Md Manawar Iqbal

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# Numpy in Python

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 import convention:

``````>>> 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

``````>>> np.save('my_array' , a)
>>> np.savez( 'array.npz', a, b)

``````

``````>>> np.loadtxt("myfile.txt")
>>> np.genfromtxt("my_file.csv", delimiter= ',')
>>> np.savetxt( "myarray.txt", a, delimiter= " ")
``````

`>>> np.info(np.ndarray.dtype)`

``````>>> 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
array([[ 2.5, 4. , 6.],[5. ,7. ,9. ]])
>>> 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<2] #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 matrixโ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 donโt change data

``````

``````>>>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.]]])]

``````