The basic use of Python's numpy __python

I. Overview of NumPy

NumPy (numerical python) provides Python support for multidimensional array objects: Ndarray, with vector computing power, fast, and space-saving. NumPy supports a large number of dimension groups and matrix operations, and also provides a large number of mathematical libraries for array operations. Second, create Ndarray arrays

Ndarray:n dimension Array Object (matrix), all elements must be of the same type.
Ndarray Property: The Ndim property that represents the number of dimensions, the Shape property, the dimension size, the Dtype property, and the data type.

To create a Ndarray array function:

code example:

#-*-Coding:utf-8-*-

import numpy;

print ' uses the list to generate a one-dimensional array '
data = [1,2,3,4,5,6]
x = Numpy.array (data)
print x #打印数组
print X.dtype #打印数组元素的类型

print ' Use list to generate two-dimensional array '
data = [[1,2],[3,4],[5,6]]
x = Numpy.array (data)
print x #打印数组
Print X.ndim # Prints the dimension of the array print
X.shape #打印数组各个维度的长度. Shape is a tuple

print ' Create an array using Zero/ones/empty: Create '
x = Numpy.zeros (6) #创建一维长度为6的 based on shape, elements are 1-d array
print X
x = Numpy.zeros ((2,3)) #创建一维长度为2, two-dimensional 0 array with two-dimensional length 3,
print x
x = Numpy.ones ((2,3)) #创建一维长度为2, two-dimensional 1 array of two-dimensional length 3
print x
x = Numpy.empty ((3,3)) #创建一维长度为2, two-dimensional length 3, uninitialized two-dimensional array
print x

print ' uses arrange to generate continuous elements '
Print Numpy.arange (6) # [0,1,2,3,4,5,] open interval
print numpy.arange (0,6,2)  # [0, 2,4]

Iii. specifying the type of Ndarray array element

NumPy Data type:

code example:

print ' generates an array of the specified element types: Setting the Dtype property '
x = Numpy.array ([1,2.6,3],dtype = Numpy.int64)
print x # element type is Int64
print X.dtype
x = Numpy.array ([1,2,3],dtype = Numpy.float64)
print x # element type is float64
print x.dtype

print ' Use Astype to copy an array and convert the type '
x = Numpy.array ([1,2.6,3],dtype = numpy.float64)
y = X.astype (numpy.int32)
print y # [1 2 3]
print x # [1.   2.6  3.]
z = Y.astype (numpy.float64)
Print Z # [1.  2.  3.]

print ' converts a string element to a numeric element '
x = Numpy.array ([' 1 ', ' 2 ', ' 3 '],dtype = numpy.string_)
y = X.astype (numpy.int32)
Print x # [' 1 ' 2 ' 3 ']
print y # [1 2 3] If the conversion fails

, the exception print ' is thrown using the data type of the other array as the parameter '
x = Numpy.array ([1., 2.6,3.] , dtype = Numpy.float32);
y = Numpy.arange (3,dtype=numpy.int32);
Print Y # [0 1 2]
print Y.astype (x.dtype) # [0.  1.  2.]

vectorization calculation of four and Ndarray

Vector operations: Operations between array keys of the same size are applied to the element
Vector and scalar operations: Broadcast-to "broadcast" a scalar to individual elements

code example:

print ' Ndarray array with scalar/array operations '
x = Numpy.array ([1,2,3]) 
print X*2 # [2 4 6]
print X>2 # [false False  true]< C4/>y = Numpy.array ([3,4,5])
print X+y # [4 6 8]
print X>y # [false false]

basic indexes and slices of ndarray arrays

Index of one-dimensional array: similar to Python's list indexing function

Index of multidimensional array: ARR[R1:R2, C1:C2] arr[1,1] equivalent arr[1][1] [:] Representing data for a dimension

code example:

