send a blank line. The web server will respond with some header information about the document and a blank line followed by the document content.
Perhaps the easiest way to show how the HTTP protocol works is to write a very simple Python program that makes a connection to a web server and follows the rules of the HTTP protocol to requests a document and display what the server sends back.
import socket mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) mysock.connect(('data.py4e.org', 80)) mysock.send('GET http://data.py4e.org/romeo.txt HTTP/1.0\n\n') while True: data = mysock.recv(512) if ( len(data) < 1 ) : break print data mysock.close()
First the program makes a connection to port 80 on the server www.py4inf.com. Since our program is playing the role of the “web browser”, the HTTP protocol says we must send the GET command followed by a blank line.
Once we send that blank line, we write a loop that receives data in 512-character chunks from the socket and prints the data out until there is no more data to read (i.e., the recv() returns an empty string).
The program produces the following output:
HTTP/1.1 200 OK Date: Sun, 14 Mar 2010 23:52:41 GMT Server: Apache Last-Modified: Tue, 29 Dec 2009 01:31:22 GMT ETag: "143c1b33-a7-4b395bea" Accept-Ranges: bytes Content-Length: 167 Connection: close Content-Type: text/plain But soft what light through yonder window breaks It is the east and Juliet is the sun Arise fair sun and kill the envious moon Who is already sick and pale with grief
The output starts with headers which the web server sends to describe the document. For example, the Content-Type header indicates that the document is a plain text document (text/plain).
After the server sends us the headers, it adds a blank line to indicate the end of the headers, and then sends the actual data of the file romeo.txt.
This example shows how to make a low-level network connection with sockets. Sockets can be used to communicate with a web server or with a mail server or many other kinds of servers. All that is needed is to find the document which describes the protocol and write the code to send and receive the data according to the protocol.
However, since the protocol that we use most commonly is the HTTP web protocol, Python has a special library specifically designed to support the HTTP protocol for the retrieval of documents and data over the web.
In the above example, we retrieved a plain text file which had newlines in the file and we simply copied the data to the screen as the program ran. We can use a similar program to retrieve an image across using HTTP. Instead of copying the data to the screen as the program runs, we accumulate the data in a string, trim off the headers, and then save the image data to a file as follows:
import socket import time mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) mysock.connect(('www.py4inf.com', 80)) mysock.send('GET http://www.py4inf.com/cover.jpg HTTP/1.0\n\n') count = 0 picture = ""; while True: data = mysock.recv(5120) if ( len(data) < 1 ) : break # time.sleep(0.25) count = count + len(data) print len(data),count picture = picture + data mysock.close() # Look for the end of the header (2 CRLF) pos = picture.find("\r\n\r\n"); print 'Header length',pos print picture[:pos] # Skip past the header and save the picture data picture = picture[pos+4:] fhand = open("stuff.jpg","wb") fhand.write(picture); fhand.close()
When the program runs it produces the following output:
$ python urljpeg.py 2920 2920 1460 4380 1460 5840 1460 7300 ... 1460 62780 1460 64240 2920 67160 1460 68620 1681 70301 Header length 240 HTTP/1.1 200 OK Date: Sat, 02 Nov 2013 02:15:07 GMT Server: Apache Last-Modified: Sat, 02 Nov 2013 02:01:26 GMT ETag: "19c141-111a9-4ea280f8354b8" Accept-Ranges: bytes Content-Length: 70057 Connection: close Content-Type: image/jpeg
You can see that for this url, the Content-Type header indicates that body of the document is an image (image/jpeg). Once the program completes, you can view the image data by opening the file stuff.jpg in an image viewer.
As the program runs, you can see that we don’t get 5120 characters each time we call the recv() method. We get as many characters as have been transferred across the network to us by the web server at the moment we call recv(). In this example, we either get 1460 or 2920 characters each time we request up to 5120 characters of data.
Your results may be different depending on your network speed. Also note that on the last call to recv() we get 1681 bytes, which is the end of the stream, and in the next call to recv() we get a zero-length string that tells us that the server has called close() on its end of the socket and there is no more data forthcoming.
