I'm working through exercises in Building Skills in Python, which to my knowledge don't have any published solutions.
In any case, I'm attempting to have a dictionary count the number of occurrences of a certain number in the original list, before duplicates are removed. For some reason, despite a number of variations on the theme below, I cant seem to increment the value for each of the 'keys' in the dictionary.
How could I code this with dictionaries?
dv = list()
# arbitrary sequence of numbers
seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
# dictionary counting number of occurances
seqDic = { }
for v in seq:
i = 1
dv.append(v)
for i in range(len(dv)-1):
if dv[i] == v:
del dv[-1]
seqDic.setdefault(v)
currentCount = seqDic[v]
currentCount += 1
print currentCount # debug
seqDic[v]=currentCount
print "orig:", seq
print "new: ", dv
print seqDic
defaultdict is not dict (it's a subclass, and may do too much of the work for you to help you learn via this exercise), so here's a simple way to do it with plain dict:
dv = list()
# arbitrary sequence of numbers
seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
# dictionary counting number of occurances
seqDic = { }
for i in seq:
if i in seqDic:
seqDic[i] += 1
else:
dv.append(i)
seqDic[i] = 1
this simple approach works particularly well here because you need the if i in seqDic test anyway for the purpose of building dv as well as seqDic. Otherwise, simpler would be:
for i in seq:
seqDic[i] = 1 + seqDic.get(i, 0)
using the handy method get of dict, which returns the second argument if the first is not a key in the dictionary. If you like this idea, here's a solution that also builds dv:
for i in seq:
seqDic[i] = 1 + seqDic.get(i, 0)
if seqDic[i] == 1: dv.append(i)
Edit: If you don't case about the order of items in dv (rather than wanting dv to be in the same order as the first occurrence of item in seq), then just using (after the simple version of the loop)
dv = seqDic.keys()
also works (in Python 2, where .keys returns a list), and so does
dv = list(seqDic)
which is fine in both Python 2 and Python 3. Under the same hypothesis (that you don't care about the order of items in dv) there are also other good solutions, such as
seqDic = dict.fromkeys(seq, 0)
for i in seq: seqDic[i] += 1
dv = list(seqDic)
here, we first use the fromkeys class method of dictionaries to build a new dict which already has 0 as the value corresponding to each key, so we can then just increment each entry without such precautions as .get or membership checks.
defaultdict makes this easy:
>>> from collections import defaultdict
>>> seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
>>> seqDic = defaultdict(int)
>>> for v in seq:
... seqDic[v] += 1
>>> print seqDic
defaultdict(<type 'int'>, {2: 4, 3: 2, 4: 2, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 47: 1})
I'm not really sure what you try to do .. count how often each number appears?
#arbitrary sequence of numbers
seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
#dictionary counting number of occurances
seqDic = {}
### what you want to do, spelled out
for number in seq:
if number in seqDic: # we had the number before
seqDic[number] += 1
else: # first time we see it
seqDic[number] = 1
#### or:
for number in seq:
current = seqDic.get(number, 0) # current count in the dict, or 0
seqDic[number] = current + 1
### or, to show you how setdefault works
for number in seq:
seqDic.setdefault(number, 0) # set to 0 if it doesnt exist
seqDic[number] += 1 # increase by one
print "orig:", seq
print seqDic
How about this:
#arbitrary sequence of numbers
seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
#dictionary counting number of occurances
seqDic = { }
for v in seq:
if v in seqDic:
seqDic[v] += 1
else:
seqDic[v] = 1
dv = seqDic.keys()
print "orig:", seq
print "new: ", dv
print seqDic
It's clean and I think it demonstrates what you are trying to learn how to do in a simple manner. It is possible to do this using defaultdict as others have pointed out, but knowing how to do it this way is instructive too.
Or, if you use Python3, you can use collections.Counter, which is essentially a dict, albeit subclassed.
>>> from collections import Counter
>>> seq = [2,4,5,2,4,6,3,8,9,3,7,2,47,2]
>>> Counter(seq)
Counter({2: 4, 3: 2, 4: 2, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 47: 1}
for v in seq:
try:
seqDic[v] += 1
except KeyError:
seqDic[v] = 1
That's the way I've always done the inner loop of things like this.
Apart from anything else, it's significantly faster than testing membership before working on the element, so if you have a few hundred thousand elements it saves a lot of time.
Related
I am trying to write a function called find_it(seq) that, given list of numbers, returns the number that appears an odd amount of times.
I have tried rearranging the return and for loop.
and tried without the else clause.
can someone point out how to format it?
thanks
def find_it(seq):
#return i for i in seq if seq.count(i) % 2 == 1 else 0
for i in seq: return i if seq.count(i) % 2 == 1 else: pass
#this is my solution without the one line and without using count()
def find_it(seq):
dic = {}
for i in seq:
if i not in dic:
dic.update({i:1})
else:
dic[i] += 1
print(dic)
for item,num in dic.items():
if num % 2 == 1:
return item
If you insist on making one-liner loop I suggest you use generator with next, this will make the code more readable
def find_it(seq):
return next((i for i in seq if seq.count(i) % 2 == 1), None)
However the more efficient way will be a simple loop
def find_it(seq):
for i in seq:
if seq.count(i) % 2 == 1:
return i
def find_it(seq):
return set([el for el in seq if seq.count(el) % 2 == 1])
> print(find_it([1, 2, 3, 1, 1, 2, 3]))
{1}
This snippet returns a set of elements present an odd number of times in a list.
