Functional Python programming :: discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads /
Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Through this book, you'll understand what fun...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Through this book, you'll understand what functional programming is all about, its impact on ... |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781788621854 1788621859 9781788627061 1788627067 |
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| 100 | 1 | |a Lott, Steven F., |e author. |0 http://id.loc.gov/authorities/names/no2016028047 | |
| 245 | 1 | 0 | |a Functional Python programming : |b discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / |c Steven F. Lott. |
| 250 | |a Second edition. | ||
| 260 | |a Birmingham, UK : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Includes index. | ||
| 520 | |a Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Through this book, you'll understand what functional programming is all about, its impact on ... | ||
| 505 | 0 | |a Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Understanding Functional Programming -- Identifying a paradigm -- Subdividing the procedural paradigm -- Using the functional paradigm -- Using a functional hybrid -- Looking at object creation -- The stack of turtles -- A classic example of functional programming -- Exploratory data analysis -- Summary -- Chapter 2: Introducing Essential Functional Concepts -- First-class functions -- Pure functions -- Higher-order functions -- Immutable data -- Strict and non-strict evaluation -- Recursion instead of an explicit loop state -- Functional type systems -- Familiar territory -- Learning some advanced concepts -- Summary -- 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 -- Using lists, dicts, and sets -- Using stateful mappings -- Using the bisect module to create a mapping -- Using stateful sets -- Summary -- 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 a simple loop -- Applying generator expressions to scalar functions -- Using any() and all() as reductions -- Using len() and sum() -- 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 reversed() to change the order. | |
| 505 | 8 | |a Using enumerate() to include a sequence number -- Summary -- 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 -- 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 -- Writing generator functions -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization -- Processing collections through recursion -- Tail-call optimization for collections -- Reductions and folding a collection from many items to one -- 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 -- Chapter 7: Additional Tuple Techniques -- Using tuples to collect data -- Using named tuples to collect data -- Building named tuples with functional constructors -- Avoiding stateful classes by using families of tuples -- Assigning statistical ranks -- Wrapping instead of state changing. | |
| 505 | 8 | |a Rewrapping instead of state changing -- Computing Spearman rank-order correlation -- Polymorphism and type-pattern matching -- Summary -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- 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() -- 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 -- Chapter 9: More Itertools Techniques -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance analysis -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Recipes -- Summary -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with lru_cache -- Defining classes with total ordering -- Defining number classes -- 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 -- Summary -- Chapter 11: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() functions -- Cross-cutting concerns -- Composite design -- Preprocessing bad data. | |
| 505 | 8 | |a Adding a parameter to a decorator -- Implementing more complex decorators -- Complex design considerations -- Summary -- Chapter 12: The Multiprocessing and Threading 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 namedtuples -- 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 -- Using the map_async(), starmap_async(), and apply_async() functions -- More complex multiprocessing architectures -- Using the concurrent.futures module -- Using concurrent.futures thread pools -- Using the threading and queue modules -- Designing concurrent processing -- Summary -- Chapter 13: Conditional Expressions and the Operator Module -- Evaluating conditional expressions -- Exploiting non-strict dictionary rules -- Filtering true conditional expressions -- Finding a matching pattern -- Using the operator module instead of lambdas -- Getting named attributes when using higher-order functions -- Starmapping with operators -- Reducing with operator module functions -- Summary -- Chapter 14: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Currying the hard way -- Functional composition and the PyMonad * operator -- Functors and applicative functors -- Using the lazy List() functor -- Monad bind() function and the>> operator -- Implementing simulation with monads -- Additional PyMonad features -- Summary. | |
| 505 | 8 | |a Chapter 15: A Functional Approach to Web Services -- The HTTP request-response model -- Injecting state through cookies -- Considering a server with a functional design -- Looking more deeply into the functional view -- Nesting the services -- The WSGI standard -- Throwing exceptions during WSGI processing -- Pragmatic WSGI applications -- Defining web services as functions -- Creating the WSGI application -- Getting raw data -- Applying a filter -- Serializing the results -- Serializing data into JSON or CSV formats -- Serializing data into XML -- Serializing data into HTML -- Tracking usage -- Summary -- Chapter 16: Optimizations and Improvements -- Memoization and caching -- Specializing memoization -- Tail recursion optimizations -- Optimizing storage -- Optimizing accuracy -- Reducing accuracy based on audience requirements -- Case study-making a chi-squared decision -- Filtering and reducing the raw data with a Counter object -- Reading summarized data -- Computing sums with a Counter object -- Computing probabilities from Counter objects -- Computing expected values and displaying a contingency table -- Computing the chi-squared value -- Computing the chi-squared threshold -- Computing the incomplete gamma function -- Computing the complete gamma function -- Computing the odds of a distribution being random -- Functional programming design patterns -- Summary -- Other Books You May Enjoy -- Index. | |
