{"id":1929,"date":"2019-06-17T14:27:37","date_gmt":"2019-06-17T14:27:37","guid":{"rendered":"http:\/\/wiki.thomasandsofia.com\/?p=1929"},"modified":"2019-06-18T02:23:57","modified_gmt":"2019-06-18T02:23:57","slug":"section-15-advanced-python-modules","status":"publish","type":"post","link":"https:\/\/wiki.thomasandsofia.com\/?p=1929","title":{"rendered":"Section 15: Advanced Python Modules"},"content":{"rendered":"

< Section 14 | Section 16 ><\/a><\/p>\n

Collections Module<\/h1>\n

https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3512826?start=0#questions<\/a><\/p>\n

The collections module is a built in module that implements specialized container datatypes providing alternatives to python’s general purpose build-in containers.\u00a0 We’ve already discussed several of the basics: dict, list, set and tuple<\/p>\n

Counter<\/h2>\n

Counter counts the number of time an item appears in a list.<\/p>\n

Counter is a dict subclass which helps count hashable objects.\u00a0 Inside of it elements are stored as dictionary keys and the counts of the objects are stored as the value<\/p>\n

from collections import Counter\r\nfrom collections import Counter\r\nmylist = [1,1,1,1,2,2,3,3,3,4,4,2,2,5,1,5,2,5,4,6,6,7]\r\nCounter(mylist)<\/pre>\n
Counter({1: 5, 2: 5, 3: 3, 4: 3, 5: 3, 6: 2, 7: 1})<\/p>\n
s = 'Mississippi'\r\nCounter(s)<\/pre>\n
Counter({‘M’: 1, ‘i’: 4, ‘s’: 4, ‘p’: 2})<\/p>\n
Count the word repititions in a sentence<\/h3>\n
s = \"How many Up times does times show times uP in show this UP sentence\"\r\nCounter(s.lower().split()<\/pre>\n
Counter({‘how’: 1,
\n‘many’: 1,
\n‘up’: 3,
\n‘times’: 3,
\n‘does’: 1,
\n‘show’: 2,
\n‘in’: 1,
\n‘this’: 1,
\n‘sentence’: 1})<\/p>\n
Common methods<\/h3>\n
s = \"How many Up times does times show times uP in show this UP sentence\"\r\nwords = s.lower().split()\r\nc = Counter(words)<\/pre>\n
.most_common(n)<\/h4>\n
c.most_common(2)<\/pre>\n
[(‘up’, 3), (‘times’, 3)]<\/p>\n
To get the Least Common elements:<\/b>
\n.most_common(:-n-1:-1)
\nHint: Use a negative step<\/p>\n
c.most_common()[:-2-1:-1]<\/pre>\n
[(‘sentence’, 1), (‘this’, 1)]<\/p>\n
List()<\/h4>\n
Shows unique elements<\/p>\n
list(c)<\/pre>\n
[‘how’, ‘many’, ‘up’, ‘times’, ‘does’, ‘show’, ‘in’, ‘this’, ‘sentence’]<\/p>\n
sum(.values())<\/h4>\n
Total of all counts<\/p>\n
sum(c.values())<\/pre>\n
14<\/p>\n
defaultdict<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3512824#questions<\/a><\/p>\n
defaultdict is a dictionary like oibject which provides all methods provided by dictionary, but takes first argument (default_factory) as default data type for the dictionary.\u00a0 using defaultdict is faster than doing the same using dict.set_default method.<\/p>\n
A defaultdict will never raise a KeyError.\u00a0 Any key that does not exist gets the value returned by the default factory.<\/p>\n
from collections import defaultdict\r\nd = {}\r\nd['one']<\/pre>\n
KeyError: ‘one’<\/p>\n
d = defaultdict(object)\r\nd['one']\r\nfor item in d:\r\n    print(item)<\/pre>\n
one<\/p>\n
Assign default values to 0 using lambda<\/h3>\n
d = defaultdict(lambda: 0)\r\nd['two]<\/pre>\n
0<\/p>\n
d['three']=3\r\nd['three']<\/pre>\n
3<\/p>\n
Ordered Dictionaries OrderedDict<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3779906#questions<\/a><\/p>\n
Standard dict object do not retain a specific order<\/p>\n
d = {}\r\nd['a']=1\r\nd['b']=2\r\nd['c']=3\r\nd['d']=4\r\nd['e']=5\r\nfor k,v in d.items():\r\n    print( k,v)<\/pre>\n
a 1
\nb 2
\ne 5
\nd 4
\nc 3<\/p>\n
from collections import OrderedDict\r\nd = orderedDict()\r\nd['a']=1\r\nd['b']=2\r\nd['c']=3\r\nd['d']=4\r\nd['e']=5\r\nfor k,v in d.items():\r\n    print( k,v)<\/pre>\n
