List comprehensions and Generator Expressions

List comprehensions and Readibility



In [1]:

    
symbols = '$¢£¥€Ω'



In [2]:

    
codes = []



In [3]:

    
for symbol in symbols:
    codes.append(ord(symbol))



In [4]:

    
codes









    Out[4]:





[36, 162, 163, 165, 8364, 937]



In [5]:

    
codes = [ord(symbol) for symbol in symbols]



In [6]:

    
codes









    Out[6]:





[36, 162, 163, 165, 8364, 937]

Listcomps Versus map and filter



In [ ]:

    
symbols = '$¢£¥€Ω'



In [7]:

    
beyond_ascii = [ord(s) for s in symbols if ord(s) > 127]



In [8]:

    
beyond_ascii









    Out[8]:





[162, 163, 165, 8364, 937]



In [9]:

    
beyond_ascii = list(filter(lambda c: c > 127, map(ord, symbols)))



In [10]:

    
beyond_ascii









    Out[10]:





[162, 163, 165, 8364, 937]



In [14]:

    
list(filter(lambda c: c > 127, list(map(ord, symbols))))









    Out[14]:





[162, 163, 165, 8364, 937]

Carsesian Products



In [1]:

    
colors = ['black', 'white']



In [2]:

    
sizes = ['S', 'M', 'L']



In [3]:

    
tshirts = [(color, size) for color in colors for size in sizes]



In [4]:

    
tshirts









    Out[4]:





[('black', 'S'),
 ('black', 'M'),
 ('black', 'L'),
 ('white', 'S'),
 ('white', 'M'),
 ('white', 'L')]



In [5]:

    
for color in colors:
    for size in sizes:
        print((color, size))









    



('black', 'S')
('black', 'M')
('black', 'L')
('white', 'S')
('white', 'M')
('white', 'L')



In [6]:

    
tshirts = [(color, size) for size in sizes for color in colors]



In [7]:

    
tshirts









    Out[7]:





[('black', 'S'),
 ('white', 'S'),
 ('black', 'M'),
 ('white', 'M'),
 ('black', 'L'),
 ('white', 'L')]

Generator Expressions



In [11]:

    
symbols = '$¢£¥€' + chr(164)



In [12]:

    
tuple(ord(symbol) for symbol in symbols)









    Out[12]:





(36, 162, 163, 165, 8364, 164)



In [13]:

    
import array



In [15]:

    
array.array('I', (ord(symbol) for symbol in symbols))









    Out[15]:





array('I', [36, 162, 163, 165, 8364, 164])



In [16]:

    
colors = ['black','white']



In [17]:

    
sizes = ['S','M','L']



In [21]:

    
for tshirt in ('%s %s' % (c, s) for c in colors for s in sizes):
    print(tshirt)









    



black S
black M
black L
white S
white M
white L

Tuples Are Not Just Immutable Lists

Tuples as Records



In [22]:

    
lax_coordinates = (33.9425, -118.408056)



In [23]:

    
city, year, pop, chg, area = ('Tokyo',2003, 32450, 0.66, 8014)



In [24]:

    
traveler_ids = [('USA','31195855'), ('BRA', 'CE342567'),('ESP','XDA205856')]



In [25]:

    
for passport in sorted(traveler_ids):
    print('%s/%s' % passport)









    



BRA/CE342567
ESP/XDA205856
USA/31195855



In [27]:

    
for country, _ in traveler_ids:
    print(country)









    



USA
BRA
ESP

Tuple Unpacking



In [28]:

    
lax_coordinates = (33.9425, -118.408056)



In [29]:

    
latitude, longitude = lax_coordinates



In [30]:

    
latitude









    Out[30]:





33.9425



In [31]:

    
longitude









    Out[31]:





-118.408056



In [32]:

    
divmod(20,8)









    Out[32]:





(2, 4)



In [33]:

    
t = (20, 8)