The basic index of print ' Ndarray '
x = Numpy.array ([[[[1,2],[3,4],[5,6]])
print x[0] # [1,2]
print x[0][1] # 2, Index
Print x[0,1] # of normal python arrays
x = Numpy.array ([[[1, 2], [3,4]], [[5, 6], [7,8]])
print  X[0] # [[1 2],[3 4]]
y = x[0].copy () # generate a replica
z = x[0] # does not generate a copy of
print y #  [[1 2],[3 4]]
print y[0,0] # 1
y[0,0] = 0 
z[0,0] =-1
print y # [[0 2],[3 4]]
print x[0] # [[-1 2],[3 4]]
print Z # [[-1 2] , [3 4]]

print ' Ndarray slice '
x = Numpy.array ([1,2,3,4,5])
print X[1:3] # [2,3] right open interval
print x[:3] # [1,2 , 3] left defaults to 0
print x[1:] # [2,3,4,5] Right defaults to element number
print X[0:4:2] # [1,3] subscript increments 2
x = Numpy.array ([[1,2],[3,4],[5, 6]]
print x[:2] # [[1 2],[3 4]]
print x[:2,:1] # [[1],[3]]
x[:2,:1] = 0 # Assign a scalar value to
print x # [[0,2],[0,4 ],[5,6]]
x[:2,:1] = [[8],[6]] # Assign a value to the array
print x # [[8,2],[6,4],[5,6]]

Boolean index and fancy index of Ndarray array

Boolean index: Use a Boolean array as an index. Arr[condition],condition is a Boolean array consisting of a condition/multiple conditions.

Example of a Boolean index code:

The Boolean index of print ' Ndarray '
x = Numpy.array ([3,2,3,1,3,0])
# The length of the Boolean array must be consistent with the axis length of the index
y = Numpy.array ([True,false, True,false,true,false]) 
print x[y] # [3,3,3] 
print X[y==false] # [2,1,0]
print x>=3 # [True False  Tru  True  false]
print x[~ (x>=3)] # [2,1,0]
print (x==2) | ( x==1) # [False to  true false  ]
print x[(x==2) | ( X==1)] # [2 1]
x[(x==2) | ( x==1)] = 0
print x # [3 0 3 0 3 0]

Fancy index: An integer array is used as an index.

Fancy Index code example:

print ' Ndarray flower index: Use integral array as index '
x = Numpy.array ([1,2,3,4,5,6])
print x[[0,1,2] # [1 2 3]
print x[[-1,-2 , -3]] # [6,5,4]
x = Numpy.array ([[[[[1,2],[3,4],[5,6]]]
print x[[0,1]] # [[1,2],[3,4]]
print x[[0,1],[0,1] ] # [1,4] printing x[0][0] and x[1][1] Print
x[[0,1]][:,[0,1] # Print 01 Rows of 01 columns [[[1,2],[3,4]]
# Use the Numpy.ix_ () function to enhance readability
Print X[numpy.ix_ ([0,1],[0,1])] #同上 printing 01 rows of 01 columns [[[1,2],[3,4]]
x[[0,1],[0,1]] = [0,0]
print x # [[0,2],[3,0],[ 5,6]]

Ndarray and axis commutation of the array of seven

The transpose/axis swap of an array returns only one view of the source data and does not modify the source data.

code example:

print ' Ndarray array transpose and Axis swap '
k = numpy.arange (9) #[0,1,.... 8]
m = K.reshape ((3,3)) # Changing the array's shape copy to generate 2-dimensional array of 3 for each dimension print K # [0 1 2 3 4 5 6 7 8
]
print m # [[0 1 2] [3 4 5] [6 7 8]]
# transpose (Matrix) array: T property: Mt[x][y] = m[y][x]
print m.t # [[0 3 6] [1 4 7] [2 5 8]]
# COMPUTE the inner product XTX print of a matrix
Numpy.dot (m,m.t) # Numpy.dot dot Multiply
# The Axis object of the high dimensional array
k = Numpy.arange (8). Reshape (2,2,2)
print k # [[[0 1],[2 3]],[[4 5] , [6 7]]]
print k[1][0][0]
# Axis transform transpose parameter: tuple
m = K.transpose ((1,0,2)) # m[y][x][z = k[x][y][z]< C14/>print m # [[0 1],[4 5]],[[2 3],[6 7]]
print m[0][1][0]
# Axis Exchange swapaxes (axes: axes), parameters: pair of axis number
m = K.swapax ES (0,1) # swaps the first axis and the second axis m[y][x][z] = k[x][y][z]
print M # [[[0 1],[4 5]],[[2 3],[6 7]]]
print m[0][1][0]
# makes  Array matrix transpose by axis switching
m = Numpy.arange (9). Reshape ((3,3))
print M # [[0 1 2] [3 4 5] [6 7 8]]
print m.swapaxes (1,0) # [[0 3 6] [1 4 7] [2 5 8]]

Viii. Ndarray General functions

A common function (UFUNC) is a function that performs element-level operations on data in Ndarray.

