-

NUMPY -Numerical Python

•NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays.

•A powerful N-dimensional array object

•Sophisticated functions

•Tools for integrating C/C++ and Fortran code

•Useful linear algebra, Fourier transform, and random number capabilities



#Single-dimensional Numpy Array
import numpy as np
a = np.array([1,2,3])

#Multi-dimensional Array
a = np.array([(1,2,3),(4,5,6)])
print(a)
------------------------------------------------------------------------------------------------------------

Python NumPyArray v/s List

We use python numpy array instead of a list because of the below three reasons:

•Less Memory

•Fast

•Convenient


#comparing memory occupied by both
import numpy as np
import sys
#memory occupied by list
s = range(1000)
print('memory occupied by List',sys.getsizeof(10)*len(s))
#memory occupied by numpy
d = np.arange(1000)
print('memory occupied by Numpy Array',d.size*d.itemsize)
-----------------------------------------------------------------------------------------------------------
#comparing speed of list and numpy array
import time
size = 10000000
#checking list taken time
l1 = range(size)
l2 = range(size)
start = time.time()
result = [(x,y) for x,y in zip(l1,l2)]
print('Time taken by List:', time.time()-start)
#checking numpy array taken time
a1 = np.arange(size)
a2 = np.arange(size)
start2 = time.time()
result_numpy = a1+a2
print('Time taken by numpy:', time.time()-start2)
-------------------------------------------------------------------------------------------------------------
import numpy as np
np.arange(1,10,3)
np.arange(1,8,3)
np.arange(1,10.1,3)
np.arange(7, 0, -3)
x = np.arange(5)
print(x)
print(x.dtype)
print(x.itemsize)
-------------------------------------------------------------------------------------------------------------
# if you provide equal values for start and stop, 
# then you will get an empty array

np.arange(2,2)
np.arange(8,2,1)
np.arange(2,8,-1)
-------------------------------------------------------------------------------------------------------------
# ndim
# Find the dimension of the array, whether it is a two-dimensional array or a single dimensional array
import numpy as np
a = np.array([(1,2,3),(4,5,6)])
a1 = np.array([((1,2,3),(4,5,6)),((1,2,3),(4,5,6))])
print(a.ndim)
print(a1.ndim)
-------------------------------------------------------------------------------------------------------------
# itemsize
# calculate the byte size of each element
import numpy as np
a = np.array([(1,2,3)])
print(a.itemsize)
-------------------------------------------------------------------------------------------------------------
# dtype
# find the data type of the elements that are stored in anarray. So, if you want to know the data type of a particular element, you can use‘dtype’ function which will print the datatypealong with the size.
import numpy as np
a = np.array([(1,2,3)])
print(a.dtype)
--------------------------------------------------------------------------------------------------------------
# ‘size’ and ‘shape’
# find the size and shape of the array using ‘size’ and ‘shape’ function respectively.
import numpy as np
a = np.array([(1,2,3,4,5,6)])
a1 = np.array([(1,2,3,3),(4,5,6,4)])
print(a.size)
print(a.shape)

Comments

Post a Comment

Popular posts from this blog

Linear Regression with single variable

-
Image
Linear Regression Algorithm From Andrew Ng’s Machine learning course The above diagram conveniently gives a brief overview of the Linear Regression Algorithm. We feed our learning algorithm with some data set(training set), which then outputs a function called Hypothesis. Hypothesis approximates a target function for mapping inputs to outputs. For linear regression in one variable, our hypothesis function is of the form — h(x) = θ0 + θ1x where θ0 and θ1 are the parameters.
Read more

Introduction Deep Learning

-
Deep learning is a branch of  machine learning   which is completely based on   artificial neural networks , as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture. A formal definition of deep learning is- neurons "Deep learning is a particular kind of machine learning that achieves great  power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones". In the human brain app...
Read more

Artificial Neural Network (ANN)

-
Image
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes. Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building blocks, unsupervised learning, Genetic algorithm, etc. What is Artificial Neural Network? The term " Artificial Neural Network " is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one...
Read more