I am using Python3. I have a list a of only integers. Now, I want to save the element and the number it repeats itself in a row in another list.
Example:
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
Output:
result = ["6,1", "0, 2", "2, 4", "1, 1", "89, 2"]
# the number before the "," represents the element, the number after the "," represents how many times it repeats itself.
How to efficiently achieve my goal ?
I believe all the solutions given are counting the total occurrences of a number in the list rather than counting the repeating runs of a number.
Here is a solution using groupby from itertools. It gathers the runs and appends them to a dictionary keyed by the number.
from itertools import groupby
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
d = dict()
for k, v in groupby(a):
d.setdefault(k, []).append(len(list(v)))
Dictionary created:
>>> d
{6: [1], 0: [2], 2: [4], 1: [1], 89: [2]}
Note that all runs only had 1 count in their list. If there where other occurrences of a number already seen, there would be multiple counts in the lists (that are the values for dictionary).
for counting an individual element,
us list.count,
i.e, here, for, say 2, we user
a.count(2),
which outputs 4,
also,
set(a) gives the unique elements in a
overall answer,
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
nums = set(a)
result = [f"{val}, {a.count(val)}" for val in set(a)]
print(result)
which gives
['0, 2', '1, 1', '2, 4', '6, 1', '89, 2']
Method 1: using for loop
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
result = []
a_set = set(a) # transform the list into a set to have unique integer
for nbr in a_set:
nbr_count = a.count(nbr)
result.append("{},{}".format(nbr, nbr_count))
print(result) # ['0,2', '1,1', '2,4', '6,1', '89,2']
Method 2: using list-comprehensions
result = ["{},{}".format(item, a.count(item)) for item in set(a)]
print(result) # ['0,2', '1,1', '2,4', '6,1', '89,2']
you can use Python List count() Method, method returns the number of elements with the specified value.
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
print ({x:a.count(x) for x in a})
output:
{6: 1, 0: 2, 2: 4, 1: 1, 89: 2}
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
dic = dict()
for i in a:
if(i in dic):
dic[i] = dic[i] + 1
else:
dic[i] = 1
result = []
for i in dic:
result.append(str(i) +"," + str(dic[i]))
Or:
from collections import Counter
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
mylist = [Counter(a)]
print(mylist)
You can use Counter from collections:
from collections import Counter
a = [6, 0, 0, 2, 2, 2, 2, 1, 89, 89]
counter = Counter(a)
result = ['{},{}'.format(k, v) for k,v in counter.items()]
I have a dictionary consisting of key:[list] in which the list is a fixed and even number of binary values, ie:
{'a':[0,1,1,0,0,1],'b':[1,1,1,0,0,0]}
For each key, I need to return a new value for each pair of values in the original dict, such that for pair(1,1) = 3, pair(0,1) = 2, pair(1,0) = 1, pair(0,0) = 0.
For the example above, output would be:
{'a':[2,1,2],'b':[3,1,0]}
New to both python and programming in general, and haven't found what I'm looking for on SO. Suggestions appreciated.
First attack just the pairing part:
def paired(binlist, map={(1, 1): 3, (0, 1): 2, (1, 0): 1, (0, 0): 0}):
return [map[tuple(binlist[i:i + 2])] for i in range(0, len(binlist), 2)]
Then apply this to your dictionary:
{k: paired(v) for k, v in input_dictionary.iteritems()}
Demo:
>>> paired([0,1,1,0,0,1])
[2, 1, 2]
>>> paired([1,1,1,0,0,0])
[3, 1, 0]
>>> input_dictionary = {'a':[0,1,1,0,0,1],'b':[1,1,1,0,0,0]}
>>> {k: paired(v) for k, v in input_dictionary.iteritems()}
{'a': [2, 1, 2], 'b': [3, 1, 0]}
>>> D = {'a': [0, 1, 1, 0, 0, 1],'b': [1, 1, 1, 0, 0, 0]}
>>> {k: [v[i] + 2 * v[i+1] for i in range(0, len(v), 2)] for k, v in D.items()}
{'a': [2, 1, 2], 'b': [3, 1, 0]}
A small snippet I could come up with was :
>>> testDict = {'a':[0,1,1,0,0,1],'b':[1,1,1,0,0,0]}
>>> referenceDict = {(0, 1):2, (0, 0):0, (1, 0):1, (1, 1):3}
>>> for key, value in testDict.items():
finalList = [referenceDict[elem] for elem in zip(value[::2], value[1::2])]
testDict[key] = finalList
>>> testDict
{'a': [2, 1, 2], 'b': [3, 1, 0]}
value[::2] is Python's Slice Notation.
