vedanshpriyadarshi / python-tricks

A collection of 50 Python tricks and tips, demonstrating efficient and elegant ways to use Python for various tasks.

Python Tricks and Tips

A collection of 50 Python tricks and tips, demonstrating efficient and elegant ways to use Python for various tasks.

1. List Comprehension

Create a list of squares using list comprehension.

squares = [x**2 for x in range(10)]
# Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

2. Unpacking Sequences

Assign multiple variables in a single line.

a, b, c = 1, 2, 3
# a = 1, b = 2, c = 3

3. Swapping Values

Swap the values of two variables without a temporary variable.

a, b = b, a
# Values of a and b are swapped

4. Enumerate in Loops

Get the index and value in a loop.

for index, value in enumerate(['a', 'b', 'c']):
    print(f"{index}: {value}")
# Output: 0: a, 1: b, 2: c

5. Dictionary Comprehensions

Create dictionaries using a similar syntax to list comprehensions.

squares_dict = {x: x**2 for x in range(5)}
# Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

6. Set Comprehensions

Similar to lists, but for sets.

evens_set = {x for x in range(10) if x % 2 == 0}
# Output: {0, 2, 4, 6, 8}

7. Using any() and all()

Check if any or all elements of a list meet a condition.

has_even = any(x % 2 == 0 for x in [1, 3, 5, 8])
all_positive = all(x > 0 for x in [1, 2, 3, 4])
# has_even = True, all_positive = True

8. Default Dictionary Value with get()

Safely get a value from a dictionary.

d = {'a': 1, 'b': 2}
value = d.get('c', 3)
# value = 3, as 'c' is not in d

9. Using zip() to Iterate Over Two Lists

Iterate over two lists in parallel.

for name, age in zip(['Alice', 'Bob'], [24, 25]):
    print(f"{name} is {age} years old")
# Output: Alice is 24 years old, Bob is 25 years old

10. Lambda Functions

Create small anonymous functions.

multiply = lambda x, y: x * y
# multiply(2, 3) returns 6

11. Function Argument Unpacking

Use * and ** to unpack lists and dictionaries into function arguments.

def func(a, b, c):
    return a + b + c

args = [1, 2, 3]
print(func(*args))
# Output: 6

12. Conditional Assignment

Assign a value based on a condition in a single line.

x = 10
y = "Greater than 5" if x > 5 else "Less or equal to 5"
# y = "Greater than 5"

13. Chaining Comparison Operators

You can chain comparison operators for succinct logic.

x = 5
is_between = 1 < x < 10
# is_between = True

14. Using collections.Counter for Frequency Counting

Easily count the frequency of elements in an iterable.

from collections import Counter
frequencies = Counter(['apple', 'banana', 'apple', 'orange', 'banana', 'apple'])
# frequencies = Counter({'apple': 3, 'banana': 

2, 'orange': 1})

15. Extended Unpacking

Assign parts of a list into separate variables and capture the rest in another variable.

a, *b, c = [1, 2, 3, 4, 5]
# a = 1, b = [2, 3, 4], c = 5

16. Using else with Loops

An else block after a loop is executed only if the loop didn't end with a break.

for i in range(3):
    if i == 3:
        break
else:
    print("Loop ended without break")
# Prints "Loop ended without break"

17. Using next() with Iterables

Get the next item from an iterator, with an option to specify a default value if the iterator is exhausted.

my_iter = iter([1, 2, 3])
first = next(my_iter, 'No items')
# first = 1

18. Using try-except-else Blocks

The else block in a try-except construct is executed if no exception is raised in the try block.

try:
    value = int("123")
except ValueError:
    print("Conversion failed")
else:
    print("Conversion successful")
# Prints "Conversion successful"

19. Dictionary Comprehension with zip

Combine two lists into a dictionary using dictionary comprehension and zip.

