How do I count the occurrences of a list item?
Question:
Given a single item, how do I count occurrences of it in a list, in Python?
A related but different problem is counting occurrences of each different element in a collection, getting a dictionary or list as a histogram result instead of a single integer. For that problem, see Using a dictionary to count the items in a list.
Answers:
If you only want a single item’s count, use the count
method:
>>> [1, 2, 3, 4, 1, 4, 1].count(1)
3
Important: this is very slow if you are counting multiple different items
Each count
call goes over the entire list of n
elements. Calling count
in a loop n
times means n * n
total checks, which can be catastrophic for performance.
If you want to count multiple items, use Counter
, which only does n
total checks.
list.count(x)
returns the number of times x
appears in a list
see:
http://docs.python.org/tutorial/datastructures.html#more-on-lists
Use Counter
if you are using Python 2.7 or 3.x and you want the number of occurrences for each element:
>>> from collections import Counter
>>> z = ['blue', 'red', 'blue', 'yellow', 'blue', 'red']
>>> Counter(z)
Counter({'blue': 3, 'red': 2, 'yellow': 1})
# Python >= 2.6 (defaultdict) && < 2.7 (Counter, OrderedDict)
from collections import defaultdict
def count_unsorted_list_items(items):
"""
:param items: iterable of hashable items to count
:type items: iterable
:returns: dict of counts like Py2.7 Counter
:rtype: dict
"""
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
# Python >= 2.2 (generators)
def count_sorted_list_items(items):
"""
:param items: sorted iterable of items to count
:type items: sorted iterable
:returns: generator of (item, count) tuples
:rtype: generator
"""
if not items:
return
elif len(items) == 1:
yield (items[0], 1)
return
prev_item = items[0]
count = 1
for item in items[1:]:
if prev_item == item:
count += 1
else:
yield (prev_item, count)
count = 1
prev_item = item
yield (item, count)
return
import unittest
class TestListCounters(unittest.TestCase):
def test_count_unsorted_list_items(self):
D = (
([], []),
([2], [(2,1)]),
([2,2], [(2,2)]),
([2,2,2,2,3,3,5,5], [(2,4), (3,2), (5,2)]),
)
for inp, exp_outp in D:
counts = count_unsorted_list_items(inp)
print inp, exp_outp, counts
self.assertEqual(counts, dict( exp_outp ))
inp, exp_outp = UNSORTED_WIN = ([2,2,4,2], [(2,3), (4,1)])
self.assertEqual(dict( exp_outp ), count_unsorted_list_items(inp) )
def test_count_sorted_list_items(self):
D = (
([], []),
([2], [(2,1)]),
([2,2], [(2,2)]),
([2,2,2,2,3,3,5,5], [(2,4), (3,2), (5,2)]),
)
for inp, exp_outp in D:
counts = list( count_sorted_list_items(inp) )
print inp, exp_outp, counts
self.assertEqual(counts, exp_outp)
inp, exp_outp = UNSORTED_FAIL = ([2,2,4,2], [(2,3), (4,1)])
self.assertEqual(exp_outp, list( count_sorted_list_items(inp) ))
# ... [(2,2), (4,1), (2,1)]
To count the number of diverse elements having a common type:
li = ['A0','c5','A8','A2','A5','c2','A3','A9']
print sum(1 for el in li if el[0]=='A' and el[1] in '01234')
gives
3
, not 6
Another way to get the number of occurrences of each item, in a dictionary:
dict((i, a.count(i)) for i in a)
I had this problem today and rolled my own solution before I thought to check SO. This:
dict((i,a.count(i)) for i in a)
is really, really slow for large lists. My solution
def occurDict(items):
d = {}
for i in items:
if i in d:
d[i] = d[i]+1
else:
d[i] = 1
return d
is actually a bit faster than the Counter solution, at least for Python 2.7.
If you want to count all values at once you can do it very fast using numpy arrays and bincount
as follows
import numpy as np
a = np.array([1, 2, 3, 4, 1, 4, 1])
np.bincount(a)
which gives
>>> array([0, 3, 1, 1, 2])
Counting the occurrences of one item in a list
For counting the occurrences of just one list item you can use count()
>>> l = ["a","b","b"]
>>> l.count("a")
1
>>> l.count("b")
2
Counting the occurrences of all items in a list is also known as “tallying” a list, or creating a tally counter.
Counting all items with count()
To count the occurrences of items in l
one can simply use a list comprehension and the count()
method
[[x,l.count(x)] for x in set(l)]
(or similarly with a dictionary dict((x,l.count(x)) for x in set(l))
)
Example:
>>> l = ["a","b","b"]
>>> [[x,l.count(x)] for x in set(l)]
[['a', 1], ['b', 2]]
>>> dict((x,l.count(x)) for x in set(l))
{'a': 1, 'b': 2}
Counting all items with Counter()
Alternatively, there’s the faster Counter
class from the collections
library
Counter(l)
Example:
>>> l = ["a","b","b"]
>>> from collections import Counter
>>> Counter(l)
Counter({'b': 2, 'a': 1})
How much faster is Counter?
I checked how much faster Counter
is for tallying lists. I tried both methods out with a few values of n
and it appears that Counter
is faster by a constant factor of approximately 2.
Here is the script I used:
from __future__ import print_function
import timeit
t1=timeit.Timer('Counter(l)',
'import random;import string;from collections import Counter;n=1000;l=[random.choice(string.ascii_letters) for x in range(n)]'
)
t2=timeit.Timer('[[x,l.count(x)] for x in set(l)]',
'import random;import string;n=1000;l=[random.choice(string.ascii_letters) for x in range(n)]'
)
print("Counter(): ", t1.repeat(repeat=3,number=10000))
print("count(): ", t2.repeat(repeat=3,number=10000)
And the output:
Counter(): [0.46062711701961234, 0.4022796869976446, 0.3974247490405105]
count(): [7.779430688009597, 7.962715800967999, 8.420845870045014]
Given an item, how can I count its occurrences in a list in Python?
