A dictionary is like a list, but more general. In a list, the index positions have to be integers; in a dictionary, the indices can be (almost) any type.
You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item.
As an example, we’ll build a dictionary that maps from English to Spanish words, so the keys and the values are all strings.
The function dict creates a new dictionary with no
items. Because dict is the name of a built-in function, you
should avoid using it as a variable name.
>>> eng2sp = dict()
>>> print(eng2sp)
{}The curly brackets, {}, represent an empty dictionary.
To add items to the dictionary, you can use square brackets:
>>> eng2sp['one'] = 'uno'This line creates an item that maps from the key 'one'
to the value “uno”. If we print the dictionary again, we see a key-value
pair with a colon between the key and value:
>>> print(eng2sp)
{'one': 'uno'}This output format is also an input format. For example, you can create a new dictionary with three items.
>>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}
>>> print(eng2sp)
{'one': 'uno', 'two': 'dos', 'three': 'tres'}Since Python 3.7x the order of key-value pairs is the same as their input order, i.e. dictionaries are now ordered structures.
But that doesn’t really matter because the elements of a dictionary are never indexed with integer indices. Instead, you use the keys to look up the corresponding values:
>>> print(eng2sp['two'])
'dos'The key 'two' always maps to the value “dos” so the
order of the items doesn’t matter.
If the key isn’t in the dictionary, you get an exception:
>>> print(eng2sp['four'])
KeyError: 'four'The len function works on dictionaries; it returns the
number of key-value pairs:
>>> len(eng2sp)
3The in operator works on dictionaries; it tells you
whether something appears as a key in the dictionary (appearing
as a value is not good enough).
>>> 'one' in eng2sp
True
>>> 'uno' in eng2sp
FalseTo see whether something appears as a value in a dictionary, you can
use the method values, which returns the values as a type
that can be converted to a list, and then use the in
operator:
>>> vals = list(eng2sp.values())
>>> 'uno' in vals
TrueThe in operator uses different algorithms for lists and
dictionaries. For lists, it uses a linear search algorithm. As the list
gets longer, the search time gets longer in direct proportion to the
length of the list. For dictionaries, Python uses an algorithm called a
hash table that has a remarkable property: the in
operator takes about the same amount of time no matter how many items
there are in a dictionary. I won’t explain why hash functions are so
magical, but you can read more about it at wikipedia.org/wiki/Hash_table.1
Exercise 1: Download a copy of the file
Write a program that reads the words in words.txt and stores
them as keys in a dictionary. It doesn’t matter what the values are.
Then you can use the in operator as a fast way to check
whether a string is in the dictionary.
Suppose you are given a string and you want to count how many times each letter appears. There are several ways you could do it:
You could create 26 variables, one for each letter of the alphabet. Then you could traverse the string and, for each character, increment the corresponding counter, probably using a chained conditional.
You could create a list with 26 elements. Then you could convert
each character to a number (using the built-in function
ord), use the number as an index into the list, and
increment the appropriate counter.
You could create a dictionary with characters as keys and counters as the corresponding values. The first time you see a character, you would add an item to the dictionary. After that you would increment the value of an existing item.
Each of these options performs the same computation, but each of them implements that computation in a different way.
An implementation is a way of performing a computation; some implementations are better than others. For example, an advantage of the dictionary implementation is that we don’t have to know ahead of time which letters appear in the string and we only have to make room for the letters that do appear.
Here is what the code might look like:
word = 'brontosaurus'
d = dict()
for c in word:
if c not in d:
d[c] = 1
else:
d[c] = d[c] + 1
print(d)We are effectively computing a histogram, which is a statistical term for a set of counters (or frequencies).
The for loop traverses the string. Each time through the
loop, if the character c is not in the dictionary, we
create a new item with key c and the initial value 1 (since
we have seen this letter once). If c is already in the
dictionary we increment d['c'].
Here’s the output of the program:
{'b': 1, 'r': 2, 'o': 2, 'n': 1, 't': 1, 's': 2, 'a': 1, 'u': 2}The histogram indicates that the letters “a” and “b” appear once; “o” appears twice, and so on.
Dictionaries have a method called get that takes a key
and a default value. If the key appears in the dictionary,
get returns the corresponding value; otherwise it returns
the default value. For example:
>>> counts = { 'chuck' : 1 , 'annie' : 42, 'jan': 100}
>>> print(counts.get('jan', 0))
100
>>> print(counts.get('tim', 0))
0We can use get to write our histogram loop more
concisely. Because the get method automatically handles the
case where a key is not in a dictionary, we can reduce four lines down
to one and eliminate the if statement.
word = 'brontosaurus'
d = dict()
for c in word:
d[c] = d.get(c,0) + 1
print(d)The use of the get method to simplify this counting loop
ends up being a very commonly used “idiom” in Python and we will use it
many times in the rest of the book. So you should take a moment and
compare the loop using the if statement and in
operator with the loop using the get method. They do
exactly the same thing, but one is more succinct.
