![3. Using ‘os’ and ‘sys’
What is OS PY?
The OS module in Python provides a way of using operating system dependent
functionality. The functions that the OS module provides allows you to interface with the
underlying operating system that Python is running on – be that Windows, Mac or Linux
What is SYS in Python?
28.1. sys — System-specific parameters and functions. This module provides access to some
variables used or maintained by the interpreter and to functions that interact strongly with the
interpreter. It is always available. ... If no script name was passed to the Python interpreter,
argv[0] is the empty string
First, let’s go over the sys module. Possibly the biggest advantage that it offers to the
programmer is the use of command line inputs to the program. Say you’ve built a large
program that will perform some task that depends on inputs from the user. For instance, in
my machine learning class last semester, I implemented the k-means clustering algorithm.
This is a learning algorithm that is given data and can classify it into groups depending on
how many clusters are given as input. It’s clear that this can be useful in many life
applications. Someone who has standardized data on medical patients’ records (e.g. blood-
sugar levels, height, weight, etc.) may want to classify patients into two “clusters,” which](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-21-320.jpg)
![could be (1) healthy or (2) ill. Or perhaps there could be n clusters, where patients classified
into lower numbered clusters have a better outlook than those with high numbers.
4. List Comprehension
List comprehensions provide a concise way to create lists.
It consists of brackets containing an expression followed by a for clause, then
zero or more for or if clauses. The expressions can be anything, meaning you can
put in all kinds of objects in lists.
The result will be a new list resulting from evaluating the expression in the
context of the for and if clauses which follow it.
The list comprehension always returns a result list.
If you used to do it like this:
new_list = []
for i in old_list:
if filter(i):
new_list.append(expressions(i))
You can obtain the same thing using list comprehension:
new_list = [expression(i) for i in old_list if filter(i)]
Syntax
The list comprehension starts with a '[' and ']', to help you remember that the
result is going to be a list.
The basic syntax is
[ expression for item in list if conditional ]
This is equivalent to:
for item in list:
if conditional:
expression
Let's break this down and see what it does
new_list = [expression(i) for i in old_list if filter(i)]
new_list
The new list (result).
expression(i)
Expression is based on the variable used for each element in the old list.
for i in old_list
The word for followed by the variable name to use, followed by the word in the
old list.
if filter(i)
Apply a filter with an If-statement.
This blog shows an example of how to visually break down the list comprehension:
new_range = [i * i for i in range(5) if i % 2 == 0]
Which corresponds to:](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-22-320.jpg)
![*result* = [*transform* *iteration* *filter* ]
The * operator is used to repeat. The filter part answers the question if the
item should be transformed.
Examples
Now when we know the syntax of list comprehensions, let's show some examples and
how you can use it.
Create a simple list
Let's start easy by creating a simple list.
x = [i for i in range(10)]
print x
# This will give the output:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
That's how you can create a simple list.
Create a list using loops and list comprehension
For the next example, assume we want to create a list of squares.
# You can either use loops:
squares = []
for x in range(10):
squares.append(x**2)
print squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
# Or you can use list comprehensions to get the same result:
squares = [x**2 for x in range(10)]
print squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Just remember the syntax: [ expression for item in list if conditional ]
Multiplying parts of a list.
Multiply every part of a list by three and assign it to a new list.
list1 = [3,4,5]
multiplied = [item*3 for item in list1]
print multiplied
[9,12,15]
Note how the item*3 multiplies each piece by 3.](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-23-320.jpg)
![Show the first letter of each word
We will take the first letter of each word and make a list out of it.
listOfWords = ["this","is","a","list","of","words"]
items = [ word[0] for word in listOfWords ]
print items
The output should be: ['t', 'i', 'a', 'l', 'o', 'w']
Lower/Upper case converter
Let's show how easy you can convert lower case / upper case letters.
>>> [x.lower() for x in ["A","B","C"]]
['a', 'b', 'c']
>>> [x.upper() for x in ["a","b","c"]]
['A', 'B', 'C']
Print numbers only from a given string
This example show how to extract all the numbers from a string.
string = "Hello 12345 World"
numbers = [x for x in string if x.isdigit()]
print numbers
>> ['1', '2', '3', '4', '5']
Change x.isdigit() to x.isalpha() if you don't want any numbers.
Parsing a file using list comprehension
In this example, we can see how to get specific lines out from a text file.
Create a text file and put in some text in it.
this is line1
this is line2
this is line3
this is line4
this is line5
Save the file as test.txt
# Then create the filter by using list comprehension:
fh = open("test.txt", "r")
result = [i for i in fh if "line3" in i]
print result
Output: ['this is line3
']](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-24-320.jpg)
![Using list comprehension in functions
Now, let's see how we can use list comprehension in functions.
# Create a function and name it double:
def double(x):
return x*2
# If you now just print that function with a value in it, it should look like this:
>>> print double(10)
20
We can easily use list comprehension on that function.
>>> [double(x) for x in range(10)]
print double
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
# You can put in conditions:
>>> [double(x) for x in range(10) if x%2==0]
[0, 4, 8, 12, 16]
# You can add more arguments:
>>> [x+y for x in [10,30,50] for y in [20,40,60]]
[30, 50, 70, 50, 70, 90, 70, 90, 110]
5. Slicing
Python slice function syntax is:
class slice(stop)
class slice(start, stop[, step])
Note that slice function returns slice object. We can pass this object as a set of indices with
sequences such as string, list, tuple etc.
Python slice function allows us to create a stepwise sub-sequence easily without doing
complete iteration on the existing sequence.
slice() function arguments
start: specifies the start of the index value. It’s optional and defaults to None.
stop: specifies the end of the index value. This is a mandatory parameter.
step: specifies the steps to take from start to stop index. It’s an optional parameter and
defaults to None.
Python slice object has read-only data attributes – start, stop and step – which return the
argument values (or default value).
