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Numpy Foundation

2022-07-26 08:54:00 【Miracle Fan】

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Create array

# Create array 
a=np.array([1,2,3])
b=np.array([(1,5,2,3),(4,5,6,7)],dtype=np.float32)
c=np.array([[(1,5,2,3),(4,5,6,7)],[(3,2,1,0),(4,5,6,7)]],dtype=np.float32)
print(a,'\n',b,'\n',c)

[1 2 3] 
 [[1. 5. 2. 3.]
 [4. 5. 6. 7.]] 
 [[[1. 5. 2. 3.]
  [4. 5. 6. 7.]]

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

Initialize placeholders

Zero matrix

np.zeros((3,4))# establish 3*4 Of 0 matrix

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1 matrix

np.ones((2,3,4),dtype=np.int16)# establish 2*3*4 Matrix , first 2 Is the number of channels 

array([[[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]],

       [[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]]], dtype=int16)

Evenly spaced array

d=np.arange(10,25,5)# Evenly spaced array , The step value is 5
np.linspace(0,2,9)# Create evenly spaced arrays , Use the number of samples to split the array

data type

data type
np.int64	Signed 64 position
np.float	Double precision floating point
np.complex	The plural
np.bool	bool value
np.object	Python object
np.string_	Fixed length string
np.unicode_	Fixed length Unicode

Array information

a.shape
len(a)
b.ndim
e.size
b.dtype
b.dtype.name
b.astype(int)

Array calculation

Arithmetic operations


`a-b`/`np.subtract(a,b)`
`a+b`/`np.add(a,b)`
`a/b`/`np.divide(a,b)`
`a*b`/`np.multiply(a,b)`
`np.exp(a)`
`np.sqrt(a)`
`np.sin(a)`/`np.cos(a)`
`np.log(a)`
`a.dot(b)`	Dot product

Compare

a=np.array([1,2,3,4])
b=np.array([3,4,5,1])

1.a==b
[False False False False]
2.a<2
[ True False False False]
3.np.array_equal(a,b)
False

Aggregate functions

function	describe
`a.sum()`	Array summary
`a.min()`	Array minimum
`a.max(axis=0)`	Maximum by row
`a.cumsum(axis=1)`	Accumulate by column
`a.mean()`	The average
`a.median()`	Median
`a.corrcoef()`	Find the correlation coefficient
`np.std(a)`	Standard deviation