One of the common uses of a dictionary is to count the occurrence of words in a file with some written text. Let’s start with a very simple file of words taken from the text of Romeo and Juliet.
For the first set of examples, we will use a shortened and simplified version of the text with no punctuation. Later we will work with the text of the scene with punctuation included.
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 griefWe will write a Python program to read through the lines of the file, break each line into a list of words, and then loop through each of the words in the line and count each word using a dictionary.
You will see that we have two for loops. The outer loop
is reading the lines of the file and the inner loop is iterating through
each of the words on that particular line. This is an example of a
pattern called nested loops because one of the loops is the
outer loop and the other loop is the inner loop.
Because the inner loop executes all of its iterations each time the outer loop makes a single iteration, we think of the inner loop as iterating “more quickly” and the outer loop as iterating more slowly.
The combination of the two nested loops ensures that we will count every word on every line of the input file.
fname = input('Enter the file name: ')
try:
fhand = open(fname)
except:
print('File cannot be opened:', fname)
exit()
counts = dict()
for line in fhand:
words = line.split()
for word in words:
if word not in counts:
counts[word] = 1
else:
counts[word] += 1
print(counts)
# Code: https://www.py4e.com/code3/count1.pyIn our else statement, we use the more compact
alternative for incrementing a variable. counts[word] += 1
is equivalent to counts[word] = counts[word] + 1. Either
method can be used to change the value of a variable by any desired
amount. Similar alternatives exist for -=, *=,
and /=.
When we run the program, we see a raw dump of all of the counts in unsorted hash order. (the romeo.txt file is available at www.py4e.com/code3/romeo.txt)
python count1.py
Enter the file name: romeo.txt
{'But': 1, 'soft': 1, 'what': 1, 'light': 1, 'through': 1, 'yonder': 1,
'window': 1, 'breaks': 1, 'It': 1, 'is': 3, 'the': 3, 'east': 1, 'and': 3,
'Juliet': 1, 'sun': 2, 'Arise': 1, 'fair': 1, 'kill': 1, 'envious': 1,
'moon': 1, 'Who': 1, 'already': 1, 'sick': 1, 'pale': 1, 'with': 1,
'grief': 1}It is a bit inconvenient to look through the dictionary to find the most common words and their counts, so we need to add some more Python code to get us the output that will be more helpful.
If you use a dictionary as the sequence in a for
statement, it traverses the keys of the dictionary. This loop prints
each key and the corresponding value:
counts = { 'chuck' : 1 , 'annie' : 42, 'jan': 100}
for key in counts:
print(key, counts[key])Here’s what the output looks like:
chuck 1
annie 42
jan 100Again, the keys are ordered.
We can use this pattern to implement the various loop idioms that we have described earlier. For example if we wanted to find all the entries in a dictionary with a value above ten, we could write the following code:
counts = { 'chuck' : 1 , 'annie' : 42, 'jan': 100}
for key in counts:
if counts[key] > 10 :
print(key, counts[key])The for loop iterates through the keys of the
dictionary, so we must use the index operator to retrieve the
corresponding value for each key. Here’s what the output looks
like:
annie 42
jan 100We see only the entries with a value above 10.
If you want to print the keys in alphabetical order, you first make a
list of the keys in the dictionary using the keys method
available in dictionary objects, and then sort that list and loop
through the sorted list, looking up each key and printing out key-value
pairs in sorted order as follows:
counts = { 'chuck' : 1 , 'annie' : 42, 'jan': 100}
lst = list(counts.keys())
print(lst)
lst.sort()
print(lst)
for key in lst:
print(key, counts[key])Here’s what the output looks like:
['chuck', 'annie', 'jan']
['annie', 'chuck', 'jan']
annie 42
chuck 1
jan 100First you see the list of keys in non-alphabetical order that we get
from the keys method. Then we see the key-value pairs in
alphabetical order from the for loop.
In the above example using the file romeo.txt, we made the file as simple as possible by removing all punctuation by hand. The actual text has lots of punctuation, as shown below.
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,Since the Python split function looks for spaces and
treats words as tokens separated by spaces, we would treat the words
“soft!” and “soft” as different words and create a separate
dictionary entry for each word.
Also since the file has capitalization, we would treat “who” and “Who” as different words with different counts.
We can solve both these problems by using the string methods
lower, punctuation, and
translate. The translate is the most subtle of
the methods. Here is the documentation for translate:
line.translate(str.maketrans(fromstr, tostr, deletestr))
Replace the characters in fromstr with the character
in the same position in tostr and delete all characters
that are in deletestr. The fromstr and
tostr can be empty strings and the deletestr
parameter can be omitted.
