Numpy

The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. Use the following improt convention:

Example:

Using Numpy to blend two images.

You can use OpenCV function addWeighted like:

cv2.addWeighted(img1,0.5,img2,0.5,0)`

import numpy as np

Numpy Arrays

Creating Arrays

a = np.array([1,2,3])
b = np.array([(1.5,2,3), (4,5,6)], dtype = float)
c = np.array([[(1.5,2,3), (4,5,6)],[(3,2,1), (4,5,6)]], dtype = float) Initial Placeholders
np.zeros((3,4)) #Create an array of zeros
np.ones((2,3,4),dtype=np.int16) #Create an array of ones
d = np.arange(10,25,5)#Create an array of evenly spaced values (step value)
np.linspace(0,2,9) #Create an array of evenlyspaced values (number of samples)
e = np.full((2,2),7)#Create a constant array
f = np.eye(2) #Create a 2X2 identity matrix
np.random.random((2,2)) #Create an array with random values
np.empty((3,2)) #Create an empty array I/O Saving & Loading on Disk
np.save('my_array' , a)
np.savez( 'array.npz', a, b)
np.load( 'my_array.npy') Saving & Loading Text Files
np.loadtxt("myfile.txt")
np.genfromtxt("my_file.csv", delimiter= ',')
np.savetxt( "myarray.txt", a, delimiter= " ") Asking For Help
np.info(np.ndarray.dtype) Inspecting Your Array
a.shape #Array dimensions
len(a)#Length of array
b.ndim #Number of array dimensions
e.size #Number of array elements
b.dtype #Data type of array elements
b.dtype.name #Name of data type
b.astype(int). #Convert an array to a different type Data Types
np.int64 #Signed 64-bit integer types
np.float32. #Standard double-precision floating point
np.complex. #Complex numbers represented by 128 floats
np.bool #Boolean type storing TRUE and FALSE values
np.object #Python object type
np.string_ #Fixed-length string type
np.unicode_ #Fixed-length unicode type Array Mathematics Arithmetic Operations
g = a - b. #Subtraction array([[-0.5,0. ,0.], [-3. , -3. , -3. ]])
np.subtract(a,b) #Subtraction
b + a #Addition array([[ 2.5, 4. , 6.],[5. ,7. ,9. ]])
np.add(b,a) #Addition
a/b #Division array([[0.66666667,1. ,1.],[0.25 ,0.4 ,0.5 ]])
np.divide(a,b) #Division
a * b #Multiplication array([[1.5, 4. ,9.],[ 4. , 10. , 18. ]])
np.multiply(a,b) #Multiplication
np.exp(b) #Exponentiation
np.sqrt(b) #Square root
np.sin(a) #Print sines of an array
np.cos(b) #Elementwise cosine
np.log(a)#Elementwise natural logarithm
e.dot(f) #Dot product array([[7.,7.],[7.,7.]]) Comparison
a == b #Elementwise comparison array([[False , True, True], [ False,False ,False ]], dtype=bool)
a < 2 #Elementwise comparison array([True, False, False], dtype=bool)
np.array_equal(a, b) #Arraywise comparison Copying Arrays h = a.view()#Create a view of the array with the same data
np.copy(a) #Create a copy of the array h = a.copy() #Create a deep copy of the array Sorting Arrays
a.sort() #Sort an array
c.sort(axis=0) #Sort the elements of an array's axis Subsetting, Slicing, Indexing Subsetting
a[2] #Select the element at the 2nd index 3
b[1,2] #Select the element at row 1 column 2(equivalent to b[1][2]) 6.0 Slicing
a[0:2]#Select items at index 0 and 1 array([1, 2])
b[0:2,1] #Select items at rows 0 and 1 in column 1 array([ 2.,5.])
b[:1] #Select all items at row0(equivalent to b[0:1, :]) array([[1.5, 2., 3.]])
c[1,...] #Same as[1,:,:] array([[[ 3., 2.,1.],[ 4.,5., 6.]]])
a[ : : -1] #Reversed array a array([3, 2, 1]) Boolean Indexing
a[a #Select elements from a less than 2 array([1]) Fancy Indexing
b[[1,0,1, 0],[0,1, 2, 0]] #Select elements(1,0),(0,1),(1,2) and(0,0) array([ 4. , 2. , 6. ,1.5])
b[[1,0,1, 0]][:,[0,1,2,0]] #Select a subset of the matrix’s rows and columns array([[ 4. ,5. , 6. , 4.],[1.5, 2. , 3. ,1.5],[ 4. ,5. , 6. , 4.],[1.5, 2. , 3. ,1.5]]) Array Manipulation Transposing Array
i = np.transpose(b) #Permute array dimensions
i.T #Permute array dimensions Changing Array Shape
b.ravel() #Flatten the array
g.reshape(3, -2) #Reshape, but don’t change data Adding/Removing Elements h.resize((2,6)) #Return a new arraywith shape(2,6)
np.append(h,g) #Append items to an array
np.insert(a,1,5) #Insert items in an array
np.delete(a,[1]) #Delete items from an array Combining Arrays
np.concatenate((a,d),axis=0) #Concatenate arrays array([1, 2, 3, 10, 15, 20])
np.vstack((a,b) #Stack arrays vertically(row wise) array([[1. , 2. , 3.],[1.5, 2. , 3.],[ 4. ,5. , 6. ]])
np.r_[e,f] #Stack arrays vertically(row wise)
np.hstack((e,f)) #Stack arrays horizontally(column wise) array([[7.,7.,1.,0.],[7.,7.,0.,1.]])
np.column_stack((a,d)) #Create stacked column wise arrays array([[1, 10],[ 2, 15],[ 3, 20]])
np.c_[a,d] #Create stacked column wise arrays Splitting Arrays
np.hsplit(a,3) #Split the array horizontally at the 3rd index [array([1]),array([2]),array([3])]
np.vsplit(c,2) #Split the array vertically at the 2nd index [array([[[ 1.5, 2. ,1.],[ 4. ,5. , 6. ]]]), array([[[ 3., 2., 3.],[ 4.,5., 6.]]])