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Numpy common function table sorting of data processing

2022-04-23 05:56:00 【ShuangTwo】

 
     function 
     Concrete realization ： In the table a, b Represents an array 
   
     Import numpy library 
    import numpy as np
   
     Use array Create array 
    np.array(([1,2,3],[4,5,6]))
   
     Creates an array of the specified data type 
    np.array([[1,2,3],[4,5,6]], dtype=np.int32)
   
     Create an array of default data types 
    np.arange(5)
   
     from 1 To 2 Generate 5 A floating point number 
    np.linspace(1,2,5)
   
     Create whole 0 Array 
    np.zeros((2,3))
   
     Create whole 0 Array 
    np.ones((2,3))
   
     The main diagonal element is 1 The other elements are 0
    arr = np.eye(3)
   
     Generate [0,1) Between random floating-point numbers 
    np.random.random((2,3))
   
     Generate [0,10) Random integer between 
    np.random.randint(0,10,(3,2))
   
     Data type of array element 
    a.dtype
   
     The number of bytes of memory occupied by array elements 
    a.dtype.itemsize
   
     The number of bytes of memory occupied by array elements 
    a.itemsize
   
     Dimension of array 
    a.shape
   
     Number of array elements 
    a.size
   
     Array row variable column , Be similar to transpose()
    a.T
   
     Row to column （ It's like matrix transposition ）
    a.transpose()
   
     View array dimensions 
    a.shape
   
     return 3 That's ok 2 Array of columns 
    a.reshape(3,2)
   
     Returns a one-dimensional array 
    a.ravel()
   
     The last element 
    a[-1]
   
     Back to page 2 To the first 5 Elements 
    a[2:5]
   
     Back to page 0 To the first 7 Elements , In steps of 3
    a[:7:3]
   
     Returns an array in reverse order 
    a[::-1]
   
     Horizontal merger 
    np.hstack((a,b))
   
     Merge vertically 
    np.vstack((a,b))
   
     Deep merge 
    np.dstack((a,b))
   
     Horizontal split , return list
    np.hsplit(a, 3)
   
     Split Vertically , return list
    np.vsplit(a, 3)
   
     Deep split , return list
    np.dsplit(a, 3)
   
     Arrays and constants can do four operations 
    a+2
   
     Arrays and arrays can do four operations 
    a/b
   
     Determine whether the array is equal 
    (a == b).all()
   
     Sum of all elements 
    a.sum()
   
     The product of all elements 
    a.prod()
   
     The arithmetic mean of all elements 
    a.mean()
   
     The maximum value of all elements 
    a.max()
   
     The minimum of all elements 
    a.min()
   
     Less than 3 The element of is replaced by 3, Greater than 4 The element of is replaced by 4
    a.clip(3,4)
   
     Return is greater than the 2 Array of elements of 
    a.compress(a>2)
   
     return python Of list
    a.tolist()
   
     Calculate variance （ The mean of the square of the difference between the element and the mean ）
    a.var()
   
     Calculate the standard deviation （ The arithmetic square root of variance ）
    a.std()
   
     Returns the difference between the maximum and minimum values of an array 
    a.ptp()
   
     Returns the index of the minimum value in a flat array 
    a.argmin()
   
     Returns the index of the maximum value in a flat array 
    a.argmax()
   
     Return all values as 2 The index of the element 
    np.where(a == 2)
   
     Returns the difference between adjacent elements 
    np.diff(a)
   
     Returns a logarithmic array 
    np.log(a)
   
     Returns an array of exponents 
    np.exp(a)
   
     Return the square root array 
    np.sqrt(a
   
     Array sorting 
    np.msort(a
   
     Returns the maximum value array of corresponding position elements in multiple arrays 
    np.maximum(a, b)
   
     Returns the minimum value array of corresponding position elements in multiple arrays 
    np.minimum(a, b)
   
     Real mathematical division of integers 
    np.true_divide(a, b)

function	Concrete realization ： In the table a, b Represents an array
Import numpy library	import numpy as np
Use array Create array	np.array(([1,2,3],[4,5,6]))
Creates an array of the specified data type	np.array([[1,2,3],[4,5,6]], dtype=np.int32)
Create an array of default data types	np.arange(5)
from 1 To 2 Generate 5 A floating point number	np.linspace(1,2,5)
Create whole 0 Array	np.zeros((2,3))
Create whole 0 Array	np.ones((2,3))
The main diagonal element is 1 The other elements are 0	arr = np.eye(3)
Generate [0,1) Between random floating-point numbers	np.random.random((2,3))
Generate [0,10) Random integer between	np.random.randint(0,10,(3,2))
Data type of array element	a.dtype
The number of bytes of memory occupied by array elements	a.dtype.itemsize
The number of bytes of memory occupied by array elements	a.itemsize
Dimension of array	a.shape
Number of array elements	a.size
Array row variable column , Be similar to transpose()	a.T
Row to column （ It's like matrix transposition ）	a.transpose()
View array dimensions	a.shape
return 3 That's ok 2 Array of columns	a.reshape(3,2)
Returns a one-dimensional array	a.ravel()
The last element	a[-1]
Back to page 2 To the first 5 Elements	a[2:5]
Back to page 0 To the first 7 Elements , In steps of 3	a[:7:3]
Returns an array in reverse order	a[::-1]
Horizontal merger	np.hstack((a,b))
Merge vertically	np.vstack((a,b))
Deep merge	np.dstack((a,b))
Horizontal split , return list	np.hsplit(a, 3)
Split Vertically , return list	np.vsplit(a, 3)
Deep split , return list	np.dsplit(a, 3)
Arrays and constants can do four operations	a+2
Arrays and arrays can do four operations	a/b
Determine whether the array is equal	(a == b).all()
Sum of all elements	a.sum()
The product of all elements	a.prod()
The arithmetic mean of all elements	a.mean()
The maximum value of all elements	a.max()
The minimum of all elements	a.min()
Less than 3 The element of is replaced by 3, Greater than 4 The element of is replaced by 4	a.clip(3,4)
Return is greater than the 2 Array of elements of	a.compress(a>2)
return python Of list	a.tolist()
Calculate variance （ The mean of the square of the difference between the element and the mean ）	a.var()
Calculate the standard deviation （ The arithmetic square root of variance ）	a.std()
Returns the difference between the maximum and minimum values of an array	a.ptp()
Returns the index of the minimum value in a flat array	a.argmin()
Returns the index of the maximum value in a flat array	a.argmax()
Return all values as 2 The index of the element	np.where(a == 2)
Returns the difference between adjacent elements	np.diff(a)
Returns a logarithmic array	np.log(a)
Returns an array of exponents	np.exp(a)
Return the square root array	np.sqrt(a
Array sorting	np.msort(a
Returns the maximum value array of corresponding position elements in multiple arrays	np.maximum(a, b)
Returns the minimum value array of corresponding position elements in multiple arrays	np.minimum(a, b)
Real mathematical division of integers	np.true_divide(a, b)

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Numpy common function table sorting of data processing

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