Python Algorithms
for practicing python
Coding Philosophies
You aren’t meant to master a Programming Language in the beginning. You’re meant to go build them
You don’t master a programming concept, you apply them in numerous project. then you continuously improve on them
SOLID
Strings
Unicodes
using ord() returns an integer representing the Unicode character
character = 'P'
# find unicode of P
unicode_char = ord(character)
print(unicode_char)
# Output: 80
The chr() method converts an integer to its unicode character and returns it.
print(chr(97))
# Output: a
print(chr(98))
# Output: b
Array Slicing
a[start:stop] # items start through stop-1
a[start:] # items start through the rest of the array
a[:stop] # items from the beginning through stop-1
a[:] # a copy of the whole array
There is also the step value, which can be used with any of the above:
a[start:stop:step] # start through not past stop, by step
The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).
The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:
a[-1] # last item in the array
a[-2:] # last two items in the array
a[:-2] # everything except the last two items
Similarly, step may be a negative number:
a[::-1] # all items in the array, reversed
a[1::-1] # the first two items, reversed
a[:-3:-1] # the last two items, reversed
a[-3::-1] # everything except the last two items, reversed
Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.
** Relationship with the slice object **.
A slice object can represent a slicing operation, i.e.:
a[start:stop:step]
is equivalent to:
a[slice(start, stop, step)]
Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].
more reference at: https://stackoverflow.com/questions/509211/understanding-slicing
List Comprehensions
** Basic syntax: **
new_list = [expression for_loop_one_or_more conditions ]Example:
Find squares of a number using for loop.
numbers = [1, 2, 3, 4]
squares = []
for n in numbers:
squares.append(n**2)
print(squares) # Output: [1, 4, 9, 16]Finding squares using list comprehensions
numbers = [1, 2, 3, 4]
squares = [n**2 for n in numbers]
print(squares) # Output: [1, 4, 9, 16]List comprehension more complex example
Find common numbers from two list using for loop.
Normal way :
list_a = [1, 2, 3, 4]
list_b = [2, 3, 4, 5]
common_num = []
for a in list_a:
for b in list_b:
if a == b:
common_num.append(a)
print(common_num) # Output [2, 3, 4]List Comprehension:
list_a = [1, 2, 3, 4]
list_b = [2, 3, 4, 5]
common_num = [a for a in list_a for b in list_b if a == b]
print(common_num) # Output: [2, 3, 4]List comprehensions can also be used to iterate over strings as shown below:
list_a = ["Hello", "World", "In", "Python"]
small_list_a = [str.lower() for str in list_a]
print(small_list_a) # Output: ['hello', 'world', 'in', 'python']List comprehension with IF only
a = ["a","A","b", "B"]
b = [i for i in a if i.islower() ]
print(b)
# [ "a", "b"] List comprehension with IF ELSE
a = ["a","A","b", "B"]
b = [i if i.islower() else 0 for i in a ]
print(b)
# [ "a", 0, "b", 0] Extract number from strings
string = "Hello 12345 World "
numbers = [x for x in string if x.isdigit()]
print(numbers) # Output ['1', '2', '3', '4', '5']MATH Operators
Division
/ → Floating point division
// → Floor division
Let’s see some examples in both Python 2.7 and in Python 3.5.
Python 2.7.10 vs. Python 3.5
print (2/3) ----> 0 Python 2.7
print (2/3) ----> 0.6666666666666666 Python 3.5
Python 2.7.10 vs. Python 3.5
print (4/2) ----> 2 Python 2.7
print (4/2) ----> 2.0 Python 3.5
Now if you want to have (in Python 2.7) the same output as in Python 3.5, you can do the following:
Python 2.7.10
from __future__ import division
print (2/3) ----> 0.6666666666666666 # Python 2.7
print (4/2) ----> 2.0 # Python 2.7
Whereas there isn't any difference between floor division in both Python 2.7 and in Python 3.5.
138.93//3 ---> 46.0 # Python 2.7
138.93//3 ---> 46.0 # Python 3.5
4//3 ---> 1 # Python 2.7
4//3 ---> 1 # Python 3.5