Packing this up into a function for use :
def testFunction(inputDict):
referenceDict = {(0, 1):2, (0, 0):0, (1, 0):1, (1, 1):3}
for key, value in inputDict.items():
finalList = [referenceDict[elem] for elem in zip(value[::2], value[1::2])]
inputDict[key] = finalList
return inputDict
Example -
>>> testFunction({'a':[0,1,1,0,0,1],'b':[1,1,1,0,0,0]})
{'a': [2, 1, 2], 'b': [3, 1, 0]}
This question already has answers here:
Using a dictionary to count the items in a list
(8 answers)
Closed 7 months ago.
Given an unordered list of values like
a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
How can I get the frequency of each value that appears in the list, like so?
# `a` has 4 instances of `1`, 4 of `2`, 2 of `3`, 1 of `4,` 2 of `5`
b = [4, 4, 2, 1, 2] # expected output
In Python 2.7 (or newer), you can use collections.Counter:
>>> import collections
>>> a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
>>> counter = collections.Counter(a)
>>> counter
Counter({1: 4, 2: 4, 5: 2, 3: 2, 4: 1})
>>> counter.values()
dict_values([2, 4, 4, 1, 2])
>>> counter.keys()
dict_keys([5, 1, 2, 4, 3])
>>> counter.most_common(3)
[(1, 4), (2, 4), (5, 2)]
>>> dict(counter)
{5: 2, 1: 4, 2: 4, 4: 1, 3: 2}
>>> # Get the counts in order matching the original specification,
>>> # by iterating over keys in sorted order
>>> [counter[x] for x in sorted(counter.keys())]
[4, 4, 2, 1, 2]
If you are using Python 2.6 or older, you can download an implementation here.
If the list is sorted, you can use groupby from the itertools standard library (if it isn't, you can just sort it first, although this takes O(n lg n) time):
from itertools import groupby
a = [5, 1, 2, 2, 4, 3, 1, 2, 3, 1, 1, 5, 2]
[len(list(group)) for key, group in groupby(sorted(a))]
Output:
[4, 4, 2, 1, 2]
Python 2.7+ introduces Dictionary Comprehension. Building the dictionary from the list will get you the count as well as get rid of duplicates.
>>> a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
>>> d = {x:a.count(x) for x in a}
>>> d
{1: 4, 2: 4, 3: 2, 4: 1, 5: 2}
>>> a, b = d.keys(), d.values()
>>> a
[1, 2, 3, 4, 5]
>>> b
[4, 4, 2, 1, 2]
Count the number of appearances manually by iterating through the list and counting them up, using a collections.defaultdict to track what has been seen so far:
from collections import defaultdict
appearances = defaultdict(int)
for curr in a:
appearances[curr] += 1
In Python 2.7+, you could use collections.Counter to count items
>>> a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
>>>
>>> from collections import Counter
>>> c=Counter(a)
>>>
>>> c.values()
[4, 4, 2, 1, 2]
>>>
>>> c.keys()
[1, 2, 3, 4, 5]
Counting the frequency of elements is probably best done with a dictionary:
b = {}
for item in a:
b[item] = b.get(item, 0) + 1
To remove the duplicates, use a set:
a = list(set(a))
You can do this:
import numpy as np
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
np.unique(a, return_counts=True)
Output:
(array([1, 2, 3, 4, 5]), array([4, 4, 2, 1, 2], dtype=int64))
The first array is values, and the second array is the number of elements with these values.
