At this point you have learned about Python’s core data structures, and you have seen some of the algorithms that use them. If you would like to know more about algorithms, this might be a good time to read Chapter [algorithms]. But you don’t have to read it before you go on; you can read it whenever you are interested.
This chapter presents a case study with exercises that let you think about choosing data structures and practice using them.
As usual, you should at least attempt the exercises before you read my solutions.
Write a program that reads a file, breaks each line into words, strips whitespace and punctuation from the words, and converts them to lowercase.
Hint: The string module provides a string named whitespace, which contains space, tab, newline, etc., and punctuation which contains the punctuation characters. Let’s see if we can make Python swear:
>>> import string
>>> string.punctuation
'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
Also, you might consider using the string methods strip, replace and translate.
Go to Project Gutenberg (http://gutenberg.org) and download your favorite out-of-copyright book in plain text format.
Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.
Then modify the program to count the total number of words in the book, and the number of times each word is used.
Print the number of different words used in the book. Compare different books by different authors, written in different eras. Which author uses the most extensive vocabulary?
Modify the program from the previous exercise to print the 20 most frequently used words in the book.
Modify the previous program to read a word list (see Section [wordlist]) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that should be in the word list, and how many of them are really obscure?
Given the same inputs, most computer programs generate the same outputs every time, so they are said to be deterministic. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.
Making a program truly nondeterministic turns out to be difficult, but there are ways to make it at least seem nondeterministic. One of them is to use algorithms that generate pseudorandom numbers. Pseudorandom numbers are not truly random because they are generated by a deterministic computation, but just by looking at the numbers it is all but impossible to distinguish them from random.
The random module provides functions that generate pseudorandom numbers (which I will simply call “random” from here on).
The function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:
import random
for i in range(10):
x = random.random()
print(x)
The function randint takes parameters low and high and returns an integer between low and high (including both).
>>> random.randint(5, 10)
5
>>> random.randint(5, 10)
9
To choose an element from a sequence at random, you can use choice:
>>> t = [1, 2, 3]
>>> random.choice(t)
2
>>> random.choice(t)
3
The random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.
Write a function named choose_from_hist that takes a histogram as
defined in Section [histogram] and returns a random value from the
histogram, chosen with probability in proportion to frequency. For
example, for this histogram:
>>> t = ['a', 'a', 'b']
>>> hist = histogram(t)
>>> hist
{'a': 2, 'b': 1}
your function should return 'a' with probability $2/3$ and 'b' with
probability $1/3$.
You should attempt the previous exercises before you go on. You can download my solution from http://thinkpython2.com/code/analyze_book1.py. You will also need http://thinkpython2.com/code/emma.txt.
Here is a program that reads a file and builds a histogram of the words in the file:
import string
def process_file(filename):
hist = dict()
fp = open(filename)
for line in fp:
process_line(line, hist)
return hist
def process_line(line, hist):
line = line.replace('-', ' ')
for word in line.split():
word = word.strip(string.punctuation + string.whitespace)
word = word.lower()
hist[word] = hist.get(word, 0) + 1
hist = process_file('emma.txt')
This program reads emma.txt, which contains the text of Emma by Jane Austen.
process_file loops through the lines of the file, passing them one at
a time to process_line. The histogram hist is being used
as an accumulator.
process_line uses the string method replace to replace
hyphens with spaces before using split to break the line
into a list of strings. It traverses the list of words and uses
strip and lower to remove punctuation and
convert to lower case. (It is a shorthand to say that strings are
“converted”; remember that strings are immutable, so methods like
strip and lower return new strings.)
Finally, process_line updates the histogram by creating a new item or
incrementing an existing one.
To count the total number of words in the file, we can add up the frequencies in the histogram:
def total_words(hist):
return sum(hist.values())
The number of different words is just the number of items in the dictionary:
def different_words(hist):
return len(hist)
Here is some code to print the results:
print('Total number of words:', total_words(hist))
print('Number of different words:', different_words(hist))
And the results:
Total number of words: 161080
Number of different words: 7214
To find the most common words, we can make a list of tuples, where each tuple contains a word and its frequency, and sort it.
