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DATA ACCESS IN PYTHON

In the previous unit, you have learnt that Python is the most commonly used programming language in the field
of data science. Now, you will learn how packages of Python helps us in accessing structured data within the
Python code. The brief description of Python packages are as follows:
NumPy

NumPy, an acronym of Numerical Python, is the fundamental package for scientific
computing with Python. The important features of NumPy are as follows:

u A powerful N-dimensional array object.
u Sophisticated (broadcasting) function
u Useful linear algebra, Fourier transform, and random number capabilities.
As you know, an array is a set of multiple values of the same datatype. They can be numbers, characters, booleans,
etc. You should always remember that only one data type can be accessed through an array. The difference
between NumPy arrays and lists are summarised in the tabular form:

S. No. NumPy Arrays Lists
1. Arrays are created by using a specific function Lists are created by simply enclosing a sequence of
from either the array module or NumPy elements into square brackets.
Packages.

2. The data of arrays is homogeneous in nature. The data of arrays is heterogeneous in nature.
3. Arrays are great for numerical operations. Lists cannot directly handle numerical operations.
4. Arrays offer more efficient data storage. Lists possess more memory space.
5. Functions such as concatenation, appending, Functions such as concatenation, appending,
extending, etc are not possible within an array. extending, etc are possible within a list.
NumPy can be imported into the Jupyter Notebook by using the given statement:

>>> import numpy # this will import the complete numpy
# package
OR
>>> import numpy as npy # this will import numpy and referred
# as npy
OR
>>> from numpy import array # this will import ONLY arrays
# from whole numy package
OR
>>> from numpy import array as ary # this will import ONLY
# arrays and referred as ary
In NumPy, we can create n-dimensional arrays and are considered as an alternative to Python lists because they
allow faster access in reading and writing items effectively and efficiently.
So, if we compare NumPy-Arrays and Python-List then:

u Array is a collection of homogeneous values whereas list is a collection of heterogeneous values.
u In arrays data of one type does not support data of another type whereas in list it works perfectly by using
data of one type by converting into another data type.

u Arrays are mainly used for mathematical operations where lists are mainly used for data management.

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