Let’s see how to create slice objects.](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-25-320.jpg)
![s = slice(1, 10, 2) # indexes 1,3,5,7,9
print(type(s))
print(s.start)
print(s.stop)
print(s.step)
s = slice(5) # indexes 0,1,2,3,4
print(s.start)
print(s.stop)
print(s.step)
Output:
1
10
2
None
5
None
Python slice object has no use on its own, it’s useful when used in conjunction with
sequences to create a sub-sequence.
6. Dictionaries and Sets
Sets and dictionaries are ideal data structures to be used when your data has no intrinsic
order, but does have a unique object that can be used to reference it (the reference object is
normally a string, but can be any hashable type). This reference object is called the “key,”
while the data is the “value.” Dictionaries and sets are almost identical, except that sets do not
actually contain values: a set is simply a collection of unique keys. As the name implies, sets
are very useful for doing set operations.
Example 4-1. Phone book lookup with a list
def find_phonenumber(phonebook, name):
for n, p in phonebook:
if n == name:
return p
return None
phonebook = [
("John Doe", "555-555-5555"),
("Albert Einstein", "212-555-5555"),
]
print "John Doe's phone number is", find_phonenumber(phonebook, "John Do
7. Copying Structures (and Basic Memory Management)
One of the major challenges in writing (somewhat) large-scale Python programs is to keep
memory usage at a minimum. However, managing memory in Python is easy—if you just
don’t care. Python allocates memory transparently, manages objects using a reference count
system, and frees memory when an object’s reference count falls to zero. In theory, it’s swell.](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-26-320.jpg)
![def f(self):
return 'hello world'
then MyClass.i and MyClass.f are valid attribute references, returning an integer and a
function object, respectively. Class attributes can also be assigned to, so you can change the
value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring
belonging to the class: "A simple example class".
Class instantiation uses function notation. Just pretend that the class object is a parameterless
function that returns a new instance of the class. For example (assuming the above class):
x = MyClass()
creates a new instance of the class and assigns this object to the local variable x.
The instantiation operation (“calling” a class object) creates an empty object. Many classes
like to create objects with instances customized to a specific initial state. Therefore a class
may define a special method named __init__(), like this:
def __init__(self):
self.data = []
When a class defines an __init__() method, class instantiation automatically invokes
__init__() for the newly-created class instance. So in this example, a new, initialized instance
can be obtained by:
x = MyClass()
Of course, the __init__() method may have arguments for greater flexibility. In that case,
arguments given to the class instantiation operator are passed on to __init__(). For example,
>>>
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
Function
You can define functions to provide the required functionality. Here are simple rules to
define a function in Python.
Function blocks begin with the keyword def followed by the function name and
parentheses ( ( ) ).
Any input parameters or arguments should be placed within these parentheses. You
can also define parameters inside these parentheses.](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-29-320.jpg)
![ The first statement of a function can be an optional statement - the documentation
string of the function or docstring.
The code block within every function starts with a colon (:) and is indented.
The statement return [expression] exits a function, optionally passing back an
expression to the caller. A return statement with no arguments is the same as return
None.
Syntax
def functionname( parameters ):
"function_docstring"
function_suite
return [expression]
By default, parameters have a positional behavior and you need to inform them in the same
order that they were defined.
Example
The following function takes a string as input parameter and prints it on standard screen.
def printme( str ):
"This prints a passed string into this function"
print str
return
Recommended links :
1. where-can-I-learn-Python-easily
2. Python-programming-language
3. Python an easy language to learn
4. Programming with Python: Hands-On Introduction for Beginners
5. Python Language Introduction
Useful Tags
python programming, data scientist, machine learning, credo systemz](https://image.slidesharecdn.com/pythonprogramming3-190911115030/85/Python-programming-for-beginners-30-320.jpg)
Python programming for beginners
- 1. PYTHON LNGUAGE Overview of Python Benish.D Cs.benish272@gmail.com
- 2. PYTHON INTRODUCTION Python is a widely used general-purpose, high level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code. Python is a programming language that lets you work quickly and integrate systems more efficiently. There are two major Python versions- Python 2 and Python 3. Both are quite different. 8 reasons you should be using Python 1. Quick to setup: Python is easy to download, even for the newbie; careful documentation takes you through the download and setup steps in either Windows, Mac, or Linux environments. Tons of support and documentation make Python learning fairly manageable. If you want to jump right into Python without any need for download on your machine, just go to one of many tutorials like Credo systemz. 2. Python is fast: Python has developed a reputation as a solid, high-performance language. Lots has been done in recent years to get to this point. The PyPy project aims to speed up Python as a whole (and is doing a great job of it). And Numba is another tool that can offer amazing speedups by implementing high performance functions written directly in Python.
- 3. 3. Python has broad support: The applications for Python are broad and varied; it’s used by individuals and big industry players alike in everything from systems automation, testing, and ETL to gaming, CGI and web development. Disney uses Python to help power their creative process. And Mozilla releases tons of open source packages built in Python. Bank of America uses Python to build new products and interfaces within the bank’s technology infrastructure. 4. Ease of use: Python gets a lot of accolades for being easy to learn, and rightfully so. The learning curve is very gradual. Other languages can be quite steep. Python places a heavy emphasis on readability, as shown by its comparison with other object-oriented languages. The first code example below is written in C++: Python is much more intuitive looking and in fact appears like everyday English. As one writer has well said, “With Python and the proper combination of ambition and attention, you could whip together a game in a day knowing nothing before you started.” 5. Big Data: Python’s growing ecosystem and support network has been significantly enhanced by the popularization of Big Data in recent years. Based on a 2013 poll, Python is the second most popular language in data science, used by 39% of respondents (after R’s 61%). Python offers a very mature numeric and scientific computing ecosystem (NumPy & SciPy) and in the trade-off between scale and sophistication, Python is seen as a nice compromise. The advantage of a rich data community with large amounts of toolkits and features makes Python a powerful tool for medium-scale data processing. 6. Money: Across a number of industry metrics and rankings, demand for Python job skills are rising sharply. For example, while the overall hiring demand for IT professionals
- 4. dipped year over year by 5% as of January 2014, the opposite was the case for Python programmers. During this period demand rose by 8.7%. The average salary for Python programmers in the U.S. is just over $100K and demand continues to rise. 7. Why not web development with Python? Python is usually associated as a scripting language heavily used in Big Data and analytics, and there’s no doubt that is where it excels. But did you know that it can be used to build online applications? In case you didn’t know. Django — the popular open source web application framework written in Python — is the foundation of such sites as Pinterest, The New York Times, The Guardian, Bit Bucket, and Instagram. Python is a growing ecosystem for web development; here are some options to explore. 8. Easy ways to learn: There are many ways to learn code today, and in particular Python – MOOCs, online tutorials, and code bootcamps (including online options). As an example, Udacity – one of the first MOOCs – on the market, offered a signature course called Intro to Computer Science, which since its inception in 2012 has introduced over 400,000 students worldwide to Python. The course links theory with practice by having students build an actual search engine using Python; the course was recently revised to also include the addition of a social network component. Learning Python today has never been easier or more accessible Disadvantages of Python As an interpreted language, Python has a slow speed of execution. It is slower than C and C++ because it works with an interpreter, not the compiler.
