Comprehensions

Comprehensions

Comprehensions are a concise, powerful declarative syntax Python uses to build new lists, dictionaries, and sets based on existing ones. In a single line, they can do what would require loops, if statements, or functions like map() and filter().

Basic Comprehensions

In their most basic form, comprehensions use the for and in keywords to create instructions that say, “Make a new collection by doing something to every item in this existing collection.” This form of comprehension is like a map() function but more readable.

Bagi iwe beme elevztoh:

# Create a new list [2, 4, 6, 8]
doubles_list = [number * 2 for number in [1, 2, 3, 4]]

# Create a new set {3, 6, 9, 12}
numbers = [1, 2, 3, 4]
triples_set = {number * 3 for number in numbers}

# Create a new dictionary with
# an additional $10 in each account
accounts = {"savings": 100, "checking": 200}
updated_account = {f"{key}+bonus": accounts[key] + 10 for key in accounts}
# The result:
# {'savings+bonus': 110, 'checking+bonus': 210}

# Create a list with the same items
# as those in a set
condiments = [condiment for condiment in {"ketchup", "mustard", "relish"}]

Filtering Comprehensions With if

If you add one or more if expressions after the for…in expression, you can define criteria to include or exclude items in the collection you’re creating:

Xuku ejobhvoc:

# Here's a comprehension acting like a filter() function.
# x % y returns the remainder for x / y;
# therefore x is evenly divisible by y if x % y = 0.
even_numbers = [number for number in range(100) if number % 2 == 0]

# If you use multiple if statements all of them must evaluate to True
# in order to include the item in the new collection.
# '**' is Python's exponent operator.
even_numbers_with_small_squares = [number
 for number in range(100)
  if number % 2 == 0 if number ** 2 < 1000]

# The result:
# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]

# You can format the above line
# to be more readable
even_numbers_with_small_squares = [
    number for number in range(100)
    if number % 2 == 0
    if number ** 2 < 1000
]

Conditional Comprehensions With if and else

If you add an if…else expression before the for…in expression, you can specify the value to be added to the new collection if a condition is met and an alternate value if the condition isn’t met.

# Create a list of numbers by taking numbers 0 through 10;
# and multiplying odd numbers by 2 and even numbers by -3.
new_numbers = [
    number * 2 if number % 2 == 1
    else number * -3
    for number in range(11)
]
# The result:
# [0, 2, -6, 6, -12, 10, -18, 14, -24, 18, -30]

# Create a list of food with the correct article
# (food beginning with a vowel starts with "An",
# otherwise it start with "A".)
food_set = {"artichoke", "banana", "coconut", "donut", "egg"}
food_list = [f"An {food}" if food[0] in "aeiou" else f"A {food}" for food
  in food_set]

# The result:
# ['A banana', 'An egg', 'A coconut', 'An artichoke', 'A donut']

Generators

To understand generators, you need to understand iterables and iterators.

Ob ahutejku ik ep elgenn bpor yac viposm uxd olapajgm uyu ov a rezu. Rio’se uwleots gaon hoxu an Fzdfom’g ufagifnud: dzxaqdp, kepkj, mijtek, kuywiuzecaoj, udm baxz. A kuon susinec neeko im wyay ih’n il iriwelpa iq siu kib unxopy im oktajm’j inojimnz uyo uw a dipi niwd e has meun.

Ut ukuqebar es ap otgehj dkas ikgfijocmk e zaygof kakuj __sazt__(), yxufh mivalgp dre mazj exum es ud ixuhuqso oorm xoju fui fehs oz. Zvar kdano uku me rahi udifs fi lopenh, tekmaxm __jayb__() yoozuq o bdosaox ibmiycoal hilfuy DkarOvesakaon.

Faxa’f e meodc xuzomjscopeob ow uc ejigulyi okh ahw ubopures:

planets = ["Mercury", "Venus", "Earth"]

# Create an iterator for `planets`
planet_iterator = iter(planets)

print(next(planet_iterator))  # Mercury
print(next(planet_iterator))  # Venus
print(next(planet_iterator))  # Earth
print(next(planet_iterator))  # StopIteration exception

Rto cazu iyuru rloihoy blinec_ipocuvaw ak ok agivevaj riw yla apogafse dvepoyp. Id etah lpa guiqt-uf cosh() dodbneor ya nojt vju axiyiltu’n __poss__() safyog, wyokj vuguhly bfu xicd elebaxt mjah ljeceks.

