curry

Curries a function.

Use functools.partial() to return a new partial object which behaves like fn with the given arguments, args, partially applied.

CODE:

from functools import partial

def curry(fn, *args):
return partial(fn, *args)

Examples:

add = lambda x, y: x + y
add10 = curry(add, 10)
add10(20) # 30

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#Basics

Return Multiple Values From Functions.

Code:

def x():
    return 1, 2, 3, 4
a, b, c, d = x()

Input:
print(a, b, c, d)

Output:
1 2 3 4

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#Basics

Find The Most Frequent Value In A List

Code:

test = [1, 2, 3, 4, 2, 2, 3, 1, 4, 4, 4]
print(max(set(test), key = test.count))

Output:
4
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#Basics

Check The Memory Usage Of An Object.

Code:

import sys
x = 1
print(sys.getsizeof(x))

Output:
28

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#Basics

Checking if two words are anagrams

Code:

from collections import Counter
def is_anagram(str1, str2):
     return Counter(str1) == Counter(str2)
  
# or without having to import anything 
def is_anagram(str1, str2): 
    return sorted(str1) == sorted(str2) 

print(is_anagram('code', 'doce'))
print(is_anagram('python', 'yton'))

Output:
True
False

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#Basics

zip() function

When we need to join many iterator objects like lists to get a single list we can use the zip function. The result shows each item to be grouped with their respective items from the other lists.

Example:

Year = (1999, 2003, 2011, 2017)
Month = ("Mar", "Jun", "Jan", "Dec")
Day = (11,21,13,5)
print zip(Year,Month,Day)

Output:
[(1999, 'Mar', 11), (2003, 'Jun', 21), (2011, 'Jan', 13), (2017, 'Dec', 5)]

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#Basics

Transpose a Matrix

Transposing a matrix involves converting columns into rows. In python we can achieve it by designing some loop structure to iterate through the elements in the matrix and change their places or we can use the following script involving zip function in conjunction with the * operator to unzip a list which becomes a transpose of the given matrix.

Example:

x = [[31,17],
[40 ,51],
[13 ,12]]
print (zip(*x))

Output:
[(31, 40, 13), (17, 51, 12)]

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#Basics

The _ Operator

The _ operator might be something that you might not have heard of. Here _ is the output of the last executed expression. Let’s check how it works.


Example:

>>> 2+ 3
5
>>> _ # the _ operator, it will return the output of the last executed statement.
>>> 5

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#Basics

Swap keys and values of a dictionary

dictionary = {"a": 1, "b": 2, "c": 3}

reversed_dictionary = {j: i for i, j in dictionary.items()}

print(reversed)

Output:
{1: 'a', 2: 'b', 3: 'c'}

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#Basics

Condition inside the print function

def is_positive(number):
print("Positive" if number > 0 else "Negative")


is_positive(-3)

Output:

Negative

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#Basics

Convert a value into a complex number

print(complex(10, 2)) 

Output:
(10+2j)

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Message to this account

Username: @Ping_TG

#numpy

NumPy

Broadcasting


Broadcasting describes how NumPy automatically brings two arrays with different shapes to a compatible shape during arithmetic operations. Generally, the smaller array is “repeated” multiple times until both arrays have the same shape. Broadcasting is memory-efficient as it doesn’t actually copy the smaller array multiple times.

Code:

import numpy as np

A = np.array([1, 2, 3])
res = A * 3 # scalar is broadcasted to [3 3 3]
print(res)

Output:
# [3 6 9]

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#numpy

NumPy

Smart use of ‘:’ to extract the right shape


Sometimes you encounter a 3-dim array that is of shape (N, T, D), while your function requires a shape of (N, D). At a time like this, reshape() will do more harm than good, so you are left with one simple solution:

Example:

for t in xrange(T):
x[:, t, :] = # ...


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Dynamic Programming:

👉 In simple words, the concept behind dynamic programming is to break the problems into sub-problems and save the result for the future so that we will not have to compute that same problem again.

👉 Dynamic programming is a problem-solving technique for resolving complex problems by recursively breaking them up into sub-problems, which are then each solved individually. Dynamic programming optimizes recursive programming and saves us the time of re-computing inputs later.

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Time complexity in above Picture

Fibonacci Number using Recursion


CODE:

def fib(n):
if n <= 0: # base case 1
return 0
if n <= 1: # base case 2
return 1
else: # recursive step
return fib(n-1) + fib(n-2)

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Time complexity in above Picture

Fibonacci Number using Dynamic Programming:

CODE:

calculated = {}

def fib(n):
if n == 0: # base case 1
return 0
if n == 1: # base case 2
return 1
elif n in calculated:
return calculated[n]
else: # recursive step
calculated[n] = fib(n-1) + fib(n-2)
return calculated[n]

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