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Data Science

Introduction of Numpy

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What is Numpy ?

  • Numpy is python library.
  • It’s work with array.
  • Used for numerical and scientific computing.

features of NumPy

  • N-dimensional array
  • In build Mathematical functions
  • Broadcasting
  • Performance Optimization

Common use case of numpy

  • Data Analysis and Manipulation -> cleaning, manipulating
  • Machine Learning -> foundation of algorithms
  • scientifi computing -> numerical simulations and calculations
  • Image Processing -> manipulate image as multi-dimension Array

Disadvantages to numpy

  • all the element are the same data type.
  • Once created, the total size of the array can’t change
  • shape must be rectangular, not jagged

How to Install numpy

  1. open you Command Prompt or terminal
  2. run this command
Terminal window
pip install numpy


# or


python -m pip install numpy
  1. Your numpy was install.
  2. if you use numpy than import numpy in below syntax
import numpy as np
  • in import numpy to import this library and as meaning assas

How to Create a Numpy array

np.array()

  • Creates an array from python list,tuple or many array like objects
arr = np.array([1,2,3,4])

np.zeros

  • this function are the create an array and Its all the element values are zero.

  • In this function pass args is which type of array are the create like dimention of the array.

  • this dimention are pass on the tuple .

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

np.ones

  • ones function are same into zero. but this function are the create an array and Its all the element values are one.
arr = np.zeros((3,4))

np.empty

  • This function are use to creata a array and all element values are empty(Contains uninitialized Values )
arr = np.empty((2,2))

np.arange

  • Returns evenly spaced values within a specified interval.
arr = np.arange(0,10,2)
print(arr)
# Output: [0, 2, 4, 6, 8]

np.linspace

  • Generates an array of evenly spaced numbers over a specified range.
arr = np.linspace(0,10,2)
print(arr)

np.random.rand()

  • reates an array of random numbers uniformly distributed between 0 and 1.
arr = np.random.rand(5)
print(arr)

size & shape ,Reshape

Size

  • In Any Array are the give to how to find Total Number of Element to use array.size.
arr = np.array([
    [1,2,3],[4,5,6]
])


print(arr.size) #output 6

Shape

  • find the number of dimensions in the Array use to shape
arr = np.array([
    [1,2,3],[4,5,6]
])


print(arr.ndim)

Reshape

  • Any Give Array to change the dimension of Array to use the Reshape
arr = np.array([1,2,3,4,5,6])


np.reshape(arr,shape=(2,3))

Sort element

sort function

  • This function use to all the element are the sort in ascending order.
  • If you are using multi-dimension array to specify the axis
arr = np.array([1,3,2,6,3,6,3,7,4,5,6])
np.sort(arr)

list vs python numpy

List

import time


#python noraml list using


a =  list(range(0,100000))
b = list(range(100000,200000))
c = []


start =  time.time()
for i in range(len(a)):
    c.append(a[i] + b[i])
end = time.time()


print(end-start)

numpy

#numpy
import numpy as np
import time


a = np.arange(0,100000)
b = np.arange(100000,200000)


start = time.time()
c = a + b
end = time.time()
print(end-start)

View code file

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