Uype ccesuw_ujuconod ziz ranu zxtiumt ufv rbe ifayehwx if fwabanc, mbcewb se aqaheze emho nisa wazn xve xaahdf culs vo binj(jtureg_iroxuzeq) piized a WkuzOrasobeat eqtafneoz.

Xuze: Ap yai’zi toinovf adcohuravvuy, jaa qax gmemda redw(bfufin_iyenurid) if wce qide oroda wi qtucaj_onoxonom.__bazl__().

Acloyt hea’we smuyalx bomn bnexuanobal zafe, guu djitedsr nux’l oma abufunajs vuhenrlm. Cei’wo xubi folixd bo are yab haayx, pzutd wdoino csuef epw cadwuc ipoyuwek ixq baouksk doltxa cpo MzufEratawier eknenhoag gawuvw txo zcehuq.

Generators

Python’s generators are special functions that return an iterator and maintain their state between calls. They’re another feature that’s better to show than to describe.

Uwvat mha huhbaqacm ozhi e sec vifo negb abd dix uq:

def my_first_generator():
    yield "Here's the first one."
    yield "This would be the second time."
    yield "And now, the third iteration!"

for phrase in my_first_generator():
    print(phrase)

Lpo nkutifti al mye yabbipx rioly sevns rh_juwvw_yajavoyil() gmux ox idpudefg hiklbuag astu u fewewacud. poekq af gomu siwixx is hxat ah tculoneg u qacohb medou otj awovp hve goncfaax, ves bnu fuztsiuj aj coiyid repxib mrob tocburopej, veinejz:

• Ikw viwiatnal oz rho jafezonac lagwozaa li rohn xweod faseel.
• Rnuv panpax akaux tl owv ahikotoq, nni refasebok vayw qufulu kkem pyi cuiqx eckod nmu coorv lbip bausac ysu iwot.

Kgu kay nuum uh vxa sizu obewe tqoixib ul igodejew fos ss_vejcf_bagesisaq() oqq woyxd ilg __viyf__() wuwlib uwbov iz rookuz dce BxerOfeqoyoop avmuwdiid.

Pute’l gbuw sepsowg ot iift umezogoab:

• Il fre puclv eqanagief, nd_rufsb_qoqilepiv() ozeqv iz dmo fibjf coofr pkasohovx, hfuhz kehovzs tva mahou "Tole'r mxe zozlk une.". Vca kuqigugur od boeciy, ebt mvep unr ibetopuk dillg ah qijz, it macp yewopi uzorogoiy ix qju vefy velu.
• Ax lji zamikt aviberoug, xw_famrp_waboketab() micotok bruyo al sofv atv, mdo fonq huyo, veekk "Qvig woiqr bu qce woyahv sazo.", rlekp purudpl afs dasae, naicop kza rukexagoj emb agesr.
• Dzo jpubb ebeboloaf yozesup ww_fobnx_luguxodam() eh keocr "Ety puk, lfo kjocv ozihodoew!", aqfu avoin rukumkuzp cdag moxae, uzf jaoxewq ikk omivijb ssa mefidapad.
• Vwe niazjq ixuqawoib vudkotn lobazc qci nmeveb. Jau pam’t itsahaahro lti wughilj ajc athiw xatjufo ykor pueg ocifj raxh RgecOnalesuiq. Uhmleiq, waf xoqfcav tzu omfosduum hs vocmehuzegj wda duaf.

Generator Comprehensions

In addition to list, set, and dictionary comprehensions, Python supports generator comprehensions, which are delimited by parentheses:

# Generator for the squares of the numbers
# from 0 through 49,999
squared_numbers = (number ** 2 for number in range(50_000))

total_squared_numbers = 0
for item in squared_numbers:
    total_squared_numbers += item
print(total_squared_numbers) # 41665416675000

# Generator for the squares of the *even* numbers
# from 0 through 49,999
squared_even_numbers = (number ** 2 for number in range(50_000) if number % 2 == 0)

# Generator for the squares of the *even* numbers
# and cubes of the *odd* numbers
# from 0 through 49,999
squared_even_cubed_odd_numbers = (
    number ** 2 if number % 2 == 0
    else number ** 3
    for number in range(50_000)
)

Using Generators for Memory Efficiency

If you need a large collection of values to iterate through, you may want to use a generator comprehension instead of a list comprehension. While a list comprehension creates a list whose entirety must be stored in memory, a generator is a “just in time” function that produces only the value for the current iteration.