It's not as efficient as can be, as checked elements present multiple times are still counted. For example, count(2) returns an int of how many 2s are in the list, but because of how the loop is, the next time the program sees a 2, it stills calculates the .count of 2 even though it's done it before.
This can be rectified by removing all the occurrences of an element from the list after it's been checked, or ignore checked elements. I was unable to find a way to do this in one line as you requested.
I digress only because OP seems intent on efficiency.
For this problem, the technique used can be influenced by the data being processed. This is best explained by example. Here are six different ways to achieve the same objective.
from collections import Counter
from timeit import timeit
# even number of 1s, odd number of 2s
list_ = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2]
def find_it_1(seq):
for k, v in Counter(seq).items():
if v % 2:
return k
def find_it_2(seq):
for i in seq:
if seq.count(i) % 2 :
return i
def find_it_3(seq):
s = set()
for e in seq:
if e not in s:
if seq.count(e) % 2:
return e
s.add(e)
def find_it_4(seq):
return next((i for i in seq if seq.count(i) % 2), None)
def find_it_5(seq):
for e in set(seq):
if seq.count(e) % 2:
return e
def find_it_6(seq):
d = {}
for e in seq:
d[e] = d.get(e, 0) + 1
for k, v in d.items():
if v % 2:
return k
for func in find_it_1, find_it_2, find_it_3, find_it_4, find_it_5, find_it_6:
print(func.__name__, timeit(lambda: func(list_)))
Output:
find_it_1 1.627880711999751
find_it_2 2.23142556699986
find_it_3 0.9605982989996846
find_it_4 2.4646536830000514
find_it_5 0.6783656980001069
find_it_6 1.9190425920000962
Now, let's change the data as follows:
list_ = [2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
Note that there are 3 occurrences of 2 and that they're at the start of the list. This results in:
find_it_1 1.574513012999887
find_it_2 0.3627374699999564
find_it_3 0.4003442379998887
find_it_4 0.5936855530007961
find_it_5 0.674294768999971
find_it_6 1.8698847380001098
Quod Erat Demonstrandum
Given a list of items, recall that the mode of the list is the item that occurs most often.
I would like to know how to create a function that can find the mode of a list but that displays a message if the list does not have a mode (e.g., all the items in the list only appear once). I want to make this function without importing any functions. I'm trying to make my own function from scratch.
You can use the max function and a key. Have a look at python max function using 'key' and lambda expression.
max(set(lst), key=lst.count)
You can use the Counter supplied in the collections package which has a mode-esque function
from collections import Counter
data = Counter(your_list_in_here)
data.most_common() # Returns all unique items and their counts
data.most_common(1) # Returns the highest occurring item
Note: Counter is new in python 2.7 and is not available in earlier versions.
Python 3.4 includes the method statistics.mode, so it is straightforward:
>>> from statistics import mode
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
You can have any type of elements in the list, not just numeric:
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red'
Taking a leaf from some statistics software, namely SciPy and MATLAB, these just return the smallest most common value, so if two values occur equally often, the smallest of these are returned. Hopefully an example will help:
>>> from scipy.stats import mode
>>> mode([1, 2, 3, 4, 5])
(array([ 1.]), array([ 1.]))
>>> mode([1, 2, 2, 3, 3, 4, 5])
(array([ 2.]), array([ 2.]))
>>> mode([1, 2, 2, -3, -3, 4, 5])
(array([-3.]), array([ 2.]))
Is there any reason why you can 't follow this convention?
There are many simple ways to find the mode of a list in Python such as:
import statistics
statistics.mode([1,2,3,3])
>>> 3
Or, you could find the max by its count
max(array, key = array.count)
The problem with those two methods are that they don't work with multiple modes. The first returns an error, while the second returns the first mode.
In order to find the modes of a set, you could use this function:
def mode(array):
most = max(list(map(array.count, array)))
return list(set(filter(lambda x: array.count(x) == most, array)))
Extending the Community answer that will not work when the list is empty, here is working code for mode:
def mode(arr):
if arr==[]:
return None
else:
return max(set(arr), key=arr.count)
In case you are interested in either the smallest, largest or all modes:
def get_small_mode(numbers, out_mode):
counts = {k:numbers.count(k) for k in set(numbers)}
modes = sorted(dict(filter(lambda x: x[1] == max(counts.values()), counts.items())).keys())
if out_mode=='smallest':
return modes[0]
elif out_mode=='largest':
return modes[-1]
else:
return modes
A little longer, but can have multiple modes and can get string with most counts or mix of datatypes.
