डिप्लोमा इन ऑफिस मैनेजमेंट एण्ड अकाउटिंग

डिप्लोमा इन ऑफिस मैनेजमेंट एण्ड अकाउटिंग

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Python Numpy tutorial

NumPy stands for Numerical Python.
NumPy is used for working for arrays.
Numpy are faster than lists because numpy arrays are stored at one continuous place in a memory.
 

Install NumPy

pip install numpy

Importing Numpy Module

import numpy

Create an array 

import numpy as np
l=[1, 2, 3]
x=np.array(l)
print(x)

Array dimensions

  1. 0-D array
  2. 1-D array
  3. 2-D array
import numpy as np 
x=np.array(0)
print(type(x))

y=np.array([1, 2, 3])
print(type(y))

z=np.array([[1, 2],[3, 4]])
print(type(z))
 

Check number of dimensions of arrays

ndim

Array indexing

import numpy as np 

x=np.array([1, 2, 3, 4, 5])
print(x[0])

y=np.array([ [1, 2, 3], [4, 5, 6]])
print(y[1, 0 ])

Slicing in 2-D

y=np.array([ [1, 2, 3], [4, 5, 6]])
print(y[1, 0:2 ])

Shape of an array

Number of elements in each dimension
array.shape
 

Reshape an array

Reshape means changing dimension of array.
import numpy as np 
x=np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
print(x.reshape(2, 5))
 

Joining Numpy arrays

Concat more than one arrays together.
concatenate(arr1, arr2)
import numpy as np
x=np.array([1,2,3])
y=np.array([4,5,6])
z=np.concatenate((x,y))
print(z)

Iterating arrays.

Splitting numpy arrays

Break a single array into multiple arrays.
array_split(arr, number_of_array)
import numpy as np
x = np.array([1, 2, 3, 4, 5, 6])
y = np.array_split(x, 2)
print(y)

Methods

dot() Return product of two array
import numpy as np 
x=np.array([ [1,2], [3,4]])
y=np.array([ [1,2], [3,4] ])
print(x.dot(y))
 
max Return max value from an array
import numpy as np 
x=np.array([ [1,2], [3,4]])
print(x.max())
 
min Return minimum value fron an array
import numpy as np 
x=np.array([ [1,2], [3,4]])
print(x.min())
prod Return product of elements of given axis.
import numpy as np 
x=np.array([ [1,2], [3,4]])
print(np.prod( x, axis=1 ))
sum() Return sum of elements of given array
import numpy as np 
x=np.array([ [1,2], [3,4]])
print(np.sum( x))
 
 
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