keys = ['a', 'b', 'c']
values = [1, 2, 3]
my_dict = {key: value for key, value in zip(keys, values)}
# my_dict = {'a': 1, 'b': 2, 'c': 3}

20. Flattening Lists Using List Comprehensions

Flatten a list of lists into a single list.

nested_list = [[1, 2], [3, 4], [5, 6]]
flat_list = [item for sublist in nested_list for item in sublist]
# flat_list = [1, 2, 3, 4, 5, 6]

21. Using set for Uniqueness and Order Preservation

Remove duplicates while preserving order (works in Python 3.6+).

items = [1, 2, 2, 3, 3, 1, 4]
unique_items = list(dict.fromkeys(items))
# unique_items = [1, 2, 3, 4]

22. Using * for Repeating Elements in Lists

Create lists with repeated elements concisely.

repeated_list = [0] * 5
# repeated_list = [0, 0, 0, 0, 0]

23. List Slicing for Removing Elements

Use slicing to remove elements from a list.

my_list = [1, 2, 3, 4, 5]
my_list[1:3] = []
# my_list = [1, 4, 5]

24. Using itertools for Advanced Iteration

The itertools module provides a variety of tools for efficient looping and iteration.

import itertools
for combination in itertools.combinations([1, 2, 3], 2):
    print(combination)
# Prints (1, 2), (1, 3), (2, 3)

25. Using join() for String Concatenation

Efficiently concatenate strings from an iterable.

words = ['Hello', 'world']
sentence = ' '.join(words)
# sentence = "Hello world"

26. Dictionary Merging with update()

Merge two dictionaries.

dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}
dict1.update(dict2)
# dict1 = {'a': 1, 'b': 3, 'c': 4}

27. List Slicing Tricks - Reverse a List

Reverse a list or create sublists with slicing.

my_list = [1, 2, 3, 4, 5]
reversed_list = my_list[::-1]
sub_list = my_list[1:4]
# reversed_list = [5, 4, 3, 2, 1], sub_list = [2, 3, 4]

28. Sorting a List of Dictionaries by Multiple Keys

Suppose you have a list of dictionaries representing books, and you

want to sort these books first by their 'year' in descending order and then by their 'title' in ascending order.

books = [
    {'title': 'Book A', 'year': 2000},
    {'title': 'Book B', 'year': 1995},
    {'title': 'Book C', 'year': 2000},
    {'title': 'Book D', 'year': 1995}
]
sorted_books = sorted(books, key=lambda x: (-x['year'], x['title']))
# Output: [{'title': 'Book A', 'year': 2000}, {'title': 'Book C', 'year': 2000}, {'title': 'Book B', 'year': 1995}, {'title': 'Book D', 'year': 1995}]

29. Creating a Nested Dictionary

Use dictionary syntax to create nested dictionaries.

nested_dict = {'dict1': {'keyA': 'valueI'}, 'dict2': {'keyB': 'valueII'}}
# Output: {'dict1': {'keyA': 'valueI'}, 'dict2': {'keyB': 'valueII'}}

30. Using List Slices to Clear All Elements

Clear a list using list slicing.

my_list = [1, 2, 3, 4, 5]
my_list.clear()
# my_list = []

31. Using a Slice to Assign to a List

Use a slice to assign multiple items to a list.

my_list = [1, 2, 4, 5]
my_list[1:3] = [7, 8, 9]
# my_list = [1, 7, 8, 9, 5]

32. Checking If an Object is Iterable

Use isinstance and Iterable from collections.abc to check if an object is iterable.

from collections.abc import Iterable
is_iterable = isinstance(my_list, Iterable)
# is_iterable = True

33. Using 'in' to Check Membership

Use in to check if an element exists in a list, string, set, or dictionary keys.

is_member = 'Hello' in words
# is_member = True

34. Using List Comprehensions with an if Condition

Create a list using list comprehension with a conditional filter.

even_squares = [x**2 for x in range(10) if x % 2 == 0]
# even_squares = [0, 4, 16, 36, 64]