- 5. The language is seen as less suitable for mobile development and game development. It is often used on desktop and server, but there are only several mobile applications that were developed with Python. Another disadvantage Python has is the runtime error. The language has a lot of design limits and needs more testing time. The programmer has the possibility to see bugs only during run time. Python has high memory consumption and is not used in web browsers because it is not secure. Language flexibility is considered among both advantages and disadvantages of Python. Developers like Python for its simplicity in learning and coding, so much that it might be difficult for some of them to learn and use other languages. In spite of all the disadvantages of Python programming language, it has a lot more pros than cons. Advantages of Python Compared to other programming languages Python is the most broadly applied by the developers lately. Within the next paragraphs, we will take a look at the advantages of Python programming language for developers in contrast with other languages. The main Python language advantages are that it is easy to read and easy to learn. It is easier to write a program in Python than in C or C++. With this language, you gain the possibility to think clearly while coding, which also makes the code easier to sustain. Which reduces the cost for maintenance of the program and seen as one of Python programming advantages. So, what are the advantages of Python that make this language special? The answer is that Python has some unique characteristics that are valuable for programmers because they make coding easier. Another advantage of Python programming is that no bug can originate a segmentation fault. An important advantage of Python language is that it has wide applicability, and is extensively used by scientists, engineers, and mathematicians. It is for this reason that Python is so useful for prototyping and all kinds of experiments. It is used in many groundbreaking fields. It is also used when producing animation for movies and in machine learning. The language includes a large library with memory management which is another one of the advantages of Python programming. Advantages of using Python over other languages C++ or any other typical scripting language might be easier to use for constructing mobile applications or games, however, Python is better for automating build systems, collecting test data, server-side applications. Going through Python pros and cons, there is a need to highlight one of the most valuable advantages of Python language. Python has far-reaching libraries and blank designs and it’s boosting developer’s productivity.
- 6. Is Python easy to learn for beginners? Our aim is to help you : Understand all about Python with our market relevant course. Learn Python coding. Understand its components from basic to complex like OOPS, inbuilt libraries, etc. Understand how to use Python in application across domains of web developments, graphics, data science etc. Become a Python professional so you can build your own apps. Give placement support so you can fully exploit enormous job opportunities that Python brings and earn big. What are topics in Python? There are many topics are available for python programming such as follow: Classes, Objects, Loops, Exceptions. List, Tuples, Dictionary. Regular expressions. CGI programming. Socket programming. Multi threading. GUI programming. Network application Programming Why Python in Trend Now? 1. Why Python programming is so much in demand nowadays1. First of all, Data science is booming nowadays. For related development, Python is put to use the most. Reason?Availability of numerous libraries such as NumPy, ScikitLearn, SciPy, PyBrain, etc Python comes with a data analytics tool, Pandas which is of great use to data scientist and developers Machine learning algorithm is easy to obtain in Python e.g.: Scikit-Learn and TensorFlow Presence of Third party module which makes it compatible with all other programming languages and platform. Due to it’s increased speed and productivity, Python is the top prior option for developers in creating complex multi-protocol network application 2. Apart from the development of artificial intelligence and Data Science application and programs. Python is extensively used for web, mobile, and desktop-based software development. Reason? The language is so much simpler to use.
- 7. 3. Owing to obvious reasons career opportunity of Python is also in an all-time rise. The major share of the traffic that websites that deal with Python share are owing to this high demand.It has to be noted that 22% of the Python developers are either 1-2 years of experience cementing the fact that there is a large requirement of new developers. Where Python is Used? Python is used in many application domains. Here's a sampling. Web and Internet Development Python offers many choices for web development: Frameworks such as Django and Pyramid. Micro-frameworks such as Flask and Bottle. Advanced content management systems such as Plone and django CMS. Python's standard library supports many Internet protocols: HTML and XML JSON E-mail processing. Support for FTP, IMAP, and other Internet protocols. Easy-to-use Socket interface. And the Package Index has yet more libraries: Request, a powerful HTTP client library. Beautiful Soup, an HTML parser that can handle all sorts of oddball HTML. Feed parser for parsing RSS/Atom feeds. Paramiko, implementing the SSH2 protocol. Twisted Python, a framework for asynchronous network programming. Scientific and Numeric: Python is widely used in Scientific and numeric computing: SciPy is a collection of packages for mathematics, science, and engineering. Pandas is a data analysis and modeling library. I Python is a powerful interactive shell that features easy editing and recording of a work session, and supports visualizations and parallel computing. The Credo Systemz teaches basic skills for scientific computing, running bootcamps and providing open-access teaching materials. Education: Python is a superb language for teaching programming, both at the introductory level and in more advanced courses.
