Basic properties of Arrays
数组：
1.任意维度，包括0(a scalar)
2.类型： np.unit8, np.int64, np.float32, np.float64
3.密集的，元素的类型相同
4.不同shape无法combine
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| a = np.array([1,2,3],[4,5,6],dtype = np.float32)
print(a.ndim, a,shape, a.dtype)
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The creation of array
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| np.zeros((6,2), dtype = np.int8)
np.ones((3,5), dtype = np.float32)
a = np.ones((2,2,3))
b = np.zeros_like(a)
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| A = np.ones((2,3))
B = np.zeros((3,3))
np.concatenate([A,B], axis = 0)
A = np.ones((4,1))
B = np.zeros((4,2))
np.concatenate([A,B], axis = 0)
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| np.random.random((10,3))
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Shaping
元素总数不能变
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| a = np.array([1,2,3,4,5,6])
b = a.reshape(3,2)
c = a.reshape(2,-1)
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Transposition
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| a = np.array([1.,2.],[3.,4.])
b = a.T
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Array sorting
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| array4 = np.array([1,0,2,-3,6,8,4,7])
array4.sort()
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Statistical operations
算术操作，element-wise
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| a = np.array([1,2,3])
b = np.array([4,4,10])
a*b
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逻辑操作，返回bool array
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| a = np.random.random((5,3))
c = a > 0.5
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计算操作
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| a = np.array([1,4],[9,16],[25,36])
b = np.sqrt(a)
a.max()
a.min()
a.mean()
a.std()
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Array spliting
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| a = np.array([1,4],[9,16],[25,36],[40,50])
first, second = np.split(a, 2, axis=0)
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Array Indexing
arr[0:6:2] => range(0,6,2)
Array Slicing
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| [2,3,1,5,6]
arr[1:4]
[3,1,5]
arr[:4]
[2,3,1,5]
arr[1:]
[3,1,5,6]
arr[:]
[2,3,1,5,6]
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Lab5 Numpy
create an array of the integers from 20 to 50
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| import numpy as np
array = np.arange(20,51)
print(array)
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| [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50]
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create an array of the integers from 0 to 50 with evenly spacing of 10
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| import numpy as np
array = np.linspace(0,50,6)
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| [ 0. 10. 20. 30. 40. 50.]
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show different properties of the numpy array
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| import numpy as np
array = np.arange(20)
print(array)
array = array.reshape(4,5)
print(array)
print(type(array))
print(array.ndim)
print(array.shape)
print(array.dtype)
print(array.itemsize)
print(array.size)
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| [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]
[15 16 17 18 19]]
<class 'numpy.ndarray'>
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(4, 5)
int32
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20
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create an array of thre integers from 9 to 31 and print all values except the first and the last
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| import numpy as np
array = arange(9,32)
print(array[1:-1])
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| [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30]
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create an array of 5 zeros, 5 ones, 5 fives
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| import numpy as np
print('An array of 5 zeros:')
array = np.zeros(5)
print(array)
print('An array of 5 ones:')
array = np.ones(5)
print(array1)
print('An array of 5 fives:')
array = np.ones(5) * 5
print(2)
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| An array of 5 zeros:
[0. 0. 0. 0. 0.]
An array of 5 ones:
[1. 1. 1. 1. 1.]
An array of 5 fives:
[5. 5. 5. 5. 5.]
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create 5x5 zero matrix with elements with the diagonal to 5,4,3,2,1
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| import numpy as np
array = np.diag([5,4,3,2,1])
print(array)
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| [[5 0 0 0 0]
[0 4 0 0 0]
[0 0 3 0 0]
[0 0 0 2 0]
[0 0 0 0 1]]
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find missing item in a given array
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| import numpy as np
array = np.array([[1,1,np.nan,1],
[np.nan,1,1,1],
[1,np.nan,1,1]])
print('\nFind the missing data of the said array:')
print(np.isnan(array))
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| Find the missing data of the said array:
[[False False True False]
[ True False False False]
[False True False False]]
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indexing row and col
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| import numpy as np
array = np.array(([5,10,15],[20,25,30],[35,40,45]))
array[1]
array[1][0]
print(array)
print('----')
print(array[:2,1:])
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| array([20, 25, 30])
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[[ 5 10 15]
[20 25 30]
[35 40 45]]
----
[[10 15]
[25 30]]
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