no unique mode; found 2 equally common values

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I would like to use statistics.mode([1,1,2,2,3]) function in python to calculate the mode, it returns no unique mode; found 2 equally common values. In this way, I would like to output either 1 or 2. Does anyone have any good idea?

For example:

lst = [1, 1, 2, 2, 3]

# test for count lst elements
lst_count = [[x, lst.count(x)] for x in set(lst)]
print lst_count
# [[1, 2], [2, 2], [3, 1]]

# remove count <= 1
lst_count = [x for x in set(lst) if lst.count(x) > 1]
print lst_count
# [1, 2]

# get 1 or 2 by index
print lst_count[0], lst_count[1]
# 1 2

Another way:

from collections import Counter

# change lst elements to str, for more readable
lst = ['a', 'a', 'b', 'b', 'c']

# get a dict, key is the elements in lst, value is count of the element
d_mem_count = Counter(lst)
print d_mem_count
# Counter({'a': 2, 'b': 2, 'c': 1})

for k in d_mem_count.keys():
    if d_mem_count[k] > 1:
        print k

# output as below
# a
# b

no unique mode; found 2 equally common values, For example: lst = [1, 1, 2, 2, 3] # test for count lst elements lst_count = [[x, lst.​count(x)] for x in set(lst)] print lst_count # [[1, 2], [2, 2], [3,  A unique mode is the most occurring number in a set of data. For example, since the set: {1,2,2,4,4,5} does not have a single most occurring data value, the two modes (2 and 4) are averaged to

Try this function, which finds the max values as mode when no unique mode:

import statistics
def find_max_mode(list1):
    list_table = statistics._counts(list1)
    len_table = len(list_table)

    if len_table == 1:
        max_mode = statistics.mode(list1)
    else:
        new_list = []
        for i in range(len_table):
            new_list.append(list_table[i][0])
        max_mode = max(new_list) # use the max value here
    return max_mode

if __name__ == '__main__':
    a = [1,1,2,2,3]
    print(find_max_mode(a)) # print 2

Statistics in Python using the statistics module, The four functions we'll use in this post are common in statistics: mean - average value; median - middle value; mode - most often value mode([1, 1, 2, 2, 3]) StatisticsError: no unique mode; found 2 equally common values. The mode function will return the modal value only if the distribution has a unique mode. If the distribution has multiple modes, python raises StatisticsError; For Example, the mode() function will report “ no unique mode; found 2 equally common values” when it is supplied of a bimodal distribution.

from collections import Counter
c = Counter([1,1,2,2,3])
c.most_common(1)
# output
>>> [(1,2)] #the key 1, 2 occurrences.

From the docs:

"most_common([n]): Returns a list of the n most common elements and their counts from the most common to the least. Elements with equal counts are ordered arbitrarily"

Python statistics, The mode of a set of data values is the value that appears most often. It is the value at which StatisticsError: no unique mode; found 2 equally common values. $\begingroup$ Generally speaking the mode is not defined to be unique. If two or more values tie as most frequently occuring they both or all are modes. It is unconventional to pick one of the modes and call it "the mode".

from scipy import stats as s
a=[1,1,2,2,3]
print(int(s.mode(a)[0]))

A (simple) more flexible implementation of the mode function than , statistics.mode(data). Traceback (most recent call last): statistics.StatisticsError: no unique mode; found 2 equally common values. >>> calc_mode(freq_tab=ft). The mode of a set of data values is the value that appears most often.It is the value at which the data is most likely to be sampled. A mode of a continuous probability distribution is often considered to be any value x at which its probability density function has a locally maximum value, so any peak is a mode.

I just faced the same issue. This how I solved it pretty simply:

def most_element(liste):
    numeral=[[liste.count(nb), nb] for nb in liste]
    numeral.sort(key=lambda x:x[0], reverse=True)
    return(numeral[0][1])

Not sure this the most elegant way but it does the job :). Hope it will help

'no unique mode; found %d equally common values' % len(table , 'no unique mode; found %d equally common values' % len(table) statistics.​StatisticsError: no unique mode; found 2 equally common values - python-3.x. Windows 10 in S mode is a version of Windows 10 that's streamlined for security and performance, while providing a familiar Windows experience. To increase security, it allows only apps from the Microsoft Store, and requires Microsoft Edge for safe browsing. For more info, see the Windows 10 in S mode page.

Calculate Mode of a distribution using Python, distribution has multiple modes, python raises StatisticsError; For Example, the mode() function will report “no unique mode; found 2 equally common values”  60 synonyms of unique from the Merriam-Webster Thesaurus, plus 78 related words, definitions, and antonyms. Find another word for unique.

statistics.StatisticsError Python Example, def mode(data): """Return the mode, the most common data point from In case there is no most frequent, the class is defined by default str :param list StatisticsError for values in ([-1], [1, -2, 3]): with self. def test_unique_data_failure(self): # Test mode exception when data points are all unique. data = list(range(10)) self. If you don’t, you’re not using S Mode. Should I Buy a PC With S Mode? Because it’s easy and free to leave S Mode, there’s no downside to buying a Windows 10 PC that comes with S Mode. Even if you don’t want S Mode, you can easily switch out of it. For example, Microsoft only sells the Surface Laptop in S Mode.

Day 0: Mean, Median, and Mode Discussions | Tutorials, Compute the mean, median, mode, and standard deviation. done based on the value returned by this (here numbers.count which count how many 1, 2, on test case#3 is 46 as there is 'no unique mode; found %d equally common values'  Touch of Modern is the most popular men's fashion site. Discover Modern Designs up to 70% Off. Guaranteed Lowest Prices.

Comments
  • What programming/coding language are you using? Best to add an appropriate language tag.
  • Thank you for your advice. I am using python.
  • @mgcy, does the below answer solves you problem?
  • I help i does't misunderstand you describe.
  • @Jayhello Yes it does. You prompted me to use the _counts function.
  • This answer was automatically tagged "low quality". As explained in the help ("Brevity is acceptable, but fuller explanations are better."), please edit your answer to tell the OP what he's doing wrong, how you reasoned and what your code is about, to show that you understood the question and to make benefit both the OP and future visitors.