numpy库的实现

简单随机抽样

indexs=[numpy.random.randint(len(data)) for _ in range(k) ]
data[indexs]#data需要是narray类型

按不同概率抽样

numpy.random.choice(a,size=None,replace=None,p=None)

该函数可以根据不同的概率进行有放回和无放回抽样，这里的p需要满足sum( p )=1

np.random.choice([1,2,3],size=(6,1),replace=True,p=[0.1,0.3,0.6])
# array([[2],
#        [2],
#        [2],
#        [2],
#        [3],
#        [3]])

Parameters
-----------
a : 1-D array-like or intIf an ndarray, a random sample is generated from its elements.If an int, the random sample is generated as if a were np.arange(a)
size : int or tuple of ints, optionalOutput shape.  If the given shape is, e.g., ``(m, n, k)``, then``m * n * k`` samples are drawn.  Default is None, in which case asingle value is returned.
replace : boolean, optionalWhether the sample is with or without replacement
p : 1-D array-like, optionalThe probabilities associated with each entry in a.If not given the sample assumes a uniform distribution over allentries in a.Returns
--------
samples : single item or ndarrayThe generated random samplesRaises
-------
ValueErrorIf a is an int and less than zero, if a or p are not 1-dimensional,if a is an array-like of size 0, if p is not a vector ofprobabilities, if a and p have different lengths, or ifreplace=False and the sample size is greater than the populationsize

pandas库的实现

 pandas.DataFrame.sample(n=None,frac=None,replace=False,weights=None,random_state=None,axis=None)

该函数主要针对DataFrame，可以进行纵向和横向的抽样，这里的weights只需要大于零就可以了

df=pd.DataFrame({'x':[1,2,3],'w':[9,5,3]})
df.sample(n=2,frac=None,replace=False,weights=df.w,random_state=1,axis=0)
# Out[65]:
#    x  w
# 0  1  9
# 1  2  5

Parameters
----------
n : int, optionalNumber of items from axis to return. Cannot be used with `frac`.Default = 1 if `frac` = None.
frac : float, optional（抽样的比例）Fraction of axis items to return. Cannot be used with `n`.
replace : boolean, optionalSample with or without replacement. Default = False.
weights : str or ndarray-like, optionalDefault 'None' results in equal probability weighting.If passed a Series, will align with target object on index. Indexvalues in weights not found in sampled object will be ignored andindex values in sampled object not in weights will be assignedweights of zero.If called on a DataFrame, will accept the name of a columnwhen axis = 0.Unless weights are a Series, weights must be same length as axisbeing sampled.If weights do not sum to 1, they will be normalized to sum to 1.Missing values in the weights column will be treated as zero.inf and -inf values not allowed.
random_state : int or numpy.random.RandomState, optionalSeed for the random number generator (if int), or numpy RandomStateobject.
axis : int or string, optionalAxis to sample. Accepts axis number or name. Default is stat axisfor given data type (0 for Series and DataFrames, 1 for Panels).Returns
-------
A new object of same type as caller.

random库的实现

无放回等概率抽样

 random.sample(population,k)

该函数实现了无放回等概率样本抽样，例子：

random.sample([1,2,3],2)

Chooses k unique random elements from a population sequence or set.Returns a new list containing elements from the population while
leaving the original population unchanged.  The resulting list is
in selection order so that all sub-slices will also be valid random
samples.  This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).Members of the population need not be hashable or unique.  If the
population contains repeats, then each occurrence is a possible
selection in the sample.To choose a sample in a range of integers, use range as an argument.
This is especially fast and space efficient for sampling from a
large population:   sample(range(10000000), 60)

参考：

pandas.DataFrame.sample的官方文件：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html

2.https://blog.csdn.net/qq_41080850/article/details/87906590?depth_1-

python中实现简单抽样的函数相关推荐

python平方数迭代器_对python中的高效迭代器函数详解
python中内置的库中有个itertools,可以满足我们在编程中绝大多数需要迭代的场合,当然也可以自己造轮子,但是有现成的好用的轮子不妨也学习一下,看哪个用的顺手~ 首先还是要先import一下: ...
python函数分几种_简单了解Python中的几种函数
python是支持多种范型的语言,可以进行所谓函数式编程,其突出体现在有这么几个函数: filter.map.reduce.lambda.yield lambda lambda函数的使用方法:在lam ...
Python中str()与repr()函数的区别——repr() 的输出追求明确性，除了对象内容，还需要展示出对象的数据类型信息，适合开发和调试阶段使用...
Python中str()与repr()函数的区别 from:https://www.jianshu.com/p/2a41315ca47e 在 Python 中要将某一类型的变量或者常量转换为字符串对象 ...
python中的高阶函数
python中的高阶函数文章目录: 1 什么是高阶函数? 1.1 高阶函数:一个函数的`函数名`作为参数传给另外一个函数 1.2 高阶函数:一个函数返回值(return)为另外一个`函数` 2 py ...
Python编程语言学习：python中与数字相关的函数(取整等)、案例应用之详细攻略
Python编程语言学习:python中与数字相关的函数(取整等).案例应用之详细攻略目录 python中与数字相关的函数 1.对小数进行向上取整 1.1.利用numpy库 1.2.利用math库
Python中字符串常用处理函数
** Python中字符串常用处理函数 ** 1.len( )函数用len( )函数计算字符串的长度 2.strip( )函数删除字符串两边的空白符(包括:'\n'.'\t'.'\r') 注:只能 ...
【Python】Python中str()和repr()函数的区别
作用在 Python 中要将某一类型的变量或者常量转换为字符串对象通常有两种方法,即 str() 或者 repr() . 区别与使用参考文章:Python 中 str() 和 repr() 函数的 ...
python中比较重要的几个函数_Python 几个重要的内置函数 python中的内置函数和关键字需要背过吗...
python重要的几个内置函数用法 python内置函数什么用忘不掉的是回忆,继续的是生活,错过的,就当是路过吧.来来往往身边出现很多人,总有一个位置,一直没有变.看看温暖的阳光,偶尔还是会想一想. ...
python items函数用法,Python中dictionary items()系列函数的用法实例
本文实例讲述了Python中dictionary items()系列函数的用法,对Python程序设计有很好的参考借鉴价值.具体分析如下: 先来看一个示例: import html # availab ...
python enumerate函数_关于python中enumerate和zip函数的用法及举例
关于python中enumerate和zip函数的用法及举例关于enumerate函数: enumerate函数可以同时返回列表或元组等可迭代对象的下标和内容,但实际上,enumerate函数实际返 ...

python中实现简单抽样的函数

numpy库的实现

简单随机抽样

按不同概率抽样

pandas库的实现

random库的实现

无放回等概率抽样

python中实现简单抽样的函数相关推荐

最新文章

热门文章