def getmode(inplist):
'''with list of items as input, returns mode
'''
dictofcounts = {}
listofcounts = []
for i in inplist:
countofi = inplist.count(i) # count items for each item in list
listofcounts.append(countofi) # add counts to list
dictofcounts[i]=countofi # add counts and item in dict to get later
maxcount = max(listofcounts) # get max count of items
if maxcount ==1:
print "There is no mode for this dataset, values occur only once"
else:
modelist = [] # if more than one mode, add to list to print out
for key, item in dictofcounts.iteritems():
if item ==maxcount: # get item from original list with most counts
modelist.append(str(key))
print "The mode(s) are:",' and '.join(modelist)
return modelist
Mode of a data set is/are the member(s) that occur(s) most frequently in the set. If there are two members that appear most often with same number of times, then the data has two modes. This is called bimodal.If there are more than 2 modes, then the data would be called multimodal. If all the members in the data set appear the same number of times, then the data set has no mode at all. Following function modes() can work to find mode(s) in a given list of data:
import numpy as np; import pandas as pd
def modes(arr):
df = pd.DataFrame(arr, columns=['Values'])
dat = pd.crosstab(df['Values'], columns=['Freq'])
if len(np.unique((dat['Freq']))) > 1:
mode = list(dat.index[np.array(dat['Freq'] == max(dat['Freq']))])
return mode
else:
print("There is NO mode in the data set")
Output:
# For a list of numbers in x as
In [1]: x = [2, 3, 4, 5, 7, 9, 8, 12, 2, 1, 1, 1, 3, 3, 2, 6, 12, 3, 7, 8, 9, 7, 12, 10, 10, 11, 12, 2]
In [2]: modes(x)
Out[2]: [2, 3, 12]
# For a list of repeated numbers in y as
In [3]: y = [2, 2, 3, 3, 4, 4, 10, 10]
In [4]: modes(y)
Out[4]: There is NO mode in the data set
# For a list of strings/characters in z as
In [5]: z = ['a', 'b', 'b', 'b', 'e', 'e', 'e', 'd', 'g', 'g', 'c', 'g', 'g', 'a', 'a', 'c', 'a']
In [6]: modes(z)
Out[6]: ['a', 'g']
If we do not want to import numpy or pandas to call any function from these packages, then to get this same output, modes() function can be written as:
def modes(arr):
cnt = []
for i in arr:
cnt.append(arr.count(i))
uniq_cnt = []
for i in cnt:
if i not in uniq_cnt:
uniq_cnt.append(i)
if len(uniq_cnt) > 1:
m = []
for i in list(range(len(cnt))):
if cnt[i] == max(uniq_cnt):
m.append(arr[i])
mode = []
for i in m:
if i not in mode:
mode.append(i)
return mode
else:
print("There is NO mode in the data set")
I wrote up this handy function to find the mode.
def mode(nums):
corresponding={}
occurances=[]
for i in nums:
count = nums.count(i)
corresponding.update({i:count})
for i in corresponding:
freq=corresponding[i]
occurances.append(freq)
maxFreq=max(occurances)
keys=corresponding.keys()
values=corresponding.values()
index_v = values.index(maxFreq)
global mode
mode = keys[index_v]
return mode
Short, but somehow ugly:
def mode(arr) :
m = max([arr.count(a) for a in arr])
return [x for x in arr if arr.count(x) == m][0] if m>1 else None
Using a dictionary, slightly less ugly:
def mode(arr) :
f = {}
for a in arr : f[a] = f.get(a,0)+1
m = max(f.values())
t = [(x,f[x]) for x in f if f[x]==m]
return m > 1 t[0][0] else None
This function returns the mode or modes of a function no matter how many, as well as the frequency of the mode or modes in the dataset. If there is no mode (ie. all items occur only once), the function returns an error string. This is similar to A_nagpal's function above but is, in my humble opinion, more complete, and I think it's easier to understand for any Python novices (such as yours truly) reading this question to understand.
def l_mode(list_in):
count_dict = {}
for e in (list_in):
count = list_in.count(e)
if e not in count_dict.keys():
count_dict[e] = count
max_count = 0
for key in count_dict:
if count_dict[key] >= max_count:
max_count = count_dict[key]
corr_keys = []
for corr_key, count_value in count_dict.items():
if count_dict[corr_key] == max_count:
corr_keys.append(corr_key)
if max_count == 1 and len(count_dict) != 1:
return 'There is no mode for this data set. All values occur only once.'
else:
corr_keys = sorted(corr_keys)
return corr_keys, max_count
For a number to be a mode, it must occur more number of times than at least one other number in the list, and it must not be the only number in the list. So, I refactored #mathwizurd's answer (to use the difference method) as follows:
def mode(array):
'''
returns a set containing valid modes
returns a message if no valid mode exists
- when all numbers occur the same number of times
- when only one number occurs in the list
- when no number occurs in the list
'''
most = max(map(array.count, array)) if array else None
mset = set(filter(lambda x: array.count(x) == most, array))
return mset if set(array) - mset else "list does not have a mode!"
These tests pass successfully:
mode([]) == None
mode([1]) == None
mode([1, 1]) == None
mode([1, 1, 2, 2]) == None
Here is how you can find mean,median and mode of a list:
import numpy as np
from scipy import stats
#to take input
size = int(input())
numbers = list(map(int, input().split()))
print(np.mean(numbers))
print(np.median(numbers))
print(int(stats.mode(numbers)[0]))
Simple code that finds the mode of the list without any imports:
nums = #your_list_goes_here
nums.sort()
counts = dict()
for i in nums:
counts[i] = counts.get(i, 0) + 1
mode = max(counts, key=counts.get)
In case of multiple modes, it should return the minimum node.
Why not just
def print_mode (thelist):
counts = {}
for item in thelist:
counts [item] = counts.get (item, 0) + 1
maxcount = 0
maxitem = None
for k, v in counts.items ():
if v > maxcount:
maxitem = k
maxcount = v
if maxcount == 1:
print "All values only appear once"
elif counts.values().count (maxcount) > 1:
print "List has multiple modes"
else:
print "Mode of list:", maxitem
This doesn't have a few error checks that it should have, but it will find the mode without importing any functions and will print a message if all values appear only once. It will also detect multiple items sharing the same maximum count, although it wasn't clear if you wanted that.