In [34]:

    
divmod(*t)









    Out[34]:





(2, 4)



In [35]:

    
quotient, remainder = divmod(*t)



In [36]:

    
quotient, remainder









    Out[36]:





(2, 4)



In [37]:

    
import os



In [38]:

    
_, filename = os.path.split('C:/Users/langestrst01/.ssh/id_rsa.pub')



In [39]:

    
filename









    Out[39]:





'id_rsa.pub'



In [40]:

    
os.path.split('C:/Users/langestrst01/.ssh/id_rsa.pub')









    Out[40]:





('C:/Users/langestrst01/.ssh', 'id_rsa.pub')



In [41]:

    
a, b, *rest = range(5)



In [42]:

    
a, b, rest









    Out[42]:





(0, 1, [2, 3, 4])



In [46]:

    
a,b, *rest = range(3)



In [48]:

    
a, b, rest









    Out[48]:





(0, 1, [2])



In [49]:

    
a, b, *rest = range(2)



In [50]:

    
a, b, rest









    Out[50]:





(0, 1, [])



In [51]:

    
a, *body, c, d = range(5)



In [52]:

    
a, body, c, d









    Out[52]:





(0, [1, 2], 3, 4)



In [53]:

    
*head, b, c, d = range(5)



In [54]:

    
head, b, c, d









    Out[54]:





([0, 1], 2, 3, 4)

Nested tuple Unpacking



In [80]:

    
metro_areas = [
    ('Tokyo', 'JP', 36.933, (35.689722, 139.691667)),
    ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),
    ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),
    ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),
    ('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833)),
]



In [81]:

    
print('{:15} | {:^9} | {:^9}'.format('', 'lat.', 'long.'))
fmt = '{:15} | {:9.4f} | {:9.4f}'
for name, cc, pop, (latitude, longitude) in metro_areas:
    if longitude <= 0:
        print(fmt.format(name, latitude, longitude))









    



                |   lat.    |   long.  
Mexico City     |   19.4333 |  -99.1333
New York-Newark |   40.8086 |  -74.0204
Sao Paulo       |  -23.5478 |  -46.6358

Named Tuples



In [82]:

    
from collections import namedtuple



In [83]:

    
City = namedtuple('City','name country population coordinates')



In [84]:

    
tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667))



In [85]:

    
tokyo









    Out[85]:





City(name='Tokyo', country='JP', population=36.933, coordinates=(35.689722, 139.691667))



In [86]:

    
tokyo.population









    Out[86]:





36.933



In [87]:

    
tokyo.coordinates









    Out[87]:





(35.689722, 139.691667)



In [88]:

    
tokyo[1]









    Out[88]:





'JP'



In [89]:

    
City._fields









    Out[89]:





('name', 'country', 'population', 'coordinates')



In [90]:

    
LatLong = namedtuple('LatLong', 'lat long')



In [91]:

    
delhi_data = ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889))



In [92]:

    
delhi = City._make(delhi_data)



In [93]:

    
delhi._asdict()









    Out[93]:





OrderedDict([('name', 'Delhi NCR'),
             ('country', 'IN'),
             ('population', 21.935),
             ('coordinates', (28.613889, 77.208889))])



In [97]:

    
for key, value in delhi._asdict().items():
    print(key + ':',value)









    



name: Delhi NCR
country: IN
population: 21.935
coordinates: (28.613889, 77.208889)



In [95]:

    
delhi









    Out[95]:





City(name='Delhi NCR', country='IN', population=21.935, coordinates=(28.613889, 77.208889))

Slicing

Why Slices and Range Exclude the Last Item



In [6]:

    
l = [10, 20, 30, 40, 50, 60]



In [7]:

    
l[:2]









    Out[7]:





[10, 20]



In [8]:

    
l[2:]









    Out[8]:





[30, 40, 50, 60]



In [9]:

    
l[:3]









    Out[9]:





[10, 20, 30]



