NumPy Library All A Data Scientist Should Know!

Original article was published by Jitendra Singh Balla on Artificial Intelligence on Medium

• Core features or functions of Numpy :

First of all , import Numpy library :

`import numpy as np`

Create an array of one dimension or two dimension as you want:

`a = np.array([(1,2,3),(4,5,6)])print(a)#output will be an array of two dimensional.`

Get dimensions of an array:

`a = np.array([(1,2,3),(4,5,6)])print(a.ndim)#output : 2`

Find data type of array:

`a = np.array([(1,2,3),(4,5,6)])print(a.dtype)#ouput: int32`

Find size of an array:

`a = np.array([(1,2,3),(4,5,6)])print(a.size)#ouput : 6 `

Find number of rows and columns:

`a = np.array([(1,2,3),(4,5,6)])print(a.shape)#output: (2,3) which means array has 2 rows and 3 columns`

Use arange function as range and find byte size of each element:

`#create an array using arange functionM = np.arange(1000)print(M.size*M.itemsize)#output will be like:4000which shows that memory occupied by numpy array`

Reshape an array:

`a = np.array([(1,2,3),(4,5,6)])a = a.reshape(3,2)print(a)#In output we'll have an array of 3 rows and 2 columns because reshape function will change shape of array`

Slicing for getting a specific element from an array:

`a = np.array([(1,2,3)],(4,5,6)])print(a[0,2])#output: 3print(a[1,1])#output: 5print(a[0:,2]) #getting 2nd element from all the rows#output: [3 5] `

Use linespace function:

`#print three values between 1 to 5b = np.linespace(1,3,5)print(b)#output: [1. 1.5 2. 2.5 3.]`

Some Math function:

`a = np.array([(1,2,3),(4,5,6)])print(a.min())#output: 1print(1.max())#output: 6print(a.sum())#output: 21`

Sum for row and column , for that we use axis where axis = 0 means column and axis = 1 means row:

`a = np.array([(1,2,3),(4,5,6)])print(a.sum(axis=0))#output: [4 6 8]print(a.sum(axis=1))#output: [6 12]`

Finding Square root and Standing deviation:

`#for Square roota = np.array([(1,2,3)])print(np.sqrt(a))#output: [1. 1.434 1.732]#for Standard deviationprint(np.std(a))#output: 1.2909944`

Basic Math function:

`#Addition of two arraysa = np.array([(1,2,3),(4,5,6)])b = np.array([(1,2,3),(4,5,6)])print(a+b)#output: [[2 4 6]         [6 8 10]]#Substraction of two arraysprint(a-b)#output: [[0 0 0]         [0 0 0]]#Multiplication of two arraysprint(a*b)##output: [[1 4 9]         [9 16 25]]#Division of two arraysprint(a/b)##output: [[1 1 1]           [1 1 1]]`

Stacking arrays:

`#vertical stackinga = np.array([(1,2,3),(4,5,6)])b = np.array([(1,2,3),(4,5,6)])print(np.vstack((a,b)))#ouput: [[1 2 3]         [3 4 5]         [1 2 3]         [3 4 5]]#horizontal Stackingprint(np.hstack((a,b)))#output:[[1 2 3 1 2 3]         [3 4 5 3 4 5]]#column Stackingprint(np.column_stack((a,b)))`

Convert whole array to a column:

`a = np.array([(1,2,3),(4,5,6)])print(a.ravel())#output: [1 2 3 4 5 6]`

Initializing Numpy array with zeros:

`import numpy as np n1 = np.zeros((2,2))print(n1)#output: [[0. 0.]          [0. 0.]]`

Initializing Numpy array with same number:

`import numpy as np n1 = np.full((2,2),10)print(n1)#output: [[10 10]          [10 10]]`

Initializing Numpy array with random numbers:

`import numpy as np n1 = np.random.randint((1,100,5)print(n1)#output: [10 23 56 86 51]`