Functional Programming in Python
Functional Programming in Python
About the Book
Functional Programming in Python
A Practical Guide to Writing Clean and Efficient Code
This book introduces functional programming concepts using Python, helping developers write clearer, more maintainable, and more predictable code.
You'll learn how to:
- Use core functional tools like
map(),filter(),reduce(), and comprehensions - Work with immutable data and pure functions
- Apply modules like
functoolsanditertoolseffectively - Structure programs using a functional approach
- Improve testability and reduce side effects in your code
Whether you're looking to adopt a functional style for specific parts of your projects or gain a deeper understanding of programming paradigms, this book offers practical examples and explanations tailored to Python programmers.
Functional Programming in Python is ideal for developers with some Python experience who want to write more reliable and modular code.
About the Author
Table of Contents
- Functional programming in Python
- Programming Paradigms
- Functional programming
- Iterators (Iterables)
- string
- list
- tuple
- filehandle
- range
- range
- range with 3 parameters
- range with 2 parameters
- range with 1 parameter
- range index and len
- filehandle index and len
- len
- index
- range with list
- range vs. list size using getsizeof
- for loop with transformation
- map
- map with list
- size of map
- map delaying function call
- map on many values
- double with lambda
- What is lambda in Python?
- lambda returning tuple
- map returning tuples
- lambda with two parameters
- map for more than one iterable
- map on uneven lists
- replace None (for Python 2)
- map on uneven lists - fixed (for Python 2)
- map mixed iterators
- map fetch value from dictionary
- Exercise: string to length
- Exercise: row to length
- Exercise: compare rows
- Solution: string to length
- Solution: row to length
- Solution: compare rows
- filter in a loop
- filter
- filter with lambda
- filter - map example
- filter - map in one expression
- filter a dictionary using dict comprehension
- Get indices of values
- reduce
- reduce empty list
- reduce with default
- reduce list of dictionaries
- zip
- Combining two lists using zip
- Creating dictionary from two lists using zip
- all, any
- Compare elements of list with scalar
- List comprehension - double
- List comprehension - simple expression
- List generator
- List comprehension
- Dict comprehension
- Lookup table with lambda
- Read lines without newlines
- Read key-value pairs
- Create index-to-value mapping in a dictionary based on a list of values
- Exercise: min, max, factorial
- Exercise: Prime numbers
- Exercise: Many validator functions
- Exercise: Calculator using lookup table
- Exercise: parse file
- Solution: min, max, factorial
- Solution: Prime numbers
- Solution: Many validator functions
- Solution: Calculator using lookup table
- map with condtion
- map with lambda
- map with lambda with condition
- List comprehension - complex
- Change list before iteration over map object
- Replace list before iteration over map object
- Iterators - with and without Itertools
- Advantages of iterators and generators
- The Fibonacci research institute
- Fibonacci plain
- Fibonacci copy-paste
- Iterators Glossary
- What are iterators and iterables?
- A file-handle is an iterator
- range is iterable but it is not an iterator
- Range with floating point steps
- Iterator: a counter
- Using iterator
- Iterator without temporary variable
- The type of the iterator
- Using iterator with next
- Mixing for and next
- Iterable which is not an iterator
- Iterator returning multiple values
- Range-like iterator
- Unbound or infinite iterator
- Unbound iterator Fibonacci
- Operations on Unbound iterator
- itertools
- itertools - count
- itertools - cycle
- Exercise: iterators - reimplement the range function
- Exercise: iterators - cycle
- Exercise: iterators - alter
- Exercise: iterators - limit Fibonacci
- Exercise: iterators - Fibonacci less memory
- Exercise: read char
- Exercise: read section
- Exercise: collect packets
- Exercise: compare files
- Solution: iterators - limit Fibonacci
- Solution: iterators - Fibonacci less memory
- Solution: read section
- Solution: compare files
- Solution: collect packets
- Generators and Generator Expressions
- Generators Glossary
- Iterators vs Generators
- List comprehension and Generator Expression
- List comprehension vs Generator Expression - less memory
- List comprehension vs Generator Expression - lazy evaluation
- Generator: function with yield - call next
- Generators - call next
- Generator with yield
- Generators - fixed counter
- Generators - counter
- Generators - counter with parameter
- Generators - my_range
- Fibonacci - generator
- Infinite series
- Integers
- Integers + 3
- Integers + Integers
- Filtered Fibonacci
- The series.py
- generator - unbound count (with yield)
- iterator - cycle
- Exercise: Alternator
- Exercise: Prime number generator
- Exercise: generator
- Exercise: Binary file reader
- Exercise: File reader with records
- yield outside of functions
- Operations on infinite lists
- Infinite random number generator
- Infinite random set generator
- Simple function (before generators)
- Filtered Fibonacci with ifilter
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