A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.
As you already know, Python gives you many built-in functions like print(), etc. but you can also create your own functions. These functions are called user-defined functions.

Table of Contents

Defining a 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.
• 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

Calling a Function

Defining a function only gives it a name, specifies the parameters that are to be included in the function and structures the blocks of code.
Once the basic structure of a function is finalized, you can execute it by calling it from another function or directly from the Python prompt. Following is the example to call printme() function −


#!/usr/bin/python

# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print str
return;

# Now you can call printme function
printme("I'm first call to user defined function!")
printme("Again second call to the same function")

When the above code is executed, it produces the following result −
I’m first call to user defined function!
Again second call to the same function

Pass by reference vs value

All parameters (arguments) in the Python language are passed by reference. It means if you change what a parameter refers to within a function, the change also reflects back in the calling function. For example −


#!/usr/bin/python

# Function definition is here
def changeme( mylist ):
"This changes a passed list into this function"
mylist.append([1,2,3,4]);
print "Values inside the function: ", mylist
return

# Now you can call changeme function
mylist = [10,20,30];
changeme( mylist );
print "Values outside the function: ", mylist

Here, we are maintaining reference of the passed object and appending values in the same object. So, this would produce the following result −
Values inside the function: [10, 20, 30, [1, 2, 3, 4]]
Values outside the function: [10, 20, 30, [1, 2, 3, 4]]
There is one more example where argument is being passed by reference and the reference is being overwritten inside the called function.


#!/usr/bin/python

# Function definition is here
def changeme( mylist ):
"This changes a passed list into this function"
mylist = [1,2,3,4]; # This would assig new reference in mylist
print "Values inside the function: ", mylist
return

# Now you can call changeme function
mylist = [10,20,30];
changeme( mylist );
print "Values outside the function: ", mylist

The parameter mylist is local to the function changeme. Changing mylist within the function does not affect mylist. The function accomplishes nothing and finally this would produce the following result −
Values inside the function: [1, 2, 3, 4]
Values outside the function: [10, 20, 30]

Function Arguments

You can call a function by using the following types of formal arguments −
• Required arguments
• Keyword arguments
• Default arguments
• Variable-length arguments

Required arguments

Required arguments are the arguments passed to a function in correct positional order. Here, the number of arguments in the function call should match exactly with the function definition.
To call the function printme(), you definitely need to pass one argument, otherwise it gives a syntax error as follows −


#!/usr/bin/python

# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print str
return;

# Now you can call printme function
printme()

When the above code is executed, it produces the following result −
Traceback (most recent call last):
File “test.py”, line 11, in <module>
printme();
TypeError: printme() takes exactly 1 argument (0 given)

Keyword arguments

Keyword arguments are related to the function calls. When you use keyword arguments in a function call, the caller identifies the arguments by the parameter name.
This allows you to skip arguments or place them out of order because the Python interpreter is able to use the keywords provided to match the values with parameters. You can also make keyword calls to the printme() function in the following ways −


#!/usr/bin/python

# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print str
return;

# Now you can call printme function
printme( str = "My string")
When the above code is executed, it produces the following result −
My string
The following example gives more clear picture. Note that the order of parameters does not matter.

#!/usr/bin/python

# Function definition is here
def printinfo( name, age ):
"This prints a passed info into this function"
print "Name: ", name
print "Age ", age
return;

# Now you can call printinfo function
printinfo( age=50, name="miki" )

When the above code is executed, it produces the following result −
Name: miki
Age 50

Default arguments

A default argument is an argument that assumes a default value if a value is not provided in the function call for that argument. The following example gives an idea on default arguments, it prints default age if it is not passed −


#!/usr/bin/python

# Function definition is here
def printinfo( name, age = 35 ):
"This prints a passed info into this function"
print "Name: ", name
print "Age ", age
return;

# Now you can call printinfo function
printinfo( age=50, name="miki" )
printinfo( name="miki" )

When the above code is executed, it produces the following result −
Name: miki
Age 50
Name: miki
Age 35

Variable-length arguments

You may need to process a function for more arguments than you specified while defining the function. These arguments are called variable-length arguments and are not named in the function definition, unlike required and default arguments.
Syntax for a function with non-keyword variable arguments is this −

def functionname([formal_args,] *var_args_tuple ):
"function_docstring"
function_suite
return [expression]

An asterisk (*) is placed before the variable name that holds the values of all nonkeyword variable arguments. This tuple remains empty if no additional arguments are specified during the function call. Following is a simple example −


#!/usr/bin/python

# Function definition is here
def printinfo( arg1, *vartuple ):
"This prints a variable passed arguments"
print "Output is: "
print arg1
for var in vartuple:
print var
return;

# Now you can call printinfo function
printinfo( 10 )
printinfo( 70, 60, 50 )

When the above code is executed, it produces the following result −
Output is:
10
Output is:
70
60
50

The Anonymous Functions

These functions are called anonymous because they are not declared in the standard manner by using the def keyword. You can use the lambda keyword to create small anonymous functions.
• Lambda forms can take any number of arguments but return just one value in the form of an expression. They cannot contain commands or multiple expressions.
• An anonymous function cannot be a direct call to print because lambda requires an expression
• Lambda functions have their own local namespace and cannot access variables other than those in their parameter list and those in the global namespace.
• Although it appears that lambda’s are a one-line version of a function, they are not equivalent to inline statements in C or C++, whose purpose is by passing function stack allocation during invocation for performance reasons.