- 8. Books such as How to Think Like a Computer Scientist, Python Programming: An Introduction to Computer Science, and Practical Programming. The Education Special Interest Group is a good place to discuss teaching issues. Desktop GUIs: The Tk GUI library is included with most binary distributions of Python. Some toolkits that are usable on several platforms are available separately: wxWidgets Kivy, for writing multitouch applications. Qt via pyqt or pyside Platform-specific toolkits are also available: GTK+ Microsoft Foundation Classes through the win32 extensions. Software Development: Python is often used as a support language for software developers, for build control and management, testing, and in many other ways. SCons for build control. Buildbot and Apache Gump for automated continuous compilation and testing. Roundup or Trac for bug tracking and project management. Business Applications: Python is also used to build ERP and e-commerce systems: Odoo is an all-in-one management software that offers a range of business applications that form a complete suite of enterprise management applications. Tryton is a three-tier high-level general purpose application platform. Benefits of Learning Python There are many benefits of learning Python, especially as your first language, which we will discuss. Other benefits include: 1) Python can be used to develop prototypes, and quickly because it is so easy to work with and read. 2) Most automation, data mining, and big data platforms rely on Python. This is because it is the ideal language to work with for general purpose tasks.
- 9. 3) Python allows for a more productive coding environment than massive languages like C# and Java. Experienced coders tend to stay more organized and productive when working with Python, as well. 4) Python is easy to read, even if you're not a skilled programmer. Anyone can begin working with the language, all it takes is a bit of patience and a lot of practice. Plus, this makes it an ideal candidate for use among multi-programmer and large development teams. 5) Python powers Django, a complete and open source web application framework. Frameworks - like Ruby on Rails - can be used to simplify the development process. 6) It has a massive support base thanks to the fact that it is open source and community developed. Millions of like-minded developers work with the language on a daily basis and continue to improve core functionality. The latest version of Python continues to receive enhancements and updates as time progresses. This is a great way to network with other developers. I believe This article gave the required information about Python. To know more click the link below Python programming language Comparing Python to Other Languages Python is often compared to other interpreted languages such as Java, JavaScript, Perl, Tcl, or Smalltalk. Comparisons to C++, Common Lisp and Scheme can also be enlightening. In this section I will briefly compare Python to each of these languages. These comparisons concentrate on language issues only. In practice, the choice of a programming language is often dictated by other real-world constraints such as cost, availability, training, and prior investment, or even emotional attachment.
- 10. Java Python programs are generally expected to run slower than Java programs, but they also take much less time to develop. Python programs are typically 3-5 times shorter than equivalent Java programs. This difference can be attributed to Python's built-in high-level data types and its dynamic typing. For example, a Python programmer wastes no time declaring the types of arguments or variables, and Python's powerful polymorphic list and dictionary types, for which rich syntactic support is built straight into the language, find a use in almost every Python program. For these reasons, Python is much better suited as a "glue" language, while Java is better characterized as a low-level implementation language. In fact, the two together make an excellent combination. Components can be developed in Java and combined to form applications in Python; Python can also be used to prototype components until their design can be "hardened" in a Java implementation. Java script Python's "object-based" subset is roughly equivalent to JavaScript. Like JavaScript (and unlike Java), Python supports a programming style that uses simple functions and variables without engaging in class definitions. However, for JavaScript, that's all there is. Python, on the other hand, supports writing much larger programs and better code reuse through a true object-oriented programming style, where classes and inheritance play an important role. Perl Python and Perl come from a similar background (Unix scripting, which both have long outgrown), and sport many similar features, but have a different philosophy. Perl emphasizes support for common application-oriented tasks, e.g. by having built-in regular expressions, file scanning and report generating features. Tcl Like Python, Tcl is usable as an application extension language, as well as a stand-alone programming language. However, Tcl, which traditionally stores all data as strings, is weak on data structures, and executes typical code much slower than Python. Tcl also lacks features needed for writing large programs, such as modular namespaces. Thus, while a "typical" large application using Tcl usually contains Tcl extensions written in C or C++ that are specific to that application, an equivalent Python application can often be written in "pure Python". Tcl 8.0 addresses the speed issuse by providing a bytecode compiler with limited data type support, and adds namespaces. However, it is still a much more cumbersome programming language. Smalltalk Perhaps the biggest difference between Python and Smalltalk is Python's more "mainstream" syntax, which gives it a leg up on programmer training. Like Smalltalk, Python has dynamic
- 11. typing and binding, and everything in Python is an object. However, Python distinguishes built-in object types from user-defined classes, and currently doesn't allow inheritance from built-in types. Python has a different philosophy regarding the development environment and distribution of code. Where Smalltalk traditionally has a monolithic "system image" which comprises both the environment and the user's program, Python stores both standard modules and user modules in individual files which can easily be rearranged or distributed outside the system. C++ Almost everything said for Java also applies for C++, just more so: where Python code is typically 3-5 times shorter than equivalent Java code, it is often 5-10 times shorter than equivalent C++ code! Anecdotal evidence suggests that one Python programmer can finish in two months what two C++ programmers can't complete in a year. Python shines as a glue language, used to combine components written in C++. Common Lisp and Scheme These languages are close to Python in their dynamic semantics, but so different in their approach to syntax that a comparison becomes almost a religious argument: is Lisp's lack of syntax an advantage or a disadvantage? It should be noted that Python has introspective capabilities similar to those of Lisp, and Python programs can construct and execute program fragments on the fly. Usually, real-world properties are decisive: Common Lisp is big (in every sense), and the Scheme world is fragmented between many incompatible versions, where Python has a single, free, compact implementation. Top 10 Reasons To Learn Python