We will not specify the tostr but we will use the
deletestr parameter to delete all of the punctuation. We
will even let Python tell us the list of characters that it considers
“punctuation”:
>>> import string
>>> string.punctuation
'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'The parameters used by translate were different in
Python 2.0.
We make the following modifications to our program:
import string
fname = input('Enter the file name: ')
try:
fhand = open(fname)
except:
print('File cannot be opened:', fname)
exit()
counts = dict()
for line in fhand:
line = line.rstrip()
# First two parameters are empty strings
line = line.translate(line.maketrans("", "", string.punctuation))
line = line.lower()
words = line.split()
for word in words:
if word not in counts:
counts[word] = 1
else:
counts[word] += 1
print(counts)
# Code: https://www.py4e.com/code3/count2.pyPart of learning the “Art of Python” or “Thinking Pythonically” is realizing that Python often has built-in capabilities for many common data analysis problems. Over time, you will see enough example code and read enough of the documentation to know where to look to see if someone has already written something that makes your job much easier.
The following is an abbreviated version of the output:
Enter the file name: romeo-full.txt
{'romeo': 40, 'and': 42, 'juliet': 32, 'act': 1, '2': 2, 'scene': 2,
'ii': 1, 'capulets': 1, 'orchard': 2, 'enter': 1, 'he': 5, 'jests': 1,
'at': 9, 'scars': 1, 'that': 30, 'never': 2, 'felt': 1, 'a': 24,
'wound': 1, 'appears': 1, 'above': 6, 'window': 2, 'but': 18,
'soft': 1, 'what': 11, 'light': 5, 'through': 2, 'yonder': 2,
'breaks': 1, ...}Looking through this output is still unwieldy and we can use Python to give us exactly what we are looking for, but to do so, we need to learn about Python tuples. We will pick up this example once we learn about tuples.
As you work with bigger datasets it can become unwieldy to debug by printing and checking data by hand. Here are some suggestions for debugging large datasets:
If possible, reduce the size of the dataset. For example if the
program reads a text file, start with just the first 10 lines, or with
the smallest example you can find. You can either edit the files
themselves, or (better) modify the program so it reads only the first
n lines.
If there is an error, you can reduce n to the smallest
value that manifests the error, and then increase it gradually as you
find and correct errors.
Instead of printing and checking the entire dataset, consider printing summaries of the data: for example, the number of items in a dictionary or the total of a list of numbers.
A common cause of runtime errors is a value that is not the right type. For debugging this kind of error, it is often enough to print the type of a value.
Sometimes you can write code to check for errors automatically. For example, if you are computing the average of a list of numbers, you could check that the result is not greater than the largest element in the list or less than the smallest. This is called a “sanity check” because it detects results that are “completely illogical”.
Another kind of check compares the results of two different computations to see if they are consistent. This is called a “consistency check”.
Again, time you spend building scaffolding can reduce the time you spend debugging.
Exercise 2: Write a program that categorizes each mail message by which day of the week the commit was done. To do this look for lines that start with “From”, then look for the third word and keep a running count of each of the days of the week. At the end of the program print out the contents of your dictionary (order does not matter).
Sample Line:
From [email protected] Sat Jan 5 09:14:16 2008Sample Execution:
python dow.py
Enter a file name: mbox-short.txt
{'Fri': 20, 'Thu': 6, 'Sat': 1}Exercise 3: Write a program to read through a mail log, build a histogram using a dictionary to count how many messages have come from each email address, and print the dictionary.
Enter file name: mbox-short.txt
{'[email protected]': 1, '[email protected]': 3,
'[email protected]': 5, '[email protected]': 1,
'[email protected]': 2, '[email protected]': 3,
'[email protected]': 4, '[email protected]': 1,
'[email protected]': 4, '[email protected]': 2,
'[email protected]': 1}Exercise 4: Add code to the above program to figure out who has the most messages in the file. After all the data has been read and the dictionary has been created, look through the dictionary using a maximum loop (see Chapter 5: Maximum and minimum loops) to find who has the most messages and print how many messages the person has.
Enter a file name: mbox-short.txt
[email protected] 5
Enter a file name: mbox.txt
[email protected] 195Exercise 5: This program records the domain name (instead of the address) where the message was sent from instead of who the mail came from (i.e., the whole email address). At the end of the program, print out the contents of your dictionary.
python schoolcount.py
Enter a file name: mbox-short.txt
{'media.berkeley.edu': 4, 'uct.ac.za': 6, 'umich.edu': 7,
'gmail.com': 1, 'caret.cam.ac.uk': 1, 'iupui.edu': 8}If you do want to learn more about hash tables, there is a course at https://www.cc4e.com that explores how the programming language C implements a Python dictionary.↩︎
If you find a mistake in this book, feel free to send me a fix using Github.