We can slow down our successive recv() calls by uncommenting the call to time.sleep(). This way, we wait a quarter of a second after each call so that the server can “get ahead” of us and send more data to us before we call recv() again. With the delay, in place the program executes as follows:
$ python urljpeg.py 1460 1460 5120 6580 5120 11700 ... 5120 62900 5120 68020 2281 70301 Header length 240 HTTP/1.1 200 OK Date: Sat, 02 Nov 2013 02:22:04 GMT Server: Apache Last-Modified: Sat, 02 Nov 2013 02:01:26 GMT ETag: "19c141-111a9-4ea280f8354b8" Accept-Ranges: bytes Content-Length: 70057 Connection: close Content-Type: image/jpeg
Now other than the first and last calls to recv(), we now get 5120 characters each time we ask for new data.
There is a buffer between the server making send() requests and our application making recv() requests. When we run the program with the delay in place, at some point the server might fill up the buffer in the socket and be forced to pause until our program starts to empty the buffer. The pausing of either the sending application or the receiving application is called “flow control”.
While we can manually send and receive data over HTTP using the socket library, there is a much simpler way to perform this common task in Python by using the urllib library.
Using urllib, you can treat a web page much like a file. You simply indicate which web page you would like to retrieve and urllib handles all of the HTTP protocol and header details.
The equivalent code to read the romeo.txt file from the web using urllib is as follows:
import urllib fhand = urllib.urlopen('http://www.py4inf.com/code/romeo.txt') for line in fhand: print line.strip()
Once the web page has been opened with urllib.urlopen, we can treat it like a file and read through it using a for loop.
When the program runs, we only see the output of the contents of the file. The headers are still sent, but the urllib code consumes the headers and only returns the data to us.
But soft what light through yonder window breaks It is the east and Juliet is the sun Arise fair sun and kill the envious moon Who is already sick and pale with grief
As an example, we can write a program to retrieve the data for romeo.txt and compute the frequency of each word in the file as follows:
import urllib counts = dict() fhand = urllib.urlopen('http://www.py4inf.com/code/romeo.txt') for line in fhand: words = line.split() for word in words: counts[word] = counts.get(word,0) + 1 print counts
Again, once we have opened the web page, we can read it like a local file.
One of the common uses of the urllib capability in Python is to scrape the web. Web scraping is when we write a program that pretends to be a web browser and retrieves pages, then examines the data in those pages looking for patterns.
As an example, a search engine such as Google will look at the source of one web page and extract the links to other pages and retrieve those pages, extracting links, and so on. Using this technique, Google spiders its way through nearly all of the pages on the web.
Google also uses the frequency of links from pages it finds to a particular page as one measure of how “important” a page is and how high the page should appear in its search results.
One simple way to parse HTML is to use regular expressions to repeatedly search for and extract substrings that match a particular pattern.
Here is a simple web page:
<h1>The First Page</h1> <p> If you like, you can switch to the <a href="http://www.dr-chuck.com/page2.htm"> Second Page</a>. </p>
We can construct a well-formed regular expression to match and extract the link values from the above text as follows:
Our regular expression looks for strings that start with “href="http://”, followed by one or more characters (“.+?”), followed by another double quote. The question mark added to the “.+?” indicates that the match is to be done in a “non-greedy” fashion instead of a “greedy” fashion. A non-greedy match tries to find the smallest possible matching string and a greedy match tries to find the largest possible matching string.
We add parentheses to our regular expression to indicate which part of our matched string we would like to extract, and produce the following program:
import urllib import re url = raw_input('Enter - ') html = urllib.urlopen(url).read() links = re.findall('href="(http://.*?)"', html) for link in links: print link
The findall regular expression method will give us a list of all of the strings that match our regular expression, returning only the link text between the double quotes.
When we run the program, we get the following output:
python urlregex.py Enter - http://www.dr-chuck.com/page1.htm http://www.dr-chuck.com/page2.htm python urlregex.py Enter - http://www.py4inf.com/book.htm http://www.greenteapress.com/thinkpython/thinkpython.html http://allendowney.com/ http://www.py4inf.com/code http://www.lib.umich.edu/espresso-book-machine http://www.py4inf.com/py4inf-slides.zip
Regular expressions work very nicely when your HTML is well formatted and predictable. But since there are a lot of “broken” HTML pages out there, a solution only using regular expressions might either miss some valid links or end up with bad data.
This can be solved by using a robust HTML parsing library.
There are a number of Python libraries which can help you parse HTML and extract data from the pages. Each of the libraries has its strengths and weaknesses and you can pick one based on your needs.