- 12. Programming languages have been around for ages, and every decade sees the launch of a new language sweeping developers off their feet. Python is considered as one of the most popular and in-demand programming language. A recent Stack Overflow survey showed that Python has taken over languages such as Java, C, C++ and has made its way to the top. This makes Python certification one of the most sought-after programming certifications. Through this blog, I will be listing down the top 10 reasons to learn Python. Get ready to fall in love with Python!! Below are the major features and applications due to which people choose Python as their first programming language: 1. Python’s Popularity & High Salary Python engineers have some of the highest salaries in the industry. The average Python Developer salary in the United States is approximately $116,028 per year. Also, Python has a strong spike in popularity over the last 1year. Refer the below screenshot taken from Google Trends. I hope my blog on “Top 10 reasons to learn Python” was relevant for you. To get in- depth knowledge on Python along with its various applications, check out our interactive, live-online Credo systemz Python training here, that comes with 24*7 support to guide you throughout your learning period. 2. Data Science Python is the leading language of many data scientist. For years, academic scholars and private researchers were using the MATLAB language for scientific research but it all started to change with the release of Python numerical engines such as ‘Numpy’ and ‘Pandas’. Python also deals with the tabular, matrix as well as statistical data and it even visualizes it with popular libraries such as ‘Matplotlib’ and ‘Seaborn‘. 3. Scripting & Automation Many people only knows that Python is a programming language, but Python can also be used as Scripting language. In scripting: The code is written in the form of scripts and get executed Machine reads and interprets the code Error checking is done during Runtime Once the code is checked, it can be used several times. So by automation, you can automate certain tasks in a program. 4. Big Data Python handles a lot of hassles of data. It supports parallel computing where you can use Python for Hadoop as well. In Python, you have a library called “Pydoop” and
- 13. you can write a MapReduce program in Python and process data present in the HDFS cluster. There are other libraries such as ‘Dask‘ and ‘Pyspark‘ for big data processing. Therefore, Python is widely used for Big Data where you can easily process it! 5. Testing Framework Python is great for validating ideas or products for established companies. Python has many built-in testing frameworks that covers debugging & fastest workflows. There are a lot of tools and modules to make things easier such as Selenium and Splinter. It supports testing with cross-platform & cross-browser with frameworks such as PyTest and RobotFramework. Testing is a tedious task and Python is the booster for it, so every tester should definitely go for it! 6. Computer Graphics Python is largely used in small, large, online or offline projects. It is used to build GUI and desktop applications. It uses ‘Tkinter‘ library to provide fast & easy way to create applications. It is also used in game development where you can write the logic of using a module ‘pygame’ which also runs on android devices. 7. Artificial Intelligence AI is the next huge development in the tech world. You can actually make a machine mimic the human brain which has the power to think, analyze and make decisions. Furthermore, libraries such as Keras and TensorFlow bring machine learning functionality into the mix. It gives the ability to learn without being explicitly programmed. Also, we have libraries such as openCv that helps computer vision or image recognition. 8. Web Development Python has an array of frameworks for developing websites. The popular frameworks are Django, Flask, Pylons etc. Since these frameworks are written in Python, its the core reason which makes the code a lot faster and stable. You can also perform web scraping where you can fetch details from any other websites. You will also be impressed as many websites such as Instagram, bit bucket, Pinterest are build on these frameworks only. 9. Portable & Extensible The portable and extensible properties of Python allow you to perform cross-language operations seamlessly. Python is supported by most platforms present in the industry today ranging from Windows to Linux to Macintosh, Solaris, Play station, among others. Python’s extensibility features allow you to integrate Java as well as .NET components. You can also invoke C and C++ libraries.
- 14. 10. Simple & Easy To Learn So at number 10, Python is extremely simple and easy to learn. It is a very powerful language and it closely resembles the English language! So, what contributes to its simplicity? Python is Free & open source High-level Interpreted Blessed with large community Furthermore, in Python, you don’t have to deal with complex syntax. If you have to print ‘hello world’, you have to write above three lines whereas in Python, just one line is sufficient to print “hello world”. It’s that SIMPLE guys! So the 10th reason lies in the simplicity of the code which makes the best suit for beginners. Conclusion I believe the trial has shown conclusively that it is both possible and desirable to use Python as the principal teaching language: it is Free (as in both cost and source code). it is trivial to install on a Windows PC allowing students to take their interest further. For many the hurdle of installing a Pascal or C compiler on a Windows machine is either too expensive or too complicated; it is a flexible tool that allows both the teaching of traditional procedural programming and modern OOP; It can be used to teach a large number of transferable skills; it is a real-world programming language that can be and is used in academia and the commercial world; it appears to be quicker to learn and, in combination with its many libraries, this offers the possibility of more rapid student development allowing the course to be made more challenging and varied; and most importantly, its clean syntax offers increased understanding and enjoyment for students; Python should be used as the first year teaching language. If used it will be possible to teach students more programming and less of the peculiarities of a particular language. Teaching a mid-level language like C in just one day is inadvisable. Too much time must be spent teaching C and not enough time teaching generic skills to students with no programming experience. In conclusion, Python offers the optimum compromise of teachability and applicability.
- 15. where-can-I-learn-Python-easily What is python ? There are two primary factors why we have to use Python® is software quality and developer productivity. It’s commonly used in a variety of domains like Web programming, Internet Scripting, database, numeric and scientific programming, Gaming thus it also known as general purpose language. The major technical strengths of this language are readability, easy to use and learn, it’s free and supported object-oriented, it is portable. Python Training has dynamically typed language so every operation can be done on the fly. Python codes can be shipped or placed on the web more securely as it execution involves Python® Virtual Machine ( PVM ) and bytecode compilation which is platform independent. Python is the high level language with features of object oriented programming. Clear syntax and expressive Supports Object Oriented and Functional Programming Highly Portable, runs almost anywhere- highend server, workstations Uses machine independent byte codes Designed to be extensible using C/C++,thus allowing access to many external libraries Below are the Python implementations that we see in the coding world. Cpython ( c ) (most common) PyPy (python) JyThon (Java) IronPython(.Net) Important Features Programs are written as text files ,and with the .py extension Each module has its own namespace Name space within a module is global .py files executed directly are programs or scripts .py files referenced by import statement are modules Variables are not needed for introduction Indentation plays a crucial role in here Click Here to Check Latest Updated – Python Interview Questions and Answers Python® can communicate to other parts of the application using the variety of component integration. For an example using CPython component, python can call C/C++ libraries and can be called from C/C++ programs. It will also have support for accessing java objects, .NET objects through Jython, IronPython respectively.