So If you want to get just array with the numbers you should use this:
np.unique(a, return_counts=True)[1]
Here's another succint alternative using itertools.groupby which also works for unordered input:
from itertools import groupby
items = [5, 1, 1, 2, 2, 1, 1, 2, 2, 3, 4, 3, 5]
results = {value: len(list(freq)) for value, freq in groupby(sorted(items))}
results
format: {value: num_of_occurencies}
{1: 4, 2: 4, 3: 2, 4: 1, 5: 2}
I would simply use scipy.stats.itemfreq in the following manner:
from scipy.stats import itemfreq
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
freq = itemfreq(a)
a = freq[:,0]
b = freq[:,1]
you may check the documentation here: http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.itemfreq.html
from collections import Counter
a=["E","D","C","G","B","A","B","F","D","D","C","A","G","A","C","B","F","C","B"]
counter=Counter(a)
kk=[list(counter.keys()),list(counter.values())]
pd.DataFrame(np.array(kk).T, columns=['Letter','Count'])
seta = set(a)
b = [a.count(el) for el in seta]
a = list(seta) #Only if you really want it.
Suppose we have a list:
fruits = ['banana', 'banana', 'apple', 'banana']
We can find out how many of each fruit we have in the list like so:
import numpy as np
(unique, counts) = np.unique(fruits, return_counts=True)
{x:y for x,y in zip(unique, counts)}
Result:
{'banana': 3, 'apple': 1}
This answer is more explicit
a = [1,1,1,1,2,2,2,2,3,3,3,4,4]
d = {}
for item in a:
if item in d:
d[item] = d.get(item)+1
else:
d[item] = 1
for k,v in d.items():
print(str(k)+':'+str(v))
# output
#1:4
#2:4
#3:3
#4:2
#remove dups
d = set(a)
print(d)
#{1, 2, 3, 4}
For your first question, iterate the list and use a dictionary to keep track of an elements existsence.
For your second question, just use the set operator.
def frequencyDistribution(data):
return {i: data.count(i) for i in data}
print frequencyDistribution([1,2,3,4])
...
{1: 1, 2: 1, 3: 1, 4: 1} # originalNumber: count
I am quite late, but this will also work, and will help others:
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
freq_list = []
a_l = list(set(a))
for x in a_l:
freq_list.append(a.count(x))
print 'Freq',freq_list
print 'number',a_l
will produce this..
Freq [4, 4, 2, 1, 2]
number[1, 2, 3, 4, 5]
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
counts = dict.fromkeys(a, 0)
for el in a: counts[el] += 1
print(counts)
# {1: 4, 2: 4, 3: 2, 4: 1, 5: 2}
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
# 1. Get counts and store in another list
output = []
for i in set(a):
output.append(a.count(i))
print(output)
# 2. Remove duplicates using set constructor
a = list(set(a))
print(a)
Set collection does not allow duplicates, passing a list to the set() constructor will give an iterable of totally unique objects. count() function returns an integer count when an object that is in a list is passed. With that the unique objects are counted and each count value is stored by appending to an empty list output
list() constructor is used to convert the set(a) into list and referred by the same variable a
Output
D:\MLrec\venv\Scripts\python.exe D:/MLrec/listgroup.py
[4, 4, 2, 1, 2]
[1, 2, 3, 4, 5]
Simple solution using a dictionary.
