Why Choose Python as a Career?
Python
is an easy-to-use programming language that encourages you to get surefire accomplishment with the
projects you make. Python is one of the top
three programming languages in the world and is poised to become the most
popular, according to ZDNet. In
fact, according to the PYPL
Index, Python is the most popular programming language worldwide,
so if you want to work in a different country, you have a good chance of
landing a job in, say, Switzerland or Australia. Where would you like to work?
Adding Python to your skill set could be your
ticket to anywhere.
Thus, after completing the Python Course, you’ll have Ample Job Opportunities and a Great package if you learn Python. If you want to learn a Complete Python Course, High Technologies
Solutions is one of the Best Python Training Institute
in Delhi.
Earning Potential
Python
is a highly demandable computer language, and ample job opportunities
according to INDEED. If you have one year of experience as a data
scientist, your starting salary is ₹6,98,413. An entry-level data scientist can
earn around ₹500,000 per annum.
Support from Renowned Corporate Sponsors
Programming
languages grow faster when a corporate sponsor backs them. For example, PHP
is backed by Facebook, Java by Oracle and Sun, and Visual Basic & C# by
Microsoft. Python Programming language is heavily backed by Facebook, Amazon
Web Services, and especially Google.
Google adopted the Python language way
back in 2006 and has used it for many applications and platforms since then.
Lots of Institutional effort and money have been devoted to the training and
success of the Python language by Google. They have even created a dedicated
portal only for Python. The list of support tools and documentation keeps on
growing for python
language in the developers' world.
Use of Libraries in Python Program
As we write large-size programs in Python, we want to maintain the code’s modularity. For the easy maintenance of the code, we split the code into different parts and we can use that code later whenever we need it. In Python, modules play that part. Instead of using the same code in different programs and making the code complex, we define mostly used functions in modules and we can just simply import them into a program wherever there is a requirement. We don't need to write that code but still, we can use its functionality by importing its module. Multiple interrelated modules are stored in a library. And whenever we need to use a module, we import it from its library. In Python, it’s a very simple job to do due to its easy syntax. We just need to use import.
TYPE OF PYTHON: -
There are two major Python versions- Python 2 and Python 3.
• On 16 October 2000, Python 2.0 was released with many new features.
• On 3rd December 2008, Python 3.0 was released with more testing and includes new features.
|
Comparison Parameter |
Python 2 |
Python 3 |
|
Year of Release |
Python 2 was released
in the year 2000. |
Python 3 was released
in the year 2008. |
|
“Print” Keyword |
In Python 2, print is
considered to be a statement and not a function. |
In Python 3, print is
considered to be a function and not a statement. |
|
Storage of Strings |
In Python 2, strings
are stored as ASCII by default. |
In Python 3, strings
are stored as UNICODE by default. |
|
Division of Integers |
On the division of two
integers, we get an integral value in Python 2. For instance, 7/2 yields 3 in
Python 2. |
On the division of two
integers, we get a floating-point value in Python 3. For instance, 7/2 yields
3.5 in Python 3. |
|
Exceptions |
In Python 2,
exceptions are enclosed in notations. |
In Python 3,
exceptions are enclosed in parentheses. |
|
Variable leakage |
The values of global
variables do change in Python 2 if they are used inside a for-loop. |
The value of variables
never changes in Python 3. |
|
Iteration |
In Python 2, the
xrange() function has been defined for iterations. |
In Python 3, the new
Range() function was introduced to perform iterations. |
|
Ease of Syntax |
Python 2 has a more
complicated syntax than Python 3. |
Python 3 has an easier
syntax compared to Python 2. |
|
Libraries |
A lot of libraries of
Python 2 are not forward-compatible. |
A lot of libraries are
created in Python 3 to be strictly used with Python 3. |
|
Usage in today’s times |
Python 2 is not longer
in use since 2020. |
Python 3 is more
popular than Python 2 and is still in use in today’s times. |
|
Backward compatibility |
Python 2 codes can be
ported to Python 3 with a lot of effort. |
Python 3 is not
backward compatible with Python 2. |
|
Application |
Python 2 was mostly
used to become a DevOps Engineer. It is no longer in use after 2020. |
Python 3 is used in a
lot of fields like Software Engineering, Data Science, etc. |
HISTORY OF PYTHON: -
It was initially designed by
Guido van Rossum in 1991 in the Netherlands and developed by Python Software
Foundation. Python was named after the BBC
TV show Monty Python’s Flying Circus. It was mainly developed for
emphasis on code readability, and its syntax allows programmers to express
concepts in fewer lines of code.
PYTHON LIBRARIES
1.
TensorFlow: It is an open-source library used for high-level
computations. It is also used in machine learning and deep learning algorithms.
It contains a large number of tensor operations.
2.
Matplotlib: This library is responsible for plotting numerical data.
And that’s why it is used in data analysis. It is also an open-source library
and plots high-defined figures like pie charts, histograms, scatterplots,
graphs, etc.
3.
Pandas: Pandas are an important library for data scientists. It is
an open-source machine learning library that provides flexible high-level data
structures and a variety of analysis tools. It eases data analysis, data
manipulation, and cleaning of data.
4.
NumPy: The name "NumPy" stands for "Numerical
Python". It is a commonly used library. It is a popular machine-learning
library that supports large matrices and multi-dimensional data. It consists of
in-built mathematical functions for easy computations.
5.
SciPy: The name "SciPy" stands for "Scientific
Python". It is an open-source library used for high-level scientific
computations. This library is built over an extension of NumPy. It works with
NumPy to handle complex computations. While NumPy allows the sorting and
indexing of array data, the numerical data code is stored in SciPy. It is also
widely used by application developers and engineers.
6.
Scikit-learn: It is a famous Python library to work with complex data.
Scikit-learn is an open-source library that supports machine learning. It
supports various supervised and unsupervised algorithms like linear
regression, classification, clustering, etc. This library works in association
with NumPy and SciPy.
7.
PyTorch: PyTorch is the largest machine learning library that
optimizes tensor computations. It has rich APIs to perform tensor computations
with strong GPU acceleration. It also helps to solve application issues related
to neural networks.
There are many more libraries in Python. We can use a suitable library for our purposes. Hence, Python libraries play a very crucial role and are very helpful to developers.
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