np.random的随机数函数(1)
rand(d0,d1,..,dn) |
根据d0‐dn创建随机数数组,浮点数, [0,1),均匀分布 |
randn(d0,d1,..,dn) |
根据d0‐dn创建随机数数组,标准正态分布 |
randint(low[,high,shape]) |
根据shape创建随机整数或整数数组,范围是[low, high) |
seed(s) |
随机数种子, s是给定的种子值 |
np.random.rand
- import numpy as np
-
- a = np.random.rand(3, 4, 5)
-
- a
- Out[3]:
- array([[[0.28576737, 0.96566496, 0.59411491, 0.47805199, 0.97454449],
- [0.15970049, 0.35184063, 0.66815684, 0.13571458, 0.41168113],
- [0.66737322, 0.91583297, 0.68033204, 0.49083857, 0.33549182],
- [0.52797439, 0.23526146, 0.39731129, 0.26576975, 0.26846021]],
-
- [[0.46860445, 0.84988491, 0.92614786, 0.76410349, 0.00283208],
- [0.88036955, 0.01402271, 0.59294569, 0.14080713, 0.72076521],
- [0.0537956 , 0.08118672, 0.59281986, 0.60544876, 0.77931621],
- [0.41678215, 0.24321042, 0.25167563, 0.94738625, 0.86642919]],
-
- [[0.36137271, 0.21672667, 0.85449629, 0.51065516, 0.16990425],
- [0.97507815, 0.78870518, 0.36101021, 0.56538782, 0.56392004],
- [0.93777677, 0.73199966, 0.97342172, 0.42147127, 0.73654324],
- [0.83139234, 0.00221262, 0.51822612, 0.60964223, 0.83029954]]])
np.random.randn
- b = np.random.randn(3, 4, 5)
-
- b
- Out[5]:
- array([[[ 0.09170952, -0.36083675, -0.18189783, -0.52370155,
- -0.61183783],
- [ 1.05285606, -0.82944771, -0.93438396, 0.32229904,
- -0.85316565],
- [ 1.41103666, -0.32534111, -0.02202953, 1.02101228,
- 1.59756695],
- [-0.33896372, 0.42234042, 0.14297587, -0.70335248,
- 0.29436318]],
-
- [[ 0.73454216, 0.35412624, -1.76199508, 1.79502353,
- 1.05694614],
- [-0.42403323, -0.36551581, 0.54033378, -0.04914723,
- 1.15092556],
- [ 0.48814148, 1.09265266, 0.65504441, -1.04280834,
- 0.70437122],
- [ 2.92946803, -1.73066859, -0.30184912, 1.04918753,
- -1.58460681]],
-
- [[ 1.24923498, -0.65467868, -1.30427044, 1.49415265,
- 0.87520623],
- [-0.26425316, -0.89014489, 0.98409579, 1.13291179,
- -0.91343016],
- [-0.71570644, 0.81026219, -0.00906133, 0.90806035,
- -0.914998 ],
- [ 0.22115875, -0.81820313, 0.66359573, -0.1490853 ,
- 0.75663096]]])
np.random.randint
- c = np.random.randint(100, 200, (3, 4))
-
- c
- Out[9]:
- array([[104, 140, 161, 193],
- [134, 147, 126, 120],
- [117, 141, 162, 137]])
numpy.random.randint的详细用法 - python
函数的作用是,返回一个随机整型数,范围从低(包括)到高(不包括),即[low, high)。如果没有写参数high的值,则返回[0,low)的值。
numpy.random.randint(low, high=None, size=None, dtype='l')
参数如下:
参数 |
描述 |
low: int |
生成的数值最低要大于等于low。
(hign = None时,生成的数值要在[0, low)区间内) |
high: int (可选) |
如果使用这个值,则生成的数值在[low, high)区间。 |
size: int or tuple of ints(可选) |
输出随机数的尺寸,比如size=(m * n* k) 则输出同规模即m * n* k 个随机数。默认是None 的,仅仅返回满足要求的单一随机数。 |
dtype: dtype(可选): |
想要输出的格式。如int64、int等等 |
输出:
返回一个随机数或随机数数组
例子
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])