In [10]:

    
l[3:]









    Out[10]:





[40, 50, 60]

Slice Objects



In [11]:

    
s = 'bicycle'



In [12]:

    
s[::3]









    Out[12]:





'bye'



In [13]:

    
s[::1]









    Out[13]:





'bicycle'



In [15]:

    
s[::-2]









    Out[15]:





'eccb'



In [14]:

    
invoice = """
0.....6.................................40...........52..55..........
1909  Pimoroni PiBrella                      $17.50      3      $52.50
1489  6mm Tactile Switch x20                  $4.95      2       $9.90
1510  Panavise Jr. - PV-201                  $28.00      1      $28.00
1601  PiTFT Mini Kit 320x240                 $34.95      1      $34.95
"""



In [2]:

    
invoice









    Out[2]:





'\n0.....6.................................40...........52..55..........\n1909 Pimoroni PiBrella                      $17.50      3      $52.50\n1489 6mm Tactile Switch x20                  $4.95      2       $9.90\n1510 Panavise Jr. - PV-201                  $28.00      1      $28.00\n1601 PiTFT Mini Kit 320x240                 $34.95      1      $34.95\n'



In [3]:

    
SKU = slice(0,6)



In [4]:

    
SKU









    Out[4]:





slice(0, 6, None)



In [5]:

    
DESCRIPTION = slice(6,40)



In [6]:

    
UNIT_PRICE = slice(52,55)



In [7]:

    
QUANTITY = slice(55, 55)



In [8]:

    
ITEM_TOTAL = slice(55, None)



In [15]:

    
line_items = invoice.split('\n')[2:]



In [16]:

    
line_items









    Out[16]:





['1909  Pimoroni PiBrella                      $17.50      3      $52.50',
 '1489  6mm Tactile Switch x20                  $4.95      2       $9.90',
 '1510  Panavise Jr. - PV-201                  $28.00      1      $28.00',
 '1601  PiTFT Mini Kit 320x240                 $34.95      1      $34.95',
 '']



In [17]:

    
for item in line_items:
    print(item[UNIT_PRICE], item[DESCRIPTION])









    



    Pimoroni PiBrella                 
    6mm Tactile Switch x20            
    Panavise Jr. - PV-201             
    PiTFT Mini Kit 320x240

Assigning to Slices



In [18]:

    
l = list(range(10))



In [19]:

    
l









    Out[19]:





[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]



In [22]:

    
l[2:5] = [20,30]



In [23]:

    
l









    Out[23]:





[0, 1, 20, 30, 5, 6, 7, 8, 9]



In [25]:

    
del l[5:7]



In [26]:

    
l









    Out[26]:





[0, 1, 20, 30, 5, 8, 9]



In [28]:

    
l[3::2] = [11,22]



In [29]:

    
l









    Out[29]:





[0, 1, 20, 11, 5, 22, 9]



In [30]:

    
l[2:5] = 100









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-30-e500d102d83b> in <module>()
----> 1 l[2:5] = 100

TypeError: can only assign an iterable



In [31]:

    
l[2:5] = [100]



In [32]:

    
l









    Out[32]:





[0, 1, 100, 22, 9]

Using + and * with Sequences



In [33]:

    
l = [1,2,3]



In [34]:

    
l * 5









    Out[34]:





[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]



In [35]:

    
5 * 'abcd'









    Out[35]:





'abcdabcdabcdabcdabcd'

Building Lists of Lists



In [36]:

    
board = [['_'] * 3 for i in range(3)]



In [37]:

    
board









    Out[37]:





[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]



In [38]:

    
board[1][2] = 'X'



In [39]:

    
board









    Out[39]:





[['_', '_', '_'], ['_', '_', 'X'], ['_', '_', '_']]



In [40]:

    
weird_board = [['_'] * 3] * 3



In [41]:

    
weird_board









    Out[41]:





[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]



