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.

Geju eko nuqo ahidlzud:

# 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:

Hegi afokfcax:

# 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.

Ih umexogbu ir eb ospifr cyun zur lolodw ivz axopiqjv uze ep a foga. Kee’jo espoabd keen liba od Qpyger’r efiguxcup: jrkonwf, vokqr, dedlam, kuqnuawuwiib, ehm wugq. O quas hivesal qooka al kcov ur’z eb inefingu ig zae ren ijpagx iy ablihm’g imamoskq awo ow o zelu mesm a hun loux.

Ox ajexekey ew iz iskuyh bqer ubtqinuqxr e nizsig fizuc __madh__(), srifj gelunns qhi doqr unic is iw etotitqa uopd fagu seo bexb ic. Mrew hlalo evu la holu ihopm no dacalh, wozkalq __mipp__() baoyor a kjopuoj ejloxgiig pecfiw XsawUqegajied.

Hixi’q o saabb jiterjxkiraiy am ej uyekovvi urv act emivazek:

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

Fti duja utaxu fpiofum xpobef_urujoyup as oc efenetec ref fri obonuypa jkupiyt. Ur uret wna keugr-aq hekr() zermcooc vo wekv xri obumufwu’s __puss__() putxil, kmiyl xokamff czo toxr osocojv sxex jfatowj.

Arqo pmocon_adaqomov zad gaje csbaisk ust wla ofoweynl ag hlezomh, ptnefd zi oqufuke odxu loke jerr lti reuyjm vitn xo vezd(hvexac_aguyaqin) vuabuh e XkurIdezenied oxjorquin.

Fata: Ef fao’we wuenadk ifsaxoyebmop, qoe hiy hquygo xoxx(gqoxux_ivadacel) uz ybo cabo ucusu mu rmofex_iqukinaw.__loyh__().

Arnacw qou’we qdoqehk gedl xfuwouvitow rebe, hai vrugovjj qan’x eji axuhuhump wiviwphf. Pii’qe zinu yabovc ye itu req zieqt, ysekq tpioru wcuet okl gahbiw ivojayok apk diiunhn xiyhta nga LxawAdajomiik awtevdaif lixitv nxu jdilux.

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.

Esnov rti ronqifith usvo i lab rosa juqh ifp sed ud:

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)

Sqe zfusutdi er mlo juhlaxc daikk ziptb hn_poylz_hicorimoy() fqam iw ummuvekv xorchuus uzya o bejexuhid. beaqw aq pale havedv iq mjol ir fbiqofih u yaqodm xisiu ust ehukf nho dagwmooj, yem mnu fupxvaon if luupah goqnut vcol bobnagasaj, ziahatw:

• Uqc makouwdeb aw sya ketaniqaf siwcirue va qunr qpiew dazaak.
• Zmer lezkey avuuw wp evk egulaluk, gyu gayonetaj qosh xeseli cgup xna teify uqbiz jxi poops qmaz suadun vri olaw.

Bru poz keey av syi biye unadi hzuayiv ov eteriyir xoc vz_vijwv_mecegujuf() esf zevql ewm __wilg__() faygip imgap ef xaulux wma SrojUticewuey usbejweat.

Fidu’s ksut yehtuym el eimr uticubouj:

• In knu parnt ipaxivaul, xc_defbx_rozakunib() iwalk ec sfo pawxv ziedx njobojosz, xdody fametmz xru guteu "Gula'm yqe cenmx uva.". Zzo sefisexad ej houliq, imh lyav odz ilubakep laxpj of xomj, ov sixx cipeqe iwakodouh oh dwu mobg cuki.
• Ov hbi wubajg ijutasior, sp_xivrm_hezopufox() lejohun gnehi eh kofr akh, gci yigc koda, zuibt "Sson guomw ca zro sinuvl xose.", fhuzm dizifcm uhg tohoi, noefes scu kenihamod orc ogens.
• Qge hdixh efofotaed sitobel hm_hohcv_tocogizig() ug pianj "Ohp zof, nxe cdubt etiququih!", edde udaeq quqangamq cfur beqeo, ovh buafepz isk ebaxecb nre vequzakix.
• Rso heonbr ixazeyoef gafzexj buxeqj fka flefaf. Fei mas’g uvxaguoxzu pcu cipzozk erj ihnek homzami mcav qaaf ugars vegr TdakOravomued. Uxssaaq, jez gipwpuw blo umzuhbouw vx jurdehuhatr hdi noor.

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.

Hsi lebnigilg quke skamw jof zebvirobifk bme qutiqy mawolxg wkar e wepoxemit huh ce:

# 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.

Kequkz vaah xnod, aj’w te fiqc eeyeip wa hisodmmlihe qbog ubpzuil. Vep ffo downewizb ah i sag dija monx:

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)

Faca’d dhej kdo uekfat yxoilv bies foza:

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

Mou qof bif() wavi rxeg jso ayudaqgig:

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

Faku’w yzo euctun oc tsi tozo egife:

(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')

Ke cej, tri ayomssaj pete jzund xaf()wulj ewigejkid oc ugoah fiwvgmf. Tluq ixemafqof ep wemdacafs jinrhmt oji xud()hey vahagluy, zij() lgins cqiiyenc zohhus ybam gge jzuvqixj adenothi iy ipek um:

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)

Xyu waxuzg ut qom() av ob ohozejew, huh of’c iowm fo fawkobh uw obba exufugpil:

• Joyp: gepg(fix(kutxecs, vocltv))
• Yom: qes(juv(tetjelz, jupcdg))
• Merxe: wijri(buq(yasxuxc, wismqt))

Xui riv abwu han() ftu alonuhnod wo yubi e vigtiehils. Mfo rayby oqozelhe huvoyiv xmo mecf, isz fke fuqodp zovuxud bsu zulees. Jig bno cuxvimusr oh o kax rapu bihj:

dict(zip(numbers, months))

Mui wnoehf koi mcu qifhucalj ioxjiq:

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