This will return all modes:
def mode(numbers)
largestCount = 0
modes = []
for x in numbers:
if x in modes:
continue
count = numbers.count(x)
if count > largestCount:
del modes[:]
modes.append(x)
largestCount = count
elif count == largestCount:
modes.append(x)
return modes
For those looking for the minimum mode, e.g:case of bi-modal distribution, using numpy.
import numpy as np
mode = np.argmax(np.bincount(your_list))
Okey! So community has already a lot of answers and some of them used another function and you don't want.
let we create our very simple and easily understandable function.
import numpy as np
#Declare Function Name
def calculate_mode(lst):
Next step is to find Unique elements in list and thier respective frequency.
unique_elements,freq = np.unique(lst, return_counts=True)
Get mode
max_freq = np.max(freq) #maximum frequency
mode_index = np.where(freq==max_freq) #max freq index
mode = unique_elements[mode_index] #get mode by index
return mode
Example
lst =np.array([1,1,2,3,4,4,4,5,6])
print(calculate_mode(lst))
>>> Output [4]
How my brain decided to do it completely from scratch. Efficient and concise :) (jk lol)
import random
def removeDuplicates(arr):
dupFlag = False
for i in range(len(arr)):
#check if we found a dup, if so, stop
if dupFlag:
break
for j in range(len(arr)):
if ((arr[i] == arr[j]) and (i != j)):
arr.remove(arr[j])
dupFlag = True
break;
#if there was a duplicate repeat the process, this is so we can account for the changing length of the arr
if (dupFlag):
removeDuplicates(arr)
else:
#if no duplicates return the arr
return arr
#currently returns modes and all there occurences... Need to handle dupes
def mode(arr):
numCounts = []
#init numCounts
for i in range(len(arr)):
numCounts += [0]
for i in range(len(arr)):
count = 1
for j in range(len(arr)):
if (arr[i] == arr[j] and i != j):
count += 1
#add the count for that number to the corresponding index
numCounts[i] = count
#find which has the greatest number of occurences
greatestNum = 0
for i in range(len(numCounts)):
if (numCounts[i] > greatestNum):
greatestNum = numCounts[i]
#finally return the mode(s)
modes = []
for i in range(len(numCounts)):
if numCounts[i] == greatestNum:
modes += [arr[i]]
#remove duplicates (using aliasing)
print("modes: ", modes)
removeDuplicates(modes)
print("modes after removing duplicates: ", modes)
return modes
def initArr(n):
arr = []
for i in range(n):
arr += [random.randrange(0, n)]
return arr
#initialize an array of random ints
arr = initArr(1000)
print(arr)
print("_______________________________________________")
modes = mode(arr)
#print result
print("Mode is: ", modes) if (len(modes) == 1) else print("Modes are: ", modes)
def mode(inp_list):
sort_list = sorted(inp_list)
dict1 = {}
for i in sort_list:
count = sort_list.count(i)
if i not in dict1.keys():
dict1[i] = count
maximum = 0 #no. of occurences
max_key = -1 #element having the most occurences
for key in dict1:
if(dict1[key]>maximum):
maximum = dict1[key]
max_key = key
elif(dict1[key]==maximum):
if(key<max_key):
maximum = dict1[key]
max_key = key
return max_key
def mode(data):
lst =[]
hgh=0
for i in range(len(data)):
lst.append(data.count(data[i]))
m= max(lst)
ml = [x for x in data if data.count(x)==m ] #to find most frequent values
mode = []
for x in ml: #to remove duplicates of mode
if x not in mode:
mode.append(x)
return mode
print mode([1,2,2,2,2,7,7,5,5,5,5])
Here is a simple function that gets the first mode that occurs in a list. It makes a dictionary with the list elements as keys and number of occurrences and then reads the dict values to get the mode.
def findMode(readList):
numCount={}
highestNum=0
for i in readList:
if i in numCount.keys(): numCount[i] += 1
else: numCount[i] = 1
for i in numCount.keys():
if numCount[i] > highestNum:
highestNum=numCount[i]
mode=i
if highestNum != 1: print(mode)
elif highestNum == 1: print("All elements of list appear once.")
If you want a clear approach, useful for classroom and only using lists and dictionaries by comprehension, you can do:
def mode(my_list):
# Form a new list with the unique elements
unique_list = sorted(list(set(my_list)))
# Create a comprehensive dictionary with the uniques and their count
appearance = {a:my_list.count(a) for a in unique_list}
# Calculate max number of appearances
max_app = max(appearance.values())
# Return the elements of the dictionary that appear that # of times
return {k: v for k, v in appearance.items() if v == max_app}
#function to find mode
def mode(data):
modecnt=0
#for count of number appearing
for i in range(len(data)):
icount=data.count(data[i])
#for storing count of each number in list will be stored
if icount>modecnt:
#the loop activates if current count if greater than the previous count
mode=data[i]
#here the mode of number is stored
modecnt=icount
#count of the appearance of number is stored
return mode
print mode(data1)
import numpy as np
def get_mode(xs):
values, counts = np.unique(xs, return_counts=True)
max_count_index = np.argmax(counts) #return the index with max value counts
return values[max_count_index]
print(get_mode([1,7,2,5,3,3,8,3,2]))
Perhaps try the following. It is O(n) and returns a list of floats (or ints). It is thoroughly, automatically tested. It uses collections.defaultdict, but I'd like to think you're not opposed to using that. It can also be found at https://stromberg.dnsalias.org/~strombrg/stddev.html
def compute_mode(list_: typing.List[float]) -> typing.List[float]:
"""
Compute the mode of list_.