The following function takes a histogram and returns a list of word-frequency tuples:
def most_common(hist):
t = []
for key, value in hist.items():
t.append((value, key))
t.sort(reverse=True)
return t
In each tuple, the frequency appears first, so the resulting list is sorted by frequency. Here is a loop that prints the ten most common words:
t = most_common(hist)
print('The most common words are:')
for freq, word in t[:10]:
print(word, freq, sep='\t')
I use the keyword argument sep to tell print to use a tab character as a “separator”, rather than a space, so the second column is lined up. Here are the results from Emma:
The most common words are:
to 5242
the 5205
and 4897
of 4295
i 3191
a 3130
it 2529
her 2483
was 2400
she 2364
This code can be simplified using the key parameter of the sort function. If you are curious, you can read about it at https://wiki.python.org/moin/HowTo/Sorting.
We have seen built-in functions and methods that take optional arguments. It is possible to write programmer-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram
def print_most_common(hist, num=10):
t = most_common(hist)
print('The most common words are:')
for freq, word in t[:num]:
print(word, freq, sep='\t')
The first parameter is required; the second is optional. The default value of num is 10.
If you only provide one argument:
print_most_common(hist)
num gets the default value. If you provide two arguments:
print_most_common(hist, 20)
num gets the value of the argument instead. In other words, the optional argument overrides the default value.
If a function has both required and optional parameters, all the required parameters have to come first, followed by the optional ones.
Finding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in the other (the words in the list).
subtract takes dictionaries d1 and d2 and returns a new dictionary that contains all the keys from d1 that are not in d2. Since we don’t really care about the values, we set them all to None.
def subtract(d1, d2):
res = dict()
for key in d1:
if key not in d2:
res[key] = None
return res
To find the words in the book that are not in words.txt, we
can use process_file to build a histogram for words.txt,
and then subtract:
words = process_file('words.txt')
diff = subtract(hist, words)
print("Words in the book that aren't in the word list:")
for word in diff:
print(word, end=' ')
Here are some of the results from Emma:
Words in the book that aren't in the word list:
rencontre jane's blanche woodhouses disingenuousness
friend's venice apartment ...
Some of these words are names and possessives. Others, like “rencontre”, are no longer in common use. But a few are common words that should really be in the list!
Python provides a data structure called set that provides many common set operations. You can read about them in Section [sets], or read the documentation at http://docs.python.org/3/library/stdtypes.html#types-set.
Write a program that uses set subtraction to find words in the book that are not in the word list. Solution: http://thinkpython2.com/code/analyze_book2.py.
To choose a random word from the histogram, the simplest algorithm is to build a list with multiple copies of each word, according to the observed frequency, and then choose from the list:
def random_word(h):
t = []
for word, freq in h.items():
t.extend([word] * freq)
return random.choice(t)
The expression * freq creates a list with freq copies of the string word. The extend method is similar to append except that the argument is a sequence.
This algorithm works, but it is not very efficient; each time you choose a random word, it rebuilds the list, which is as big as the original book. An obvious improvement is to build the list once and then make multiple selections, but the list is still big.
An alternative is:
Use keys to get a list of the words in the book.
Build a list that contains the cumulative sum of the word frequencies (see Exercise [cumulative]). The last item in this list is the total number of words in the book, $n$.
Choose a random number from 1 to $n$. Use a bisection search (See Exercise [bisection]) to find the index where the random number would be inserted in the cumulative sum.
Use the index to find the corresponding word in the word list.
[randhist]
Write a program that uses this algorithm to choose a random word from the book. Solution: http://thinkpython2.com/code/analyze_book3.py.
