Beginner's Guide to Python Python Tutorials

Beginners Guide to Python 3 – Python for Loop

By on Thursday, May 13, 2021

Python for Loop

The for statement in Python is a bit different from what you usually use in other programming languages.

Rather than iterating over a numeric progression, Python’s for statement iterates over the items of any iterable (list, tuple, dictionary, set, or string). The items are iterated in the order that they appear in the iterable.

Syntax

Here’s the syntax of the for statement:

Python for Loop Syntax

Basic Examples

# Iterate through a list
colors = ['red', 'green', 'blue', 'yellow']
for x in colors:
    print(x)
# Prints red green blue yellow
# Iterate through a string
S = 'python'
for x in S:
    print(x)
# Prints p y t h o n

Break in for Loop

Python break statement is used to exit the loop immediately. It simply jumps out of the loop altogether, and the program continues after the loop.

# Break the loop at 'blue'
colors = ['red', 'green', 'blue', 'yellow']
for x in colors:
    if x == 'blue':
        break
    print(x)
# Prints red green

Continue in for Loop

The continue statement skips the current iteration of a loop and continues with the next iteration.

# Skip 'blue'
colors = ['red', 'green', 'blue', 'yellow']
for x in colors:
    if x == 'blue':
        continue
    print(x)
# Prints red green yellow

Else in for Loop

Python allows an optional else clause at the end of a for loop. The else clause will be executed if the loop terminates naturally (through exhaustion).

colors = ['red', 'green', 'blue', 'yellow']
for x in colors:
    print(x)
else:
    print('Done!')
# Prints red green blue yellow
# Prints Done!

If the loop terminates prematurely with break, the else clause won’t be executed.

colors = ['red', 'green', 'blue', 'yellow']
for x in colors:
    if x == 'blue':
        break
    print(x)
else:
    print('Done!')
# Prints red green

range() function in for loop

If you need to execute a group of statements for a specified number of times, use built-in function range().

The range(start,stop,step) function generates a sequence of numbers from 0 up to (but not including) specified number.

# Generate a sequence of numbers from 0 6
for x in range(7):
    print(x)
# Prints 0 1 2 3 4 5 6

range() provides a simple way to repeat an action a specific number of times.

# Print 'Hello!' three times
for x in range(3):
    print('Hello!')
# Prints Hello!
# Prints Hello!
# Prints Hello!

The range starts from 0 by default. But, you can start the range at another number by specifying start parameter.

# Generate a sequence of numbers from 2 to 6
for x in range(2, 7):
    print(x)
# Prints 2 3 4 5 6

You can generate a range of negative numbers as well.

for x in range(-5,0):
    print(x)
# Prints -5 -4 -3 -2 -1

The range increments by 1 by default. But, you can specify a different increment by adding a step parameter.

# Increment the range with 2
for x in range(2, 7, 2):
    print(x)
# Prints 2 4 6

Nested for Loop

A loop inside another loop is called a nested loop.

# Flatten a nested list
list = [[1, 2, 3],[4, 5, 6],[7, 8, 9]]   
for sublist in list:
    for number in sublist:
        print(number)
# Prints 1 2 3 4 5 6 7 8 9

Access Index in for Loop

To iterate over the indices of a sequence, you can combine range() and len() as follows:

colors = ['red', 'green', 'blue']
for index in range(len(colors)):
    print(index, colors[index])
# Prints 0 red
# Prints 1 green
# Prints 2 blue

However, in most such cases it is convenient to use the enumerate() function.

colors = ['red', 'green', 'blue']
for index, value in enumerate(colors):
    print(index, value)
# Prints 0 red
# Prints 1 green
# Prints 2 blue

Unpacking in a for loop

Below for loop does a multiple assignment (unpack the current tuple) each time through the loop.

# Tuple unpacking
T = [(1, 2), (3, 4), (5, 6)]
for (a, b) in T:
    print(a, b)
# Prints 1 2
# Prints 3 4
# Prints 5 6

Likewise, you can iterate through both keys and values in a dictionary.

# Dictionary unpacking
D = {'name': 'Bob', 'age': 25}
for x, y in D.items():
	print(x, y)
# Prints age 25
# Prints name Bob

Modify a List While Iterating

Don’t alter mutable objects while looping on them. It may create an infinite loop.

# infinite loop
colors = ['red', 'green', 'blue']
for x in colors:
    if x == 'red':
        colors.insert(0, 'orange')
        print(colors)

It is recommended that you first make a copy. The slicing operator makes this especially convenient.

colors = ['red', 'green', 'blue']
for x in colors[:]:
	if x == 'red':
		colors.insert(0, 'orange')
print(colors)
# Prints ['orange', 'red', 'green', 'blue']

Looping Through Multiple Lists

Using built-in zip() function you can loop through multiple lists at once.

# Loop through two lists at once
name = ['Bob', 'Sam', 'Max']
age = [25, 35, 30]
for x, y in zip(name, age):
    print(x, y)
# Prints Bob 25
# Prints Sam 35
# Prints Max 30

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
0
Related Posts