a 1
\nb 2
\nc 3
\nd 4
\ne 5<\/p>\n
With OrderedDict, order is important<\/h3>\n
d1={'a':1, 'b':2}\r\nd2={'b':2, 'a':1}\r\nd1 == d2<\/pre>\n
True
\nthis would fail with OrderedDict, because the order of the keys would not match.<\/b><\/p>\n
namedtuple<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3512830#questions<\/a><\/p>\n
These are similar to creating classes with both named and indexed values.<\/p>\n
Named values are sent as a string with spaces between each key value.<\/p>\n
from collections import namedtuple\r\nDog = namedtuple('Dog','age breed name')\r\nsam = Dog(age=2, breed='Lab', name='Sammy')\r\nprint(sam.breed)\r\nprint(sam[0])<\/pre>\n
Lab
\n2<\/p>\n
Datetime<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3547908#questions<\/a><\/p>\n
datetime.time<\/h2>\n
import datetime\r\n#datetime.time( hours, minutes, seconds, microseconds)\r\nt = datetime.time(5, 25, 1)\r\nprint(t.hour)\r\nprint(t.minute)\r\nprint(t.second)\r\nprint(t.microsecond)\r\nprint(t.resolution<\/pre>\n
5
\n25
\n1
\n0
\n0:00:00.000001<\/p>\n
datetime.date<\/h2>\n
today = datetime.date.today()\r\nprint(today)<\/pre>\n
2019-06-17<\/p>\n
today.timetuple()<\/pre>\n
time.struct_time(tm_year=2019, tm_mon=6, tm_mday=17, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=0, tm_yday=168, tm_isdst=-1)<\/p>\n
d1 = datetime.date(2019, 6, 17)\r\nd2 = d1.replace(year = 2018)\r\nprint(d2)<\/pre>\n
2018-06-17<\/p>\n
Math on Dates<\/h3>\n
lastyear = today.replace(year = 2018)\r\nprint(today - lastyear)<\/pre>\n
365 days, 0:00:00<\/p>\n
Python Debugger pdb<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3547912#questions<\/a><\/p>\n
Tool good for tracing through your code and being able to see where errors occur.<\/p>\n
import pdb\r\nx = [1,3,5]\r\ny = 4\r\nz = 7\r\nresult = y +z\r\nprint(result)\r\nresult2 = x + y\r\nprint(result2)<\/pre>\n
11
\nTypeError: can only concatenate list (not “int”) to list<\/p>\n
Modify your code to stop before the error point and perform some checking
\nTo stop, press ‘Q’<\/b><\/p>\n
import pdb\r\nx = [1,3,5]\r\ny = 4\r\nz = 7\r\nresult = y +z\r\nprint(result)\r\npdb.set_trace()\r\nresult2 = x + y\r\nprint(result2)<\/pre>\n
11<\/p>\n
(Pdb) x<\/pre>\n
[1, 3, 5]<\/p>\n
Timing your Code<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3547914#questions<\/a><\/p>\n
This is useful for checking small sections of code for optimizing.\u00a0 Running these small snipits multiple times will provide more accurate results than a single iteration.<\/p>\n
The command is passed to the function as a string.<\/b><\/p>\n
import timeit\r\n# How long does it take to create the string: '0-1-2-3...99'\r\ntimeit.timeit('\"-\".join(str(n) for n in range(100))', number = 10000)<\/pre>\n
0.6442793710000387<\/p>\n
# as a list comprehension\r\ntimeit.timeit('\"-\".join([str(n) for n in range(100)])', number=10000)<\/pre>\n
0.5642282049999494<\/p>\n
# as a map function\r\ntimeit.timeit('\"-\".join(map(str, range(100)))', number=10000)<\/pre>\n
0.3922121389999802<\/p>\n
To use Jupiter Notebooks built in magic fucntion<\/h2>\n
%timeit \"-\".join(str(n) for n in range(100))<\/pre>\n
61.9 \u00b5s \u00b1 1.04 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)<\/p>\n
%timeit \"-\".join(map(str, range(100)))<\/pre>\n
39.4 \u00b5s \u00b1 1.28 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)<\/p>\n
Regular Expressions – re<\/h1>\n
https:\/\/www.udemy.com\/complete-python-bootcamp\/learn\/lecture\/3547904#questions<\/a><\/p>\n
These are a common interview question!<\/p>\n
Regular expressions are text matching patters used for<\/p>\n