Python is a high level and general purpose language. This can be used for any purpose depending upon the industry requirement. Every IT firm has implied Python in fields like web development, servers, server site, shell scripting, research, data science, data analysis, data visualization, machine learning, deep learning, security, just name the field and you will find the expert using Python occupying the prestige.
For the completely unfamiliar lads begin the journey with an adorable and explanatory course with coding instances here.
The ones who want to make their career in data science, choose Python. Really day and night bright upcoming decades of data experiments. It’s the data which encourages research to be done more precisely.
Python for Data Science is in great demand and one can opt a course for the same that provides stronger knowledge and skills for the logistics and reasoning to control the rapid rise in data. The rise in Big Data is growing exponentially and to handle such an enormous amount one needs to know the proper measures. The fresh and workable techniques to tackle structured, unstructured as well as semi-structured data into the limelight.
Before discussing the lovable features Python has firmed its feet elsewhere too i.e., in Artificial Intelligence.
System Programming
System programming is strictly associated with the part of the operating system programs. The section covers pipes, threads, forks, shell scripting. Primarily focusing on the data streams such as stdin, stdout, stderr and their redirection too.
The Python modularity with Linux shell encourages the developers to code in Python.
Well said!
Machine Learning and Deep Learning with Python
The Python became the first choice of the AI (Artificial Intelligence) pioneers. All the model developments are done using Python majorly. Even the research centers prefer Python in their labs after C, Fortran, and Pascal.
Machine Learning is sub-domain of AI which has its fulcrum set at analysis, clustering, classification algorithms. Here too Python wins the race amongst Java, Scala, and R.
Similarly, deep learning concerns mainly with the development of use cases using neural networks, Python has no match here too.
So the geeks who seek a better future in Machine Learning to create the expert systems must opt for Python for Machine Learning course which covers the conceptual knowledge to the practical implementation of Machine Learning algorithms. Better insights can be drawn from the data so gathered.
The course enhances the existing skills of the amateur to some extent of a professional.
There are some features which drool the developers like:
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Generators
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Collections Module
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Itertools Module
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Functools Module
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Packing / Unpacking
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Decorators
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Context Managers
Generators
A generator is responsible for creating a series of values. It’s an object and can be used as an iterator. So use this object either as in for loop or with the next functions to achieve the next values. The iteration of the values occurs only once.
The ‘yield’ keyword creates the generator, whenever a generator function is called the object of the generator is formulated itself.
# Yield Operator
def fib_generator():
x, y = 0, 1
while True:
yield x
x, y = y, x + y
# Print all the numbers of the Fibonacci sequence that are lower than 1000
for cntr in fib_generator():
if cntr > 1000:
break
print(cntr)
# Generator Expression
x = (a * a for a in range(100))
# a is a generator object
print(type(x))
# Sum all the numbers of the generator
print(sum(x))
# There are no elements left in the generator
print(sum(x))
Collection Module
A collection is a component in the standard library which executes alternative container datatypes.
For instance, ‘counter’ is a collection where the data is saved as dictionary keys and their poll (counts) is saved as dictionary values:
# Counter
from collections import Counter
x = Counter('black')
v = Counter('dull black')
print(x)
print(v)
print((x + v).most_common(3))
from collections import defaultdict
my_dict = defaultdict(lambda: 'Default Value')
my_dict['x'] = 42
print(my_dict['x'])
print(my_dict['v'])
from collections import defaultdict
import json
def tree():
"""
Factory that creates a defaultdict that also uses this factory
"""
return defaultdict(tree)
root = tree()
root['Page']['Python']['defaultdict']['Title'] = 'Using defaultdict'
root['Page']['Python']['defaultdict']['Subtitle'] = 'Create a tree'
root['Page']['Java'] = None
print(json.dumps(root, indent=4))
Itertools Module
Itertools is a consignment in the standard library which facilitates you to form iterators for the efficient application of loops.
Just like permutations provide you every possible way of ordering the given set of things:
from itertools import permutations
for p in permutations([1,2,3]):
print(p)
from itertools import combinations
for c in combinations([1, 2, 3, 4], 2):
print(c)
from itertools import chain
for c in chain(range(3), range(12, 15)):
print(c)
Packing / Unpacking
The '*' operator known by the name of unpack/splat operator provides you the soothing transformations, starting from lists or tuples to draw lines between the variables or argument and this happens vice - versa too.
a, *b, c = [2, 7, 5, 6, 3, 4, 1]
print(a)
print(b)
print(c)
# Unpacking arguments
def repeat(count, name):
for i in range(count):
print(name)
print("Call function repeat using a list of arguments:")
args = [4, "cats"]
repeat(*args)
print("Call function repeat using a dictionary of keyword arguments:")
kwargs = {'count': 4, 'name': 'cats'}
repeat(**kwargs)
# Keyword Arguments
def f(*args, **kwargs):
print("Arguments: ", args)
print("Keyword arguments: ", kwargs)
f(3, 4, 9, foo=42, bar=7)
Decorators
A decorator is another function which has a parameter: function and even returns the function output.
Create a function named ‘xyz’ to remember the Fibonacci numbers that have already been created and the function ‘xyz’ is our decorator.
def xyz(function):
xyz_values = {} # Contains already computed values
def wrapping_function(*args):
if args not in xyz_values:
# Call the function only if we haven't already done it for those parameters
xyz_values[args] = function(*args)
return xyz_values[args]
return wrapping_function
@xyz
def fibonacci(n):
print('calling fibonacci(%d)' % n)
if n < 2:
return n
return fibonacci(n-1) + fibonacci(n-2)
print([fibonacci(n) for n in range(1, 9)])
Another approach will be using Iru_<functionName> like here it will be @Iru_xyz
from functools import lru_cache
@lru_xyz(maxsize=None)
def fibonacci(n):
print('calling fibonacci(%d)' % n)
if n < 2:
return n
return fibonacci(n-1) + fibonacci(n-2)
print([fibonacci(n) for n in range(1, 9)])
Context Manager
So to manage the resources properly ‘context managers’ are implemented. Mostly the context managers are used there where we open the files for various operations.
with open(‘filename’, ‘r’) as f: //[Write the name of the file instead of filename]
In actual practice, the context manager is another class that executes methods like __enter__, __exit__.
Here’s the example of computing time for prime numbers
from time import time
class Timer():
def __init__(self, message):
self.message = message
def __enter__(self):
self.start = time()
return None # could return anything, to be used like this: with Timer("Message") as value:
def __exit__(self, type, value, traceback):
elapsed_time = (time() - self.start) * 1000
print(self.message.format(elapsed_time))
with Timer("Elapsed time to compute some prime numbers: {}ms"):
primes = []
for x in range(2, 500):
if not any(x % p == 0 for p in primes):
primes.append(x)
print("Primes: {}".format(primes))
The reason for choosing Python is not just the features stated above but also due to its acceptance in other domains too.
The Final Words
Having so many features and the huge demand in the industry for Python, the professionals have no alternative. The ones who already are doing business with Python, never wish to make a switch. There are even more features like multi-threading, parallel processing, and parallel programming which just binds the developers to use Python for development.