One dollar Ufunc:

Unary Ufunc code example:

print ' unary Ufunc sample '
x = Numpy.arange (6)
print x # [0 1 2 3 4 5]
print Numpy.square (x) # [0  1  4 9 16
x = Numpy.array ([1.5,1.6,1.7,1.8])
y,z = NUMPY.MODF (x)
print y # [0.5 0.6 0.7  0.8]
Print Z # [1.  1.  1.  1.]

Binary Ufunc:

Binary Ufunc code example:

print ' Two Ufunc sample '
x = Numpy.array ([[[[[1,4],[6,7]])
y = Numpy.array ([[[2,3],[5,8]])
print numpy.maximum (x , y) # [[2,4],[6,8]]
print numpy.minimum (x,y) # [[1,3],[5,7]]

Nine, numpy where function is used

Np.where (condition, x, y), the first argument is a Boolean array, and the second and third arguments can be either scalar or array.

code example:

Use of print ' where function '
cond = Numpy.array ([true,false,true,false])
x = Numpy.where (cond,-2,2)
print x # [-2  2-2  2]
cond = Numpy.array ([1,2,3,4])
x = Numpy.where (cond>2,-2,2)
print x # [2  2-2 -2]
  
   y1 = Numpy.array ([ -1,-2,-3,-4])
y2 = Numpy.array ([1,2,3,4])
x = Numpy.where (cond>2,y1,y2) # length must match
The print x # [1,2,-3,-4]

print ' where function is nested using '
y1 = Numpy.array ([ -1,-2,-3,-4,-5,-6])
y2 = Numpy.array ([ 1,2,3,4,5,6])
y3 = Numpy.zeros (6)
cond = Numpy.array ([1,2,3,4,5,6])
x = Numpy.where (Cond>5,y3, Numpy.where (cond>2,y1,y2))
print x # [1.  2.-3. -4. -5.  0.]

  

10, Ndarray commonly used statistical methods

The data of the entire array/axis can be computed statistically by these basic statistical methods.

code example:

The basic statistical method of print ' NumPy '
x = Numpy.array ([[[1,2],[3,3],[1,2]]) #同一维度上的数组长度须一致
print X.mean () # 2
print X.mean ( Axis=1) # for the elements of each row average
print X.mean (axis=0) # for the elements of each column average
print x.sum () #同理
print X.sum (Axis=1) # [3 6 3]
Print X.max () # 3
print X.max (Axis=1) # [2 3 2]
print x.cumsum () # [1  3 6 9  ]
print X.cum Prod () # [1  2  6 18 18 36]

Statistical method for Boolean arrays: sum: Counts the number of true in a dimension of an array/array any: Statistics whether one/more of the dimensions in an array/array is true all: statistics whether the array/array is true in a dimension

code example:

print ' statistical method for Boolean arrays '
x = Numpy.array ([[[True,false],[true,false]])
print x.sum () # 2
print x.sum (Axis=1) # [1,1]
print X.any (axis=0) # [True,false]
print X.all (axis=1) # [False,false]

Use sort to sort arrays/arrays in place (the array itself is modified).

code example:

In-place ordering of print '. Sort '
x = Numpy.array ([[[1,6,2],[6,1,3],[1,5,2]]]
x.sort (axis=1) 
print x # [[1 2 6] [1 3 6] [1 2 5]]
#非就地排序: Numpy.sort () can produce a copy of an array

11, Ndarray of the array and set operation

code example: (Method return type is one-dimensional array (1d))

print ' Ndarray and set operations '
x = Numpy.array ([[[[1,6,2],[6,1,3],[1,5,2]])
print numpy.unique (x) # [1,2,3,5,6]
y = Numpy.array ([1,6,5])
print numpy.in1d (x,y) # [True True to  false  true True  false  True False]
print numpy.setdiff1d (x,y) # [2 3]
print numpy.intersect1d (x,y) # [1 5 6]

12. Linear algebra in NumPy

Import Numpy.linalg module. Linear algebra (linear algebra)

Common NUMPY.LINALG Module functions:

code example:

print ' linear algebra '
import numpy.linalg as NLA
print ' matrix dot multiply '
x = Numpy.array ([[1,2],[3,4]])
y = Numpy.array ([[[1,3],[2,4]])
Print X.dot (y) # [[5 11][11]]
print Numpy.dot (x,y) # [[5 11][11]]
print ' matrix reverse '
x = Numpy.array ([[ 1,1],[1,2]]
y = NLA.INV (x) # matrix inversion (if the inverse of the matrix exists)
print X.dot (y) # unit matrix [1.  0.][0.  1.]]
print Nla.det (x) # Find the determinant

13. Random number generation in NumPy

Import Numpy.random module.