Here’s an example list:
>>> l = list('aaaaabbbbcccdde')
>>> l
['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'e']
list.count
There’s the list.count
method
>>> l.count('b')
4
This works fine for any list. Tuples have this method as well:
>>> t = tuple('aabbbffffff')
>>> t
('a', 'a', 'b', 'b', 'b', 'f', 'f', 'f', 'f', 'f', 'f')
>>> t.count('f')
6
collections.Counter
And then there’s collections.Counter. You can dump any iterable into a Counter, not just a list, and the Counter will retain a data structure of the counts of the elements.
Usage:
>>> from collections import Counter
>>> c = Counter(l)
>>> c['b']
4
Counters are based on Python dictionaries, their keys are the elements, so the keys need to be hashable. They are basically like sets that allow redundant elements into them.
Further usage of collections.Counter
You can add or subtract with iterables from your counter:
>>> c.update(list('bbb'))
>>> c['b']
7
>>> c.subtract(list('bbb'))
>>> c['b']
4
And you can do multi-set operations with the counter as well:
>>> c2 = Counter(list('aabbxyz'))
>>> c - c2 # set difference
Counter({'a': 3, 'c': 3, 'b': 2, 'd': 2, 'e': 1})
>>> c + c2 # addition of all elements
Counter({'a': 7, 'b': 6, 'c': 3, 'd': 2, 'e': 1, 'y': 1, 'x': 1, 'z': 1})
>>> c | c2 # set union
Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1, 'y': 1, 'x': 1, 'z': 1})
>>> c & c2 # set intersection
Counter({'a': 2, 'b': 2})
Silly answer, sum
There are good builtin answers, but this example is slightly instructive. Here we sum all the occurences where the character, c, is equal to 'b'
:
>>> sum(c == 'b' for c in l)
4
Not great for this use-case, but if you need to have a count of iterables where the case is True
it works perfectly fine to sum the boolean results, since True
is equivalent to 1
.
Why not pandas?
Another answer suggests:
Why not use pandas?
Pandas is a common library, but it’s not in the standard library. Adding it as a requirement is non-trivial.
There are builtin solutions for this use-case in the list object itself as well as in the standard library.
If your project does not already require pandas, it would be foolish to make it a requirement just for this functionality.
Why not using Pandas?
import pandas as pd
my_list = ['a', 'b', 'c', 'd', 'a', 'd', 'a']
# converting the list to a Series and counting the values
my_count = pd.Series(my_list).value_counts()
my_count
Output:
a 3
d 2
b 1
c 1
dtype: int64
If you are looking for a count of a particular element, say a, try:
my_count['a']
Output:
3
sum([1 for elem in <yourlist> if elem==<your_value>])
This will return the amount of occurences of your_value
I’ve compared all suggested solutions (and a few new ones) with perfplot (a small project of mine).
Counting one item
For large enough arrays, it turns out that
numpy.sum(numpy.array(a) == 1)
is slightly faster than the other solutions.
Counting all items
numpy.bincount(a)
is what you want.
Code to reproduce the plots:
from collections import Counter
from collections import defaultdict
import numpy
import operator
import pandas
import perfplot
def counter(a):
return Counter(a)
def count(a):
return dict((i, a.count(i)) for i in set(a))
def bincount(a):
return numpy.bincount(a)
def pandas_value_counts(a):
return pandas.Series(a).value_counts()
def occur_dict(a):
d = {}
for i in a:
if i in d:
d[i] = d[i]+1
else:
d[i] = 1
return d
def count_unsorted_list_items(items):
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
def operator_countof(a):
return dict((i, operator.countOf(a, i)) for i in set(a))
perfplot.show(
setup=lambda n: list(numpy.random.randint(0, 100, n)),
n_range=[2**k for k in range(20)],
kernels=[
counter, count, bincount, pandas_value_counts, occur_dict,
count_unsorted_list_items, operator_countof
],
equality_check=None,
logx=True,
logy=True,
)
from collections import Counter
from collections import defaultdict
import numpy
import operator
import pandas
import perfplot
def counter(a):
return Counter(a)
def count(a):
return dict((i, a.count(i)) for i in set(a))
def bincount(a):
return numpy.bincount(a)
def pandas_value_counts(a):
return pandas.Series(a).value_counts()
def occur_dict(a):
d = {}
for i in a:
if i in d:
d[i] = d[i] + 1
else:
d[i] = 1
return d
def count_unsorted_list_items(items):
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
def operator_countof(a):
return dict((i, operator.countOf(a, i)) for i in set(a))
b = perfplot.bench(
setup=lambda n: list(numpy.random.randint(0, 100, n)),
n_range=[2 ** k for k in range(20)],
kernels=[
counter,
count,
bincount,
pandas_value_counts,
occur_dict,
count_unsorted_list_items,
operator_countof,
],
equality_check=None,
)
b.save("out.png")
b.show()
If you can use pandas
, then value_counts
is there for rescue.
>>> import pandas as pd
>>> a = [1, 2, 3, 4, 1, 4, 1]
>>> pd.Series(a).value_counts()
1 3
4 2
3 1
2 1
dtype: int64
It automatically sorts the result based on frequency as well.
If you want the result to be in a list of list, do as below
>>> pd.Series(a).value_counts().reset_index().values.tolist()
[[1, 3], [4, 2], [3, 1], [2, 1]]
May not be the most efficient, requires an extra pass to remove duplicates.