35. Using map() to Apply a Function to an Iterable

Apply a function to each item of an iterable and return a list of the results.

squared = list(map(lambda x: x**2, range(10)))
# squared = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

36. Using filter() to Filter an Iterable

Filter elements of an iterable, returning only those for which the function returns True.

even_numbers = list(filter(lambda x: x % 2 == 0, range(10)))
# even_numbers = [0, 2, 4, 6, 8]

37. Using zip() to Create a Dictionary from Two Lists

Pair elements from two lists to create a dictionary.

keys = ['a', 'b', 'c']
values = [1, 2, 3]
my_dict = dict(zip(keys, values))
# my_dict = {'a': 1, 'b': 2, 'c': 3}

38. Using reversed() to Reverse an Iterable

Reverse an iterable without altering the original data structure.

for item in reversed(range(1, 4)):
    print(item)
# Prints 3, 2, 1

39. Using any() with a Generator Expression

Check if any element of an iterable satisfies a certain condition.

has_positive = any(x > 0 for x in [-1, 0, 1, 2])
# has_positive = True

40. Using all() with a Generator Expression

Check if all elements of an iterable satisfy a certain condition.

all_positive = all(x > 0 for x in [1, 2, 3])
# all_positive = True

41. Using a Dictionary to Store Functions

Use a dictionary to store and call functions.

operations = {
    'add': lambda x, y:

 x + y,
    'subtract': lambda x, y: x - y
}
result = operations['add'](5, 3)
# result = 8

42. Using defaultdict for Default Dictionary Values

Use defaultdict to automatically assign default values to non-existent keys.

from collections import defaultdict
default_dict = defaultdict(int)
default_dict['key'] += 1
# default_dict = defaultdict(<class 'int'>, {'key': 1})

43. Using namedtuple for Readable Tuple Access

Create readable tuples with namedtuple.

from collections import namedtuple
Point = namedtuple('Point', ['x', 'y'])
point = Point(1, 2)
x_coordinate = point.x
# x_coordinate = 1

44. Using List Slices to Insert Elements

Insert elements into a list using list slices.

my_list = [1, 2, 4]
my_list[2:2] = [3]
# my_list = [1, 2, 3, 4]

45. Using chain from itertools to Combine Iterables

Combine multiple iterables into one.

combined = list(itertools.chain(range(3), range(4, 6)))
# combined = [0, 1, 2, 4, 5]

46. Using groupby from itertools for Grouping Data

Group elements of an iterable based on a key function.

data = sorted([('type1', 1), ('type1', 2), ('type2', 1)], key=lambda x: x[0])
for key, group in itertools.groupby(data, key=lambda x: x[0]):
    print(f"{key}: {list(group)}")
# Prints 'type1: [('type1', 1), ('type1', 2)]' and 'type2: [('type2', 1)]'

47. Using slice to Create Reusable Slice Objects

Create a slice object for slicing lists and other sequences.

my_slice = slice(2, 5)
sliced_list = my_list[my_slice]
# sliced_list = [3, 4]

48. Using Comprehension with enumerate()

Create a dictionary from an iterable with index-value pairs.

indexed_fruits = {index: fruit for index, fruit in enumerate(fruits)}
# indexed_fruits = {0: 'apple', 1: 'banana', 2: 'cherry'}

49. Using min() and max() with a Key Function

Find the minimum or maximum item in a collection based on a key function.

oldest_book = max(books, key=lambda x: x['year'])
# oldest_book = {'title': 'Book B', 'year': 1995}

50. Using sorted() with a Custom Key Function

Sort a collection based on a custom key function.

sorted_by_title = sorted(books, key=lambda x: x['title'])
# sorted_by_title = [{'title': 'Book A', 'year': 2000}, {'title': 'Book B', 'year': 1995}, {'title': 'Book C', 'year': 2000}, {'title': 'Book D', 'year': 1995}]