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| contents | Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Understanding Functional Programming -- Identifying a paradigm -- Subdividing the procedural paradigm -- Using the functional paradigm -- Using a functional hybrid -- Looking at object creation -- The stack of turtles -- A classic example of functional programming -- Exploratory data analysis -- Summary -- Chapter 2: Introducing Essential Functional Concepts -- First-class functions -- Pure functions -- Higher-order functions -- Immutable data -- Strict and non-strict evaluation -- Recursion instead of an explicit loop state -- Functional type systems -- Familiar territory -- Learning some advanced concepts -- Summary -- 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 -- Using lists, dicts, and sets -- Using stateful mappings -- Using the bisect module to create a mapping -- Using stateful sets -- Summary -- 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 a simple loop -- Applying generator expressions to scalar functions -- Using any() and all() as reductions -- Using len() and sum() -- 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 reversed() to change the order. Using enumerate() to include a sequence number -- Summary -- 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 -- 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 -- Writing generator functions -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization -- Processing collections through recursion -- Tail-call optimization for collections -- Reductions and folding a collection from many items to one -- 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 -- Chapter 7: Additional Tuple Techniques -- Using tuples to collect data -- Using named tuples to collect data -- Building named tuples with functional constructors -- Avoiding stateful classes by using families of tuples -- Assigning statistical ranks -- Wrapping instead of state changing. Rewrapping instead of state changing -- Computing Spearman rank-order correlation -- Polymorphism and type-pattern matching -- Summary -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- 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() -- 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 -- Chapter 9: More Itertools Techniques -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance analysis -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Recipes -- Summary -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with lru_cache -- Defining classes with total ordering -- Defining number classes -- 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 -- Summary -- Chapter 11: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() functions -- Cross-cutting concerns -- Composite design -- Preprocessing bad data. Adding a parameter to a decorator -- Implementing more complex decorators -- Complex design considerations -- Summary -- Chapter 12: The Multiprocessing and Threading 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 namedtuples -- 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 -- Using the map_async(), starmap_async(), and apply_async() functions -- More complex multiprocessing architectures -- Using the concurrent.futures module -- Using concurrent.futures thread pools -- Using the threading and queue modules -- Designing concurrent processing -- Summary -- Chapter 13: Conditional Expressions and the Operator Module -- Evaluating conditional expressions -- Exploiting non-strict dictionary rules -- Filtering true conditional expressions -- Finding a matching pattern -- Using the operator module instead of lambdas -- Getting named attributes when using higher-order functions -- Starmapping with operators -- Reducing with operator module functions -- Summary -- Chapter 14: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Currying the hard way -- Functional composition and the PyMonad * operator -- Functors and applicative functors -- Using the lazy List() functor -- Monad bind() function and the>> operator -- Implementing simulation with monads -- Additional PyMonad features -- Summary. Chapter 15: A Functional Approach to Web Services -- The HTTP request-response model -- Injecting state through cookies -- Considering a server with a functional design -- Looking more deeply into the functional view -- Nesting the services -- The WSGI standard -- Throwing exceptions during WSGI processing -- Pragmatic WSGI applications -- Defining web services as functions -- Creating the WSGI application -- Getting raw data -- Applying a filter -- Serializing the results -- Serializing data into JSON or CSV formats -- Serializing data into XML -- Serializing data into HTML -- Tracking usage -- Summary -- Chapter 16: Optimizations and Improvements -- Memoization and caching -- Specializing memoization -- Tail recursion optimizations -- Optimizing storage -- Optimizing accuracy -- Reducing accuracy based on audience requirements -- Case study-making a chi-squared decision -- Filtering and reducing the raw data with a Counter object -- Reading summarized data -- Computing sums with a Counter object -- Computing probabilities from Counter objects -- Computing expected values and displaying a contingency table -- Computing the chi-squared value -- Computing the chi-squared threshold -- Computing the incomplete gamma function -- Computing the complete gamma function -- Computing the odds of a distribution being random -- Functional programming design patterns -- Summary -- Other Books You May Enjoy -- Index. |
| ctrlnum | (OCoLC)1032707716 |
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| discipline | Informatik |
| edition | Second edition. |