If you choose words from the book at random, you can get a sense of the vocabulary, but you probably won’t get a sentence:
this the small regard harriet which knightley's it most things
A series of random words seldom makes sense because there is no relationship between successive words. For example, in a real sentence you would expect an article like “the” to be followed by an adjective or a noun, and probably not a verb or adverb.
One way to measure these kinds of relationships is Markov analysis, which characterizes, for a given sequence of words, the probability of the words that might come next. For example, the song Eric, the Half a Bee begins:
Half a bee, philosophically,
Must, ipso facto, half not be.
But half the bee has got to be
Vis a vis, its entity. D’you see?
But can a bee be said to be
Or not to be an entire bee
When half the bee is not a bee
Due to some ancient injury?\
In this text, the phrase “half the” is always followed by the word “bee”, but the phrase “the bee” might be followed by either “has” or “is”.
The result of Markov analysis is a mapping from each prefix (like “half the” and “the bee”) to all possible suffixes (like “has” and “is”).
Given this mapping, you can generate a random text by starting with any prefix and choosing at random from the possible suffixes. Next, you can combine the end of the prefix and the new suffix to form the next prefix, and repeat.
For example, if you start with the prefix “Half a”, then the next word has to be “bee”, because the prefix only appears once in the text. The next prefix is “a bee”, so the next suffix might be “philosophically”, “be” or “due”.
In this example the length of the prefix is always two, but you can do Markov analysis with any prefix length.
Markov analysis:
Write a program to read a text from a file and perform Markov analysis. The result should be a dictionary that maps from prefixes to a collection of possible suffixes. The collection might be a list, tuple, or dictionary; it is up to you to make an appropriate choice. You can test your program with prefix length two, but you should write the program in a way that makes it easy to try other lengths.
Add a function to the previous program to generate random text based on the Markov analysis. Here is an example from Emma with prefix length 2:
He was very clever, be it sweetness or be angry, ashamed or only amused, at such a stroke. She had never thought of Hannah till you were never meant for me?“ ”I cannot make speeches, Emma:” he soon cut it all himself.
For this example, I left the punctuation attached to the words. The result is almost syntactically correct, but not quite. Semantically, it almost makes sense, but not quite.
What happens if you increase the prefix length? Does the random text make more sense?
Once your program is working, you might want to try a mash-up: if you combine text from two or more books, the random text you generate will blend the vocabulary and phrases from the sources in interesting ways.
Credit: This case study is based on an example from Kernighan and Pike, The Practice of Programming, Addison-Wesley, 1999.
You should attempt this exercise before you go on; then you can download my solution from http://thinkpython2.com/code/markov.py. You will also need http://thinkpython2.com/code/emma.txt.
Using Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:
How to represent the prefixes.
How to represent the collection of possible suffixes.
How to represent the mapping from each prefix to the collection of possible suffixes.
The last one is easy: a dictionary is the obvious choice for a mapping from keys to corresponding values.
For the prefixes, the most obvious options are string, list of strings, or tuple of strings.
For the suffixes, one option is a list; another is a histogram (dictionary).
How should you choose? The first step is to think about the operations you will need to implement for each data structure. For the prefixes, we need to be able to remove words from the beginning and add to the end. For example, if the current prefix is “Half a”, and the next word is “bee”, you need to be able to form the next prefix, “a bee”.
Your first choice might be a list, since it is easy to add and remove elements, but we also need to be able to use the prefixes as keys in a dictionary, so that rules out lists. With tuples, you can’t append or remove, but you can use the addition operator to form a new tuple:
def shift(prefix, word):
return prefix[1:] + (word,)
shift takes a tuple of words, prefix, and a string, word, and forms a new tuple that has all the words in prefix except the first, and word added to the end.
For the collection of suffixes, the operations we need to perform include adding a new suffix (or increasing the frequency of an existing one), and choosing a random suffix.
Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise [randhist]).
So far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.