Note that the return value is a list, because sometimes there is a tie for "most common value".
See https://stackoverflow.com/questions/10797819/finding-the-mode-of-a-list
"""
if not list_:
raise ValueError('Empty list')
if len(list_) == 1:
raise ValueError('Single-element list')
value_to_count_dict: typing.DefaultDict[float, int] = collections.defaultdict(int)
for element in list_:
value_to_count_dict[element] += 1
count_to_values_dict = collections.defaultdict(list)
for value, count in value_to_count_dict.items():
count_to_values_dict[count].append(value)
counts = list(count_to_values_dict)
if len(counts) == 1:
raise ValueError('All elements in list are the same')
maximum_occurrence_count = max(counts)
if maximum_occurrence_count == 1:
raise ValueError('No element occurs more than once')
minimum_occurrence_count = min(counts)
if maximum_occurrence_count <= minimum_occurrence_count:
raise ValueError('Maximum count not greater than minimum count')
return count_to_values_dict[maximum_occurrence_count]
lst = [1,2,3,4,1]
I want to know 1 occurs twice in this list, is there any efficient way to do?
lst.count(1) would return the number of times it occurs. If you're going to be counting items in a list, O(n) is what you're going to get.
The general function on the list is list.count(x), and will return the number of times x occurs in a list.
Are you asking whether every item in the list is unique?
len(set(lst)) == len(lst)
Whether 1 occurs more than once?
lst.count(1) > 1
Note that the above is not maximally efficient, because it won't short-circuit -- even if 1 occurs twice, it will still count the rest of the occurrences. If you want it to short-circuit you will have to write something a little more complicated.
Whether the first element occurs more than once?
lst[0] in lst[1:]
How often each element occurs?
import collections
collections.Counter(lst)
Something else?
For multiple occurrences, this give you the index of each occurence:
>>> lst=[1,2,3,4,5,1]
>>> tgt=1
>>> found=[]
>>> for index, suspect in enumerate(lst):
... if(tgt==suspect):
... found.append(index)
...
>>> print len(found), "found at index:",", ".join(map(str,found))
2 found at index: 0, 5
If you want the count of each item in the list:
>>> lst=[1,2,3,4,5,2,2,1,5,5,5,5,6]
>>> count={}
>>> for item in lst:
... count[item]=lst.count(item)
...
>>> count
{1: 2, 2: 3, 3: 1, 4: 1, 5: 5, 6: 1}
def valCount(lst):
res = {}
for v in lst:
try:
res[v] += 1
except KeyError:
res[v] = 1
return res
u = [ x for x,y in valCount(lst).iteritems() if y > 1 ]
u is now a list of all values which appear more than once.
Edit:
#katrielalex: thank you for pointing out collections.Counter, of which I was not previously aware. It can also be written more concisely using a collections.defaultdict, as demonstrated in the following tests. All three methods are roughly O(n) and reasonably close in run-time performance (using collections.defaultdict is in fact slightly faster than collections.Counter).
My intention was to give an easy-to-understand response to what seemed a relatively unsophisticated request. Given that, are there any other senses in which you consider it "bad code" or "done poorly"?
import collections
import random
import time
def test1(lst):
res = {}
for v in lst:
try:
res[v] += 1
except KeyError:
res[v] = 1
return res
def test2(lst):
res = collections.defaultdict(lambda: 0)
for v in lst:
res[v] += 1
return res
def test3(lst):
return collections.Counter(lst)
def rndLst(lstLen):
r = random.randint
return [r(0,lstLen) for i in xrange(lstLen)]
def timeFn(fn, *args):
st = time.clock()
res = fn(*args)
return time.clock() - st
def main():
reps = 5000
res = []
tests = [test1, test2, test3]
for t in xrange(reps):
lstLen = random.randint(10,50000)
lst = rndLst(lstLen)
res.append( [lstLen] + [timeFn(fn, lst) for fn in tests] )
res.sort()
return res
And the results, for random lists containing up to 50,000 items, are as follows:
(Vertical axis is time in seconds, horizontal axis is number of items in list)
Another way to get all items that occur more than once:
lst = [1,2,3,4,1]
d = {}
for x in lst:
d[x] = x in d
print d[1] # True
print d[2] # False
print [x for x in d if d[x]] # [1]
You could also sort the list which is O(n*log(n)), then check the adjacent elements for equality, which is O(n). The result is O(n*log(n)). This has the disadvantage of requiring the entire list be sorted before possibly bailing when a duplicate is found.
For a large list with a relatively rare duplicates, this could be the about the best you can do. The best way to approach this really does depend on the size of the data involved and its nature.
I have a list of phone numbers that have been dialed (nums_dialed).
I also have a set of phone numbers which are the number in a client's office (client_nums)
How do I efficiently figure out how many times I've called a particular client (total)
For example:
>>>nums_dialed=[1,2,2,3,3]
>>>client_nums=set([2,3])
>>>???
total=4
Problem is that I have a large-ish dataset: len(client_nums) ~ 10^5; and len(nums_dialed) ~10^3.
which client has 10^5 numbers in his office? Do you do work for an entire telephone company?
Anyway:
print sum(1 for num in nums_dialed if num in client_nums)
That will give you as fast as possible the number.
If you want to do it for multiple clients, using the same nums_dialed list, then you could cache the data on each number first:
nums_dialed_dict = collections.defaultdict(int)
for num in nums_dialed:
nums_dialed_dict[num] += 1
Then just sum the ones on each client:
sum(nums_dialed_dict[num] for num in this_client_nums)
That would be a lot quicker than iterating over the entire list of numbers again for each client.
