NumPy - Part1

NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides a multidimensional arrayobject, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays,including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Why we need numpy when we have list?/Why numpy is so popular?

• Provide faster operation for Large Scale operation
• Behind the scene optimizations written in C
• Vectorization via broadcasting(avoiding loops)s
• Backbone of other python scientifi c packages

Numpy dataTypes and Attributes

Main datatype of Numpy is ndarray, where ndarray represent n dimentional array.

import numpy as np

import pandas as pd #Details of pandas library will be cover in Pandas chapter.

a1 = np.array([1,2,3])

print(a1)

print("Type of the array is {}".format(type(a1)))

print("\nData type of the array is {}".format(a1.dtype))

print("\nShape of the array is {}".format(a1.shape))

print("\nSize of the array is {}".format(a1.size))

print("\nDimention of the array is {}".format(a1.ndim))

print("\n",pd.DataFrame(a1))

O/P

[1 2 3]

Type of the array is <class 'numpy.ndarray'>

Data type of the array is int64

Shape of the array is (3,)

Size of the array is 3

Dimention of the array is 1

    0

0  1

1  2

2  3

Compilation vs. interpretation - advantages and disadvantages

	COMPILATION	INTERPRETATION
ADVANTAGES	the execution of the translated code is usually faster; only the user has to have the compiler - the end-user may use the code without it; the translated code is stored using machine language - as it is very hard to understand it, your own inventions and programming tricks are likely to remain your secret.	you can run the code as soon as you complete it - there are no additional phases of translation; the code is stored using programming language, not the machine one - this means that it can be run on computers using different machine languages; you don't compile your code separately for each different architecture.
DISADVANTAGES	the compilation itself may be a very time-consuming process - you may not be able to run your code immediately after any amendment; you have to have as many compilers as hardware platforms you want your code to be run on.	don't expect that interpretation will ramp your code to high speed - your code will share the computer's power with the interpreter, so it can't be really fast; both you and the end user have to have the interpreter to run your code.