Python统计模块中的mode（）函数-yiteyi-C++库

这个模式一组数据值的 最常出现 。它是最有可能对数据进行采样的值。连续概率分布的模式通常被认为是其 概率密度函数有一个局部最大值，所以任何峰值都是一个模式。 Python在统计和处理一组大范围的值时非常健壮。统计模块有大量的功能来处理非常大的数据集。mode（）函数就是这样的方法之一。此函数返回给定数据集范围内中心数据点的稳健度量。

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例子：

Given data-set is :  [1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]The mode of the given data-set is 4Logic: 4 is the most occurring/ most common element from the given list

Syntax :mode([data-set])Parameters : [data-set] which is a tuple, list or a iterator of real valued numbers as well as Strings.Return type : Returns the most-common data point from discrete or nominal data.Errors and Exceptions : Raises StatisticsError when data set is empty.

代码#1： 这篇文章将通过一个简单的例子演示mode（）函数。

Python3

                         # Python code to demonstrate the                       
                         # use of mode() function                       
           
                         # mode() function a sub-set of the statistics module                       
                         # We need to import the statistics module before doing any work                       
                         import                                     statistics                       
           
                         # declaring a simple data-set consisting of real valued                       
                         # positive integers.                       
                         set1                                     =                                     [                                     1                                     ,                                     2                                     ,                                     3                                     ,                                     3                                     ,                                     4                                     ,                                     4                                     ,                                     4                                     ,                                     5                                     ,                                     5                                     ,                                     6                                     ]                       
           
                         # In the given data-set                       
                         # Count of 1 is 1                       
                         # Count of 2 is 1                       
                         # Count of 3 is 2                       
                         # Count of 4 is 3                       
                         # Count of 5 is 2                       
                         # Count of 6 is 1                       
                         # We can infer that 4 has the highest population distribution                       
                         # So mode of set1 is 4                       
           
                         # Printing out mode of given data-set                       
                         print                                     (                                     "Mode of given data set is % s"                                     %                                     (statistics.mode(set1)))                       

输出

Mode of given data set is 4

代码#2： 在这段代码中，我们将在各种数据集上演示mode（）函数。

Python3

                         # Python code to demonstrate the                       
                         # working of mode() function                       
                         # on a various range of data types                       
           
                         # Importing the statistics module                       
                         from                                     statistics                                     import                                     mode                       
           
                         # Importing fractions module as fr                       
                         # Enables to calculate harmonic_mean of a                       
                         # set in Fraction                       
                         from                                     fractions                                     import                                     Fraction as fr                       
           
                         # tuple of positive integer numbers                       
                         data1                                     =                                     (                                     2                                     ,                                     3                                     ,                                     3                                     ,                                     4                                     ,                                     5                                     ,                                     5                                     ,                                     5                                     ,                                     5                                     ,                                     6                                     ,                                     6                                     ,                                     6                                     ,                                     7                                     )                       
           
                         # tuple of a set of floating point values                       
                         data2                                     =                                     (                                     2.4                                     ,                                     1.3                                     ,                                     1.3                                     ,                                     1.3                                     ,                                     2.4                                     ,                                     4.6                                     )                       
           
                         # tuple of a set of fractional numbers                       
                         data3                                     =                                     (fr(                                     1                                     ,                                     2                                     ), fr(                                     1                                     ,                                     2                                     ), fr(                                     10                                     ,                                     3                                     ), fr(                                     2                                     ,                                     3                                     ))                       
           
                         # tuple of a set of negative integers                       
                         data4                                     =                                     (                                     -                                     1                                     ,                                     -                                     2                                     ,                                     -                                     2                                     ,                                     -                                     2                                     ,                                     -                                     7                                     ,                                     -                                     7                                     ,                                     -                                     9                                     )                       
           
                         # tuple of strings                       
                         data5                                     =                                     (                                     "red"                                     ,                                     "blue"                                     ,                                     "black"                                     ,                                     "blue"                                     ,                                     "black"                                     ,                                     "black"                                     ,                                     "brown"                                     )                       
           
           
                         # Printing out the mode of the above data-sets                       
                         print                                     (                                     "Mode of data set 1 is % s"                                     %                                     (mode(data1)))                       
                         print                                     (                                     "Mode of data set 2 is % s"                                     %                                     (mode(data2)))                       
                         print                                     (                                     "Mode of data set 3 is % s"                                     %                                     (mode(data3)))                       
                         print                                     (                                     "Mode of data set 4 is % s"                                     %                                     (mode(data4)))                       
                         print                                     (                                     "Mode of data set 5 is % s"                                     %                                     (mode(data5)))                       

输出

Mode of data set 1 is 5Mode of data set 2 is 1.3Mode of data set 3 is 1/2Mode of data set 4 is -2Mode of data set 5 is black

代码#3： 在这段代码中，我们将演示 统计误差 他长大了

Python3

                         # Python code to demonstrate the                       
                         # statistics error in mode function                       
                                   
                         '''                       
                         StatisticsError is raised while using mode when there are                       
                         two equal modes present in a data set and when the data set                       
                         is empty or null                       
                         '''                       
                                   
                         # importing statistics module                       
                         import                                     statistics                       
                                   
                         # creating a data set consisting of two equal data-sets                       
                         data1                                     =                                     [                                     1                                     ,                                     1                                     ,                                     1                                     ,                                     -                                     1                                     ,                                     -                                     1                                     ,                                     -                                     1                                     ]                       
                                   
                         # In the above data set                       
                         # Count of 1 is 3                       
                         # Count of -1 is also 3                       
                         # StatisticsError will be raised                       
                                   
                         print                                     (statistics.mode(data1))                       

输出

Traceback (most recent call last):  File "/home/38fbe95fe09d5f65aaa038e37aac20fa.py", line 20, in     print(statistics.mode(data1))  File "/usr/lib/python3.5/statistics.py", line 474, in mode    raise StatisticsError('no mode for empty data') from Nonestatistics.StatisticsError: no mode for empty data

注意：在较新版本的Python中，如Python 3.8，当序列有多个模式时，实际的数学概念将被应用，其中最小的元素被视为一个模式。

例如，对于上述代码，-1和1的频率是相同的，但是，-1将是模式，因为它的值较小。

应用： mode（）是一个统计函数，主要用于金融部门，将价值/价格与过去的细节进行比较，根据价格分布集计算/预测未来可能的价格。mean（）不是单独使用的，而是与其他两个统计支柱mean和median一起创建了一个非常强大的工具，可以用来揭示数据的任何方面。

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