Syntax

The syntax of lambda functions contains only a single statement, which is as follows −
lambda [arg1 [,arg2,…..argn]]:expression
Following is the example to show how lambda form of function works −


#!/usr/bin/python

# Function definition is here
sum = lambda arg1, arg2: arg1 + arg2;

# Now you can call sum as a function
print "Value of total : ", sum( 10, 20 )
print "Value of total : ", sum( 20, 20 )

When the above code is executed, it produces the following result −
Value of total : 30
Value of total : 40

The return Statement

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.
All the above examples are not returning any value. You can return a value from a function as follows −


#!/usr/bin/python

# Function definition is here
def sum( arg1, arg2 ):
# Add both the parameters and return them."
total = arg1 + arg2
print "Inside the function : ", total
return total;

# Now you can call sum function
total = sum( 10, 20 );
print "Outside the function : ", total

When the above code is executed, it produces the following result −
Inside the function : 30
Outside the function : 30

Scope of Variables

All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable.
The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python −
• Global variables
• Local variables

Global vs. Local variables

Variables that are defined inside a function body have a local scope, and those defined outside have a global scope.
This means that local variables can be accessed only inside the function in which they are declared, whereas global variables can be accessed throughout the program body by all functions. When you call a function, the variables declared inside it are brought into scope. Following is a simple example −


#!/usr/bin/python

total = 0; # This is global variable.
# Function definition is here
def sum( arg1, arg2 ):
# Add both the parameters and return them."
total = arg1 + arg2; # Here total is local variable.
print "Inside the function local total : ", total
return total;

# Now you can call sum function
sum( 10, 20 );
print "Outside the function global total : ", total

When the above code is executed, it produces the following result −
Inside the function local total : 30
Outside the function global total : 0

So, this brings us to the end of blog. This Tecklearn ‘Functions in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Python Programming domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period.

Python with Data Science Training

About the Course

Python with Data Science training lets you master the concepts of the widely used and powerful programming language, Python. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, NumPy, Matplotlib which are essential for Data Science. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.

Why Should you take Python with Data Science Training?

• Python is the preferred language for new technologies such as Data Science and Machine Learning.
• Average salary of Python Certified Developer is $123,656 per annum – Indeed.com
• Python is by far the most popular language for data science. Python held 65.6% of the data science market.

What you will Learn in this Course?

Introduction to Python

• Define Python
• Understand the need for Programming
• Know why to choose Python over other languages
• Setup Python environment
• Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
• Illustrate String formatting
• Understand Command Line Parameters and Flow control

Python Environment Setup and Essentials

• Python installation
• Windows, Mac & Linux distribution for Anaconda Python
• Deploying Python IDE
• Basic Python commands, data types, variables, keywords and more

Python language Basic Constructs

• Looping in Python
• Data Structures: List, Tuple, Dictionary, Set
• First Python program
• Write a Python Function (with and without parameters)
• Create a member function and a variable
• Tuple
• Dictionary
• Set and Frozen Set
• Lambda function

OOP (Object Oriented Programming) in Python

• Object-Oriented Concepts

Working with Modules, Handling Exceptions and File Handling

• Standard Libraries
• Modules Used in Python (OS, Sys, Date and Time etc.)
• The Import statements
• Module search path
• Package installation ways
• Errors and Exception Handling
• Handling multiple exceptions

Introduction to NumPy

• Introduction to arrays and matrices
• Indexing of array, datatypes, broadcasting of array math
• Standard deviation, Conditional probability
• Correlation and covariance
• NumPy Exercise Solution

Introduction to Pandas

• Pandas for data analysis and machine learning
• Pandas for data analysis and machine learning Continued
• Time series analysis
• Linear regression
• Logistic Regression
• ROC Curve
• Neural Network Implementation
• K Means Clustering Method

Data Visualisation

• Matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots – bar graphs, pie charts, histograms
• Contour plots

Data Manipulation

• Perform function manipulations on Data objects
• Perform Concatenation, Merging and Joining on DataFrames
• Iterate through DataFrames
• Explore Datasets and extract insights from it

Scikit-Learn for Natural Language Processing

• What is natural language processing, working with NLP on text data
• Scikit-Learn for Natural Language Processing
• The Scikit-Learn machine learning algorithms
• Sentimental Analysis – Twitter

Introduction to Python for Hadoop

• Deploying Python coding for MapReduce jobs on Hadoop framework.
• Python for Apache Spark coding
• Deploying Spark code with Python
• Machine learning library of Spark MLlib
• Deploying Spark MLlib for Classification, Clustering and Regression

Got a question for us? Please mention it in the comments section and we will get back to you.

342