Functional implementation :
arr = np.array(['a','a','b','b','b','c'])
print(set(map(lambda x : (x , list(arr).count(x)) , arr)))
returns :
{('c', 1), ('b', 3), ('a', 2)}
or return as dict
:
print(dict(map(lambda x : (x , list(arr).count(x)) , arr)))
returns :
{'b': 3, 'c': 1, 'a': 2}
if you want a number of occurrences for the particular element:
>>> from collections import Counter
>>> z = ['blue', 'red', 'blue', 'yellow', 'blue', 'red']
>>> single_occurrences = Counter(z)
>>> print(single_occurrences.get("blue"))
3
>>> print(single_occurrences.values())
dict_values([3, 2, 1])
def countfrequncyinarray(arr1):
r=len(arr1)
return {i:arr1.count(i) for i in range(1,r+1)}
arr1=[4,4,4,4]
a=countfrequncyinarray(arr1)
print(a)
It was suggested to use numpy’s bincount, however it works only for 1d arrays with non-negative integers. Also, the resulting array might be confusing (it contains the occurrences of the integers from min to max of the original list, and sets to 0 the missing integers).
A better way to do it with numpy is to use the unique function with the attribute return_counts
set to True. It returns a tuple with an array of the unique values and an array of the occurrences of each unique value.
# a = [1, 1, 0, 2, 1, 0, 3, 3]
a_uniq, counts = np.unique(a, return_counts=True) # array([0, 1, 2, 3]), array([2, 3, 1, 2]
and then we can pair them as
dict(zip(a_uniq, counts)) # {0: 2, 1: 3, 2: 1, 3: 2}
It also works with other data types and “2d lists”, e.g.
>>> a = [['a', 'b', 'b', 'b'], ['a', 'c', 'c', 'a']]
>>> dict(zip(*np.unique(a, return_counts=True)))
{'a': 3, 'b': 3, 'c': 2}
Count of all elements with itertools.groupby()
Antoher possiblity for getting the count of all elements in the list could be by means of itertools.groupby()
.
With “duplicate” counts
from itertools import groupby
L = ['a', 'a', 'a', 't', 'q', 'a', 'd', 'a', 'd', 'c'] # Input list
counts = [(i, len(list(c))) for i,c in groupby(L)] # Create value-count pairs as list of tuples
print(counts)
Returns
[('a', 3), ('t', 1), ('q', 1), ('a', 1), ('d', 1), ('a', 1), ('d', 1), ('c', 1)]
Notice how it combined the first three a
‘s as the first group, while other groups of a
are present further down the list. This happens because the input list L
was not sorted. This can be a benefit sometimes if the groups should in fact be separate.
With unique counts
If unique group counts are desired, just sort the input list:
counts = [(i, len(list(c))) for i,c in groupby(sorted(L))]
print(counts)
Returns
[('a', 5), ('c', 1), ('d', 2), ('q', 1), ('t', 1)]
Note: For creating unique counts, many of the other answers provide easier and more readable code compared to the groupby
solution. But it is shown here to draw a parallel to the duplicate count example.
Below are the three solutions:
Fastest is using a for loop and storing it in a Dict.
import time
from collections import Counter
def countElement(a):
g = {}
for i in a:
if i in g:
g[i] +=1
else:
g[i] =1
return g
z = [1,1,1,1,2,2,2,2,3,3,4,5,5,234,23,3,12,3,123,12,31,23,13,2,4,23,42,42,34,234,23,42,34,23,423,42,34,23,423,4,234,23,42,34,23,4,23,423,4,23,4]
#Solution 1 - Faster
st = time.monotonic()
for i in range(1000000):
b = countElement(z)
et = time.monotonic()
print(b)
print('Simple for loop and storing it in dict - Duration: {}'.format(et - st))
#Solution 2 - Fast
st = time.monotonic()
for i in range(1000000):
a = Counter(z)
et = time.monotonic()
print (a)
print('Using collections.Counter - Duration: {}'.format(et - st))
#Solution 3 - Slow
st = time.monotonic()
for i in range(1000000):
g = dict([(i, z.count(i)) for i in set(z)])
et = time.monotonic()
print(g)
print('Using list comprehension - Duration: {}'.format(et - st))
Result
#Solution 1 - Faster
{1: 4, 2: 5, 3: 4, 4: 6, 5: 2, 234: 3, 23: 10, 12: 2, 123: 1, 31: 1, 13: 1, 42: 5, 34: 4, 423: 3}
Simple for loop and storing it in dict - Duration: 12.032000000000153
#Solution 2 - Fast
Counter({23: 10, 4: 6, 2: 5, 42: 5, 1: 4, 3: 4, 34: 4, 234: 3, 423: 3, 5: 2, 12: 2, 123: 1, 31: 1, 13: 1})
Using collections.Counter - Duration: 15.889999999999418
#Solution 3 - Slow
{1: 4, 2: 5, 3: 4, 4: 6, 5: 2, 34: 4, 423: 3, 234: 3, 42: 5, 12: 2, 13: 1, 23: 10, 123: 1, 31: 1}
Using list comprehension - Duration: 33.0
Although it is very old question, since i didn’t find a one liner, i made one.