>>> client_nums = set([2, 3])
>>> nums_dialed = [1, 2, 2, 3, 3]
>>> count = 0
>>> for num in nums_dialed:
... if num in client_nums:
... count += 1
...
>>> count
4
>>>
Should be quite efficient even for the large numbers you quote.
Using collections.Counter from Python 2.7:
dialed_count = collections.Counter(nums_dialed)
count = sum(dialed_count[t] for t in client_nums)
Thats very popular way to do some combination of sorted lists in single pass:
nums_dialed = [1, 2, 2, 3, 3]
client_nums = [2,3]
nums_dialed.sort()
client_nums.sort()
c = 0
i = iter(nums_dialed)
j = iter(client_nums)
try:
a = i.next()
b = j.next()
while True:
if a < b:
a = i.next()
continue
if a > b:
b = j.next()
continue
# a == b
c += 1
a = i.next() # next dialed
except StopIteration:
pass
print c
Because "set" is unordered collection (don't know why it uses hashes, but not binary tree or sorted list) and it's not fair to use it there. You can implement own "set" through "bisect" if you like lists or through something more complicated that will produce ordered iterator.
The method I use is to simply convert the set into a list and then use the len() function to count its values.
set_var = {"abc", "cba"}
print(len(list(set_var)))
Output:
2
What is an efficient way to find the most common element in a Python list?
My list items may not be hashable so can't use a dictionary.
Also in case of draws the item with the lowest index should be returned. Example:
>>> most_common(['duck', 'duck', 'goose'])
'duck'
>>> most_common(['goose', 'duck', 'duck', 'goose'])
'goose'
A simpler one-liner:
def most_common(lst):
return max(set(lst), key=lst.count)
Borrowing from here, this can be used with Python 2.7:
from collections import Counter
def Most_Common(lst):
data = Counter(lst)
return data.most_common(1)[0][0]
Works around 4-6 times faster than Alex's solutions, and is 50 times faster than the one-liner proposed by newacct.
On CPython 3.6+ (any Python 3.7+) the above will select the first seen element in case of ties. If you're running on older Python, to retrieve the element that occurs first in the list in case of ties you need to do two passes to preserve order:
# Only needed pre-3.6!
def most_common(lst):
data = Counter(lst)
return max(lst, key=data.get)
With so many solutions proposed, I'm amazed nobody's proposed what I'd consider an obvious one (for non-hashable but comparable elements) -- [itertools.groupby][1]. itertools offers fast, reusable functionality, and lets you delegate some tricky logic to well-tested standard library components. Consider for example:
import itertools
import operator
def most_common(L):
# get an iterable of (item, iterable) pairs
SL = sorted((x, i) for i, x in enumerate(L))
# print 'SL:', SL
groups = itertools.groupby(SL, key=operator.itemgetter(0))
# auxiliary function to get "quality" for an item
def _auxfun(g):
item, iterable = g
count = 0
min_index = len(L)
for _, where in iterable:
count += 1
min_index = min(min_index, where)
# print 'item %r, count %r, minind %r' % (item, count, min_index)
return count, -min_index
# pick the highest-count/earliest item
return max(groups, key=_auxfun)[0]
This could be written more concisely, of course, but I'm aiming for maximal clarity. The two print statements can be uncommented to better see the machinery in action; for example, with prints uncommented:
print most_common(['goose', 'duck', 'duck', 'goose'])
emits:
SL: [('duck', 1), ('duck', 2), ('goose', 0), ('goose', 3)]
item 'duck', count 2, minind 1
item 'goose', count 2, minind 0
goose
As you see, SL is a list of pairs, each pair an item followed by the item's index in the original list (to implement the key condition that, if the "most common" items with the same highest count are > 1, the result must be the earliest-occurring one).
groupby groups by the item only (via operator.itemgetter). The auxiliary function, called once per grouping during the max computation, receives and internally unpacks a group - a tuple with two items (item, iterable) where the iterable's items are also two-item tuples, (item, original index) [[the items of SL]].
Then the auxiliary function uses a loop to determine both the count of entries in the group's iterable, and the minimum original index; it returns those as combined "quality key", with the min index sign-changed so the max operation will consider "better" those items that occurred earlier in the original list.
This code could be much simpler if it worried a little less about big-O issues in time and space, e.g....:
def most_common(L):
groups = itertools.groupby(sorted(L))
def _auxfun((item, iterable)):
return len(list(iterable)), -L.index(item)
return max(groups, key=_auxfun)[0]
same basic idea, just expressed more simply and compactly... but, alas, an extra O(N) auxiliary space (to embody the groups' iterables to lists) and O(N squared) time (to get the L.index of every item). While premature optimization is the root of all evil in programming, deliberately picking an O(N squared) approach when an O(N log N) one is available just goes too much against the grain of scalability!-)
Finally, for those who prefer "oneliners" to clarity and performance, a bonus 1-liner version with suitably mangled names:-).
from itertools import groupby as g
def most_common_oneliner(L):
return max(g(sorted(L)), key=lambda(x, v):(len(list(v)),-L.index(x)))[0]
What you want is known in statistics as mode, and Python of course has a built-in function to do exactly that for you:
>>> from statistics import mode
>>> mode([1, 2, 2, 3, 3, 3, 3, 3, 4, 5, 6, 6, 6])
3
Note that if there is no "most common element" such as cases where the top two are tied, this will raise StatisticsError on Python
<=3.7, and on 3.8 onwards it will return the first one encountered.