In [42]:

    
weird_board[1][2] = '0'



In [43]:

    
weird_board









    Out[43]:





[['_', '_', '0'], ['_', '_', '0'], ['_', '_', '0']]



In [57]:

    
row = ['_'] * 3
board = []
for i in range(3):
    board.append(row)



In [58]:

    
board









    Out[58]:





[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]



In [59]:

    
board[2][0] = 'X'



In [60]:

    
board









    Out[60]:





[['X', '_', '_'], ['X', '_', '_'], ['X', '_', '_']]



In [53]:

    
board = []
for i in range(3):
    row = ['_'] * 3
    board.append(row)



In [54]:

    
board









    Out[54]:





[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]



In [55]:

    
board[2][0] = 'X'



In [56]:

    
board









    Out[56]:





[['_', '_', '_'], ['_', '_', '_'], ['X', '_', '_']]

Augmented Assignment with Sequences



In [1]:

    
l = [1, 2, 3]



In [2]:

    
id(l)









    Out[2]:





744635471112



In [3]:

    
l *=2



In [4]:

    
l









    Out[4]:





[1, 2, 3, 1, 2, 3]



In [5]:

    
id(l)









    Out[5]:





744635471112



In [6]:

    
t = (1, 2, 3)



In [7]:

    
id(t)









    Out[7]:





744650701704



In [8]:

    
t *= 2



In [9]:

    
t









    Out[9]:





(1, 2, 3, 1, 2, 3)



In [10]:

    
id(t)









    Out[10]:





744648070824

A += Assignment Puzzler



In [11]:

    
t = (1, 2, [30, 40])



In [15]:

    
t[2] += [50, 60]









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-9f190ddb433c> in <module>()
----> 1 t[2] += [50, 60]

TypeError: 'tuple' object does not support item assignment



In [16]:

    
t









    Out[16]:





(1, 2, [30, 40, 50, 60])



In [22]:

    
import dis
dis.dis('s[a] += b')









    



  1           0 LOAD_NAME                0 (s)
              3 LOAD_NAME                1 (a)
              6 DUP_TOP_TWO
              7 BINARY_SUBSCR
              8 LOAD_NAME                2 (b)
             11 INPLACE_ADD
             12 ROT_THREE
             13 STORE_SUBSCR
             14 LOAD_CONST               0 (None)
             17 RETURN_VALUE

list.sort and the sorted Built-In Function



In [23]:

    
fruits = ['grape', 'raspberry', 'apple', 'banana']



In [24]:

    
sorted(fruits)









    Out[24]:





['apple', 'banana', 'grape', 'raspberry']



In [25]:

    
fruits









    Out[25]:





['grape', 'raspberry', 'apple', 'banana']



In [26]:

    
sorted(fruits, reverse = True)









    Out[26]:





['raspberry', 'grape', 'banana', 'apple']



In [27]:

    
sorted(fruits, key=len)









    Out[27]:





['grape', 'apple', 'banana', 'raspberry']



In [28]:

    
sorted(fruits, key=len, reverse=True)









    Out[28]:





['raspberry', 'banana', 'grape', 'apple']



In [29]:

    
fruits









    Out[29]:





['grape', 'raspberry', 'apple', 'banana']



In [30]:

    
fruits.sort()



In [31]:

    
fruits









    Out[31]:





['apple', 'banana', 'grape', 'raspberry']

Managing Ordered Sequences with bisect

Searching with bisect



In [32]:

    
import bisect
import sys



In [33]:

    
HAYSTACK = [1, 4, 5, 6, 8, 12, 15, 20, 21, 23, 26, 29, 30]
NEEDLES = [0, 1, 2, 5, 8, 10, 22, 23, 29, 30, 31]



In [60]:

    
ROW_FMT = '{0:2d} @ {1:2d}   {2}{0:<2d}'



