Download E-books Fluent Python PDF

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By Luciano Ramalho

Python’s simplicity enables you to turn into efficient quick, yet this usually capacity you aren’t utilizing every little thing it has to supply. With this hands-on advisor, you’ll the right way to write powerful, idiomatic Python code via leveraging its best—and in all likelihood such a lot neglected—features. writer Luciano Ramalho takes you thru Python’s center language beneficial properties and libraries, and indicates you ways to make your code shorter, quicker, and extra readable on the comparable time.

Many skilled programmers attempt to bend Python to slot styles they discovered from different languages, and not observe Python beneficial properties open air in their adventure. With this publication, these Python programmers will completely how you can turn into trained in Python 3.

This ebook covers:

  • Python info model: know how distinct equipment are the most important to the constant habit of objects
  • Data structures: take complete benefit of integrated forms, and comprehend the textual content vs bytes duality within the Unicode age
  • Functions as objects: view Python features as top notch items, and know how this impacts renowned layout patterns
  • Object-oriented idioms: construct periods via studying approximately references, mutability, interfaces, operator overloading, and a number of inheritance
  • Control flow: leverage context managers, turbines, coroutines, and concurrency with the concurrent.futures and asyncio packages
  • Metaprogramming: know the way homes, characteristic descriptors, type decorators, and metaclasses work

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933, (35. 689722, 139. 691667)) ('Mexico City', 'MX', 20. 142, (19. 433333, -99. 133333)) ('New York-Newark', 'US', 20. 104, (40. 808611, -74. 020386)) should you cross a number of index arguments to itemgetter, the functionality it builds will go back tuples with the extracted values: >>> cc_name = itemgetter(1, zero) >>> for urban in metro_data: ... print(cc_name(city)) ... ('JP', 'Tokyo') ('IN', 'Delhi NCR') ('MX', 'Mexico City') ('US', 'New York-Newark') ('BR', 'Sao Paulo') >>> simply because itemgetter makes use of the [] operator, it helps not just sequences but in addition map‐ pings and any classification that implements __getitem__. A sibling of itemgetter is attrgetter, which creates features to extract item at‐ tributes by means of identify. if you happen to cross attrgetter numerous characteristic names as arguments, it additionally returns a tuple of values. furthermore, if any argument identify encompasses a . (dot), attrget ter navigates via nested gadgets to retrieve the characteristic. those behaviors are proven in instance 5-24. this isn't the shortest console consultation simply because we have to construct a nested constitution to show off the dealing with of dotted attributes through attrgetter. instance 5-24. Demo of attrgetter to strategy a formerly outlined checklist of namedtuple known as metro_data (the comparable checklist that looks in instance 5-23) >>> from collections import namedtuple >>> LatLong = namedtuple('LatLong', 'lat long') # >>> city = namedtuple('Metropolis', 'name cc pop coord') # programs for practical Programming | 157 >>> metro_areas = [Metropolis(name, cc, pop, LatLong(lat, long)) # ... for identify, cc, pop, (lat, lengthy) in metro_data] >>> metro_areas[0] Metropolis(name='Tokyo', cc='JP', pop=36. 933, coord=LatLong(lat=35. 689722, long=139. 691667)) >>> metro_areas[0]. coord. lat # 35. 689722 >>> from operator import attrgetter >>> name_lat = attrgetter('name', 'coord. lat') # >>> >>> for urban in sorted(metro_areas, key=attrgetter('coord. lat')): # ... print(name_lat(city)) # ... ('Sao Paulo', -23. 547778) ('Mexico City', 19. 433333) ('Delhi NCR', 28. 613889) ('Tokyo', 35. 689722) ('New York-Newark', forty. 808611) Use namedtuple to outline LatLong. additionally outline city. construct metro_areas checklist with city circumstances; be aware the nested tuple unpacking to extract (lat, lengthy) and use them to construct the LatLong for the coord characteristic of city. succeed in into aspect metro_areas[0] to get its range. outline an attrgetter to retrieve the identify and the coord. lat nested characteristic. Use attrgetter back to variety checklist of towns via range. Use the attrgetter outlined in to teach in basic terms urban identify and range. here's a partial checklist of features outlined in operator (names beginning with _ are passed over, simply because they're ordinarily implementation details): >>> [name for identify in dir(operator) if now not identify. startswith('_')] ['abs', 'add', 'and_', 'attrgetter', 'concat', 'contains', 'countOf', 'delitem', 'eq', 'floordiv', 'ge', 'getitem', 'gt', 'iadd', 'iand', 'iconcat', 'ifloordiv', 'ilshift', 'imod', 'imul', 'index', 'indexOf', 'inv', 'invert', 'ior', 'ipow', 'irshift', 'is_', 'is_not', 'isub', 'itemgetter', 'itruediv', 'ixor', 'le', 'length_hint', 'lshift', 'lt', 'methodcaller', 'mod', 'mul', 'ne', 'neg', 'not_', 'or_', 'pos', 'pow', 'rshift', 'setitem', 'sub', 'truediv', 'truth', 'xor'] lots of the fifty two names indexed are self-evident.

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