Without the requirement about the lowest index, you can use collections.Counter for this:
from collections import Counter
a = [1936, 2401, 2916, 4761, 9216, 9216, 9604, 9801]
c = Counter(a)
print(c.most_common(1)) # the one most common element... 2 would mean the 2 most common
[(9216, 2)] # a set containing the element, and it's count in 'a'
If they are not hashable, you can sort them and do a single loop over the result counting the items (identical items will be next to each other). But it might be faster to make them hashable and use a dict.
def most_common(lst):
cur_length = 0
max_length = 0
cur_i = 0
max_i = 0
cur_item = None
max_item = None
for i, item in sorted(enumerate(lst), key=lambda x: x[1]):
if cur_item is None or cur_item != item:
if cur_length > max_length or (cur_length == max_length and cur_i < max_i):
max_length = cur_length
max_i = cur_i
max_item = cur_item
cur_length = 1
cur_i = i
cur_item = item
else:
cur_length += 1
if cur_length > max_length or (cur_length == max_length and cur_i < max_i):
return cur_item
return max_item
This is an O(n) solution.
mydict = {}
cnt, itm = 0, ''
for item in reversed(lst):
mydict[item] = mydict.get(item, 0) + 1
if mydict[item] >= cnt :
cnt, itm = mydict[item], item
print itm
(reversed is used to make sure that it returns the lowest index item)
Sort a copy of the list and find the longest run. You can decorate the list before sorting it with the index of each element, and then choose the run that starts with the lowest index in the case of a tie.
A one-liner:
def most_common (lst):
return max(((item, lst.count(item)) for item in set(lst)), key=lambda a: a[1])[0]
I am doing this using scipy stat module and lambda:
import scipy.stats
lst = [1,2,3,4,5,6,7,5]
most_freq_val = lambda x: scipy.stats.mode(x)[0][0]
print(most_freq_val(lst))
Result:
most_freq_val = 5
# use Decorate, Sort, Undecorate to solve the problem
def most_common(iterable):
# Make a list with tuples: (item, index)
# The index will be used later to break ties for most common item.
lst = [(x, i) for i, x in enumerate(iterable)]
lst.sort()
# lst_final will also be a list of tuples: (count, index, item)
# Sorting on this list will find us the most common item, and the index
# will break ties so the one listed first wins. Count is negative so
# largest count will have lowest value and sort first.
lst_final = []
# Get an iterator for our new list...
itr = iter(lst)
# ...and pop the first tuple off. Setup current state vars for loop.
count = 1
tup = next(itr)
x_cur, i_cur = tup
# Loop over sorted list of tuples, counting occurrences of item.
for tup in itr:
# Same item again?
if x_cur == tup[0]:
# Yes, same item; increment count
count += 1
else:
# No, new item, so write previous current item to lst_final...
t = (-count, i_cur, x_cur)
lst_final.append(t)
# ...and reset current state vars for loop.
x_cur, i_cur = tup
count = 1
# Write final item after loop ends
t = (-count, i_cur, x_cur)
lst_final.append(t)
lst_final.sort()
answer = lst_final[0][2]
return answer
print most_common(['x', 'e', 'a', 'e', 'a', 'e', 'e']) # prints 'e'
print most_common(['goose', 'duck', 'duck', 'goose']) # prints 'goose'
Building on Luiz's answer, but satisfying the "in case of draws the item with the lowest index should be returned" condition:
from statistics import mode, StatisticsError
def most_common(l):
try:
return mode(l)
except StatisticsError as e:
# will only return the first element if no unique mode found
if 'no unique mode' in e.args[0]:
return l[0]
# this is for "StatisticsError: no mode for empty data"
# after calling mode([])
raise
Example:
>>> most_common(['a', 'b', 'b'])
'b'
>>> most_common([1, 2])
1
>>> most_common([])
StatisticsError: no mode for empty data
Simple one line solution
moc= max([(lst.count(chr),chr) for chr in set(lst)])
It will return most frequent element with its frequency.
You probably don't need this anymore, but this is what I did for a similar problem. (It looks longer than it is because of the comments.)