# original numbers in list
l = [1, 2, 2, 3, 3, 3, 4]
# empty dictionary to hold pair of number and its count
d = {}
# loop through all elements and store count
[ d.update( {i:d.get(i, 0)+1} ) for i in l ]
print(d)
# {1: 1, 2: 2, 3: 3, 4: 1}
I would use filter()
, take Lukasz’s example:
>>> lst = [1, 2, 3, 4, 1, 4, 1]
>>> len(filter(lambda x: x==1, lst))
3
Given a list X
import numpy as np
X = [1, -1, 1, -1, 1]
The dictionary which shows i: frequency(i) for elements of this list is:
{i:X.count(i) for i in np.unique(X)}
Output:
{-1: 2, 1: 3}
use %timeit to see which operation is more efficient. np.array counting operations should be faster.
from collections import Counter
mylist = [1,7,7,7,3,9,9,9,7,9,10,0]
types_counts=Counter(mylist)
print(types_counts)
test = [409.1, 479.0, 340.0, 282.4, 406.0, 300.0, 374.0, 253.3, 195.1, 269.0, 329.3, 250.7, 250.7, 345.3, 379.3, 275.0, 215.2, 300.0]
for i in test:
print('{} numbers {}'.format(i, test.count(i)))
Alternatively, you can also implement the counter by yourself. This is the way I do:
item_list = ['me', 'me', 'you', 'you', 'you', 'they']
occ_dict = {}
for item in item_list:
if item not in occ_dict:
occ_dict[item] = 1
else:
occ_dict[item] +=1
print(occ_dict)
Output: {'me': 2, 'you': 3, 'they': 1}
mot = ["compte", "france", "zied"]
lst = ["compte", "france", "france", "france", "france"]
dict((x, lst.count(x)) for x in set(mot))
this gives
{'compte': 1, 'france': 4, 'zied': 0}
import pandas as pd
test = [409.1, 479.0, 340.0, 282.4, 406.0, 300.0, 374.0, 253.3, 195.1, 269.0, 329.3, 250.7, 250.7, 345.3, 379.3, 275.0, 215.2, 300.0]
#turning the list into a temporary dataframe
test = pd.DataFrame(test)
#using the very convenient value_counts() function
df_counts = test.value_counts()
df_counts
then you can use df_counts.index
and df_counts.values
to get the data.
x = ['Jess', 'Jack', 'Mary', 'Sophia', 'Karen',
'Addison', 'Joseph','Jack', 'Jack', 'Eric', 'Ilona', 'Jason']
the_item = input('Enter the item that you wish to find : ')
how_many_times = 0
for occurrence in x:
if occurrence == the_item :
how_many_times += 1
print('The occurrence of', the_item, 'in', x,'is',how_many_times)
Created a list of names wherein the name ‘Jack’ is repeated.
In order to check its Occurrence, I ran a for loop in the list named x
.
Upon each iteration, if the loop variable attains the value same that of received from the user and stored in the variable the_item
, the variable how_many_times
gets incremented by 1.
After attaining some value…We print how_many_times
which stores the value of the occurance of the word ‘jack’
For this topic I know the below six ways by which you can count the number of occurrences of the element in the list:
1) Using count()
method
count()
is the in-built function by which python count occurrences in list. It is the easiest among all other methods used to count the occurrence. Count()
methods take one argument, i.e., the element for which the number of occurrences is to be counted.
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
print(sample_list.count("a"))
print(sample_list.count("ab"))
Output
2
3
2) Using a loop
Another simple approach to counting the occurrence is by using a loop with the counter variable. Here, the counter variable keeps increasing its value by one each time after traversing through the given element. At last, the value of the counter variable displays the number of occurrences of the element.
For example:
def countElement(sample_list, element):
return sample_list.count(element)
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
element = "ab"
print('{} has occurred {} times'.format(element, countElement(sample_list, element)))
Output
ab has occurred 3 times
3) Using countof()
method
Operator module from python library consists of countof()
method which helps to return the number of occurrence of the element from the lists. This method takes two arguments, i.e., the list in which the count needs to be performed and the element which needs to be found. Moreover, you have to import the operator module before beginning the program using the import
keyword as shown below:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
import operator as op
print(op.countOf(sample_list,"a"))
Output
2
4) Using counter()
method
Python possesses an in-built module named collections, including multiple methods to ease your programming. One such method is a counter()
method where elements are stored as a dictionary with keys and counts as values.
Therefore, the counter()
method helps you return the total number of occurrences of a given element inside the given list by taking one parameter as the list in which the element is to be counted. Remember that you have to import the collections module to use the counter()
method as shown in the below example:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
from collections import Counter
print(Counter(sample_list))
c = Counter(sample_list)
print(c["a"])
Output
Counter({'ab': 3, 'a': 2, 'abc': 1})
2
5) Using pandas
library
Pandas is the in-built python library, highly popular for data analysis and data manipulation. It is an open-source tool with a large range of features and is widely used in the domains like machine learning and artificial intelligence.
Pandas possess a wide range of default methods, one of which is the value_count() method. Along with the value_count()
method, pandas use series, i.e., a one-dimensional array with axis label.
To count the occurrence of elements using pandas, you have to convert the given list into the series and then use the value_count()
method, which returns the object in descending order. By these, you can easily note that the first element is always the most frequently occurring element.
Check out the below example for a better understanding of the Pandas library
For example:
import pandas as pd
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
count = pd.Series(sample_list).value_counts()
print(count["a"])
Output
2
6) Using loops and dict in python
This is the most traditional method by which python count occurrences in the list that is by using the loop, conditional statement, and dictionaries. By this method, you have to create the empty dictionary and then iterate over the list. Later, check if the element present in the list is available in the dictionary or not. If yes, then increase its value by one; otherwise, introduce a new element in the dictionary and assign 1 to it. Repeat the same process until all the elements in the lists are visited.
Remember that this method is quite different from the previous method using the loop and the counter variable. The early mentioned method does not make use of dictionary data structure, whereas this one does. At last, print the count of occurrence of each element as shown in the below example:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
def countOccurrence(a):
k = {}
for j in a:
if j in k:
k[j] +=1
else:
k[j] =1
return k
print(countOccurrence(sample_list))
Output
{'a': 2, 'ab': 3, 'abc': 1}
Given a single item, how do I count occurrences of it in a list, in Python?