- 16. As in Python, everything is the object, every operation seems to be easier than other scripting languages. Python® codes are equal to one third and one-fifth of C/C++ and Java programs in term of a number of lines. It has powerful memory management to reuse garbage collections. Click Here to Check Latest Updated – Python Tutorials Python supports different types of objects like numbers, string, tuples, list, the dictionary to store data and do operations on stored data. It has common methods and operations on sequence objects ( list, string, tuples ) like indexing, slicing, extended slicing. Why it is in Trend ? This is because Python is in trend with its myriad uses in data science, machine learning, deep learning, artificial intelligence, etc. And since so many of the current students are learning Python, it is obvious that its importance will increase even more in the future. So Why is Python So Popular? We have already established that Python is the fastest growing programming language and also well on the way to becoming the most popular programming language in the world. Now, let’s try to understand the reasons for this incredible phenomenon. 1. Python is Easy To Use Who likes excessively complicated things? No one, that’s who. And that’s one of the reasons for the growing popularity of Python. It is simple with an easily readable syntax. In addition to this, Python is also supremely efficient. It allows developers to complete more work using fewer lines of code. 2. Python has a Supportive Community Python has been around since 1990 and that is ample time to create a supportive community. Because of this support, Python learners can easily improve their knowledge, which only leads to increasing popularity. 3. Python has multiple Libraries and Frameworks Python is already quite popular and consequently, it has hundreds of different libraries and frameworks that can be used by developers. Some of the popular libraries of Python are NumPy and SciPy for scientific computing, Django for web development, BeautifulSoup for XML and HTML parsing, scikit-learn for machine learning applications, nltk for natural language processing, etc.
- 17. 4. Python has Corporate Support Did you think that a language becomes popular just like that? No….Corporate support is a big part of it. Many top companies such as Google, Facebook, Mozilla, Amazon, Quora, etc use Python for their products. In fact, Google has practically adopted Python for many of its platforms and applications. It also provides various guides and tutorials for working with Python in Credo systemz. 5. Python is used in Big Data and Machine Learning Big Data and Machine Learning are the hottest trends in modern times. And Python is used for much of the research and development in these fields. There are many Python tools for analytics and data science such as Scikit-Learn, Theano, etc. Also, Python is used with big data using tools such as Pandas, PySpark, etc. Why Learn Python ? If you are new to coding, you should start with the Python language because it is powerful without being overly complicated. Python is a relatively new language, so it’s more streamlined than older languages, making it more intuitive and quicker to pick up. If you are looking to add a language to your existing quiver, the demand for Python programmers is huge. The average Python developer salary in the US is over $120,000. That’s not shabby! If you are really interested to learn Python language. There is a Python Course available at Credo Systemz which is the leading Python Training Institute in Chennai . And also provides a one-hour free demo session in Python. You can decide whether to join or not after the session. Why should I learn python from Besant Technologies? We are the best Python training institute in Chennai where the training’s in python are provided with real time example. Our Python Course in Chennai Syllabus is designed according to the needs of the corporate client which will surely help you to clear the interviews easily. From the past 12 years, our goal is to build a successful career of the students. At Credo systemz, we provide knowledge and skills at an affordable cost in the shortest possible time duration because we value your time. Our mission is to help students to get into their dream MNC’s. Below given are 10 reasons that will clear all your doubts before joining us. Best Faculty: Whether you are new to python or already know python our faculty will deal with you accordingly. The faculty at Credo systemz are professionals in the field
- 18. of programming especially python with 10+ years of experience. Our faculty members have proven their knowledge in python with the success of their students. Updated and Relevant content: Our courses are designed by the faculty which has more than 10 years of experience in Python. We periodically update our courses and keep them simple so that it is easy to understand by a learner. Personal attention to each member: Our faculty provides attention to each and every learner. Our faculty is highly committed and is always ready to clear doubts of every student. If required, they also provide extra attention to the learner, in order to make them understand the concepts of python. Real world application: After completion of this course, you will be able to use this knowledge in the real world. You can develop a game or an application. We design our courses in a way that you get an insight into scenarios that are occurring in industries these days. Assured placement: Our courses focus mostly on job- related skills, which is helpful in placements. During this program, you will be provided with interview preparation material and mock interviews will also be conducted. We have a huge network in the corporate industry, which helps us to connect to various companies for the placements. Provide anytime, anywhere learning: We provide both online and offline courses. If you are not able to attend the classroom program. Then you can opt for our online classes. There you will get 24/7 support. You can clear your doubts at any time at your own pace. Numerous practice assignments: After each topic, you will be able to test your understanding of the topic. The assignment hours are more than 20 hours. You will get enough practice to master python skills. One-year access to the course: Even if the course is finished, you can access the course for one year. You can revise the course again and again to master the skill. Our motive is to provide a good amount of time and practice to the learner. 1000+ satisfied learners: Credo systemz have provided python training to about 1000+ learners. Some of them were from the different domain but after completion of the course, all were satisfied. To learn python from Credo systemz all you need is English and basic mathematics. Pocket-friendly courses: Our python courses are super affordable. We understand the students and hence we have kept our courses pocket-friendly. And on this occasion of the new year, you will be provided with a discount, which is flat 10-15% off on all the courses. Python Training Reviews Credo systemz Reviews have given by our students already completed the training with us. Please give your feedback as well if you are a student.