itemList = ['hi', 'hi', 'hello', 'bye']
counter = {}
maxItemCount = 0
for item in itemList:
try:
# Referencing this will cause a KeyError exception
# if it doesn't already exist
counter[item]
# ... meaning if we get this far it didn't happen so
# we'll increment
counter[item] += 1
except KeyError:
# If we got a KeyError we need to create the
# dictionary key
counter[item] = 1
# Keep overwriting maxItemCount with the latest number,
# if it's higher than the existing itemCount
if counter[item] > maxItemCount:
maxItemCount = counter[item]
mostPopularItem = item
print mostPopularItem
ans = [1, 1, 0, 0, 1, 1]
all_ans = {ans.count(ans[i]): ans[i] for i in range(len(ans))}
print(all_ans)
all_ans={4: 1, 2: 0}
max_key = max(all_ans.keys())
4
print(all_ans[max_key])
1
#This will return the list sorted by frequency:
def orderByFrequency(list):
listUniqueValues = np.unique(list)
listQty = []
listOrderedByFrequency = []
for i in range(len(listUniqueValues)):
listQty.append(list.count(listUniqueValues[i]))
for i in range(len(listQty)):
index_bigger = np.argmax(listQty)
for j in range(listQty[index_bigger]):
listOrderedByFrequency.append(listUniqueValues[index_bigger])
listQty[index_bigger] = -1
return listOrderedByFrequency
#And this will return a list with the most frequent values in a list:
def getMostFrequentValues(list):
if (len(list) <= 1):
return list
list_most_frequent = []
list_ordered_by_frequency = orderByFrequency(list)
list_most_frequent.append(list_ordered_by_frequency[0])
frequency = list_ordered_by_frequency.count(list_ordered_by_frequency[0])
index = 0
while(index < len(list_ordered_by_frequency)):
index = index + frequency
if(index < len(list_ordered_by_frequency)):
testValue = list_ordered_by_frequency[index]
testValueFrequency = list_ordered_by_frequency.count(testValue)
if (testValueFrequency == frequency):
list_most_frequent.append(testValue)
else:
break
return list_most_frequent
#tests:
print(getMostFrequentValues([]))
print(getMostFrequentValues([1]))
print(getMostFrequentValues([1,1]))
print(getMostFrequentValues([2,1]))
print(getMostFrequentValues([2,2,1]))
print(getMostFrequentValues([1,2,1,2]))
print(getMostFrequentValues([1,2,1,2,2]))
print(getMostFrequentValues([3,2,3,5,6,3,2,2]))
print(getMostFrequentValues([1,2,2,60,50,3,3,50,3,4,50,4,4,60,60]))
Results:
[]
[1]
[1]
[1, 2]
[2]
[1, 2]
[2]
[2, 3]
[3, 4, 50, 60]
Here:
def most_common(l):
max = 0
maxitem = None
for x in set(l):
count = l.count(x)
if count > max:
max = count
maxitem = x
return maxitem
I have a vague feeling there is a method somewhere in the standard library that will give you the count of each element, but I can't find it.
This is the obvious slow solution (O(n^2)) if neither sorting nor hashing is feasible, but equality comparison (==) is available:
def most_common(items):
if not items:
raise ValueError
fitems = []
best_idx = 0
for item in items:
item_missing = True
i = 0
for fitem in fitems:
if fitem[0] == item:
fitem[1] += 1
d = fitem[1] - fitems[best_idx][1]
if d > 0 or (d == 0 and fitems[best_idx][2] > fitem[2]):
best_idx = i
item_missing = False
break
i += 1
if item_missing:
fitems.append([item, 1, i])
return items[best_idx]
But making your items hashable or sortable (as recommended by other answers) would almost always make finding the most common element faster if the length of your list (n) is large. O(n) on average with hashing, and O(n*log(n)) at worst for sorting.
>>> li = ['goose', 'duck', 'duck']
>>> def foo(li):
st = set(li)
mx = -1
for each in st:
temp = li.count(each):
if mx < temp:
mx = temp
h = each
return h
>>> foo(li)
'duck'
I needed to do this in a recent program. I'll admit it, I couldn't understand Alex's answer, so this is what I ended up with.
def mostPopular(l):
mpEl=None
mpIndex=0
mpCount=0
curEl=None
curCount=0
for i, el in sorted(enumerate(l), key=lambda x: (x[1], x[0]), reverse=True):
curCount=curCount+1 if el==curEl else 1
curEl=el
if curCount>mpCount \
or (curCount==mpCount and i<mpIndex):
mpEl=curEl
mpIndex=i
mpCount=curCount
return mpEl, mpCount, mpIndex
I timed it against Alex's solution and it's about 10-15% faster for short lists, but once you go over 100 elements or more (tested up to 200000) it's about 20% slower.
def most_frequent(List):
counter = 0
num = List[0]
for i in List:
curr_frequency = List.count(i)
if(curr_frequency> counter):
counter = curr_frequency
num = i
return num
List = [2, 1, 2, 2, 1, 3]
print(most_frequent(List))
Hi this is a very simple solution, with linear time complexity
L = ['goose', 'duck', 'duck']
def most_common(L):
current_winner = 0
max_repeated = None
for i in L:
amount_times = L.count(i)
if amount_times > current_winner:
current_winner = amount_times
max_repeated = i
return max_repeated
print(most_common(L))
"duck"
Where number, is the element in the list that repeats most of the time
numbers = [1, 3, 7, 4, 3, 0, 3, 6, 3]
max_repeat_num = max(numbers, key=numbers.count) *# which number most* frequently
max_repeat = numbers.count(max_repeat_num) *#how many times*
print(f" the number {max_repeat_num} is repeated{max_repeat} times")
def mostCommonElement(list):
count = {} // dict holder
max = 0 // keep track of the count by key
result = None // holder when count is greater than max
for i in list:
if i not in count:
count[i] = 1
else:
count[i] += 1
if count[i] > max:
max = count[i]
result = i
return result
mostCommonElement(["a","b","a","c"]) -> "a"
The most common element should be the one which is appearing more than N/2 times in the array where N being the len(array). The below technique will do it in O(n) time complexity, with just consuming O(1) auxiliary space.
from collections import Counter
def majorityElement(arr):
majority_elem = Counter(arr)
size = len(arr)
for key, val in majority_elem.items():
if val > size/2:
return key
return -1
def most_common(lst):
if max([lst.count(i)for i in lst]) == 1:
return False
else:
return max(set(lst), key=lst.count)
def popular(L):
C={}
for a in L:
C[a]=L.count(a)
for b in C.keys():
if C[b]==max(C.values()):
return b
L=[2,3,5,3,6,3,6,3,6,3,7,467,4,7,4]
print popular(L)