A related but different problem is counting occurrences of each different element in a collection, getting a dictionary or list as a histogram result instead of a single integer. For that problem, see Using a dictionary to count the items in a list.
If you only want a single item’s count, use the count
method:
>>> [1, 2, 3, 4, 1, 4, 1].count(1)
3
Important: this is very slow if you are counting multiple different items
Each count
call goes over the entire list of n
elements. Calling count
in a loop n
times means n * n
total checks, which can be catastrophic for performance.
If you want to count multiple items, use Counter
, which only does n
total checks.
list.count(x)
returns the number of times x
appears in a list
see:
http://docs.python.org/tutorial/datastructures.html#more-on-lists
Use Counter
if you are using Python 2.7 or 3.x and you want the number of occurrences for each element:
>>> from collections import Counter
>>> z = ['blue', 'red', 'blue', 'yellow', 'blue', 'red']
>>> Counter(z)
Counter({'blue': 3, 'red': 2, 'yellow': 1})
# Python >= 2.6 (defaultdict) && < 2.7 (Counter, OrderedDict)
from collections import defaultdict
def count_unsorted_list_items(items):
"""
:param items: iterable of hashable items to count
:type items: iterable
:returns: dict of counts like Py2.7 Counter
:rtype: dict
"""
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
# Python >= 2.2 (generators)
def count_sorted_list_items(items):
"""
:param items: sorted iterable of items to count
:type items: sorted iterable
:returns: generator of (item, count) tuples
:rtype: generator
"""
if not items:
return
elif len(items) == 1:
yield (items[0], 1)
return
prev_item = items[0]
count = 1
for item in items[1:]:
if prev_item == item:
count += 1
else:
yield (prev_item, count)
count = 1
prev_item = item
yield (item, count)
return
import unittest
class TestListCounters(unittest.TestCase):
def test_count_unsorted_list_items(self):
D = (
([], []),
([2], [(2,1)]),
([2,2], [(2,2)]),
([2,2,2,2,3,3,5,5], [(2,4), (3,2), (5,2)]),
)
for inp, exp_outp in D:
counts = count_unsorted_list_items(inp)
print inp, exp_outp, counts
self.assertEqual(counts, dict( exp_outp ))
inp, exp_outp = UNSORTED_WIN = ([2,2,4,2], [(2,3), (4,1)])
self.assertEqual(dict( exp_outp ), count_unsorted_list_items(inp) )
def test_count_sorted_list_items(self):
D = (
([], []),
([2], [(2,1)]),
([2,2], [(2,2)]),
([2,2,2,2,3,3,5,5], [(2,4), (3,2), (5,2)]),
)
for inp, exp_outp in D:
counts = list( count_sorted_list_items(inp) )
print inp, exp_outp, counts
self.assertEqual(counts, exp_outp)
inp, exp_outp = UNSORTED_FAIL = ([2,2,4,2], [(2,3), (4,1)])
self.assertEqual(exp_outp, list( count_sorted_list_items(inp) ))
# ... [(2,2), (4,1), (2,1)]
To count the number of diverse elements having a common type:
li = ['A0','c5','A8','A2','A5','c2','A3','A9']
print sum(1 for el in li if el[0]=='A' and el[1] in '01234')
gives
3
, not 6
Another way to get the number of occurrences of each item, in a dictionary:
dict((i, a.count(i)) for i in a)
I had this problem today and rolled my own solution before I thought to check SO. This:
dict((i,a.count(i)) for i in a)
is really, really slow for large lists. My solution
def occurDict(items):
d = {}
for i in items:
if i in d:
d[i] = d[i]+1
else:
d[i] = 1
return d
is actually a bit faster than the Counter solution, at least for Python 2.7.
If you want to count all values at once you can do it very fast using numpy arrays and bincount
as follows
import numpy as np
a = np.array([1, 2, 3, 4, 1, 4, 1])
np.bincount(a)
which gives
>>> array([0, 3, 1, 1, 2])
Counting the occurrences of one item in a list
For counting the occurrences of just one list item you can use count()
>>> l = ["a","b","b"]
>>> l.count("a")
1
>>> l.count("b")
2
Counting the occurrences of all items in a list is also known as “tallying” a list, or creating a tally counter.
Counting all items with count()
To count the occurrences of items in l
one can simply use a list comprehension and the count()
method
[[x,l.count(x)] for x in set(l)]
(or similarly with a dictionary dict((x,l.count(x)) for x in set(l))
)
Example:
>>> l = ["a","b","b"]
>>> [[x,l.count(x)] for x in set(l)]
[['a', 1], ['b', 2]]
>>> dict((x,l.count(x)) for x in set(l))
{'a': 1, 'b': 2}
Counting all items with Counter()
Alternatively, there’s the faster Counter
class from the collections
library
Counter(l)
Example:
>>> l = ["a","b","b"]
>>> from collections import Counter
>>> Counter(l)
Counter({'b': 2, 'a': 1})
How much faster is Counter?
I checked how much faster Counter
is for tallying lists. I tried both methods out with a few values of n
and it appears that Counter
is faster by a constant factor of approximately 2.
Here is the script I used:
from __future__ import print_function
import timeit
t1=timeit.Timer('Counter(l)',
'import random;import string;from collections import Counter;n=1000;l=[random.choice(string.ascii_letters) for x in range(n)]'
)
t2=timeit.Timer('[[x,l.count(x)] for x in set(l)]',
'import random;import string;n=1000;l=[random.choice(string.ascii_letters) for x in range(n)]'
)
print("Counter(): ", t1.repeat(repeat=3,number=10000))
print("count(): ", t2.repeat(repeat=3,number=10000)
And the output:
Counter(): [0.46062711701961234, 0.4022796869976446, 0.3974247490405105]
count(): [7.779430688009597, 7.962715800967999, 8.420845870045014]
Given an item, how can I count its occurrences in a list in Python?