- 19. 10 Things To Know About Python let’s discuss ten of some of the important concepts that Python programmers should know. 1. Python Version Numbers While this is technically not a programming feature, it’s still crucial to know the current versions of Python just so we’re all on the same page. Python versions are numbered as A.B.C., where the three letters represent (in decreasing order) important changes in the language. So, for instance, going from 2.7.3 to 2.7.4 means that the Python build made some minor bug fixes, while going from 2.xx to 3.xx represents a major change. Note the ‘x’ here, which is intentional; if a Python feature can apply to version number 2.7.C for any valid ‘C’ value, then we put in an ‘x’ and refer to Python 2.7.x. We can also omit the ‘x’ entirely and just use 2.7. As of this writing (July 2013), Python has two stable versions commonly used: 2.7 and 3.3. Less important is the third character, but right now 2.7.5 and 3.3.2 are the current versions, both of which were released on May 15, 2013. The short answer is that, while both 2.7 and 3.3 are perfectly fine to use, 3.3 is the future of the language and someone just starting Python today should probably use Python 3.3 over 2.7. Of course, if one is in the middle of an extensive research project that makes heavy use of 2.7, then it might not make sense to upgrade to 3.3 right away. This is actually quite similar to my current situation, since I’m using a good number of my own Python 2.7 scripts to help me analyze algorithmic combinatorics on words. Once August arrives, I’ll fully transition to Python 3.3. In the
- 20. meantime, though, I’ve done quite a bit of reading on Python 3’s new versions and I have 3.3.2 installed (in addition to 2.7.4) on my laptop, so this post and its code syntax will assume that we’re using Python 3. One important thing to note, though, is that Python 3 is intentionally backwards incompatible. Backward compatibility is often a desired feature of programming languages and software that routinely undergo revisions, since it means that input from older versions (e.g. older Python programs) can still run under the latest builds. In this case, Python 2 code will not be guaranteed to run successfully if using Python 3, so some conversion may be necessary to allow code to properly run. Backwards incompatibility was necessary in order to allow Python 3 to be more clear, concise and use additional features. 2. Using the Python Shell Python is an interpreter language. It means it executes the code line by line. Python provides a Python Shell (also known as Python Interactive Shell) which is used to execute a single Python command and get the result. Python Shell waits for the input command from the user. As soon as the user enters the command, it executes it and displays the result.
- 21. 3. Using ‘os’ and ‘sys’ What is OS PY? The OS module in Python provides a way of using operating system dependent functionality. The functions that the OS module provides allows you to interface with the underlying operating system that Python is running on – be that Windows, Mac or Linux What is SYS in Python? 28.1. sys — System-specific parameters and functions. This module provides access to some variables used or maintained by the interpreter and to functions that interact strongly with the interpreter. It is always available. ... If no script name was passed to the Python interpreter, argv[0] is the empty string First, let’s go over the sys module. Possibly the biggest advantage that it offers to the programmer is the use of command line inputs to the program. Say you’ve built a large program that will perform some task that depends on inputs from the user. For instance, in my machine learning class last semester, I implemented the k-means clustering algorithm. This is a learning algorithm that is given data and can classify it into groups depending on how many clusters are given as input. It’s clear that this can be useful in many life applications. Someone who has standardized data on medical patients’ records (e.g. blood- sugar levels, height, weight, etc.) may want to classify patients into two “clusters,” which
- 22. could be (1) healthy or (2) ill. Or perhaps there could be n clusters, where patients classified into lower numbered clusters have a better outlook than those with high numbers. 4. List Comprehension List comprehensions provide a concise way to create lists. It consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The expressions can be anything, meaning you can put in all kinds of objects in lists. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. The list comprehension always returns a result list. If you used to do it like this: new_list = [] for i in old_list: if filter(i): new_list.append(expressions(i)) You can obtain the same thing using list comprehension: new_list = [expression(i) for i in old_list if filter(i)] Syntax The list comprehension starts with a '[' and ']', to help you remember that the result is going to be a list. The basic syntax is [ expression for item in list if conditional ] This is equivalent to: for item in list: if conditional: expression Let's break this down and see what it does new_list = [expression(i) for i in old_list if filter(i)] new_list The new list (result). expression(i) Expression is based on the variable used for each element in the old list. for i in old_list The word for followed by the variable name to use, followed by the word in the old list. if filter(i) Apply a filter with an If-statement. This blog shows an example of how to visually break down the list comprehension: new_range = [i * i for i in range(5) if i % 2 == 0] Which corresponds to:
- 23. *result* = [*transform* *iteration* *filter* ] The * operator is used to repeat. The filter part answers the question if the item should be transformed. Examples Now when we know the syntax of list comprehensions, let's show some examples and how you can use it. Create a simple list Let's start easy by creating a simple list. x = [i for i in range(10)] print x # This will give the output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] That's how you can create a simple list. Create a list using loops and list comprehension For the next example, assume we want to create a list of squares. # You can either use loops: squares = [] for x in range(10): squares.append(x**2) print squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] # Or you can use list comprehensions to get the same result: squares = [x**2 for x in range(10)] print squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] Just remember the syntax: [ expression for item in list if conditional ] Multiplying parts of a list. Multiply every part of a list by three and assign it to a new list. list1 = [3,4,5] multiplied = [item*3 for item in list1] print multiplied [9,12,15] Note how the item*3 multiplies each piece by 3.
- 24. Show the first letter of each word We will take the first letter of each word and make a list out of it. listOfWords = ["this","is","a","list","of","words"] items = [ word[0] for word in listOfWords ] print items The output should be: ['t', 'i', 'a', 'l', 'o', 'w'] Lower/Upper case converter Let's show how easy you can convert lower case / upper case letters. >>> [x.lower() for x in ["A","B","C"]] ['a', 'b', 'c'] >>> [x.upper() for x in ["a","b","c"]] ['A', 'B', 'C'] Print numbers only from a given string This example show how to extract all the numbers from a string. string = "Hello 12345 World" numbers = [x for x in string if x.isdigit()] print numbers >> ['1', '2', '3', '4', '5'] Change x.isdigit() to x.isalpha() if you don't want any numbers. Parsing a file using list comprehension In this example, we can see how to get specific lines out from a text file. Create a text file and put in some text in it. this is line1 this is line2 this is line3 this is line4 this is line5 Save the file as test.txt # Then create the filter by using list comprehension: fh = open("test.txt", "r") result = [i for i in fh if "line3" in i] print result Output: ['this is line3 ']
- 25. Using list comprehension in functions Now, let's see how we can use list comprehension in functions. # Create a function and name it double: def double(x): return x*2 # If you now just print that function with a value in it, it should look like this: >>> print double(10) 20 We can easily use list comprehension on that function. >>> [double(x) for x in range(10)] print double [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] # You can put in conditions: >>> [double(x) for x in range(10) if x%2==0] [0, 4, 8, 12, 16] # You can add more arguments: >>> [x+y for x in [10,30,50] for y in [20,40,60]] [30, 50, 70, 50, 70, 90, 70, 90, 110] 5. Slicing Python slice function syntax is: class slice(stop) class slice(start, stop[, step]) Note that slice function returns slice object. We can pass this object as a set of indices with sequences such as string, list, tuple etc. Python slice function allows us to create a stepwise sub-sequence easily without doing complete iteration on the existing sequence. slice() function arguments start: specifies the start of the index value. It’s optional and defaults to None. stop: specifies the end of the index value. This is a mandatory parameter. step: specifies the steps to take from start to stop index. It’s an optional parameter and defaults to None. Python slice object has read-only data attributes – start, stop and step – which return the argument values (or default value). Let’s see how to create slice objects.