def frequency(l):
d = {}
for i in l:
if i in d.keys():
d[i] += 1
else:
d[i] = 1
for k, v in d.iteritems():
if v ==max (d.values()):
return k,d.keys()
print(frequency([10,10,10,10,20,20,20,20,40,40,50,50,30]))
#!usr/bin/python
def frq(words):
freq = {}
for w in words:
if w in freq:
freq[w] = freq.get(w)+1
else:
freq[w] =1
return freq
fp = open("poem","r")
list = fp.read()
fp.close()
input = list.split()
print input
d = frq(input)
print "frequency of input\n: "
print d
fp1 = open("output.txt","w+")
for k,v in d.items():
fp1.write(str(k)+':'+str(v)+"\n")
fp1.close()
from collections import OrderedDict
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
def get_count(lists):
dictionary = OrderedDict()
for val in lists:
dictionary.setdefault(val,[]).append(1)
return [sum(val) for val in dictionary.values()]
print(get_count(a))
>>>[4, 4, 2, 1, 2]
To remove duplicates and Maintain order:
list(dict.fromkeys(get_count(a)))
>>>[4, 2, 1]
i'm using Counter to generate a freq. dict from text file words in 1 line of code
def _fileIndex(fh):
''' create a dict using Counter of a
flat list of words (re.findall(re.compile(r"[a-zA-Z]+"), lines)) in (lines in file->for lines in fh)
'''
return Counter(
[wrd.lower() for wrdList in
[words for words in
[re.findall(re.compile(r'[a-zA-Z]+'), lines) for lines in fh]]
for wrd in wrdList])
For the record, a functional answer:
>>> L = [1,1,1,1,2,2,2,2,3,3,4,5,5]
>>> import functools
>>> >>> functools.reduce(lambda acc, e: [v+(i==e) for i, v in enumerate(acc,1)] if e<=len(acc) else acc+[0 for _ in range(e-len(acc)-1)]+[1], L, [])
[4, 4, 2, 1, 2]
It's cleaner if you count zeroes too:
>>> functools.reduce(lambda acc, e: [v+(i==e) for i, v in enumerate(acc)] if e<len(acc) else acc+[0 for _ in range(e-len(acc))]+[1], L, [])
[0, 4, 4, 2, 1, 2]
An explanation:
we start with an empty acc list;
if the next element e of L is lower than the size of acc, we just update this element: v+(i==e) means v+1 if the index i of acc is the current element e, otherwise the previous value v;
if the next element e of L is greater or equals to the size of acc, we have to expand acc to host the new 1.
The elements do not have to be sorted (itertools.groupby). You'll get weird results if you have negative numbers.
Another approach of doing this, albeit by using a heavier but powerful library - NLTK.
import nltk
fdist = nltk.FreqDist(a)
fdist.values()
fdist.most_common()
Found another way of doing this, using sets.
#ar is the list of elements
#convert ar to set to get unique elements
sock_set = set(ar)
#create dictionary of frequency of socks
sock_dict = {}
for sock in sock_set:
sock_dict[sock] = ar.count(sock)
For an unordered list you should use:
[a.count(el) for el in set(a)]
The output is
[4, 4, 2, 1, 2]
Yet another solution with another algorithm without using collections:
def countFreq(A):
n=len(A)
count=[0]*n # Create a new list initialized with '0'
for i in range(n):
count[A[i]]+= 1 # increase occurrence for value A[i]
return [x for x in count if x] # return non-zero count
num=[3,2,3,5,5,3,7,6,4,6,7,2]
print ('\nelements are:\t',num)
count_dict={}
for elements in num:
count_dict[elements]=num.count(elements)
print ('\nfrequency:\t',count_dict)
You can use the in-built function provided in python
l.count(l[i])
d=[]
for i in range(len(l)):
if l[i] not in d:
d.append(l[i])
print(l.count(l[i])
The above code automatically removes duplicates in a list and also prints the frequency of each element in original list and the list without duplicates.
Two birds for one shot ! X D
This approach can be tried if you don't want to use any library and keep it simple and short!
a = [1,1,1,1,2,2,2,2,3,3,4,5,5]
marked = []
b = [(a.count(i), marked.append(i))[0] for i in a if i not in marked]
print(b)
o/p
[4, 4, 2, 1, 2]