ndarray各种数组

numpy学习专题

二、ndarray各种数组

ndarray数组

import numpy as np
dt = np.array([1,2,3,4,5])
print(dt)
print(type(dt))

[1 2 3 4 5]

元素类型要相同

import numpy as np
arr =np.array([1,2,3,4,5.5,6,7,8])
print(arr)
print(type(arr))

[1.  2.  3.  4.  5.5 6.  7.  8. ]

多维数组

import numpy as np
arr = np.array([[1,2,3],[1,2,3]])
print(arr)

[[1 2 3]
 [1 2 3]]

import numpy as np
arr = np.array([1,2,3,4,5,6,7,8,0],ndmin=5)
print(arr)

[[[[[1 2 3 4 5 6 7 8 0]]]]]

dtype 参数

import numpy as np
arr = np.array([1,2,3,4,5,6] , dtype = float)
print(arr)

[1. 2. 3. 4. 5. 6.]

结构化数据类型

import numpy as np
student = np.dtype([("names","S20"),("age","i4"),("marks","f")])
arr = np.array([("ZJM",19,2.0),("ZLJ",43,1.0)],dtype = student)
print(arr)

[(b'ZJM', 19, 2.) (b'ZLJ', 43, 1.)]

asarray函数 – 与array功能类似

import numpy as np
arr = np.asarray(([1,2,3],[4,5,6]))
print(arr)

[[1 2 3]
 [4 5 6]]

import numpy as np
arr = np.asarray([(1,2,3),(4,5)])
print(arr)

[(1, 2, 3) (4, 5)]

import numpy as np
arr = np.asarray([(1,2,3,2,3,5)],dtype = float)
arr

array([[1., 2., 3., 2., 3., 5.]])

empty函数 – 所有元素随机数来填充

import numpy as np
arr = np.empty([3,2],dtype = int)
print(arr)

[[1 2]
 [3 4]
 [5 6]]

zeros函数 – 所有元素用0来填充

import numpy as np
arr = np.zeros([2,3],dtype = int)
print(arr)

[[0 0 0]
 [0 0 0]]

import numpy as np
arr = np.zeros([3,4],dtype = [("x",float),("y","S1")])
print(arr)

[[(0., b'') (0., b'') (0., b'') (0., b'')]
 [(0., b'') (0., b'') (0., b'') (0., b'')]
 [(0., b'') (0., b'') (0., b'') (0., b'')]]

ones 函数 – 所有元素用1来填充

import numpy as np
arr = np.ones([3,4],dtype = [("X",float),("Y","S20")])
print(arr)

[[(1., b'1') (1., b'1') (1., b'1') (1., b'1')]
 [(1., b'1') (1., b'1') (1., b'1') (1., b'1')]
 [(1., b'1') (1., b'1') (1., b'1') (1., b'1')]]

full函数 – 指定value来填充

import numpy as np
arr = np.full([5,6],fill_value = 3.14,dtype = float)
print(arr)

[[3.14 3.14 3.14 3.14 3.14 3.14]
 [3.14 3.14 3.14 3.14 3.14 3.14]
 [3.14 3.14 3.14 3.14 3.14 3.14]
 [3.14 3.14 3.14 3.14 3.14 3.14]
 [3.14 3.14 3.14 3.14 3.14 3.14]]

eye函数 – 对角线全部为1，其他地方全部为0

import numpy as np
arr = np.eye(10,dtype="i4")
print(arr)

[[1 0 0 0 0 0 0 0 0 0]
 [0 1 0 0 0 0 0 0 0 0]
 [0 0 1 0 0 0 0 0 0 0]
 [0 0 0 1 0 0 0 0 0 0]
 [0 0 0 0 1 0 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0]
 [0 0 0 0 0 0 1 0 0 0]
 [0 0 0 0 0 0 0 1 0 0]
 [0 0 0 0 0 0 0 0 1 0]
 [0 0 0 0 0 0 0 0 0 1]]

arange函数 – 在一个范围内，按照规定的步长进行输出

import numpy as np 
arr = np.arange(10,20,3,dtype = float)
print(arr)

[10. 13. 16. 19.]

frombuffer函数 – 依次输出字符串

import numpy as np
arr = b"ZJM IS A HANDSOME BOYS"
str =np.frombuffer(arr,dtype = "i1")
#如果是数字，则输出ASCII码
print(str)

[90 74 77 32 73 83 32 65 32 72 65 78 68 83 79 77 69 32 66 79 89 83]

import numpy as np
arr = b"YOU ARE SO EXCELLENT"
str = np.frombuffer(arr,dtype = "S4")
print(str)

[b'YOU ' b'ARE ' b'SO E' b'XCEL' b'LENT']

import numpy as np
arr = b"ZJM IS A HANDSOME BOY"
str = np.frombuffer(arr,dtype = "S1",count = len(arr)-2 , offset = 0)
print(str)

[b'Z' b'J' b'M' b' ' b'I' b'S' b' ' b'A' b' ' b'H' b'A' b'N' b'D' b'S'
 b'O' b'M' b'E' b' ' b'B']

fromiter函数

import numpy as np
x = [1,2,3,4,5]
z = iter(x)
print(z)
print(type(z))
arr = np.fromiter (z,dtype='f')
print(arr)

[1. 2. 3. 4. 5.]

linspace函数 – 等差输出数据

import numpy as np
arr = np.linspace(1,40,20,True,True,int)
print(arr)

(array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,
       35, 37, 40]), 2.0526315789473686)

logspace函数 – 等比输出数据

import numpy as np
arr = np.logspace(1,4,20,float)
print(arr)

[   10.            14.38449888    20.69138081    29.76351442
    42.81332399    61.58482111    88.58667904   127.42749857
   183.29807108   263.66508987   379.26901907   545.55947812
   784.75997035  1128.83789168  1623.77673919  2335.72146909
  3359.81828628  4832.93023857  6951.92796178 10000.        ]

一定要支持作者哦