But often you don’t know ahead of time which implementation will be faster. One option is to implement both of them and see which is better. This approach is called benchmarking. A practical alternative is to choose the data structure that is easiest to implement, and then see if it is fast enough for the intended application. If so, there is no need to go on. If not, there are tools, like the profile module, that can identify the places in a program that take the most time.
The other factor to consider is storage space. For example, using a histogram for the collection of suffixes might take less space because you only have to store each word once, no matter how many times it appears in the text. In some cases, saving space can also make your program run faster, and in the extreme, your program might not run at all if you run out of memory. But for many applications, space is a secondary consideration after run time.
One final thought: in this discussion, I have implied that we should use one data structure for both analysis and generation. But since these are separate phases, it would also be possible to use one structure for analysis and then convert to another structure for generation. This would be a net win if the time saved during generation exceeded the time spent in conversion.
When you are debugging a program, and especially if you are working on a hard bug, there are five things to try:
Beginning programmers sometimes get stuck on one of these activities and forget the others. Each activity comes with its own failure mode.
For example, reading your code might help if the problem is a typographical error, but not if the problem is a conceptual misunderstanding. If you don’t understand what your program does, you can read it 100 times and never see the error, because the error is in your head.
Running experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call “random walk programming”, which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time.
You have to take time to think. Debugging is like an experimental science. You should have at least one hypothesis about what the problem is. If there are two or more possibilities, try to think of a test that would eliminate one of them.
But even the best debugging techniques will fail if there are too many errors, or if the code you are trying to fix is too big and complicated. Sometimes the best option is to retreat, simplifying the program until you get to something that works and that you understand.
Beginning programmers are often reluctant to retreat because they can’t stand to delete a line of code (even if it’s wrong). If it makes you feel better, copy your program into another file before you start stripping it down. Then you can copy the pieces back one at a time.
Finding a hard bug requires reading, running, ruminating, and sometimes retreating. If you get stuck on one of these activities, try the others.
The “rank” of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.
Zipf’s law describes a relationship between the ranks and frequencies of words in natural languages (http://en.wikipedia.org/wiki/Zipf's_law). Specifically, it predicts that the frequency, $f$, of the word with rank $r$ is:
\(f = c r^{-s}\) where $s$ and $c$ are parameters that depend on the language and the text. If you take the logarithm of both sides of this equation, you get:
\(\log f = \log c - s \log r\) So if you plot log $f$ versus log $r$, you should get a straight line with slope $-s$ and intercept log $c$.
Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log $f$ and log $r$. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of $s$?
Solution: http://thinkpython2.com/code/zipf.py. To run my solution, you need the plotting module matplotlib. If you installed Anaconda, you already have matplotlib; otherwise you might have to install it.
This chapter introduces the idea of “persistent” programs that keep data in permanent storage, and shows how to use different kinds of permanent storage, like files and databases.
Most of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate.
Other programs are persistent: they run for a long time (or all the time); they keep at least some of their data in permanent storage (a hard drive, for example); and if they shut down and restart, they pick up where they left off.
Examples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network.
One of the simplest ways for programs to maintain their data is by reading and writing text files. We have already seen programs that read text files; in this chapter we will see programs that write them.
An alternative is to store the state of the program in a database. In this chapter I will present a simple database and a module, pickle, that makes it easy to store program data.
A text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section [wordlist].
To write a file, you have to open it with mode 'w' as a second
parameter:
>>> fout = open('output.txt', 'w')
If the file already exists, opening it in write mode clears out the old data and starts fresh, so be careful! If the file doesn’t exist, a new one is created.
open returns a file object that provides methods for working with the file. The write method puts data into the file.
>>> line1 = "This here's the wattle,\n"
>>> fout.write(line1)
24
The return value is the number of characters that were written. The file object keeps track of where it is, so if you call write again, it adds the new data to the end of the file.
>>> line2 = "the emblem of our land.\n"
>>> fout.write(line2)
24
When you are done writing, you should close the file.