In [53]:

    
def demo(bisect_fn):
    for needle in reversed(NEEDLES):
        position = bisect_fn(HAYSTACK, needle)
        offset = position * '  |'
        print(ROW_FMT.format(needle, position, offset))



In [66]:

    
bisect_fn = bisect.bisect
print('DEMO:', bisect_fn.__name__)
print('haystack->', ' '.join('%2d' % n for n in HAYSTACK))
demo(bisect_fn)









    



DEMO: bisect
haystack->  1  4  5  6  8 12 15 20 21 23 26 29 30
31 @ 13     |  |  |  |  |  |  |  |  |  |  |  |  |31
30 @ 13     |  |  |  |  |  |  |  |  |  |  |  |  |30
29 @ 12     |  |  |  |  |  |  |  |  |  |  |  |29
23 @ 10     |  |  |  |  |  |  |  |  |  |23
22 @  9     |  |  |  |  |  |  |  |  |22
10 @  5     |  |  |  |  |10
 8 @  5     |  |  |  |  |8 
 5 @  3     |  |  |5 
 2 @  1     |2 
 1 @  1     |1 
 0 @  0   0



In [65]:

    
bisect_fn = bisect.bisect_left
print('DEMO:', bisect_fn.__name__)
print('haystack->', ' '.join('%2d' % n for n in HAYSTACK))
demo(bisect_fn)









    



DEMO: bisect_left
haystack->  1  4  5  6  8 12 15 20 21 23 26 29 30
31 @ 13     |  |  |  |  |  |  |  |  |  |  |  |  |31
30 @ 12     |  |  |  |  |  |  |  |  |  |  |  |30
29 @ 11     |  |  |  |  |  |  |  |  |  |  |29
23 @  9     |  |  |  |  |  |  |  |  |23
22 @  9     |  |  |  |  |  |  |  |  |22
10 @  5     |  |  |  |  |10
 8 @  4     |  |  |  |8 
 5 @  2     |  |5 
 2 @  1     |2 
 1 @  0   1 
 0 @  0   0



In [67]:

    
def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
    i = bisect.bisect(breakpoints, score)
    return grades[i]



In [68]:

    
[grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]









    Out[68]:





['F', 'A', 'C', 'C', 'B', 'A', 'A']

Inserting with bisect.insort



In [86]:

    
import bisect
import random



In [87]:

    
SIZE = 7



In [88]:

    
random.seed(1729)



In [89]:

    
my_list =[]
for i in range(SIZE):
    new_item = random.randrange(SIZE*2)
    bisect.insort(my_list, new_item)
    print('%2d ->' % new_item, my_list )









    



10 -> [10]
 0 -> [0, 10]
 6 -> [0, 6, 10]
 8 -> [0, 6, 8, 10]
 7 -> [0, 6, 7, 8, 10]
 2 -> [0, 2, 6, 7, 8, 10]
10 -> [0, 2, 6, 7, 8, 10, 10]

When a List is Not the Answer

Arrays



In [33]:

    
from array import array
from random import random



In [34]:

    
floats = array('d', (random() for i in range(10**7)))



In [35]:

    
floats[-1]









    Out[35]:





0.5451360798336022



In [36]:

    
fp = open('floats.bin', 'wb')



In [37]:

    
floats.tofile(fp)



In [38]:

    
fp.close()



In [7]:

    
floats2 = array('d')



In [8]:

    
fp = open('floats.bin','rb')



In [9]:

    
floats2.fromfile(fp,10**7)



In [10]:

    
fp.close()



In [11]:

    
floats2[-1]









    Out[11]:





0.2495075550059842



In [12]:

    
floats2 == floats









    Out[12]:





True

Memory Views



In [2]:

    
import array
numbers = array.array('h', [-2, -1, 0, 1, 2])



In [3]:

    
memv = memoryview(numbers)



In [4]:

    
len(memv)









    Out[4]:





5



In [5]:

    
memv[0]









    Out[5]:





-2



In [7]:

    
memv_oct = memv.cast('B')



In [8]:

    
memv_oct.tolist()









    Out[8]:





[254, 255, 255, 255, 0, 0, 1, 0, 2, 0]



In [10]:

    
memv_oct[5] = 4



In [11]:

    
numbers









    Out[11]:





array('h', [-2, -1, 1024, 1, 2])

NumPy and SciPy



In [15]:

    
import numpy



In [16]:

    
a = numpy.arange(12)



In [17]:

    
a









    Out[17]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])



In [18]:

    
type(a)









    Out[18]:





numpy.ndarray



In [19]:

    
a.shape









    Out[19]:





(12,)



In [20]:

    
a.shape = 3, 4



In [21]:

    
a









    Out[21]:





array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])



In [22]:

    
a[2]









    Out[22]:





array([ 8,  9, 10, 11])



In [23]:

    
a[2,1]









    Out[23]:





9



In [24]:

    
a[:,1]









    Out[24]:





array([1, 5, 9])



In [25]:

    
a.transpose()









    Out[25]:





array([[ 0,  4,  8],
       [ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11]])



In [59]:

    
floats = array('d')
fp = open('floats.bin','rb')
floats.fromfile(fp,10**6)
fp.close()



In [60]:

    
floats = numpy.asarray(floats)



In [61]:

    
floats









    Out[61]:





array([ 0.63800449,  0.43174904,  0.72977767, ...,  0.78848915,
        0.62317728,  0.05762697])



In [62]:

    
floats[-3:]









    Out[62]:





array([ 0.78848915,  0.62317728,  0.05762697])



In [63]:

    
floats *= .5



In [64]:

    
floats[-3:]









    Out[64]:





array([ 0.39424457,  0.31158864,  0.02881349])



In [65]:

    
from time import perf_counter as pc



In [66]:

    
t0 = pc(); floats /=3; pc() - t0









    Out[66]:





0.0030416026502280147



In [67]:

    
numpy.save('floats-10M', floats)









    



---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-67-d5a03c5ab968> in <module>()
----> 1 numpy.save('floats-10M', floats)

c:\users\langestrst01\appdata\local\continuum\anaconda3\envs\fluentpy\lib\site-packages\numpy\lib\npyio.py in save(file, arr, allow_pickle, fix_imports)
    475         if not file.endswith('.npy'):
    476             file = file + '.npy'
--> 477         fid = open(file, "wb")
    478         own_fid = True
    479     else:

OSError: [Errno 22] Invalid argument: 'floats-10M.npy'



In [52]:

    
floats2 = numpy.load('floats-10M.npy', 'r+')



In [53]:

    
floats2 *= 6



In [54]:

    
floats2[-3:]









    Out[54]:





memmap([ 1.5769783 ,  1.24635457,  0.11525394])

Deques and Other Queues



In [70]:

    
from collections import deque



In [71]:

    
dq = deque(range(10), maxlen=10)



In [72]:

    
dq









    Out[72]:





deque([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])



In [73]:

    
dq.rotate(3)



In [74]:

    
dq









    Out[74]:





deque([7, 8, 9, 0, 1, 2, 3, 4, 5, 6])



In [75]:

    
dq.rotate(-4)



In [76]:

    
dq









    Out[76]:





deque([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])



In [77]:

    
dq.appendleft(-1)



In [78]:

    
dq









    Out[78]:





deque([-1, 1, 2, 3, 4, 5, 6, 7, 8, 9])



In [79]:

    
dq.extend([11, 22, 33])



In [80]:

    
dq









    Out[80]:





deque([3, 4, 5, 6, 7, 8, 9, 11, 22, 33])



In [81]:

    
dq.extendleft([10, 20, 30, 40])



In [82]:

    
dq









    Out[82]:





deque([40, 30, 20, 10, 3, 4, 5, 6, 7, 8])



In [ ]: