Data Structures¶
Data structures, such as lists, can represent complex data. While lists are quite useful on their own, Python provides several other built-in data structures to make it easier to represent complex data. By the end of this lesson, students will be able to:
- Apply list comprehensions to define basic
listsequences. - Apply
setoperations to store and retrieve values in a set. - Apply
dictoperations to store and retrieve values in a dictionary. - Describe the difference between the various data structures' properties (
list,set,dict,tuple).
In [1]:
import doctest
List comprehensions¶
Another one of the best features of Python is the list comprehension. A list comprehension provides a concise expression for building-up a list of values by looping over any type of sequence.
We already know how to create a list counting all the numbers between 0 and 10 (exclusive) by looping over a range.
In [2]:
nums = []
for i in range(10):
nums.append(i)
nums
Out[2]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
A list comprehension provides a shorter expression for achieving the same result.
In [3]:
[i for i in range(10)]
Out[3]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
What if we wanted to compute all these values squared? A list comprehension can help us with this as well.
In [4]:
[i ** 2 for i in range(10)]
Out[4]:
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Or, what if we wanted to only include values of i that are even?
In [5]:
[i ** 2 for i in range(10) if i % 2 == 0]
Out[5]:
[0, 4, 16, 36, 64]
In [7]:
nums = []
for i in range(10):
if i % 2 == 0:
nums.append(i ** 2)
nums
Out[7]:
[0, 4, 16, 36, 64]
Before running the next block of code, what do you think will output?
In [8]:
words = "I saw a dog today".split()
[word[0] for word in words if len(word) >= 2]
Out[8]:
['s', 'd', 't']
In [10]:
[word[0]
for word in words
if len(word) >= 2]
Out[10]:
['s', 'd', 't']
Practice: Fun numbers¶
Fill in the blank with a list comprehension to complete the definition for fun_numbers.
In [11]:
def fun_numbers(start, stop):
"""
Returns an increasing list of all fun numbers between start (inclusive) and stop (exclusive).
A fun number is defined as a number that is either divisible by 2 or divisible by 5.
>>> fun_numbers(2, 16)
[2, 4, 5, 6, 8, 10, 12, 14, 15]
"""
# As your list comprehensions grow in complexity, you'll oftentimes want to break things up
# into smaller steps, or extra temporary variables that you use to show your thinking and work.
return [i for i in range(start, stop) if i % 2 == 0 or i % 5 == 0]
doctest.run_docstring_examples(fun_numbers, globals())
Tuples¶
Whereas lists represent mutable sequences of any elements, tuples (pronounced "two pull" or like "supple") represent immutable sequences of any elements. Just like strings, tuples are immutable, so the sequence of elements in a tuple cannot be modified after the tuple has been created.
While lists are defined with square brackets, tuples are defined by commas alone as in the expression 1, 2, 3. We often add parentheses around the structure for clarity. In fact, when representing a tuple, Python will use parentheses to indicate a tuple.
In [12]:
1, 2, 3
Out[12]:
(1, 2, 3)
In [13]:
(1, 2, 3)
Out[13]:
(1, 2, 3)
We learned that there are many list functions, most of which modify the original list. Since tuples are immutable, there isn't an equivalent list of tuple functions. So why use tuples when we could just use lists instead?
Your choice of data structure communicates information to other programmers. If you know exactly how many elements should go in your data structure and those elements don't need to change, a
tupleis right for the job. By choosing to use a tuple in this situation, we communicate to other programmers that the sequence of elements in this data structure cannot change! Everyone working on your project doesn't have to worry about passing atupleto a function and that function somehow destroying the data.
Tuples provide a helpful way to return more than one value from a function. For example, we can write a function that returns both the first letter and the second letter from a word.
In [14]:
def first_two_letters(word):
return word[0], word[1]
a, b = first_two_letters("goodbye")
a
Out[14]:
'g'
In [15]:
b
Out[15]:
'o'
In [17]:
result = [word[0] for word in words if len(word) >= 2]
result
Out[17]:
['s', 'd', 't']
In [18]:
a, b, c = [word[0] for word in words if len(word) >= 2]
a
Out[18]:
's'
In [19]:
b
Out[19]:
'd'
In [20]:
c
Out[20]:
't'
In [25]:
a, b, _ = [word[0] for word in words if len(word) >= 2]
_
Out[25]:
't'
In [26]:
a = [word[0] for word in words if len(word) >= 2][0]
a
Out[26]:
's'
In [27]:
a, b, c, d = [word[0] for word in words if len(word) >= 2]
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[27], line 1 ----> 1 a, b, c, d = [word[0] for word in words if len(word) >= 2] ValueError: not enough values to unpack (expected 4, got 3)
Sets¶
Whereas lists represent mutable sequences of any elements, sets represent mutable unordered collections of unique elements. Unlike lists, sets are not sequences so we cannot index into a set in the same way that we could for lists and tuples. Sets only represent unique elements, so attempts to add duplicate elements are ignored.
- Sequences (like lists and tuples) are indexable. "Unordered collection" (as in a set) is not indexable.
- Sequences (like lists and tuples) do allow duplicates, but unique collections (sets) do not have duplicates.
In [28]:
nums = set()
nums.add(1)
nums.add(2)
nums.add(3)
nums.add(2) # duplicate ignored
nums.add(-1)
nums
Out[28]:
{-1, 1, 2, 3}
In [29]:
nums[0]
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[29], line 1 ----> 1 nums[0] TypeError: 'set' object is not subscriptable
So what's the point of using a set over a list? Sets are often much faster than lists at determining whether a particular element is contained in the set. We can see this in action by comparing the time it takes to count the number of unique words in a large document. Using a list results in much slower code.
In [30]:
def count_unique(path):
unique = []
with open(path) as f:
for line in f.readlines():
for token in line.split():
if token not in unique:
unique.append(token)
return len(unique)
%time count_unique("moby-dick.txt")
CPU times: user 13.4 s, sys: 4.65 ms, total: 13.4 s Wall time: 13.4 s
Out[30]:
32553
In [32]:
def count_unique(path):
unique = set()
with open(path) as f:
for line in f.readlines():
for token in line.split():
# Do not need to check for unique tokens explicitly because sets
unique.add(token)
return len(unique)
%time count_unique("moby-dick.txt")
CPU times: user 45.1 ms, sys: 0 ns, total: 45.1 ms Wall time: 43.7 ms
Out[32]:
32553
By combining sets and list comprehensions, we can compose our programs in more "Pythonic" ways.
In [36]:
def count_unique(path):
# f.read().split() -> list of all the tokens in the file f
with open(path) as f:
# This may or may not be more readable
return len(set(f.read().split()))
%time count_unique("moby-dick.txt")
CPU times: user 37.6 ms, sys: 7.07 ms, total: 44.7 ms Wall time: 42.9 ms
Out[36]:
32553
Practice: Area codes¶
Fill in the blank to compose a "Pythonic" program that returns the number of unique area codes from the given list of phone numbers formatted as strings like "123-456-7890". The area code is defined as the first 3 digits in a phone number.
In [43]:
def area_codes(phone_numbers):
"""
Returns the number of unique area codes in the given sequence.
>>> area_codes([
... '123-456-7890',
... '206-123-4567',
... '123-000-0000',
... '425-999-9999'
... ])
3
"""
# Generator expression (super fancy Python you can look up if you like)
# Avoids creating a temporary list for the list comprehension step
# return len(set( pn[:3] for pn in phone_numbers ))
return len(set( [pn[:3] for pn in phone_numbers] ))
doctest.run_docstring_examples(area_codes, globals())
Dictionaries¶
A dictionary represents mutable unordered collections of key-value pairs, where the keys are immutable and unique. In other words, dictionaries are more flexible than lists. A list could be considered a dictionary where the "keys" are non-negative integers counting from 0 to the length minus 1.
values = ['a', 'b', 'c']
values[0]
values = {0: 'a', 1: 'b', 2: 'c'}
values[0]
values = {'the': 2, 'best': 7}
values['the']
Dictionaries are often helpful for counting occurrences. Whereas the above example counted the total number of unique words in a text file, a dictionary can help us count the number of occurrences of each unique word in that file.
In [44]:
def count_tokens(path):
counts = {}
with open(path) as f:
for token in f.read().split():
if token not in counts:
counts[token] = 1
else:
counts[token] += 1
return counts
# Dictionaries in Python (at least for more recent versions) have guaranteed display order:
# They will display key-value pairs in the order that the key-value pairs were first added
# to the dictionary.
%time count_tokens("moby-dick.txt")
CPU times: user 54.7 ms, sys: 10.1 ms, total: 64.8 ms Wall time: 62.9 ms
Out[44]:
{'MOBY': 1,
'DICK;': 1,
'OR': 2,
'THE': 59,
'WHALE': 3,
'by': 1072,
'Herman': 1,
'Melville': 1,
'CHAPTER': 149,
'1': 1,
'Loomings.': 1,
'Call': 2,
'me': 336,
'Ishmael.': 3,
'Some': 33,
'years': 59,
'ago--never': 1,
'mind': 52,
'how': 166,
'long': 249,
'precisely--having': 1,
'little': 230,
'or': 665,
'no': 435,
'money': 6,
'in': 3751,
'my': 559,
'purse,': 4,
'and': 5803,
'nothing': 79,
'particular': 40,
'to': 4368,
'interest': 10,
'on': 889,
'shore,': 4,
'I': 1714,
'thought': 127,
'would': 401,
'sail': 50,
'about': 270,
'a': 4393,
'see': 186,
'the': 13411,
'watery': 23,
'part': 118,
'of': 6364,
'world.': 18,
'It': 254,
'is': 1530,
'way': 150,
'have': 734,
'driving': 9,
'off': 162,
'spleen': 1,
'regulating': 1,
'circulation.': 1,
'Whenever': 1,
'find': 51,
'myself': 41,
'growing': 8,
'grim': 10,
'mouth;': 3,
'whenever': 14,
'it': 1490,
'damp,': 4,
'drizzly': 1,
'November': 2,
'soul;': 6,
'involuntarily': 11,
'pausing': 7,
'before': 248,
'coffin': 19,
'warehouses,': 1,
'bringing': 10,
'up': 403,
'rear': 12,
'every': 211,
'funeral': 3,
'meet;': 1,
'especially': 40,
'hypos': 1,
'get': 91,
'such': 322,
'an': 569,
'upper': 29,
'hand': 107,
'me,': 143,
'that': 2641,
'requires': 6,
'strong': 21,
'moral': 3,
'principle': 7,
'prevent': 5,
'from': 1036,
'deliberately': 6,
'stepping': 5,
'into': 511,
'street,': 3,
'methodically': 2,
'knocking': 7,
"people's": 4,
'hats': 2,
'off--then,': 1,
'account': 31,
'high': 79,
'time': 192,
'sea': 132,
'as': 1573,
'soon': 102,
'can.': 6,
'This': 94,
'substitute': 3,
'for': 1337,
'pistol': 3,
'ball.': 2,
'With': 53,
'philosophical': 1,
'flourish': 2,
'Cato': 1,
'throws': 3,
'himself': 134,
'upon': 526,
'his': 2385,
'sword;': 1,
'quietly': 15,
'take': 109,
'ship.': 31,
'There': 81,
'surprising': 7,
'this.': 10,
'If': 50,
'they': 554,
'but': 1018,
'knew': 39,
'it,': 234,
'almost': 178,
'all': 1284,
'men': 108,
'their': 596,
'degree,': 4,
'some': 560,
'other,': 31,
'cherish': 9,
'very': 305,
'nearly': 32,
'same': 197,
'feelings': 2,
'towards': 106,
'ocean': 30,
'with': 1617,
'me.': 61,
'now': 455,
'your': 233,
'insular': 4,
'city': 10,
'Manhattoes,': 1,
'belted': 3,
'round': 208,
'wharves': 2,
'Indian': 42,
'isles': 8,
'coral': 6,
'reefs--commerce': 1,
'surrounds': 2,
'her': 272,
'surf.': 1,
'Right': 46,
'left,': 4,
'streets': 8,
'you': 725,
'waterward.': 1,
'Its': 3,
'extreme': 11,
'downtown': 1,
'battery,': 2,
'where': 165,
'noble': 41,
'mole': 2,
'washed': 4,
'waves,': 10,
'cooled': 2,
'breezes,': 3,
'which': 532,
'few': 89,
'hours': 27,
'previous': 25,
'were': 638,
'out': 427,
'sight': 67,
'land.': 5,
'Look': 26,
'at': 1198,
'crowds': 4,
'water-gazers': 1,
'there.': 31,
'Circumambulate': 1,
'dreamy': 4,
'Sabbath': 3,
'afternoon.': 2,
'Go': 7,
'Corlears': 1,
'Hook': 1,
'Coenties': 1,
'Slip,': 1,
'thence,': 2,
'Whitehall,': 1,
'northward.': 1,
'What': 95,
'do': 227,
'see?--Posted': 1,
'like': 538,
'silent': 21,
'sentinels': 1,
'around': 33,
'town,': 3,
'stand': 59,
'thousands': 14,
'mortal': 36,
'fixed': 30,
'reveries.': 2,
'leaning': 23,
'against': 129,
'spiles;': 1,
'seated': 24,
'pier-heads;': 1,
'looking': 53,
'over': 356,
'bulwarks': 10,
'ships': 53,
'China;': 1,
'aloft': 28,
'rigging,': 14,
'if': 393,
'striving': 8,
'still': 275,
'better': 51,
'seaward': 1,
'peep.': 1,
'But': 637,
'these': 351,
'are': 555,
'landsmen;': 1,
'week': 4,
'days': 52,
'pent': 2,
'lath': 1,
'plaster--tied': 1,
'counters,': 1,
'nailed': 10,
'benches,': 2,
'clinched': 3,
'desks.': 1,
'How': 48,
'then': 334,
'this?': 7,
'Are': 7,
'green': 35,
'fields': 5,
'gone?': 2,
'here?': 5,
'look!': 2,
'here': 135,
'come': 117,
'more': 422,
'crowds,': 1,
'pacing': 8,
'straight': 40,
'water,': 49,
'seemingly': 9,
'bound': 15,
'dive.': 2,
'Strange!': 1,
'Nothing': 9,
'will': 332,
'content': 16,
'them': 269,
'extremest': 1,
'limit': 2,
'land;': 5,
'loitering': 1,
'under': 109,
'shady': 1,
'lee': 10,
'yonder': 12,
'warehouses': 1,
'not': 1017,
'suffice.': 2,
'No.': 3,
'They': 68,
'must': 267,
'just': 107,
'nigh': 37,
'water': 67,
'possibly': 28,
'can': 167,
'without': 142,
'falling': 12,
'in.': 11,
'And': 313,
'there': 411,
'stand--miles': 1,
'them--leagues.': 1,
'Inlanders': 1,
'all,': 50,
'lanes': 1,
'alleys,': 1,
'avenues--north,': 1,
'east,': 4,
'south,': 1,
'west.': 1,
'Yet': 36,
'unite.': 1,
'Tell': 10,
'does': 77,
'magnetic': 7,
'virtue': 15,
'needles': 5,
'compasses': 4,
'those': 291,
'attract': 1,
'thither?': 1,
'Once': 5,
'more.': 15,
'Say': 5,
'country;': 2,
'land': 34,
'lakes.': 1,
'Take': 12,
'any': 303,
'path': 6,
'please,': 5,
'ten': 40,
'one': 740,
'carries': 16,
'down': 296,
'dale,': 1,
'leaves': 15,
'pool': 3,
'stream.': 3,
'magic': 6,
'it.': 170,
'Let': 29,
'most': 268,
'absent-minded': 4,
'be': 960,
'plunged': 9,
'deepest': 5,
'reveries--stand': 1,
'man': 236,
'legs,': 8,
'set': 58,
'feet': 88,
'a-going,': 1,
'he': 1479,
'infallibly': 11,
'lead': 10,
'region.': 1,
'Should': 2,
'ever': 171,
'athirst': 3,
'great': 269,
'American': 30,
'desert,': 1,
'try': 25,
'this': 1122,
'experiment,': 2,
'caravan': 1,
'happen': 5,
'supplied': 9,
'metaphysical': 2,
'professor.': 1,
'Yes,': 24,
'knows,': 4,
'meditation': 2,
'wedded': 3,
'ever.': 2,
'artist.': 1,
'He': 183,
'desires': 4,
'paint': 3,
'dreamiest,': 1,
'shadiest,': 1,
'quietest,': 1,
'enchanting': 1,
'bit': 18,
'romantic': 6,
'landscape': 3,
'valley': 6,
'Saco.': 1,
'chief': 23,
'element': 8,
'employs?': 1,
'trees,': 4,
'each': 124,
'hollow': 17,
'trunk,': 4,
'hermit': 3,
'crucifix': 1,
'within;': 6,
'sleeps': 5,
'meadow,': 1,
'sleep': 28,
'cattle;': 2,
'cottage': 1,
'goes': 50,
'sleepy': 5,
'smoke.': 2,
'Deep': 1,
'distant': 18,
'woodlands': 2,
'winds': 15,
'mazy': 1,
'way,': 60,
'reaching': 12,
'overlapping': 2,
'spurs': 1,
'mountains': 3,
'bathed': 2,
'hill-side': 1,
'blue.': 4,
'though': 270,
'picture': 14,
'lies': 19,
'thus': 98,
'tranced,': 1,
'pine-tree': 1,
'shakes': 4,
'its': 364,
'sighs': 1,
"shepherd's": 2,
'head,': 65,
'yet': 237,
'vain,': 3,
'unless': 25,
'eye': 44,
'stream': 6,
'him.': 109,
'visit': 10,
'Prairies': 1,
'June,': 2,
'when': 496,
'scores': 4,
'miles': 19,
'wade': 3,
'knee-deep': 1,
'among': 155,
'Tiger-lilies--what': 1,
'charm': 6,
'wanting?--Water--there': 1,
'drop': 21,
'there!': 30,
'Were': 4,
'Niagara': 1,
'cataract': 1,
'sand,': 3,
'travel': 3,
'thousand': 49,
'it?': 18,
'Why': 22,
'did': 219,
'poor': 92,
'poet': 1,
'Tennessee,': 1,
'suddenly': 42,
'receiving': 11,
'two': 252,
'handfuls': 4,
'silver,': 1,
'deliberate': 5,
'whether': 65,
'buy': 3,
'him': 548,
'coat,': 7,
'sadly': 11,
'needed,': 2,
'invest': 6,
'pedestrian': 2,
'trip': 2,
'Rockaway': 1,
'Beach?': 1,
'robust': 3,
'healthy': 5,
'boy': 6,
'soul': 36,
'him,': 231,
'other': 339,
'crazy': 9,
'go': 140,
'sea?': 2,
'first': 193,
'voyage': 37,
'passenger,': 1,
'yourself': 14,
'feel': 53,
'mystical': 8,
'vibration,': 3,
'told': 36,
'ship': 224,
'land?': 2,
'old': 409,
'Persians': 1,
'hold': 68,
'holy?': 1,
'Greeks': 2,
'give': 64,
'separate': 19,
'deity,': 1,
'own': 178,
'brother': 4,
'Jove?': 1,
'Surely': 4,
'meaning.': 3,
'deeper': 17,
'meaning': 10,
'story': 36,
'Narcissus,': 1,
'who': 250,
'because': 67,
'could': 204,
'grasp': 4,
'tormenting,': 1,
'mild': 20,
'image': 7,
'saw': 90,
'fountain,': 2,
'was': 1548,
'drowned.': 1,
'image,': 3,
'we': 376,
'ourselves': 12,
'rivers': 1,
'oceans.': 4,
'ungraspable': 1,
'phantom': 11,
'life;': 8,
'key': 6,
'all.': 30,
'Now,': 84,
'say': 87,
'am': 74,
'habit': 2,
'going': 78,
'begin': 11,
'grow': 19,
'hazy': 1,
'eyes,': 36,
'conscious': 8,
'lungs,': 4,
'mean': 19,
'inferred': 4,
'passenger.': 1,
'For': 164,
'passenger': 2,
'needs': 18,
'purse': 1,
'rag': 2,
'something': 107,
'Besides,': 25,
'passengers': 4,
'sea-sick--grow': 1,
"quarrelsome--don't": 1,
'nights--do': 1,
'enjoy': 4,
'themselves': 43,
'much,': 9,
'general': 56,
'thing;--no,': 1,
'never': 185,
'passenger;': 1,
'nor,': 4,
'salt,': 2,
'Commodore,': 1,
'Captain,': 19,
'Cook.': 1,
'abandon': 3,
'glory': 12,
'distinction': 3,
'offices': 1,
'them.': 52,
'part,': 20,
'abominate': 1,
'honourable': 6,
'respectable': 1,
'toils,': 1,
'trials,': 1,
'tribulations': 1,
'kind': 22,
'whatsoever.': 3,
'quite': 40,
'much': 197,
'care': 8,
'myself,': 15,
'taking': 47,
'ships,': 17,
'barques,': 1,
'brigs,': 1,
'schooners,': 1,
'what': 374,
'not.': 14,
'cook,--though': 1,
'confess': 5,
'considerable': 26,
'that,': 99,
'cook': 9,
'being': 198,
'sort': 138,
'officer': 10,
'ship-board--yet,': 1,
'somehow,': 11,
'fancied': 6,
'broiling': 1,
'fowls;--though': 1,
'once': 119,
'broiled,': 1,
'judiciously': 2,
'buttered,': 1,
'judgmatically': 1,
'salted': 6,
'peppered,': 1,
'speak': 30,
'respectfully,': 1,
'reverentially,': 1,
'broiled': 2,
'fowl': 6,
'than': 300,
'will.': 6,
'idolatrous': 1,
'dotings': 1,
'Egyptians': 2,
'ibis': 1,
'roasted': 1,
'river': 4,
'horse,': 3,
'mummies': 2,
'creatures': 20,
'huge': 25,
'bake-houses': 1,
'pyramids.': 1,
'No,': 17,
'sea,': 101,
'simple': 10,
'sailor,': 13,
'right': 64,
'mast,': 10,
'plumb': 1,
'forecastle,': 10,
'royal': 24,
'mast-head.': 2,
'True,': 7,
'rather': 61,
'order': 46,
'some,': 4,
'make': 102,
'jump': 12,
'spar': 4,
'spar,': 3,
'grasshopper': 1,
'May': 4,
'meadow.': 1,
'first,': 8,
'thing': 130,
'unpleasant': 2,
'enough.': 6,
'touches': 2,
"one's": 11,
'sense': 10,
'honour,': 3,
'particularly': 7,
'established': 3,
'family': 8,
'land,': 21,
'Van': 1,
'Rensselaers,': 1,
'Randolphs,': 1,
'Hardicanutes.': 1,
'putting': 16,
'tar-pot,': 1,
'been': 394,
'lording': 1,
'country': 12,
'schoolmaster,': 2,
'making': 41,
'tallest': 1,
'boys': 4,
'awe': 6,
'you.': 10,
'The': 521,
'transition': 4,
'keen': 16,
'one,': 60,
'assure': 5,
'you,': 45,
'schoolmaster': 2,
'decoction': 1,
'Seneca': 1,
'Stoics': 1,
'enable': 4,
'grin': 5,
'bear': 29,
'even': 166,
'wears': 6,
'time.': 34,
'hunks': 2,
'sea-captain': 1,
'orders': 15,
'broom': 3,
'sweep': 12,
'decks?': 1,
'indignity': 1,
'amount': 6,
'to,': 27,
'weighed,': 1,
'mean,': 8,
'scales': 2,
'New': 47,
'Testament?': 1,
'Do': 21,
'think': 94,
'archangel': 7,
'Gabriel': 12,
'thinks': 15,
'anything': 36,
'less': 47,
'promptly': 1,
'respectfully': 3,
'obey': 5,
'instance?': 1,
'Who': 18,
"ain't": 17,
'slave?': 1,
'that.': 20,
'Well,': 23,
'then,': 169,
'however': 51,
'sea-captains': 2,
'may': 205,
'about--however': 1,
'thump': 4,
'punch': 2,
'about,': 18,
'satisfaction': 2,
'knowing': 14,
'right;': 6,
'everybody': 6,
'else': 30,
'served': 8,
'way--either': 1,
'physical': 4,
'point': 54,
'view,': 9,
'is;': 9,
'so': 776,
'universal': 7,
'passed': 33,
'round,': 15,
'hands': 79,
'should': 176,
'rub': 1,
"other's": 13,
'shoulder-blades,': 1,
'content.': 1,
'Again,': 4,
'always': 75,
'paying': 3,
'trouble,': 2,
'whereas': 6,
'pay': 16,
'single': 36,
'penny': 3,
'heard': 85,
'of.': 6,
'On': 35,
'contrary,': 8,
'pay.': 1,
'difference': 13,
'world': 73,
'between': 108,
'paid.': 1,
'act': 26,
'perhaps': 47,
'uncomfortable': 3,
'infliction': 1,
'orchard': 2,
'thieves': 2,
'entailed': 1,
'us.': 20,
'BEING': 1,
'PAID,--what': 1,
'compare': 5,
'urbane': 1,
'activity': 6,
'receives': 3,
'really': 30,
'marvellous,': 2,
'considering': 27,
'earnestly': 7,
'believe': 22,
'root': 3,
'earthly': 15,
'ills,': 1,
'monied': 1,
'enter': 8,
'heaven.': 8,
'Ah!': 6,
'cheerfully': 2,
'consign': 1,
'perdition!': 1,
'Finally,': 2,
'wholesome': 2,
'exercise': 2,
'pure': 7,
'air': 42,
'fore-castle': 2,
'deck.': 26,
'world,': 32,
'head': 136,
'far': 150,
'prevalent': 1,
'astern': 5,
'(that': 5,
'is,': 96,
'violate': 1,
'Pythagorean': 1,
'maxim),': 1,
'Commodore': 5,
'quarter-deck': 8,
'gets': 3,
'atmosphere': 4,
'second': 50,
'sailors': 32,
'forecastle.': 8,
'breathes': 4,
'first;': 1,
'so.': 20,
'In': 213,
'commonalty': 1,
'leaders': 2,
'many': 153,
'things,': 25,
'suspect': 3,
'wherefore': 5,
'after': 234,
'having': 60,
'repeatedly': 3,
'smelt': 3,
'merchant': 11,
'whaling': 78,
'voyage;': 5,
'invisible': 13,
'police': 1,
'Fates,': 2,
'has': 276,
'constant': 5,
'surveillance': 1,
'secretly': 2,
'dogs': 4,
'influences': 6,
'unaccountable': 15,
'way--he': 1,
'answer': 16,
'else.': 7,
'And,': 13,
'doubtless,': 6,
'voyage,': 34,
'formed': 20,
'grand': 39,
'programme': 1,
'Providence': 1,
'drawn': 19,
'ago.': 4,
'came': 117,
'brief': 8,
'interlude': 3,
'solo': 1,
'extensive': 3,
'performances.': 1,
'bill': 5,
'run': 40,
'this:': 15,
'"GRAND': 1,
'CONTESTED': 1,
'ELECTION': 1,
'FOR': 3,
'PRESIDENCY': 1,
'OF': 20,
'UNITED': 1,
'STATES.': 1,
'"WHALING': 1,
'VOYAGE': 1,
'BY': 9,
'ONE': 3,
'ISHMAEL.': 1,
'"BLOODY': 1,
'BATTLE': 1,
'IN': 11,
'AFFGHANISTAN."': 1,
'Though': 39,
'cannot': 63,
'tell': 105,
'why': 50,
'exactly': 21,
'stage': 5,
'managers,': 1,
'put': 68,
'shabby': 3,
'others': 21,
'magnificent': 2,
'parts': 30,
'tragedies,': 1,
'short': 29,
'easy': 22,
'genteel': 1,
'comedies,': 1,
'jolly': 14,
'farces--though': 1,
'exactly;': 1,
'yet,': 41,
'recall': 5,
'circumstances,': 15,
'springs': 2,
'motives': 3,
'cunningly': 1,
'presented': 17,
'various': 36,
'disguises,': 1,
'induced': 4,
'performing': 3,
'did,': 9,
'besides': 15,
'cajoling': 1,
'delusion': 1,
'choice': 5,
'resulting': 2,
'unbiased': 1,
'freewill': 1,
'discriminating': 1,
'judgment.': 2,
'Chief': 5,
'overwhelming': 1,
'idea': 28,
'whale': 371,
'himself.': 17,
'Such': 30,
'portentous': 4,
'mysterious': 8,
'monster': 29,
'roused': 3,
'curiosity.': 2,
'Then': 35,
'wild': 74,
'seas': 35,
'rolled': 41,
'island': 14,
'bulk;': 1,
'undeliverable,': 1,
'nameless': 15,
'perils': 16,
'whale;': 32,
'these,': 12,
'attending': 4,
'marvels': 6,
'Patagonian': 4,
'sights': 9,
'sounds,': 3,
'helped': 12,
'sway': 4,
'wish.': 1,
'men,': 60,
'perhaps,': 30,
'things': 85,
'inducements;': 1,
'tormented': 14,
'everlasting': 9,
'itch': 1,
'remote.': 1,
'love': 16,
'forbidden': 1,
'seas,': 18,
'barbarous': 2,
'coasts.': 1,
'Not': 39,
'ignoring': 1,
'good,': 6,
'quick': 20,
'perceive': 7,
'horror,': 1,
'social': 10,
'it--would': 1,
'let': 98,
'me--since': 1,
'well': 124,
'friendly': 3,
'terms': 8,
'inmates': 2,
'place': 69,
'lodges': 1,
'By': 47,
'reason': 56,
'welcome;': 1,
'flood-gates': 2,
...}
As an aside, there's also a more Pythonic way to write this program using collections.Counter, which is a specialized dictionary. The Counter type also sorts the results in order from greatest to least.