>>> fout.close()
If you don’t close the file, it gets closed for you when the program ends.
The argument of write has to be a string, so if we want to put other values in a file, we have to convert them to strings. The easiest way to do that is with str:
>>> x = 52
>>> fout.write(str(x))
An alternative is to use the format operator, %. When applied to integers, % is the modulus operator. But when the first operand is a string, % is the format operator.
The first operand is the format string, which contains one or more format sequences, which specify how the second operand is formatted. The result is a string.
For example, the format sequence '%d' means that the second operand
should be formatted as a decimal integer:
>>> camels = 42
>>> '%d' % camels
'42'
The result is the string '42', which is not to be confused with the
integer value 42.
A format sequence can appear anywhere in the string, so you can embed a value in a sentence:
>>> 'I have spotted %d camels.' % camels
'I have spotted 42 camels.'
If there is more than one format sequence in the string, the second argument has to be a tuple. Each format sequence is matched with an element of the tuple, in order.
The following example uses '%d' to format an integer, '%g' to format
a floating-point number, and '%s' to format a string:
>>> 'In %d years I have spotted %g %s.' % (3, 0.1, 'camels')
'In 3 years I have spotted 0.1 camels.'
The number of elements in the tuple has to match the number of format sequences in the string. Also, the types of the elements have to match the format sequences:
>>> '%d %d %d' % (1, 2)
TypeError: not enough arguments for format string
>>> '%d' % 'dollars'
TypeError: %d format: a number is required, not str
In the first example, there aren’t enough elements; in the second, the element is the wrong type.
For more information on the format operator, see https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting. A more powerful alternative is the string format method, which you can read about at https://docs.python.org/3/library/stdtypes.html#str.format.
Files are organized into directories (also called “folders”). Every running program has a “current directory”, which is the default directory for most operations. For example, when you open a file for reading, Python looks for it in the current directory.
The os module provides functions for working with files and directories (“os” stands for “operating system”). os.getcwd returns the name of the current directory:
>>> import os
>>> cwd = os.getcwd()
>>> cwd
'/home/dinsdale'
cwd stands for “current working directory”. The result in this example is /home/dinsdale, which is the home directory of a user named dinsdale.
A string like '/home/dinsdale' that identifies a file or directory is
called a path.
A simple filename, like memo.txt is also considered a path, but it is a relative path because it relates to the current directory. If the current directory is /home/dinsdale, the filename memo.txt would refer to /home/dinsdale/memo.txt.
A path that begins with / does not depend on the current directory; it is called an absolute path. To find the absolute path to a file, you can use os.path.abspath:
>>> os.path.abspath('memo.txt')
'/home/dinsdale/memo.txt'
os.path provides other functions for working with filenames and paths. For example, os.path.exists checks whether a file or directory exists:
>>> os.path.exists('memo.txt')
True
If it exists, os.path.isdir checks whether it’s a directory:
>>> os.path.isdir('memo.txt')
False
>>> os.path.isdir('/home/dinsdale')
True
Similarly, os.path.isfile checks whether it’s a file.
os.listdir returns a list of the files (and other directories) in the given directory:
>>> os.listdir(cwd)
['music', 'photos', 'memo.txt']
To demonstrate these functions, the following example “walks” through a directory, prints the names of all the files, and calls itself recursively on all the directories.
def walk(dirname):
for name in os.listdir(dirname):
path = os.path.join(dirname, name)
if os.path.isfile(path):
print(path)
else:
walk(path)
os.path.join takes a directory and a file name and joins them into a complete path.
The os module provides a function called walk that is similar to this one but more versatile. As an exercise, read the documentation and use it to print the names of the files in a given directory and its subdirectories. You can download my solution from http://thinkpython2.com/code/walk.py.
