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Python Numpy.py
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Python Numpy.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 15 18:51:04 2018
@author: Manoj.Prabhakar
"""
import numpy as np
# numpy is the basic block of python libraries and it is incredibly fast. It is for all algebraic functions
# Python list to an array
mylist = [1,2,3]
np.array(mylist)
# To create matrices
myarr = [[1,2,3],[4,5,6],[7,8,9]]
np.array(myarr)
# Range function in numpy
np.arange(0,10)
np.arange(0,11,2)
np.zeros(3)
np.zeros((4,4))
np.ones((2,2))
np.linspace(0,5,10) # Evenly spaced points between starting value and ending value
# TO create an Identity Matrix
np.eye(4)
# To generate random samples of a uniform distribution
np.random.rand(5)
# To get an array of two dimensional array of uniform distribution
np.random.rand(5,5)
# To get it from Normal distribution
np.random.randn(2)
np.random.randn(3,3)
# To get random integers from low number to high number (lowest is included and highest is excluded, last digit provides how many numbers we need)
np.random.randint(1,100)
np.random.randint(1,100,5)
arra = np.arange(0,16,1)
arra
# To reshape an array
arr=arra.reshape(4,4)
arr.shape
# Numpy array indexing
arra = np.arange(0,11)
arra[1]
arra[0:3]
arr[:5]
arra[5:]
arra[0:4]=100
arra
arra=np.arange(0,11)
slic_arra = arra[0:5]
slic_arra[:] = 99
slic_arra
arra
arra_copy = arra.copy()
arra
arra_copy
arra_copy[:] = 9
arra
arra_copy
arra_2d = np.array([[5,10,15],[20,25,30],[30,35,40]])
arra_2d
# To get individual value in an array
arra_2d[2][2]
# To get the entire row
arra_2d[1]
# Conditional Selection
arra = np.arange(1,11)
bool_arra = arra > 4
bool_arra
arra[bool_arra] # Conditionally select those values for which the condition is true
arra[arra>4]
# numpy operations
arra = np.arange(0,11)
arra+arra
arra - arra
arra * arra
arra + 100
arra * 100
arra/arra # Only runtime warning and no error on executing the code
1/ arra
arra ** 2
# Universal aarray functions
np.sqrt(arra)
np.exp(arra)
np.max(arra)
np.sin(arra)
np.cos(arra)
np.log(arra)
#https://docs.scipy.org/doc/numpy-1.14.0/reference/ufuncs.html - To get all functions in numpy
# Questions
1. Create an array of 100 zeroes
2. Create an array of 10 Tens
3. Create an identity matrix of 5*5
4. Create an array of integers between 10, 100
5. Create an 10* 10 matrix of 0 to 99
6. Random number from uniform distribution
7. Random Number from Normal distribution
8. Guess the output
np.arange(1,101,1).reshape(10,10)/100
9. Create 30 Linearly spaced points
10. Standard deviation of matrix