>>>np.random.randint(2, high=10, size=(2,3))
array([[6, 8, 7],
[2, 5, 2]])
np.random.seed
随机种子生成器,使下一次生成的随机数为由种子数决定的“特定”的随机数,如果seed中参数为空,则生成的随机数“完全”随机。参考和文档。
- np.random.seed(10)
-
- np.random.randint(100, 200, (3 ,4))
- Out[11]:
- array([[109, 115, 164, 128],
- [189, 193, 129, 108],
- [173, 100, 140, 136]])
-
- np.random.seed(10)
-
- np.random.randint(100 ,200, (3, 4))
- Out[13]:
- array([[109, 115, 164, 128],
- [189, 193, 129, 108],
- [173, 100, 140, 136]])
np.random的随机数函数(2)
shuffle(a) |
根据数组a的第1轴(也就是最外层的维度)进行随排列,改变数组x |
permutation(a) |
根据数组a的第1轴产生一个新的乱序数组,不改变数组x |
choice(a[,size,replace,p]) |
从一维数组a中以概率p抽取元素,形成size形状新数组replace表示是否可以重用元素,默认为False |
np.random.shuffle
- a = np.random.randint(100, 200, (3, 4))
-
- a
- Out[15]:
- array([[116, 111, 154, 188],
- [162, 133, 172, 178],
- [149, 151, 154, 177]])
-
- np.random.shuffle(a)
-
- a
- Out[17]:
- array([[116, 111, 154, 188],
- [149, 151, 154, 177],
- [162, 133, 172, 178]])
-
- np.random.shuffle(a)
-
- a
- Out[19]:
- array([[162, 133, 172, 178],
- [116, 111, 154, 188],
- [149, 151, 154, 177]])
可以看到,a发生了变化,轴。
np.random.permutation
- b = np.random.randint(100, 200, (3, 4))
-
- b
- Out[21]:
- array([[113, 192, 186, 130],
- [130, 189, 112, 165],
- [131, 157, 136, 127]])
-
- np.random.permutation(b)
- Out[22]:
- array([[113, 192, 186, 130],
- [130, 189, 112, 165],
- [131, 157, 136, 127]])
-
- b
- Out[24]:
- array([[113, 192, 186, 130],
- [130, 189, 112, 165],
- [131, 157, 136, 127]])
可以看到,b没有发生改变。
np.random.choice
- c = np.random.randint(100, 200, (8,))
-
- c
- Out[26]: array([123, 194, 111, 128, 174, 188, 109, 115])
-
- np.random.choice(c, (3, 2))
- Out[27]:
- array([[111, 123],
- [109, 115],
- [123, 128]])#默认可以出现重复值
-
- np.random.choice(c, (3, 2), replace=False)
- Out[28]:
- array([[188, 111],
- [123, 115],
- [174, 128]])#不允许出现重复值
-
- np.random.choice(c, (3, 2),p=c/np.sum(c))
- Out[29]:
- array([[194, 188],
- [109, 111],
- [174, 109]])#指定每个值出现的概率
np.random的随机数函数(3)
uniform(low,high,size) |
产生具有均匀分布的数组,low起始值,high结束值,size形状 |
normal(loc,scale,size) |
产生具有正态分布的数组,loc均值,scale标准差,size形状 |
poisson(lam,size) |
产生具有泊松分布的数组,lam随机事件发生率,size形状 |
- u = np.random.uniform(0, 10, (3, 4))
-
- u
- Out[31]:
- array([[9.83020867, 4.67403279, 8.75744495, 2.96068699],
- [1.31291053, 8.42817933, 6.59036304, 5.95439605],
- [4.36353698, 3.56250327, 5.87130925, 1.49471337]])
-
- n = np.random.normal(10, 5, (3, 4))
-
- n
- Out[33]:
- array([[ 8.17771928, 4.17423265, 3.28465058, 17.2669643 ],
- [10.00584724, 9.94039808, 13.57941572, 4.07115727],
- [ 6.81836048, 6.94593078, 3.40304302, 7.19135792]])
-
- p = np.random.poisson(2.0, (3, 4))
-
- p
- Out[35]:
- array([[0, 2, 2, 1],
- [2, 0, 1, 3],
- [4, 2, 0, 3]])
数据分析师分析问题第一步,必须明确这是不是一个问题!!!