In [45]:
def count_tokens(path):
from collections import Counter
with open(path) as f:
return Counter(f.read().split())
%time count_tokens("moby-dick.txt")
CPU times: user 48.7 ms, sys: 3.03 ms, total: 51.7 ms Wall time: 50.6 ms
Out[45]:
Counter({'the': 13411,
'of': 6364,
'and': 5803,
'a': 4393,
'to': 4368,
'in': 3751,
'that': 2641,
'his': 2385,
'I': 1714,
'with': 1617,
'as': 1573,
'was': 1548,
'is': 1530,
'it': 1490,
'he': 1479,
'for': 1337,
'all': 1284,
'at': 1198,
'this': 1122,
'by': 1072,
'from': 1036,
'but': 1018,
'not': 1017,
'be': 960,
'on': 889,
'so': 776,
'one': 740,
'had': 740,
'have': 734,
'you': 725,
'or': 665,
'were': 638,
'But': 637,
'their': 596,
'an': 569,
'some': 560,
'my': 559,
'are': 555,
'they': 554,
'him': 548,
'like': 538,
'which': 532,
'upon': 526,
'The': 521,
'into': 511,
'when': 496,
'now': 455,
'no': 435,
'out': 427,
'more': 422,
'there': 411,
'old': 409,
'up': 403,
'would': 401,
'been': 394,
'if': 393,
'we': 376,
'what': 374,
'whale': 371,
'its': 364,
'over': 356,
'these': 351,
'only': 342,
'other': 339,
'me': 336,
'then': 334,
'will': 332,
'such': 322,
'And': 313,
'very': 305,
'any': 303,
'than': 300,
'down': 296,
'those': 291,
'has': 276,
'still': 275,
'her': 272,
'seemed': 272,
'about': 270,
'though': 270,
'them': 269,
'great': 269,
'most': 268,
'must': 267,
'ye': 256,
'It': 254,
'two': 252,
'who': 250,
'long': 249,
'before': 248,
'said': 244,
'yet': 237,
'man': 236,
'it,': 234,
'after': 234,
'your': 233,
'Ahab': 232,
'him,': 231,
'little': 230,
'do': 227,
'ship': 224,
'three': 223,
'did': 219,
'In': 213,
'every': 211,
'round': 208,
'last': 208,
'thou': 207,
'may': 205,
'could': 204,
'through': 202,
'being': 198,
'same': 197,
'much': 197,
'while': 194,
'first': 193,
'our': 193,
'time': 192,
'see': 186,
'never': 185,
'He': 183,
'almost': 178,
'own': 178,
'should': 176,
'might': 174,
'ever': 171,
'it.': 170,
'then,': 169,
'can': 167,
'how': 166,
'even': 166,
'where': 165,
'whale,': 165,
'For': 164,
'off': 162,
'Captain': 162,
'good': 159,
'among': 155,
'many': 153,
'made': 153,
'way': 150,
'far': 150,
'CHAPTER': 149,
'white': 149,
'us': 145,
'me,': 143,
'without': 142,
'go': 140,
'sort': 138,
'cried': 137,
'head': 136,
'here': 135,
'himself': 134,
'sea': 132,
'thing': 130,
'against': 129,
'thought': 127,
'whales': 126,
'whole': 126,
'Sperm': 125,
'each': 124,
'well': 124,
'boat': 124,
'Whale': 121,
'once': 119,
'part': 118,
'come': 117,
'came': 117,
'A': 116,
'seen': 116,
'till': 115,
'away': 115,
'know': 114,
'As': 112,
'take': 109,
'under': 109,
'him.': 109,
'men': 108,
'between': 108,
'again': 108,
'both': 108,
'hand': 107,
'just': 107,
'something': 107,
'Queequeg': 107,
'towards': 106,
'small': 106,
'tell': 105,
'there,': 105,
'full': 105,
'Ahab,': 105,
'found': 103,
'thy': 103,
'soon': 102,
'make': 102,
'sea,': 101,
'that,': 99,
'thus': 98,
'let': 98,
'called': 98,
'eyes': 98,
'Stubb': 98,
'look': 97,
'is,': 96,
'What': 95,
'This': 94,
'think': 94,
'side': 93,
'she': 93,
'poor': 92,
'So': 92,
'man,': 92,
'get': 91,
"don't": 91,
'say,': 91,
'saw': 90,
'too': 90,
'day': 90,
'now,': 90,
'it;': 90,
'few': 89,
'went': 89,
'White': 89,
'feet': 88,
'another': 88,
'along': 88,
'say': 87,
'back': 87,
'him;': 86,
'sperm': 86,
'heard': 85,
'things': 85,
'"I': 85,
'Now,': 84,
'certain': 84,
'whose': 83,
'I,': 82,
'half': 82,
'boats': 82,
'Stubb,': 82,
'There': 81,
'At': 81,
'them,': 81,
'strange': 80,
'seem': 80,
'nothing': 79,
'high': 79,
'hands': 79,
'going': 78,
'whaling': 78,
'stood': 78,
'does': 77,
'shall': 77,
'ye,': 77,
'ship,': 77,
"ship's": 77,
'which,': 76,
'Moby': 76,
'always': 75,
'nor': 75,
'seems': 75,
'am': 74,
'wild': 74,
'sometimes': 74,
'world': 73,
"whale's": 73,
'times': 72,
'life': 72,
'Pequod': 72,
'young': 71,
'so,': 71,
'time,': 70,
'place': 69,
'body': 69,
"he's": 69,
'Queequeg,': 69,
'present': 69,
'They': 68,
'hold': 68,
'put': 68,
'and,': 68,
'standing': 68,
"Ahab's": 68,
'sight': 67,
'water': 67,
'because': 67,
'black': 67,
'boat,': 67,
'Starbuck': 67,
'dead': 66,
'this,': 66,
'head,': 65,
'whether': 65,
'beneath': 65,
'hard': 65,
'deck,': 65,
'crew': 65,
'give': 64,
'right': 64,
'ere': 64,
'moment': 64,
'living': 64,
'known': 64,
'cannot': 63,
'vast': 63,
'often': 63,
'looked': 62,
'keep': 62,
'me.': 61,
'rather': 61,
"it's": 61,
'four': 61,
'sea.': 61,
'again,': 61,
'also': 61,
'way,': 60,
'one,': 60,
'having': 60,
'men,': 60,
'too,': 60,
'large': 60,
'end': 60,
'THE': 59,
'years': 59,
'stand': 59,
'line': 59,
'face': 59,
'Nor': 59,
'set': 58,
'turned': 58,
'whale.': 58,
'Starbuck,': 58,
'night': 57,
'whales,': 57,
'hand,': 57,
'here,': 57,
'captain': 57,
'general': 56,
'reason': 56,
'matter': 56,
"I'll": 56,
'left': 56,
'within': 56,
'lay': 55,
'iron': 55,
'That': 55,
'live': 55,
'point': 54,
'open': 54,
'peculiar': 54,
'With': 53,
'looking': 53,
'ships': 53,
'feel': 53,
'since': 53,
'His': 53,
'To': 53,
'near': 53,
'side,': 53,
'mind': 52,
'days': 52,
'them.': 52,
'Oh,': 52,
'find': 51,
'better': 51,
'however': 51,
'best': 51,
'further': 51,
'who,': 51,
'began': 51,
'call': 51,
'entire': 51,
'heart': 51,
'sail': 50,
'If': 50,
'all,': 50,
'goes': 50,
'second': 50,
'why': 50,
'sun': 50,
'least': 50,
'water,': 49,
'thousand': 49,
'took': 49,
'hundred': 49,
'true': 49,
"that's": 49,
'arm': 49,
'God': 49,
'How': 48,
"man's": 48,
'When': 48,
'comes': 48,
'case': 48,
'was,': 48,
'turn': 48,
'length': 48,
'instant': 48,
'Mr.': 48,
'taking': 47,
'New': 47,
'less': 47,
'perhaps': 47,
'By': 47,
'hear': 47,
'fine': 47,
'curious': 47,
'forth': 47,
'deck': 47,
'lower': 47,
"Pequod's": 47,
'Right': 46,
'order': 46,
'business': 46,
'Jonah': 46,
'turning': 46,
'several': 46,
'taken': 46,
'you,': 45,
'broad': 45,
'name': 45,
'kept': 45,
'All': 45,
'eye': 44,
'night,': 44,
'"The': 44,
'up,': 44,
'used': 44,
'harpooneer': 44,
'got': 44,
'sir,': 44,
'close': 44,
'leg': 44,
'above': 44,
'thee': 44,
'themselves': 43,
'enough': 43,
'Now': 43,
'dark': 43,
'touching': 43,
'Indian': 42,
'suddenly': 42,
'air': 42,
'on,': 42,
'ivory': 42,
'next': 42,
'coming': 42,
'word': 42,
'slowly': 42,
'means': 42,
'myself': 41,
'noble': 41,
'making': 41,
'yet,': 41,
'rolled': 41,
'common': 41,
'mighty': 41,
'running': 41,
'aye,': 41,
"Whale's": 41,
'particular': 40,
'especially': 40,
'straight': 40,
'ten': 40,
'quite': 40,
'run': 40,
'day,': 40,
'light': 40,
'broken': 40,
'whatever': 40,
'last,': 40,
'sea;': 40,
'indeed,': 40,
'knew': 39,
'grand': 39,
'Though': 39,
'Not': 39,
'harpoon': 39,
'sat': 39,
'mere': 39,
'oil': 39,
'whalemen': 39,
'English': 39,
'Nantucket': 38,
'wide': 38,
'gone': 38,
'when,': 38,
'caught': 38,
'deep': 38,
'fish': 38,
'concerning': 38,
'But,': 38,
'So,': 38,
'wondrous': 38,
'cut': 38,
"boat's": 38,
'nigh': 37,
'voyage': 37,
'behind': 37,
'itself': 37,
'whom': 37,
'We': 37,
'No': 37,
'felt': 37,
'gave': 37,
'plainly': 37,
'makes': 37,
'air,': 37,
'mortal': 36,
'Yet': 36,
'soul': 36,
'told': 36,
'story': 36,
'eyes,': 36,
'anything': 36,
'single': 36,
'various': 36,
'brought': 36,
'struck': 36,
'out,': 36,
'You': 36,
'five': 36,
'lost': 36,
'Whale,': 36,
'thee,': 36,
'green': 35,
'On': 35,
'Then': 35,
'seas': 35,
'sharp': 35,
'though,': 35,
'long,': 35,
'human': 35,
'savage': 35,
'board': 35,
'placed': 35,
'leaving': 35,
'chance': 35,
'more,': 35,
'work': 35,
'them;': 35,
'us,': 35,
'given': 35,
'land': 34,
'time.': 34,
'voyage,': 34,
'chase': 34,
'indeed': 34,
'ready': 34,
'help': 34,
'back,': 34,
'sudden': 34,
'"Aye,': 34,
'Flask,': 34,
'Some': 33,
'around': 33,
'passed': 33,
'people': 33,
'harpooneers': 33,
'art': 33,
'bottom': 33,
'turns': 33,
"there's": 33,
'nearly': 32,
'world,': 32,
'sailors': 32,
'whale;': 32,
'famous': 32,
'wind': 32,
'held': 32,
'darted': 32,
'unknown': 32,
'heavy': 32,
'completely': 32,
'new': 32,
'sound': 32,
'therefore': 32,
'sailed': 32,
'waters': 32,
'said,': 32,
'times,': 32,
'Bildad,': 32,
'crew,': 32,
'Greenland': 32,
'Dick': 32,
'account': 31,
'ship.': 31,
'other,': 31,
'there.': 31,
'Cape': 31,
'voice': 31,
'blue': 31,
'holding': 31,
'himself,': 31,
'but,': 31,
'seamen': 31,
'want': 31,
'clear': 31,
'again.': 31,
'in,': 31,
'show': 31,
'fell': 31,
'shot': 31,
'SAILOR.': 31,
'ocean': 30,
'fixed': 30,
'American': 30,
'there!': 30,
'all.': 30,
'speak': 30,
'else': 30,
'really': 30,
'parts': 30,
'Such': 30,
'perhaps,': 30,
'sign': 30,
'red': 30,
'says': 30,
'bed': 30,
'be,': 30,
'drawing': 30,
'morning': 30,
'hardly': 30,
'bones': 30,
'carried': 30,
'cast': 30,
'cabin': 30,
'fast': 30,
'down,': 30,
'remained': 30,
'boats,': 30,
'upper': 29,
'Let': 29,
'bear': 29,
'short': 29,
'monster': 29,
'sure': 29,
'final': 29,
'head.': 29,
'stranger': 29,
"won't": 29,
'top': 29,
'somehow': 29,
'proper': 29,
'there;': 29,
'craft': 29,
'done,': 29,
'Pequod,': 29,
'sailing': 29,
'Flask': 29,
'spout': 29,
'de': 29,
'tail': 29,
'aloft': 28,
'possibly': 28,
'sleep': 28,
'idea': 28,
'already': 28,
'I.': 28,
'tossed': 28,
'itself,': 28,
'rest': 28,
'fifty': 28,
'devil': 28,
'heads': 28,
'become': 28,
'man.': 28,
'cry': 28,
'done': 28,
'moment,': 28,
'thing,': 28,
'mark': 28,
'"And': 28,
'object': 28,
'"What': 28,
'strangely': 28,
'Whale;': 28,
'hours': 27,
'to,': 27,
'considering': 27,
'her,': 27,
'stands': 27,
'fire': 27,
'sailor': 27,
'fair': 27,
'none': 27,
"I've": 27,
'seeing': 27,
'eyeing': 27,
'pretty': 27,
'course': 27,
'me;': 27,
'entirely': 27,
'rolling': 27,
'While': 27,
'Aye,': 27,
'wholly': 27,
'except': 27,
'blood': 27,
'use': 27,
'Peleg': 27,
'natural': 27,
'captain,': 27,
'Ahab.': 27,
'fish,': 27,
'distance': 27,
'Look': 26,
'considerable': 26,
'act': 26,
'deck.': 26,
'Nantucket,': 26,
'flying': 26,
'wooden': 26,
'getting': 26,
'giving': 26,
'touch': 26,
"Queequeg's": 26,
'creature': 26,
'Here': 26,
'From': 26,
'across': 26,
'waves': 26,
'ancient': 26,
'forward': 26,
'away,': 26,
'rising': 26,
'hoisted': 26,
'previous': 25,
'try': 25,
'unless': 25,
'Besides,': 25,
'huge': 25,
'things,': 25,
'either': 25,
'passage': 25,
'hung': 25,
'mass': 25,
'enormous': 25,
'somewhat': 25,
'bed,': 25,
'together': 25,
"I'm": 25,
'saying': 25,
'light,': 25,
'one.': 25,
'takes': 25,
'form': 25,
'death': 25,
'sideways': 25,
'Oh!': 25,
'will,': 25,
'O': 25,
'not,': 25,
'pointed': 25,
'mate': 25,
'line,': 25,
'For,': 25,
'Tashtego': 25,
'gentlemen,': 25,
'Leviathan': 25,
'seated': 24,
'Yes,': 24,
'royal': 24,
'air.': 24,
'middle': 24,
'suppose': 24,
'lie': 24,
'low': 24,
'received': 24,
'One': 24,
'generally': 24,
'certainly': 24,
'home': 24,
'fresh': 24,
'plain': 24,
'view': 24,
'carry': 24,
'fishery,': 24,
'bearing': 24,
'twenty': 24,
'bodily': 24,
'skeleton': 24,
'vessel': 24,
'lance': 24,
'sails': 24,
'rope': 24,
'watery': 23,
'leaning': 23,
'chief': 23,
'Well,': 23,
'started': 23,
'became': 23,
'foot': 23,
'followed': 23,
'hour': 23,
'look,': 23,
'queer': 23,
'altogether': 23,
'alone': 23,
'tail,': 23,
'Upon': 23,
'over,': 23,
'seldom': 23,
'feeling': 23,
'similar': 23,
'during': 23,
'cutting': 23,
'third': 23,
'Nevertheless,': 23,
'instead': 23,
'therefore,': 23,
'fact': 23,
'dropped': 23,
'Peleg,': 23,
'Bildad': 23,
'life,': 23,
'secret': 23,
'mate,': 23,
'brow': 23,
'Why': 22,
'kind': 22,
'believe': 22,
'easy': 22,
'soul,': 22,
'cold': 22,
'thick': 22,
'watch': 22,
'air;': 22,
'jet': 22,
'fiery': 22,
'lines': 22,
'floating': 22,
'bone': 22,
'mariners': 22,
"can't": 22,
'thrown': 22,
'thinking': 22,
'Lord': 22,
"It's": 22,
'real': 22,
'ran': 22,
'short,': 22,
'hope': 22,
'drew': 22,
'AND': 22,
'bows': 22,
'read': 22,
'manner': 22,
'strike': 22,
'purpose': 22,
'thirty': 22,
'born': 22,
'whales.': 22,
'glance': 22,
'different': 22,
"d'ye": 22,
'man;': 22,
'visible': 22,
'Ahab;': 22,
'off,': 22,
'Dutch': 22,
"Stubb's": 22,
'sharks': 22,
'carpenter': 22,
'strong': 21,
'silent': 21,
'drop': 21,
'land,': 21,
'Do': 21,
'exactly': 21,
'others': 21,
'place,': 21,
'filled': 21,
'swift': 21,
'fellow': 21,
'however,': 21,
'six': 21,
'flew': 21,
'thoughts': 21,
'precisely': 21,
'Of': 21,
'doubt': 21,
'hot': 21,
'hidden': 21,
'instant,': 21,
'for,': 21,
'commanded': 21,
'steel': 21,
'looks': 21,
'knows': 21,
'also,': 21,
'sweet': 21,
'killed': 21,
'deadly': 21,
'regular': 21,
'cabin,': 21,
'gold': 21,
'arms': 21,
'mast-head': 21,
'forehead': 21,
'smoke': 21,
'free': 21,
'suspended': 21,
'complete': 21,
'waters,': 21,
'hammer': 21,
'dashed': 21,
'Thou': 21,
'remains': 21,
'gazing': 21,
'bulk': 21,
'species': 21,
'St.': 21,
'Pip': 21,
'mild': 20,
'part,': 20,
'creatures': 20,
'that.': 20,
'us.': 20,
'so.': 20,
'formed': 20,
'OF': 20,
'quick': 20,
'stop': 20,
'late': 20,
'darkness': 20,
'door': 20,
'book': 20,
'house': 20,
'were,': 20,
"didn't": 20,
'teeth': 20,
'masts': 20,
'kill': 20,
'original': 20,
'fancy': 20,
"'em": 20,
'on.': 20,
'spare': 20,
'soft': 20,
'higher': 20,
'passing': 20,
'now;': 20,
'degree': 20,
'slightest': 20,
'oil,': 20,
'lofty': 20,
'here.': 20,
'beat': 20,
'learned': 20,
'power': 20,
'pass': 20,
'harpoon,': 20,
'wonder': 20,
'water;': 20,
'according': 20,
'start': 20,
'leg,': 20,
'owing': 20,
'blow': 20,
'again;': 20,
'level': 20,
'end,': 20,
'coffin': 19,
'lies': 19,
'miles': 19,
'separate': 19,
'grow': 19,
'mean': 19,
'Captain,': 19,
'drawn': 19,
'way.': 19,
'sitting': 19,
'words': 19,
'mouth,': 19,
'number': 19,
'"But': 19,
'fain': 19,
'weather': 19,
'interval': 19,
'morning,': 19,
'break': 19,
'"He': 19,
'mouth': 19,
'fear': 19,
'outer': 19,
'hat': 19,
'quickly': 19,
'hearts': 19,
'Like': 19,
'God,': 19,
'former': 19,
'bows,': 19,
'finally': 19,
'speaking': 19,
'laid': 19,
'hands,': 19,
'nature': 19,
'hanging': 19,
'jaw': 19,
...})
Practice: Count lengths¶
Suppose we want to compute a histogram (counts) for the number of words that begin with each character in a given text file. Your coworker has written the following code and would like your help to finish the program. Explain your fix.
In [47]:
def count_lengths(words):
counts = {}
for word in words:
first_letter = word[0]
# Make sure that we add the key-value pair for the first time
# we encounter this first letter in the loop!
if first_letter not in counts:
counts[first_letter] = 0
counts[first_letter] += 1
return counts
count_lengths(['cats', 'dogs', 'deers'])
Out[47]:
{'c': 1, 'd': 2}