As an example, we will simply parse some HTML input and extract links using the BeautifulSoup library. You can download and install the BeautifulSoup code from:
You can download and “install” BeautifulSoup or you can simply place the BeautifulSoup.py file in the same folder as your application.
Even though HTML looks like XML1 and some pages are carefully constructed to be XML, most HTML is generally broken in ways that cause an XML parser to reject the entire page of HTML as improperly formed. BeautifulSoup tolerates highly flawed HTML and still lets you easily extract the data you need.
We will use urllib to read the page and then use BeautifulSoup to extract the href attributes from the anchor (a) tags.
import urllib from BeautifulSoup import * url = raw_input('Enter - ') html = urllib.urlopen(url).read() soup = BeautifulSoup(html) # Retrieve all of the anchor tags tags = soup('a') for tag in tags: print tag.get('href', None)
The program prompts for a web address, then opens the web page, reads the data and passes the data to the BeautifulSoup parser, and then retrieves all of the anchor tags and prints out the href attribute for each tag.
When the program runs it looks as follows:
python urllinks.py Enter - http://www.dr-chuck.com/page1.htm http://www.dr-chuck.com/page2.htm python urllinks.py Enter - http://www.py4inf.com/book.htm http://www.greenteapress.com/thinkpython/thinkpython.html http://allendowney.com/ http://www.si502.com/ http://www.lib.umich.edu/espresso-book-machine http://www.py4inf.com/code http://www.pythonlearn.com/
You can use BeautifulSoup to pull out various parts of each tag as follows:
import urllib from BeautifulSoup import * url = raw_input('Enter - ') html = urllib.urlopen(url).read() soup = BeautifulSoup(html) # Retrieve all of the anchor tags tags = soup('a') for tag in tags: # Look at the parts of a tag print 'TAG:',tag print 'URL:',tag.get('href', None) print 'Content:',tag.contents print 'Attrs:',tag.attrs
This produces the following output:
python urllink2.py Enter - http://www.dr-chuck.com/page1.htm TAG: <a href="http://www.dr-chuck.com/page2.htm"> Second Page</a> URL: http://www.dr-chuck.com/page2.htm Content: [u'\nSecond Page'] Attrs: [(u'href', u'http://www.dr-chuck.com/page2.htm')]
These examples only begin to show the power of BeautifulSoup when it comes to parsing HTML. See the documentation and samples at http://www.crummy.com/software/BeautifulSoup/ for more detail.
Sometimes you want to retrieve a non-text (or binary) file such as an image or video file. The data in these files is generally not useful to print out, but you can easily make a copy of a URL to a local file on your hard disk using urllib.
The pattern is to open the URL and use read to download the entire contents of the document into a string variable (img) then write that information to a local file as follows:
img = urllib.urlopen('http://www.py4inf.com/cover.jpg').read() fhand = open('cover.jpg', 'w') fhand.write(img) fhand.close()
This program reads all of the data in at once across the network and stores it in the variable img in the main memory of your computer, then opens the file cover.jpg and writes the data out to your disk. This will work if the size of the file is less than the size of the memory of your computer.
However if this is a large audio or video file, this program may crash or at least run extremely slowly when your computer runs out of memory. In order to avoid running out of memory, we retrieve the data in blocks (or buffers) and then write each block to your disk before retrieving the next block. This way the program can read any size file without using up all of the memory you have in your computer.
import urllib img = urllib.urlopen('http://www.py4inf.com/cover.jpg') fhand = open('cover.jpg', 'w') size = 0 while True: info = img.read(100000) if len(info) < 1 : break size = size + len(info) fhand.write(info) print size,'characters copied.' fhand.close()
In this example, we read only 100,000 characters at a time and then write those characters to the cover.jpg file before retrieving the next 100,000 characters of data from the web.
This program runs as follows:
python curl2.py 568248 characters copied.
If you have a Unix or Macintosh computer, you probably have a command built in to your operating system that performs this operation as follows:
curl -O http://www.py4inf.com/cover.jpg
The command curl is short for “copy URL” and so these two examples are cleverly named curl1.py and curl2.py on www.py4inf.com/code as they implement similar functionality to the curl command. There is also a curl3.py sample program that does this task a little more effectively, in case you actually want to use this pattern in a program you are writing.