Here’s an example list:
>>> l = list('aaaaabbbbcccdde')
>>> l
['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'e']
list.count
There’s the list.count
method
>>> l.count('b')
4
This works fine for any list. Tuples have this method as well:
>>> t = tuple('aabbbffffff')
>>> t
('a', 'a', 'b', 'b', 'b', 'f', 'f', 'f', 'f', 'f', 'f')
>>> t.count('f')
6
collections.Counter
And then there’s collections.Counter. You can dump any iterable into a Counter, not just a list, and the Counter will retain a data structure of the counts of the elements.
Usage:
>>> from collections import Counter
>>> c = Counter(l)
>>> c['b']
4
Counters are based on Python dictionaries, their keys are the elements, so the keys need to be hashable. They are basically like sets that allow redundant elements into them.
Further usage of collections.Counter
You can add or subtract with iterables from your counter:
>>> c.update(list('bbb'))
>>> c['b']
7
>>> c.subtract(list('bbb'))
>>> c['b']
4
And you can do multi-set operations with the counter as well:
>>> c2 = Counter(list('aabbxyz'))
>>> c - c2 # set difference
Counter({'a': 3, 'c': 3, 'b': 2, 'd': 2, 'e': 1})
>>> c + c2 # addition of all elements
Counter({'a': 7, 'b': 6, 'c': 3, 'd': 2, 'e': 1, 'y': 1, 'x': 1, 'z': 1})
>>> c | c2 # set union
Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1, 'y': 1, 'x': 1, 'z': 1})
>>> c & c2 # set intersection
Counter({'a': 2, 'b': 2})
Silly answer, sum
There are good builtin answers, but this example is slightly instructive. Here we sum all the occurences where the character, c, is equal to 'b'
:
>>> sum(c == 'b' for c in l)
4
Not great for this use-case, but if you need to have a count of iterables where the case is True
it works perfectly fine to sum the boolean results, since True
is equivalent to 1
.
Why not pandas?
Another answer suggests:
Why not use pandas?
Pandas is a common library, but it’s not in the standard library. Adding it as a requirement is non-trivial.
There are builtin solutions for this use-case in the list object itself as well as in the standard library.
If your project does not already require pandas, it would be foolish to make it a requirement just for this functionality.
Why not using Pandas?
import pandas as pd
my_list = ['a', 'b', 'c', 'd', 'a', 'd', 'a']
# converting the list to a Series and counting the values
my_count = pd.Series(my_list).value_counts()
my_count
Output:
a 3
d 2
b 1
c 1
dtype: int64
If you are looking for a count of a particular element, say a, try:
my_count['a']
Output:
3
sum([1 for elem in <yourlist> if elem==<your_value>])
This will return the amount of occurences of your_value
I’ve compared all suggested solutions (and a few new ones) with perfplot (a small project of mine).
Counting one item
For large enough arrays, it turns out that
numpy.sum(numpy.array(a) == 1)
is slightly faster than the other solutions.
Counting all items
numpy.bincount(a)
is what you want.
Code to reproduce the plots:
from collections import Counter
from collections import defaultdict
import numpy
import operator
import pandas
import perfplot
def counter(a):
return Counter(a)
def count(a):
return dict((i, a.count(i)) for i in set(a))
def bincount(a):
return numpy.bincount(a)
def pandas_value_counts(a):
return pandas.Series(a).value_counts()
def occur_dict(a):
d = {}
for i in a:
if i in d:
d[i] = d[i]+1
else:
d[i] = 1
return d
def count_unsorted_list_items(items):
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
def operator_countof(a):
return dict((i, operator.countOf(a, i)) for i in set(a))
perfplot.show(
setup=lambda n: list(numpy.random.randint(0, 100, n)),
n_range=[2**k for k in range(20)],
kernels=[
counter, count, bincount, pandas_value_counts, occur_dict,
count_unsorted_list_items, operator_countof
],
equality_check=None,
logx=True,
logy=True,
)
from collections import Counter
from collections import defaultdict
import numpy
import operator
import pandas
import perfplot
def counter(a):
return Counter(a)
def count(a):
return dict((i, a.count(i)) for i in set(a))
def bincount(a):
return numpy.bincount(a)
def pandas_value_counts(a):
return pandas.Series(a).value_counts()
def occur_dict(a):
d = {}
for i in a:
if i in d:
d[i] = d[i] + 1
else:
d[i] = 1
return d
def count_unsorted_list_items(items):
counts = defaultdict(int)
for item in items:
counts[item] += 1
return dict(counts)
def operator_countof(a):
return dict((i, operator.countOf(a, i)) for i in set(a))
b = perfplot.bench(
setup=lambda n: list(numpy.random.randint(0, 100, n)),
n_range=[2 ** k for k in range(20)],
kernels=[
counter,
count,
bincount,
pandas_value_counts,
occur_dict,
count_unsorted_list_items,
operator_countof,
],
equality_check=None,
)
b.save("out.png")
b.show()
If you can use pandas
, then value_counts
is there for rescue.
>>> import pandas as pd
>>> a = [1, 2, 3, 4, 1, 4, 1]
>>> pd.Series(a).value_counts()
1 3
4 2
3 1
2 1
dtype: int64
It automatically sorts the result based on frequency as well.
If you want the result to be in a list of list, do as below
>>> pd.Series(a).value_counts().reset_index().values.tolist()
[[1, 3], [4, 2], [3, 1], [2, 1]]
May not be the most efficient, requires an extra pass to remove duplicates.