Common Numpy.random Module functions:

code example:

print ' numpy.random random number generation '
import numpy.random as NPR

x = Npr.randint (0,2,size=100000) #抛硬币
print (x>0). The result of the sum () # positive
print Npr.normal (size= (2,2)) #正态分布随机数数组 shape = (2,2)

14, Ndarray array Remodeling

code example:

print ' Ndarray array reshape '
x = Numpy.arange (0,6) #[0 1 2 3 4]
print x #[0 1 2 3 4]
print x.reshape ((2,3)) # [[0 1 2][ 3 4 5]]
print x #[0 1 2 3 4]
print x.reshape ((2,3)). Reshape ((3,2)) # [[0 1][2 3][4 5]]
y = Numpy.array ([[1,1 , 1],[1,1,1]]
x = X.reshape (y.shape)
print x # [[0 1 2][3 4 5]]
print X.flatten () # [0 1 2 3 4 5]
X.FL Atten () [0] =-1 # Flatten returns copy
print X # [0 1 2][3 4 5]]
print x.ravel () # [0 1 2 3 4 + 5]
x.ravel () [0] =- 1 # Ravel returns the view (reference) 
print x # [[-1 1 2][3 4 5]]
print ' Dimension size auto derivation '
arr = numpy.arange
print Arr.resh Ape ((5,-1)) # 15/5 = 3

15. Splitting and merging of Ndarray arrays

code example:

print ' array merging and splitting ' x = Numpy.array ([[[1, 2, 3], [4, 5, 6]]) y = Numpy.array ([[7, 8, 9], [A, X]]) print Numpy.concatenat  E ([x, y], Axis = 0) # vertical combination [[1 2 3][4 5 6][7 8 9][10]] Print numpy.concatenate ([x, y], Axis = 1) # horizontal combination [[1 2 3 7 8 9][4 5 6]] print ' vertical stack and horizontal stack ' Print numpy.vstack ((x, y)) # vertical stack: Relative to vertical combined print numpy.hs
Tack ((x, y)) # Horizontal stack: # Dstack by depth stack print numpy.split (x,2,axis=0) # by row [Array ([1, 2, 3]]), Array ([[4, 5, 6]])] Print Numpy.split (X,3,axis=1) # Split by column [Array ([[[1],[4]]), Array ([[[2],[5]]), Array ([[[[3],[6]]]] # Stacking auxiliary class import NumPy as NP a rr = Np.arange (6) arr1 = Arr.reshape ((3, 2)) arr2 = Np.random.randn (3, 2) print ' R_ used to stack ' print np.r_[arr1, arr2 by row ' [[        0.1.          ] [2.          3.] [4. 5.] [0.22621904 0.39719794] [ -1.2201912-0.23623549] [ -0.83229114-0.72678578]] ' print ' c_ is used to stack ' Print n by column '          P.C_[NP.R_[ARR1, arr2], arr] ' [0.        1.0.   ] [2.       3.1.          ] [4.        5.2.        ] [0.22621904 0.39719794 3.        ] [ -1.2201912-0.23623549 4.        ] [ -0.83229114-0.72678578 5.
 ] "' print ' slices directly into array ' Print np.c_[1:6, -10:-5] ' [[1-10] [2-9] [3-8] [4-7] [5-6]] '

16, the elements of the array repeat operation

code example:

The elements of the print ' array '
x = Numpy.array ([[[1,2],[3,4]]]
print x.repeat (2) # is repeated by element [1 1 2 2 3 3 4 4]
print x.repeat (2, axis=0) # Repeat by line [[1 2][1 2][3 4][3 4]]
print x.repeat (2,axis=1) # Repeat in columns [[1 1 2 2][3 3 4 4]]
x = Numpy.array ([1,2]) C6/>print Numpy.tile (x,2) # Tile Tiles: [1 2 1 2]
print numpy.tile (x, (2, 2))  # Specifies the number of times from the low dimension to the high dimension to replicate. 
# [[1 2 1 2][1 2 1 2]]

All code: Github