A lot of things can go wrong when you try to read and write files. If you try to open a file that doesn’t exist, you get an IOError:
>>> fin = open('bad_file')
IOError: [Errno 2] No such file or directory: 'bad_file'
If you don’t have permission to access a file:
>>> fout = open('/etc/passwd', 'w')
PermissionError: [Errno 13] Permission denied: '/etc/passwd'
And if you try to open a directory for reading, you get
>>> fin = open('/home')
IsADirectoryError: [Errno 21] Is a directory: '/home'
To avoid these errors, you could use functions like os.path.exists and os.path.isfile, but it would take a lot of time and code to check all the possibilities (if “Errno 21” is any indication, there are at least 21 things that can go wrong).
It is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if…else statement:
try:
fin = open('bad_file')
except:
print('Something went wrong.')
Python starts by executing the try clause. If all goes well, it skips the except clause and proceeds. If an exception occurs, it jumps out of the try clause and runs the except clause.
Handling an exception with a try statement is called catching an exception. In this example, the except clause prints an error message that is not very helpful. In general, catching an exception gives you a chance to fix the problem, or try again, or at least end the program gracefully.
A database is a file that is organized for storing data. Many databases are organized like a dictionary in the sense that they map from keys to values. The biggest difference between a database and a dictionary is that the database is on disk (or other permanent storage), so it persists after the program ends.
The module dbm provides an interface for creating and updating database files. As an example, I’ll create a database that contains captions for image files.
Opening a database is similar to opening other files:
>>> import dbm
>>> db = dbm.open('captions', 'c')
The mode 'c' means that the database should be created if it doesn’t
already exist. The result is a database object that can be used (for
most operations) like a dictionary.
When you create a new item, dbm updates the database file.
>>> db['cleese.png'] = 'Photo of John Cleese.'
When you access one of the items, dbm reads the file:
>>> db['cleese.png']
b'Photo of John Cleese.'
The result is a bytes object, which is why it begins with b. A bytes object is similar to a string in many ways. When you get farther into Python, the difference becomes important, but for now we can ignore it.
If you make another assignment to an existing key, dbm replaces the old value:
>>> db['cleese.png'] = 'Photo of John Cleese doing a silly walk.'
>>> db['cleese.png']
b'Photo of John Cleese doing a silly walk.'
Some dictionary methods, like keys and items, don’t work with database objects. But iteration with a for loop works:
for key in db:
print(key, db[key])
As with other files, you should close the database when you are done:
>>> db.close()
A limitation of dbm is that the keys and values have to be strings or bytes. If you try to use any other type, you get an error.
The pickle module can help. It translates almost any type of object into a string suitable for storage in a database, and then translates strings back into objects.
pickle.dumps takes an object as a parameter and returns a string representation (dumps is short for “dump string”):
>>> import pickle
>>> t = [1, 2, 3]
>>> pickle.dumps(t)
b'\x80\x03]q\x00(K\x01K\x02K\x03e.'
The format isn’t obvious to human readers; it is meant to be easy for pickle to interpret. pickle.loads (“load string”) reconstitutes the object:
>>> t1 = [1, 2, 3]
>>> s = pickle.dumps(t1)
>>> t2 = pickle.loads(s)
>>> t2
[1, 2, 3]
Although the new object has the same value as the old, it is not (in general) the same object:
>>> t1 == t2
True
>>> t1 is t2
False
In other words, pickling and then unpickling has the same effect as copying the object.
You can use pickle to store non-strings in a database. In fact, this combination is so common that it has been encapsulated in a module called shelve.
Most operating systems provide a command-line interface, also known as a shell. Shells usually provide commands to navigate the file system and launch applications. For example, in Unix you can change directories with cd, display the contents of a directory with ls, and launch a web browser by typing (for example) firefox.
Any program that you can launch from the shell can also be launched from Python using a pipe object, which represents a running program.