- 26. s = slice(1, 10, 2) # indexes 1,3,5,7,9 print(type(s)) print(s.start) print(s.stop) print(s.step) s = slice(5) # indexes 0,1,2,3,4 print(s.start) print(s.stop) print(s.step) Output: 1 10 2 None 5 None Python slice object has no use on its own, it’s useful when used in conjunction with sequences to create a sub-sequence. 6. Dictionaries and Sets Sets and dictionaries are ideal data structures to be used when your data has no intrinsic order, but does have a unique object that can be used to reference it (the reference object is normally a string, but can be any hashable type). This reference object is called the “key,” while the data is the “value.” Dictionaries and sets are almost identical, except that sets do not actually contain values: a set is simply a collection of unique keys. As the name implies, sets are very useful for doing set operations. Example 4-1. Phone book lookup with a list def find_phonenumber(phonebook, name): for n, p in phonebook: if n == name: return p return None phonebook = [ ("John Doe", "555-555-5555"), ("Albert Einstein", "212-555-5555"), ] print "John Doe's phone number is", find_phonenumber(phonebook, "John Do 7. Copying Structures (and Basic Memory Management) One of the major challenges in writing (somewhat) large-scale Python programs is to keep memory usage at a minimum. However, managing memory in Python is easy—if you just don’t care. Python allocates memory transparently, manages objects using a reference count system, and frees memory when an object’s reference count falls to zero. In theory, it’s swell.
- 27. In practice, you need to know a few things about Python memory management to get a memory-efficient program running. One of the things you should know, or at least get a good feel about, is the sizes of basic Python objects. Another thing is how Python manages its memory internally. So let us begin with the size of basic objects. In Python, there’s not a lot of primitive data types: there are ints, longs (an unlimited precision version of ints), floats (which are doubles), tuples, strings, lists, dictionaries, and classes. 8. Generators Generators are functions that can be paused and resumed on the fly, returning an object that can be iterated over. Unlike lists, they are lazy and thus produce items one at a time and only when asked. So they are much more memory efficient when dealing with large data sets. This article details how to create generator functions and expressions as well as why you would want to use them in the first place What are generators in Python? There is a lot of overhead in building an iterator in Python; we have to implement a class with __iter__() and __next__() method, keep track of internal states, raise StopIteration when there was no values to be returned etc. This is both lengthy and counter intuitive. Generator comes into rescue in such situations. Python generators are a simple way of creating iterators. All the overhead we mentioned above are automatically handled by generators in Python. Simply speaking, a generator is a function that returns an object (iterator) which we can iterate over (one value at a time). How to create a generator in Python? It is fairly simple to create a generator in Python. It is as easy as defining a normal function with yield statement instead of a return statement. If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function. Both yield and return will return some value from a function. The difference is that, while a return statement terminates a function entirely, yield statement pauses the function saving all its states and later continues from there on successive calls. 9. File Management
- 28. he term file management in the context of computers refers to the manipulation of data in a file or files and documents on a computer. Though everybody has an understanding of the term file, we present a formal definition anyway: A file or a computer file is a chunk of logically related data or information which can be used by computer programs. Usually a file is kept on a permanent storage media, e.g. a hard drive disk. A unique name and path is used by human users or in programs or scripts to access a file for reading and modification purposes. The term "file" - as we have described it in the previous paragraph - appeared in the history of computers very early. Usage can be tracked down to the year 1952, when punch cards where used. A programming language without the capability to store and retrieve previously stored information would be hardly useful. The most basic tasks involved in file manipulation are reading data from files and writing or appending data to files. 10. Classes and Functions What are classes and objects in Python? Python is an object oriented programming language. Unlike procedure oriented programming, where the main emphasis is on functions, object oriented programming stress on objects. Object is simply a collection of data (variables) and methods (functions) that act on those data. And, class is a blueprint for the object. We can think of class as a sketch (prototype) of a house. It contains all the details about the floors, doors, windows etc. Based on these descriptions we build the house. House is the object. As, many houses can be made from a description, we can create many objects from a class. An object is also called an instance of a class and the process of creating this object is called instantiation. Class Objects Class objects support two kinds of operations: attribute references and instantiation. Attribute references use the standard syntax used for all attribute references in Python: obj.name. Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this: class MyClass: """A simple example class""" i = 12345
- 29. def f(self): return 'hello world' then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class". Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class): x = MyClass() creates a new instance of the class and assigns this object to the local variable x. The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named __init__(), like this: def __init__(self): self.data = [] When a class defines an __init__() method, class instantiation automatically invokes __init__() for the newly-created class instance. So in this example, a new, initialized instance can be obtained by: x = MyClass() Of course, the __init__() method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to __init__(). For example, >>> >>> class Complex: ... def __init__(self, realpart, imagpart): ... self.r = realpart ... self.i = imagpart ... >>> x = Complex(3.0, -4.5) >>> x.r, x.i (3.0, -4.5) Function You can define functions to provide the required functionality. Here are simple rules to define a function in Python. Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ). Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
- 30. The first statement of a function can be an optional statement - the documentation string of the function or docstring. The code block within every function starts with a colon (:) and is indented. The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None. Syntax def functionname( parameters ): "function_docstring" function_suite return [expression] By default, parameters have a positional behavior and you need to inform them in the same order that they were defined. Example The following function takes a string as input parameter and prints it on standard screen. def printme( str ): "This prints a passed string into this function" print str return Recommended links : 1. where-can-I-learn-Python-easily 2. Python-programming-language 3. Python an easy language to learn 4. Programming with Python: Hands-On Introduction for Beginners 5. Python Language Introduction Useful Tags python programming, data scientist, machine learning, credo systemz