Numpy

Numpy : 

1. Array doesn't support multidimensional then where we can use third party  package : numpy 

2. NumPy stand for numerical python

3. Used for performing mathematical and logical operations on arrays

4. Provides features for operations on multidimensional arrays and matrix in python 

5. In tuple, list can't directly handle maths operations that's why we use array 

import numpy as np

#1d array 

a = np. array([1,2,3])

a[0]= 0

print(a[:2])                         # slicing 

print(a[1])                         # indexing 

print(type(a))                   # type

print(a[0:])

print(a.size)               #1. size

print(a.dtype)            #2. data type

print(a.ndim)           #3.ndim no of dim

print(a)

d= a.reshape(3,1)  #4 reshpe new variable 

print(d)                      d value is 0,2,3

#2d array 

x = np.array([[4,5,6],[7,8,9]])

print(x[0])

print(x[0:])

print(x[:2])

print(x.size) # size

print(x.dtype) # data type

print(x.ndim) # no of dimensio 2d

#5. return the shape

print(x.shape) 

#change the shape

x.shape = (3,2) 

print(x.shape[0])

print(x)

Different ways to create numpy araay :

# 1.linspace(start,stop,part)

linspace is an in-built function in Python's NumPy library. It is used to create an evenly spaced sequence in a specified interval. 

b = np.linspace(3,10,5)    # 5 part output 5

print(b)

#2. arange(start,stop,step)

arrange() It creates an array by using the evenly spaced values over the given interval.

c = np.arange(3,10,5)

print(c)

#3. logspace(start,stop,e)

It creates an array by using the numbers that are evenly separated on a log scale

d = np.logspace(3,10,5)

print(d)

#4 zeros(r,c)

The numpy. zeros() function provide a new array of given shape and type, which is filled with zeros.

e = np.zeros((3,4))

print(e)

#5. ones(r,c)  rows and colum 

The numpy.ones() function returns a new array of given shape and type, with ones.

f = np.ones((3,4))

print(f)

#6 full(r,c) rows and colum 

Return a new array with the same shape and type as a given array filled with a fill_value.

g = np.full((3,4),5)

print(g)

#7. random rand, randn, randint

Python Random module is an in-built module of Python which is used to generate random numbers.

h = np.random.randn(3,4)

print(h)

# Numpy array mathematics 

#mathematics calculations on 2 variable 

import numpy as np

a, b = 40,20

print(np.sum([8,9])) # directly add value  

print(np.sum([a, b]))  *** sum([ ])

print(np.subtract(a, b)) 

print(np.multiply(a,b)) 

print(np.divide(a,b)) 

print(np.sqrt(a)) 

print(np.exp(a)) 

print(np.log(a)) 

print(np.sin(a)) 

print(np.cos(a)) 

#mathematics calculations on 2 Array 

a = (np. array([10,20,30]))

b = (np. array([40,50,60]))

print(sum([a, b]))

print(np.subtract(a,b))

print (np.multiply(a,b))

print(np.divide(a,b))

print(np.sqrt(a))

print(np.log(a))

print(np.exp(a))

print(np.sin(a))

print(np.cos(a))

# aggregate function 

a = [1,2,3]

b = [4,5,6]

print(np.sum([a, b]))

print(np.min(a))              # lowest value 

print(np.mean(a))  #add and then divide 

print(np.median(a))      # calculate h.m no

print(np.corrcoef(a))

print(np.std(a))

# array comparison

1. Element wise comparison 

x = [1,2,3]

y = [4,5,3]

z = [1,2,3]

print(np.equal(x,y))

2.  Array wise comparison 

print(np.array_equal(x,z))

Array manipulations 

import numpy as np 

a = ([[1,2,3],[0,7,8]])

b = ([[4,5,6],[7,8,9]])

print(np.concatenate((a,b)))

#stack

print("\n")

print(np.vstack((a, b)))

print("\n")

print(np.hstack((a, b)))

print("\n")

print(np.stack((a, b)))

print("\n")

#split

x = np.arange(1,7)

print(x)

print(np.split(x,3))

print("\n")

print(np.split(x,6,0))