For example, the Unix command ls -l normally displays the contents of the current directory in long format. You can launch ls with os.popen[^1]:
>>> cmd = 'ls -l'
>>> fp = os.popen(cmd)
The argument is a string that contains a shell command. The return value is an object that behaves like an open file. You can read the output from the ls process one line at a time with readline or get the whole thing at once with read:
>>> res = fp.read()
When you are done, you close the pipe like a file:
>>> stat = fp.close()
>>> print(stat)
None
The return value is the final status of the ls process; None means that it ended normally (with no errors).
For example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a “checksum”. You can read about MD5 at http://en.wikipedia.org/wiki/Md5. This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).
You can use a pipe to run md5sum from Python and get the result:
>>> filename = 'book.tex'
>>> cmd = 'md5sum ' + filename
>>> fp = os.popen(cmd)
>>> res = fp.read()
>>> stat = fp.close()
>>> print(res)
1e0033f0ed0656636de0d75144ba32e0 book.tex
>>> print(stat)
None
Any file that contains Python code can be imported as a module. For example, suppose you have a file named wc.py with the following code:
def linecount(filename):
count = 0
for line in open(filename):
count += 1
return count
print(linecount('wc.py'))
If you run this program, it reads itself and prints the number of lines in the file, which is 7. You can also import it like this:
>>> import wc
7
Now you have a module object wc:
>>> wc
<module 'wc' from 'wc.py'>
The module object provides linecount:
>>> wc.linecount('wc.py')
7
So that’s how you write modules in Python.
The only problem with this example is that when you import the module it runs the test code at the bottom. Normally when you import a module, it defines new functions but it doesn’t run them.
Programs that will be imported as modules often use the following idiom:
if __name__ == '__main__':
print(linecount('wc.py'))
__name__ is a built-in variable that is set when the program starts.
If the program is running as a script, __name__ has the value
'__main__'; in that case, the test code runs. Otherwise, if the module
is being imported, the test code is skipped.
As an exercise, type this example into a file named wc.py
and run it as a script. Then run the Python interpreter and import
wc. What is the value of __name__ when the module is being
imported?
Warning: If you import a module that has already been imported, Python does nothing. It does not re-read the file, even if it has changed.
If you want to reload a module, you can use the built-in function reload, but it can be tricky, so the safest thing to do is restart the interpreter and then import the module again.
When you are reading and writing files, you might run into problems with whitespace. These errors can be hard to debug because spaces, tabs and newlines are normally invisible:
>>> s = '1 2\t 3\n 4'
>>> print(s)
1 2 3
4
The built-in function repr can help. It takes any object as an argument and returns a string representation of the object. For strings, it represents whitespace characters with backslash sequences:
>>> print(repr(s))
'1 2\t 3\n 4'
This can be helpful for debugging.
One other problem you might run into is that different systems use
different characters to indicate the end of a line. Some systems use a
newline, represented \n. Others use a return character, represented
\r. Some use both. If you move files between different systems, these
inconsistencies can cause problems.
For most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at http://en.wikipedia.org/wiki/Newline. Or, of course, you could write one yourself.
Write a function called sed that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.
If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution: http://thinkpython2.com/code/sed.py.
If you download my solution to Exercise [anagrams] from
http://thinkpython2.com/code/anagram_sets.py, you’ll see that it
creates a dictionary that maps from a sorted string of letters to the
list of words that can be spelled with those letters. For example,
'opst' maps to the list
['opts', 'post', 'pots', 'spot', 'stop', 'tops'].
Write a module that imports anagram_sets and provides two new
functions: store_anagrams should store the anagram dictionary in a
“shelf”; read_anagrams should look up a word and return a list of its
anagrams. Solution: http://thinkpython2.com/code/anagram_db.py.
[checksum]
In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.
Write a program that searches a directory and all of its subdirectories, recursively, and returns a list of complete paths for all files with a given suffix (like .mp3). Hint: os.path provides several useful functions for manipulating file and path names.
To recognize duplicates, you can use md5sum to compute a “checksum” for each files. If two files have the same checksum, they probably have the same contents.
To double-check, you can use the Unix command diff.