Functional Python Programming, 3rd Edition Use a Functional Approach to Write Succinct, Expressive, and Efficient Python Code
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| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing, Limited
2022
Packt Publishing Limited |
| Edition: | 3 |
| Subjects: | |
| ISBN: | 1803232579, 9781803232577 |
| Online Access: | Get full text |
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| Author | Bánffy, Ricardo Lott, Steven F |
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| Author_xml | – sequence: 1 fullname: Lott, Steven F – sequence: 2 fullname: Bánffy, Ricardo |
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| DOI | 10.0000/9781803236568 |
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| Discipline | Computer Science |
| EISBN | 1803236566 9781803236568 |
| Edition | 3 |
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| PublicationCentury | 2000 |
| PublicationDate | 2022 [2022] |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – year: 2022 text: 2022 |
| PublicationDecade | 2020 |
| PublicationPlace | Birmingham |
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| PublicationYear | 2022 |
| Publisher | Packt Publishing, Limited Packt Publishing Limited |
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| SubjectTerms | COMPUTERS / Programming / General COMPUTERS / Programming / Object Oriented COMPUTERS / Programming Languages / Python Functional programming (Computer science) Python (Computer program language) |
| Subtitle | Use a Functional Approach to Write Succinct, Expressive, and Efficient Python Code |
| TableOfContents | Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Understanding Functional Programming -- The functional style of programming -- Comparing and contrasting procedural and functional styles -- Using the functional paradigm -- Using a functional hybrid -- The stack of turtles -- A classic example of functional programming -- Exploratory data analysis -- Summary -- Exercises -- Convert an imperative algorithm to functional code -- Convert step-wise computation to functional code -- Revise the sqrt() function -- Data cleansing steps -- (Advanced) Optimize this functional code -- Chapter 2: Introducing Essential Functional Concepts -- Functions as first-class objects -- Pure functions -- Higher-order functions -- Immutable data -- Strict and non-strict evaluation -- Lazy and eager evaluation -- Recursion instead of an explicit loop state -- Functional type systems -- Familiar territory -- Learning some advanced concepts -- Summary -- Exercises -- Apply map() to a sequence of values -- Function vs. lambda design question -- Optimize a recursion -- Chapter 3: Functions, Iterators, and Generators -- Writing pure functions -- Functions as first-class objects -- Using strings -- Using tuples and named tuples -- Using generator expressions -- Exploring the limitations of generators -- Combining generator expressions -- Cleaning raw data with generator functions -- Applying generators to built-in collections -- Generators for lists, dicts, and sets -- Using stateful mappings -- Using the bisect module to create a mapping -- Using stateful sets -- Summary -- Exercises -- Rewrite the some_function() function -- Alternative Mersenne class definition -- Alternative algorithm implementations -- Map and filter -- Dictionary comprehension -- Raw data cleanup -- Chapter 4: Working with Collections -- An overview of function varieties Working with iterables -- Parsing an XML file -- Parsing a file at a higher level -- Pairing up items from a sequence -- Using the iter() function explicitly -- Extending an iteration -- Applying generator expressions to scalar functions -- Using any() and all() as reductions -- Using len() and sum() on collections -- Using sums and counts for statistics -- Using zip() to structure and flatten sequences -- Unzipping a zipped sequence -- Flattening sequences -- Structuring flat sequences -- Structuring flat sequences - an alternative approach -- Using sorted() and reversed() to change the order -- Using enumerate() to include a sequence number -- Summary -- Exercises -- Palindromic numbers -- Hands of cards -- Replace legs() with pairwise() -- Chapter 5: Higher-Order Functions -- Using max() and min() to find extrema -- Using Python lambda forms -- Lambdas and the lambda calculus -- Using the map() function to apply a function to a collection -- Working with lambda forms and map() -- Using map() with multiple sequences -- Using the filter() function to pass or reject data -- Using filter() to identify outliers -- The iter() function with a sentinel value -- Using sorted() to put data in order -- Overview of writing higher-order functions -- Writing higher-order mappings and filters -- Unwrapping data while mapping -- Wrapping additional data while mapping -- Flattening data while mapping -- Structuring data while filtering -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Exercises -- Classification of state -- Classification of state, Part II -- Optimizing a file parser -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing manual tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization Creating pairs with pairwise()) -- Filtering with compress() -- Picking subsets with islice() -- Stateful