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reversed() to change the order.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Using enumerate() to include a sequence number -- Summary -- 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 -- 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 -- Writing generator functions -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization -- Processing collections through recursion -- Tail-call optimization for collections -- Reductions and folding a collection from many items to one -- 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 -- Chapter 7: Additional Tuple Techniques -- Using tuples to collect data -- Using named tuples to collect data -- Building named tuples with functional constructors -- Avoiding stateful classes by using families of tuples -- Assigning statistical ranks -- Wrapping instead of state changing.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Rewrapping instead of state changing -- Computing Spearman rank-order correlation -- Polymorphism and type-pattern matching -- Summary -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- 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() -- 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 -- Chapter 9: More Itertools Techniques -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance analysis -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Recipes -- Summary -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with lru_cache -- Defining classes with total ordering -- Defining number classes -- 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 -- Summary -- Chapter 11: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() functions -- Cross-cutting concerns -- Composite design -- Preprocessing bad data.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Adding a parameter to a decorator -- Implementing more complex decorators -- Complex design considerations -- Summary -- Chapter 12: The Multiprocessing and Threading 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 namedtuples -- 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 -- Using the map_async(), starmap_async(), and apply_async() functions -- More complex multiprocessing architectures -- Using the concurrent.futures module -- Using concurrent.futures thread pools -- Using the threading and queue modules -- Designing concurrent processing -- Summary -- Chapter 13: Conditional Expressions and the Operator Module -- Evaluating conditional expressions -- Exploiting non-strict dictionary rules -- Filtering true conditional expressions -- Finding a matching pattern -- Using the operator module instead of lambdas -- Getting named attributes when using higher-order functions -- Starmapping with operators -- Reducing with operator module functions -- Summary -- Chapter 14: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Currying the hard way -- Functional composition and the PyMonad * operator -- Functors and applicative functors -- Using the lazy List() functor -- Monad bind() function and the>> operator -- Implementing simulation with monads -- Additional PyMonad features -- Summary.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 15: A Functional Approach to Web Services -- The HTTP request-response model -- Injecting state through cookies -- Considering a server with a functional design -- Looking more deeply into the functional view -- Nesting the services -- The WSGI standard -- Throwing exceptions during WSGI processing -- Pragmatic WSGI applications -- Defining web services as functions -- Creating the WSGI application -- Getting raw data -- Applying a filter -- Serializing the results -- Serializing data into JSON or CSV formats -- Serializing data into XML -- Serializing data into HTML -- Tracking usage -- Summary -- Chapter 16: Optimizations and Improvements -- Memoization and caching -- Specializing memoization -- Tail recursion optimizations -- Optimizing storage -- Optimizing accuracy -- Reducing accuracy based on audience requirements -- Case study-making a chi-squared decision -- Filtering and reducing the raw data with a Counter object -- Reading summarized data -- Computing sums with a Counter object -- Computing probabilities from Counter objects -- Computing expected values and displaying a contingency table -- Computing the chi-squared value -- Computing the chi-squared threshold -- Computing the incomplete gamma function -- Computing the complete gamma function -- Computing the odds of a distribution being random -- Functional programming design patterns -- Summary -- Other Books You May Enjoy -- Index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh96008834</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Application software</subfield><subfield code="x">Development.</subfield><subfield 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Lott, Steven.</subfield><subfield code="t">Functional Python Programming : Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads, 2nd Edition.</subfield><subfield code="d">Birmingham : Packt Publishing, ©2018</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1795477</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">BDZ0036647439</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBL - Ebook Library</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL5353672</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">1795477</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">15302009</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBA-on1032707716 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:19Z |
| institution | BVB |
| isbn | 9781788621854 1788621859 9781788627061 1788627067 |
| language | English |
| oclc_num | 1032707716 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Lott, Steven F., author. http://id.loc.gov/authorities/names/no2016028047 Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / Steven F. Lott. Second edition. Birmingham, UK : Packt Publishing, 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes index. Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Through this book, you'll understand what functional programming is all about, its impact on ... Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Understanding Functional Programming -- Identifying a paradigm -- Subdividing the procedural paradigm -- Using the functional paradigm -- Using a functional hybrid -- Looking at object creation -- The stack of turtles -- A classic example of functional programming -- Exploratory data analysis -- Summary -- Chapter 2: Introducing Essential Functional Concepts -- First-class functions -- Pure functions -- Higher-order functions -- Immutable data -- Strict and non-strict evaluation -- Recursion instead of an explicit loop state -- Functional type systems -- Familiar territory -- Learning some advanced concepts -- Summary -- 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 -- Using lists, dicts, and sets -- Using stateful mappings -- Using the bisect module to create a mapping -- Using stateful sets -- Summary -- 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 a simple loop -- Applying generator expressions to scalar functions -- Using any() and all() as reductions -- Using len() and sum() -- 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 reversed() to change the order. Using enumerate() to include a sequence number -- Summary -- 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 -- 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 -- Writing generator functions -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization -- Processing collections through recursion -- Tail-call optimization for collections -- Reductions and folding a collection from many items to one -- 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 -- Chapter 7: Additional Tuple Techniques -- Using tuples to collect data -- Using named tuples to collect data -- Building named tuples with functional constructors -- Avoiding stateful classes by using families of tuples -- Assigning statistical ranks -- Wrapping instead of state changing. Rewrapping instead of state changing -- Computing Spearman rank-order correlation -- Polymorphism and type-pattern matching -- Summary -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- 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() -- 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 -- Chapter 9: More Itertools Techniques -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance analysis -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Recipes -- Summary -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with lru_cache -- Defining classes with total ordering -- Defining number classes -- 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 -- Summary -- Chapter 11: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() functions -- Cross-cutting concerns -- Composite design -- Preprocessing bad data. Adding a parameter to a decorator -- Implementing more complex decorators -- Complex design considerations -- Summary -- Chapter 12: The Multiprocessing and Threading 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 namedtuples -- 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 -- Using the map_async(), starmap_async(), and apply_async() functions -- More complex multiprocessing architectures -- Using the concurrent.futures module -- Using concurrent.futures thread pools -- Using the threading and queue modules -- Designing concurrent processing -- Summary -- Chapter 13: Conditional Expressions and the Operator Module -- Evaluating conditional expressions -- Exploiting non-strict dictionary rules -- Filtering true conditional expressions -- Finding a matching pattern -- Using the operator module instead of lambdas -- Getting named attributes when using higher-order functions -- Starmapping with operators -- Reducing with operator module functions -- Summary -- Chapter 14: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Currying the hard way -- Functional composition and the PyMonad * operator -- Functors and applicative functors -- Using the lazy List() functor -- Monad bind() function and the>> operator -- Implementing simulation with monads -- Additional PyMonad features -- Summary. Chapter 15: A Functional Approach to Web Services -- The HTTP request-response model -- Injecting state through cookies -- Considering a server with a functional design -- Looking more deeply into the functional view -- Nesting the services -- The WSGI standard -- Throwing exceptions during WSGI processing -- Pragmatic WSGI applications -- Defining web services as functions -- Creating the WSGI application -- Getting raw data -- Applying a filter -- Serializing the results -- Serializing data into JSON or CSV formats -- Serializing data into XML -- Serializing data into HTML -- Tracking usage -- Summary -- Chapter 16: Optimizations and Improvements -- Memoization and caching -- Specializing memoization -- Tail recursion optimizations -- Optimizing storage -- Optimizing accuracy -- Reducing accuracy based on audience requirements -- Case study-making a chi-squared decision -- Filtering and reducing the raw data with a Counter object -- Reading summarized data -- Computing sums with a Counter object -- Computing probabilities from Counter objects -- Computing expected values and displaying a contingency table -- Computing the chi-squared value -- Computing the chi-squared threshold -- Computing the incomplete gamma function -- Computing the complete gamma function -- Computing the odds of a distribution being random -- Functional programming design patterns -- Summary -- Other Books You May Enjoy -- Index. Print version record. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast Electronic book. Print version: F. Lott, Steven. Functional Python Programming : Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads, 2nd Edition. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1795477 Volltext |
| spellingShingle | Lott, Steven F. Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Understanding Functional Programming -- Identifying a paradigm -- Subdividing the procedural paradigm -- Using the functional paradigm -- Using a functional hybrid -- Looking at object creation -- The stack of turtles -- A classic example of functional programming -- Exploratory data analysis -- Summary -- Chapter 2: Introducing Essential Functional Concepts -- First-class functions -- Pure functions -- Higher-order functions -- Immutable data -- Strict and non-strict evaluation -- Recursion instead of an explicit loop state -- Functional type systems -- Familiar territory -- Learning some advanced concepts -- Summary -- 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 -- Using lists, dicts, and sets -- Using stateful mappings -- Using the bisect module to create a mapping -- Using stateful sets -- Summary -- 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 a simple loop -- Applying generator expressions to scalar functions -- Using any() and all() as reductions -- Using len() and sum() -- 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 reversed() to change the order. Using enumerate() to include a sequence number -- Summary -- 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 -- 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 -- Writing generator functions -- Building higher-order functions with callables -- Assuring good functional design -- Review of some design patterns -- Summary -- Chapter 6: Recursions and Reductions -- Simple numerical recursions -- Implementing tail-call optimization -- Leaving recursion in place -- Handling difficult tail-call optimization -- Processing collections through recursion -- Tail-call optimization for collections -- Reductions and folding a collection from many items to one -- 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 -- Chapter 7: Additional Tuple Techniques -- Using tuples to collect data -- Using named tuples to collect data -- Building named tuples with functional constructors -- Avoiding stateful classes by using families of tuples -- Assigning statistical ranks -- Wrapping instead of state changing. Rewrapping instead of state changing -- Computing Spearman rank-order correlation -- Polymorphism and type-pattern matching -- Summary -- Chapter 8: The Itertools Module -- Working with the infinite iterators -- Counting with count() -- Counting with float arguments -- Re-iterating a cycle with cycle() -- 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() -- 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 -- Chapter 9: More Itertools Techniques -- Enumerating the Cartesian product -- Reducing a product -- Computing distances -- Getting all pixels and all colors -- Performance analysis -- Rearranging the problem -- Combining two transformations -- Permuting a collection of values -- Generating all combinations -- Recipes -- Summary -- Chapter 10: The Functools Module -- Function tools -- Memoizing previous results with lru_cache -- Defining classes with total ordering -- Defining number classes -- 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 -- Summary -- Chapter 11: Decorator Design Techniques -- Decorators as higher-order functions -- Using the functools update_wrapper() functions -- Cross-cutting concerns -- Composite design -- Preprocessing bad data. Adding a parameter to a decorator -- Implementing more complex decorators -- Complex design considerations -- Summary -- Chapter 12: The Multiprocessing and Threading 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 namedtuples -- 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 -- Using the map_async(), starmap_async(), and apply_async() functions -- More complex multiprocessing architectures -- Using the concurrent.futures module -- Using concurrent.futures thread pools -- Using the threading and queue modules -- Designing concurrent processing -- Summary -- Chapter 13: Conditional Expressions and the Operator Module -- Evaluating conditional expressions -- Exploiting non-strict dictionary rules -- Filtering true conditional expressions -- Finding a matching pattern -- Using the operator module instead of lambdas -- Getting named attributes when using higher-order functions -- Starmapping with operators -- Reducing with operator module functions -- Summary -- Chapter 14: The PyMonad Library -- Downloading and installing -- Functional composition and currying -- Using curried higher-order functions -- Currying the hard way -- Functional composition and the PyMonad * operator -- Functors and applicative functors -- Using the lazy List() functor -- Monad bind() function and the>> operator -- Implementing simulation with monads -- Additional PyMonad features -- Summary. Chapter 15: A Functional Approach to Web Services -- The HTTP request-response model -- Injecting state through cookies -- Considering a server with a functional design -- Looking more deeply into the functional view -- Nesting the services -- The WSGI standard -- Throwing exceptions during WSGI processing -- Pragmatic WSGI applications -- Defining web services as functions -- Creating the WSGI application -- Getting raw data -- Applying a filter -- Serializing the results -- Serializing data into JSON or CSV formats -- Serializing data into XML -- Serializing data into HTML -- Tracking usage -- Summary -- Chapter 16: Optimizations and Improvements -- Memoization and caching -- Specializing memoization -- Tail recursion optimizations -- Optimizing storage -- Optimizing accuracy -- Reducing accuracy based on audience requirements -- Case study-making a chi-squared decision -- Filtering and reducing the raw data with a Counter object -- Reading summarized data -- Computing sums with a Counter object -- Computing probabilities from Counter objects -- Computing expected values and displaying a contingency table -- Computing the chi-squared value -- Computing the chi-squared threshold -- Computing the incomplete gamma function -- Computing the complete gamma function -- Computing the odds of a distribution being random -- Functional programming design patterns -- Summary -- Other Books You May Enjoy -- Index. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / |
| title_auth | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / |
| title_exact_search | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / |
| title_full | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / Steven F. Lott. |
| title_fullStr | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / Steven F. Lott. |
| title_full_unstemmed | Functional Python programming : discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / Steven F. Lott. |
| title_short | Functional Python programming : |
| title_sort | functional python programming discover the power of functional programming generator functions lazy evaluation the built in itertools library and monads |
| title_sub | discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Application software Development. Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. Application software Development Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1795477 |
| work_keys_str_mv | AT lottstevenf functionalpythonprogrammingdiscoverthepoweroffunctionalprogramminggeneratorfunctionslazyevaluationthebuiltinitertoolslibraryandmonads |