Functional implementation :
arr = np.array(['a','a','b','b','b','c'])
print(set(map(lambda x : (x , list(arr).count(x)) , arr)))
returns :
{('c', 1), ('b', 3), ('a', 2)}
or return as dict
:
print(dict(map(lambda x : (x , list(arr).count(x)) , arr)))
returns :
{'b': 3, 'c': 1, 'a': 2}
if you want a number of occurrences for the particular element:
>>> from collections import Counter
>>> z = ['blue', 'red', 'blue', 'yellow', 'blue', 'red']
>>> single_occurrences = Counter(z)
>>> print(single_occurrences.get("blue"))
3
>>> print(single_occurrences.values())
dict_values([3, 2, 1])
def countfrequncyinarray(arr1):
r=len(arr1)
return {i:arr1.count(i) for i in range(1,r+1)}
arr1=[4,4,4,4]
a=countfrequncyinarray(arr1)
print(a)
It was suggested to use numpy’s bincount, however it works only for 1d arrays with non-negative integers. Also, the resulting array might be confusing (it contains the occurrences of the integers from min to max of the original list, and sets to 0 the missing integers).
A better way to do it with numpy is to use the unique function with the attribute return_counts
set to True. It returns a tuple with an array of the unique values and an array of the occurrences of each unique value.
# a = [1, 1, 0, 2, 1, 0, 3, 3]
a_uniq, counts = np.unique(a, return_counts=True) # array([0, 1, 2, 3]), array([2, 3, 1, 2]
and then we can pair them as
dict(zip(a_uniq, counts)) # {0: 2, 1: 3, 2: 1, 3: 2}
It also works with other data types and “2d lists”, e.g.
>>> a = [['a', 'b', 'b', 'b'], ['a', 'c', 'c', 'a']]
>>> dict(zip(*np.unique(a, return_counts=True)))
{'a': 3, 'b': 3, 'c': 2}
Count of all elements with itertools.groupby()
Antoher possiblity for getting the count of all elements in the list could be by means of itertools.groupby()
.
With “duplicate” counts
from itertools import groupby
L = ['a', 'a', 'a', 't', 'q', 'a', 'd', 'a', 'd', 'c'] # Input list
counts = [(i, len(list(c))) for i,c in groupby(L)] # Create value-count pairs as list of tuples
print(counts)
Returns
[('a', 3), ('t', 1), ('q', 1), ('a', 1), ('d', 1), ('a', 1), ('d', 1), ('c', 1)]
Notice how it combined the first three a
‘s as the first group, while other groups of a
are present further down the list. This happens because the input list L
was not sorted. This can be a benefit sometimes if the groups should in fact be separate.
With unique counts
If unique group counts are desired, just sort the input list:
counts = [(i, len(list(c))) for i,c in groupby(sorted(L))]
print(counts)
Returns
[('a', 5), ('c', 1), ('d', 2), ('q', 1), ('t', 1)]
Note: For creating unique counts, many of the other answers provide easier and more readable code compared to the groupby
solution. But it is shown here to draw a parallel to the duplicate count example.
Below are the three solutions:
Fastest is using a for loop and storing it in a Dict.
import time
from collections import Counter
def countElement(a):
g = {}
for i in a:
if i in g:
g[i] +=1
else:
g[i] =1
return g
z = [1,1,1,1,2,2,2,2,3,3,4,5,5,234,23,3,12,3,123,12,31,23,13,2,4,23,42,42,34,234,23,42,34,23,423,42,34,23,423,4,234,23,42,34,23,4,23,423,4,23,4]
#Solution 1 - Faster
st = time.monotonic()
for i in range(1000000):
b = countElement(z)
et = time.monotonic()
print(b)
print('Simple for loop and storing it in dict - Duration: {}'.format(et - st))
#Solution 2 - Fast
st = time.monotonic()
for i in range(1000000):
a = Counter(z)
et = time.monotonic()
print (a)
print('Using collections.Counter - Duration: {}'.format(et - st))
#Solution 3 - Slow
st = time.monotonic()
for i in range(1000000):
g = dict([(i, z.count(i)) for i in set(z)])
et = time.monotonic()
print(g)
print('Using list comprehension - Duration: {}'.format(et - st))
Result
#Solution 1 - Faster
{1: 4, 2: 5, 3: 4, 4: 6, 5: 2, 234: 3, 23: 10, 12: 2, 123: 1, 31: 1, 13: 1, 42: 5, 34: 4, 423: 3}
Simple for loop and storing it in dict - Duration: 12.032000000000153
#Solution 2 - Fast
Counter({23: 10, 4: 6, 2: 5, 42: 5, 1: 4, 3: 4, 34: 4, 234: 3, 423: 3, 5: 2, 12: 2, 123: 1, 31: 1, 13: 1})
Using collections.Counter - Duration: 15.889999999999418
#Solution 3 - Slow
{1: 4, 2: 5, 3: 4, 4: 6, 5: 2, 34: 4, 423: 3, 234: 3, 42: 5, 12: 2, 13: 1, 23: 10, 123: 1, 31: 1}
Using list comprehension - Duration: 33.0
Although it is very old question, since i didn’t find a one liner, i made one.