filtering with dropwhile() and takewhile() -- Two approaches to filtering with filterfalse() and filter() -- Applying a function to data via starmap() and map() -- Cloning iterators with tee() -- The itertools recipes -- Summary -- Exercises -- Optimize the find_first() function -- Compare Chapter 4 with the itertools.pairwise() recipe -- Compare Chapter 4 with itertools.tee() recipe -- Splitting a dataset for training and testing purposes -- Rank ordering -- Chapter 9: Itertools for Combinatorics - Permutations and Combinations -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance improvements -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Combinations with replacement -- Recipes -- Summary -- Exercises -- Alternative distance computations -- Actual domain of pixel color values -- Cribbage hand scoring -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with cache -- Defining classes with total ordering -- Applying partial arguments with partial() -- Reducing sets of data with the reduce() function -- Combining map() and reduce() -- Using the reduce() and partial() functions -- Using the map() and reduce() functions to sanitize raw data -- Using the groupby() and reduce() functions -- Avoiding problems with reduce() -- Handling multiple types with singledispatch -- Summary -- Exercises -- Compare string.join() and reduce() -- Extend the comma_fix() function -- Revise the clean_sum() function -- Chapter 11: The Toolz Package -- The itertools star map function -- Reducing with operator module functions -- Using the toolz package -- Some itertoolz functions More complex multiprocessing architectures Processing collections through recursion -- Tail-call optimization for collections -- Using the assignment (sometimes called the "walrus") operator in recursions -- Reductions and folding a collection from many items to one -- Tail-call optimization using deques -- Group-by reduction from many items to fewer -- Building a mapping with Counter -- Building a mapping by sorting -- Grouping or partitioning data by key values -- Writing more general group-by reductions -- Writing higher-order reductions -- Writing file parsers -- Parsing CSV files -- Parsing plain text files with headers -- Summary -- Exercises -- Multiple recursion and caching -- Refactor the all_print() function -- Parsing CSV files -- Classification of state, Part III -- Diesel engine data -- Chapter 7: Complex Stateless Objects -- Using tuples to collect data -- Using NamedTuple to collect data -- Using frozen dataclasses to collect data -- Complicated object initialization and property computations -- Using pyrsistent to collect data -- Avoiding stateful classes by using families of tuples -- Computing Spearman's rank-order correlation -- Polymorphism and type pattern matching -- Summary -- Exercises -- Frozen dictionaries -- Dictionary-like sequences -- Revise the rank_xy() function to use native types -- Revise the rank_corr() function -- Revise the legs() function to use pyrsistent -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- Using cycle() for data sampling -- Repeating a single value with repeat() -- Using the finite iterators -- Assigning numbers with enumerate() -- Running totals with accumulate() -- Combining iterators with chain() -- Partitioning an iterator with groupby() -- Merging iterables with zip_longest() and zip() Some dicttoolz functions -- Some functoolz functions -- Summary -- Exercises -- Replace true division with a fraction -- Color file parsing -- Anscombe's quartet parsing -- Waypoint computations -- Waypoint geofence -- Callable object for the row_counter() function -- Chapter 12: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() function -- Cross-cutting concerns -- Composite design -- Preprocessing bad data -- Adding a parameter to a decorator -- Implementing more complex decorators -- Complicated design considerations -- Summary -- Exercises -- Datetime conversions -- Optimize a decorator -- None-tolerant functions -- Logging -- Dry-run check -- Chapter 13: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Functional composition with PyMonad -- Functors - making everything a function -- Using the lazy ListMonad() monad -- Monad bind() function -- Implementing simulation with monads -- Additional PyMonad features -- Summary -- Exercises -- Revise the arctangent series -- Statistical computations -- Data validation -- Multiple models -- Chapter 14: The Multiprocessing, Threading, and Concurrent.Futures Modules -- Functional programming and concurrency -- What concurrency really means -- The boundary conditions -- Sharing resources with process or threads -- Where benefits will accrue -- Using multiprocessing pools and tasks -- Processing many large files -- Parsing log files - gathering the rows -- Parsing log lines into named tuples -- Parsing additional fields of an Access object -- Filtering the access details -- Analyzing the access details -- The complete analysis process -- Using a multiprocessing pool for concurrent processing -- Using apply() to make a single request Functional Python Programming, 3rd edition: Use a functional approach to write succinct, expressive, and efficient Python code |
| Title | Functional Python Programming, 3rd Edition |
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