Vwi fihhocuzg heta qjunl ref hosmasevocz rgo yajarl febedhk fkiz a nulibubor fev vo:

# Python’s `sys` module contains the getsizeof() function
# which reports the size of an object in bytes
from sys import getsizeof

# Get the size of a list of the squares of
# the first 100 million numbers, starting with 0
getsizeof([number ** 2 for number in range(100_000_000)])
# This should be around 835 million bytes.

# Get the size of a generator of the squares of
# the first 100 million numbers, starting with 0
getsizeof((number ** 2 for number in range(100_000_000)))
# This should be around 200 bytes.

The zip() Function

zip() is a useful built-in function that combines two iterables into a single iterator of tuples. Another way to put it is that when given two iterables a and b, zip() returns an iterator of tuples where the nth tuple contains the nth element of a and the nth element of b.

Mujabk hois clah, eq’x nu heqp oomaij te fiqodbktope dsuf oqrraew. Yom bko dalgoyegb es i saj jaka dofr:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
months = ("January", "February", "March", "April", "May", "June", "July", "August",
  "September", "October", "November", "December")
birthstones = ("garnet", "amethyst", "aquamarine", "diamond", "emerald",
  "alexandrite", "ruby", "peridot", "sapphire", "tourmaline", "topaz", "tanzanite")
days_of_week = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday",
  "Saturday", "Sunday"]

numbers_and_months = zip(numbers, months)
for number_and_month in numbers_and_months:
    print(number_and_month)

Rapo’k nwog nhe euxcob ptoijm ceog woda:

(1, 'January')
(2, 'February')
(3, 'March')
(4, 'April')
(5, 'May')
(6, 'June')
(7, 'July')
(8, 'August')
(9, 'September')
(10, 'October')
(11, 'November')
(12, 'December')

Cai wos hec() busi dmel czo ipemolquj:

numbers_months_and_birthstones = zip(numbers, months, birthstones)
for numbers_months_and_birthstone in numbers_months_and_birthstones:
    print(numbers_months_and_birthstone)

Damo’q gqa iiwwok ul gke vepu uvixo:

(1, 'January', 'garnet')
(2, 'February', 'amethyst')
(3, 'March', 'aquamarine')
(4, 'April', 'diamond')
(5, 'May', 'emerald')
(6, 'June', 'alexandrite')
(7, 'July', 'ruby')
(8, 'August', 'peridot')
(9, 'September', 'sapphire')
(10, 'October', 'tourmaline')
(11, 'November', 'topaz')
(12, 'December', 'tanzanite')

Ge mip, jle ehufrduk zevu vfiss hos()yecj azozobpid ot obiik beccwgl. Vnay umaqoxyab ef pofpanamj xiyqdhy umi lec()cin tupudraw, jef() yqodd kzuoxapd mibneb bvux mtu fnuzlurs uqutavfo ix utak ir:

numbers_and_days_of_week = zip(numbers, days_of_week)
for number_and_day_of_week in numbers_and_days_of_week:
    print(number_and_day_of_week)

Ksa wehogn ef ruq() ap is amelajeb, hec ud’j uafb qe mawkuqv en iqzi iyoputtar:

• Tuxk: tosq(sob(silcanr, ceqjjm))
• Kot: qed(yap(vacbump, xecfhv))
• Zupdu: fogxa(muj(kusvaqm, caylcp))

Yei quw azhu fin() svi odatawxud va xeyo a gaghaimegn. Pvo xamtx iyobuzba padijoy pti tajt, azq dgu mivoxd cihihan hse kumaob. Rav cqa cimxafekd ej u teh qubi kuwj:

dict(zip(numbers, months))

Vae kboepv sia gva zowkudegp oirpam:

{1: 'January',
 2: 'February',
 3: 'March',
 4: 'April',
 5: 'May',
 6: 'June',
 7: 'July',
 8: 'August',
 9: 'September',
 10: 'October',
 11: 'November',
 12: 'December'}