# original numbers in list
l = [1, 2, 2, 3, 3, 3, 4]
# empty dictionary to hold pair of number and its count
d = {}
# loop through all elements and store count
[ d.update( {i:d.get(i, 0)+1} ) for i in l ]
print(d)
# {1: 1, 2: 2, 3: 3, 4: 1}
I would use filter()
, take Lukasz’s example:
>>> lst = [1, 2, 3, 4, 1, 4, 1]
>>> len(filter(lambda x: x==1, lst))
3
Given a list X
import numpy as np
X = [1, -1, 1, -1, 1]
The dictionary which shows i: frequency(i) for elements of this list is:
{i:X.count(i) for i in np.unique(X)}
Output:
{-1: 2, 1: 3}
use %timeit to see which operation is more efficient. np.array counting operations should be faster.
from collections import Counter
mylist = [1,7,7,7,3,9,9,9,7,9,10,0]
types_counts=Counter(mylist)
print(types_counts)
test = [409.1, 479.0, 340.0, 282.4, 406.0, 300.0, 374.0, 253.3, 195.1, 269.0, 329.3, 250.7, 250.7, 345.3, 379.3, 275.0, 215.2, 300.0]
for i in test:
print('{} numbers {}'.format(i, test.count(i)))
Alternatively, you can also implement the counter by yourself. This is the way I do:
item_list = ['me', 'me', 'you', 'you', 'you', 'they']
occ_dict = {}
for item in item_list:
if item not in occ_dict:
occ_dict[item] = 1
else:
occ_dict[item] +=1
print(occ_dict)
Output: {'me': 2, 'you': 3, 'they': 1}
mot = ["compte", "france", "zied"]
lst = ["compte", "france", "france", "france", "france"]
dict((x, lst.count(x)) for x in set(mot))
this gives
{'compte': 1, 'france': 4, 'zied': 0}
import pandas as pd
test = [409.1, 479.0, 340.0, 282.4, 406.0, 300.0, 374.0, 253.3, 195.1, 269.0, 329.3, 250.7, 250.7, 345.3, 379.3, 275.0, 215.2, 300.0]
#turning the list into a temporary dataframe
test = pd.DataFrame(test)
#using the very convenient value_counts() function
df_counts = test.value_counts()
df_counts
then you can use df_counts.index
and df_counts.values
to get the data.
x = ['Jess', 'Jack', 'Mary', 'Sophia', 'Karen',
'Addison', 'Joseph','Jack', 'Jack', 'Eric', 'Ilona', 'Jason']
the_item = input('Enter the item that you wish to find : ')
how_many_times = 0
for occurrence in x:
if occurrence == the_item :
how_many_times += 1
print('The occurrence of', the_item, 'in', x,'is',how_many_times)
Created a list of names wherein the name ‘Jack’ is repeated.
In order to check its Occurrence, I ran a for loop in the list named x
.
Upon each iteration, if the loop variable attains the value same that of received from the user and stored in the variable the_item
, the variable how_many_times
gets incremented by 1.
After attaining some value…We print how_many_times
which stores the value of the occurance of the word ‘jack’
For this topic I know the below six ways by which you can count the number of occurrences of the element in the list:
1) Using count()
method
count()
is the in-built function by which python count occurrences in list. It is the easiest among all other methods used to count the occurrence. Count()
methods take one argument, i.e., the element for which the number of occurrences is to be counted.
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
print(sample_list.count("a"))
print(sample_list.count("ab"))
Output
2 3
2) Using a loop
Another simple approach to counting the occurrence is by using a loop with the counter variable. Here, the counter variable keeps increasing its value by one each time after traversing through the given element. At last, the value of the counter variable displays the number of occurrences of the element.
For example:
def countElement(sample_list, element):
return sample_list.count(element)
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
element = "ab"
print('{} has occurred {} times'.format(element, countElement(sample_list, element)))
Output
ab has occurred 3 times
3) Using countof()
method
Operator module from python library consists of countof()
method which helps to return the number of occurrence of the element from the lists. This method takes two arguments, i.e., the list in which the count needs to be performed and the element which needs to be found. Moreover, you have to import the operator module before beginning the program using the import
keyword as shown below:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
import operator as op
print(op.countOf(sample_list,"a"))
Output
2
4) Using counter()
method
Python possesses an in-built module named collections, including multiple methods to ease your programming. One such method is a counter()
method where elements are stored as a dictionary with keys and counts as values.
Therefore, the counter()
method helps you return the total number of occurrences of a given element inside the given list by taking one parameter as the list in which the element is to be counted. Remember that you have to import the collections module to use the counter()
method as shown in the below example:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
from collections import Counter
print(Counter(sample_list))
c = Counter(sample_list)
print(c["a"])
Output
Counter({'ab': 3, 'a': 2, 'abc': 1}) 2
5) Using pandas
library
Pandas is the in-built python library, highly popular for data analysis and data manipulation. It is an open-source tool with a large range of features and is widely used in the domains like machine learning and artificial intelligence.
Pandas possess a wide range of default methods, one of which is the value_count() method. Along with the value_count()
method, pandas use series, i.e., a one-dimensional array with axis label.
To count the occurrence of elements using pandas, you have to convert the given list into the series and then use the value_count()
method, which returns the object in descending order. By these, you can easily note that the first element is always the most frequently occurring element.
Check out the below example for a better understanding of the Pandas library
For example:
import pandas as pd
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
count = pd.Series(sample_list).value_counts()
print(count["a"])
Output
2
6) Using loops and dict in python
This is the most traditional method by which python count occurrences in the list that is by using the loop, conditional statement, and dictionaries. By this method, you have to create the empty dictionary and then iterate over the list. Later, check if the element present in the list is available in the dictionary or not. If yes, then increase its value by one; otherwise, introduce a new element in the dictionary and assign 1 to it. Repeat the same process until all the elements in the lists are visited.
Remember that this method is quite different from the previous method using the loop and the counter variable. The early mentioned method does not make use of dictionary data structure, whereas this one does. At last, print the count of occurrence of each element as shown in the below example:
For example:
sample_list = ["a", "ab", "a", "abc", "ab", "ab"]
def countOccurrence(a):
k = {}
for j in a:
if j in k:
k[j] +=1
else:
k[j] =1
return k
print(countOccurrence(sample_list))
Output
{'a': 2, 'ab': 3, 'abc': 1}