Learn Python Coding

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Absolute value (module) of a number Let's say you have a negative number and you want to get its absolute value. For this, you can use the abs() function. The abs() function returns the absolute value of any number (positive, negative, and complex). Below is shown how to get a list of absolute values from a list that contains both negative and positive numbers. We use list comprehension.

list1 = [-12, -45, -67, -89, 34, 67, -13]

print([abs(num) for num in list1])

[12, 45, 67, 89, 34, 67, 13] Also, abs() can be applied to a floating-point number, and it will return the absolute value. See below:

num = -23.12

print(abs(num))

23.12 ➡️ Using the math module If you need more advanced mathematical functions, you can use fabs() from the math module. This function always returns a float.

import math

num = -23.12
absolute_value = math.fabs(num)
absolute_value

23.12 ➡️ Using a lambda function You can also use lambda to turn a negative number into its absolute value. The code below checks if x is less than zero (that is, if it's a negative value). If so, it returns -x, essentially removing the minus and making the number positive. If x is not negative (greater than or equal to 0), it returns x as it is.

num = -23.12
absolute_value = (lambda x: -x if x < 0 else x)(num)
absolute_value

23.12

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✨ Quiz: The pandas DataFrame: Make Working With Data Delightful ✨ 📖 Test your pandas skills! Practice DataFrame basics, column access, creation, sorting, and data manipulation in this interactive quiz. 🏷️ #intermediate #data-science

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A bit of #Python basics. Day 8 - Flatten a nested list I'll show you three (3) ways to flatten a two-dimensional list. The first method uses a for loop, the second uses the itertools module, and the third uses list comprehension. ⚙️ Using a for loop: For this method, we use a nested for loop. The outer loop iterates over the inner lists, and the inner loop accesses the elements in the inner lists. # In [19]: list1 = [[1, 2, 3],[4, 5, 6]] newlist = [] for list2 in list1:     for j in list2:         newlist.append(j) print(newlist) [1, 2, 3, 4, 5, 6] ⚙️ Using the itertools module: The itertools.chain.from_iterable() function from the itertools module can be used to flatten a nested list. This method may not be suitable for deeply nested lists. # In [20]: import itertools list1 = [[1, 2, 3],[4, 5, 6]] flat_list = list(itertools.chain.from_iterable(list1)) print(flat_list) [1, 2, 3, 4, 5, 6] You can see that the nested loop has been flattened. ⚙️ Using list comprehension If you don't want to import itertools or write a regular for loop, you can simply use list comprehension. # In [21]: list1 = [[1, 2, 3], [4, 5, 6]] flat_list = [i for j in list1 for i in j] print(flat_list) [1, 2, 3, 4, 5, 6] List comprehension is well suited for moderately nested lists. For deeply nested lists, it is not suitable, as the code becomes harder to read. ⚙️ Using a generator function You can create a generator function that yields elements from the nested list, and then convert the generator into a list.

# In [22]:
def flatten_generator(nested_list):
    for sublist in nested_list:
        for item in sublist:
            yield item

list1 = [[1, 2, 3], [4, 5, 6]]

flat_list = list(flatten_generator(list1))
flat_list

Out[22]: [1, 2, 3, 4, 5, 6] The generator method is suitable for flattening large or deeply nested lists. This is because generators are memory-efficient. 👉 https://t.me/DataScience4

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reversed() in Python - what supports it and what doesn't The function reversed() is built-in in Python, but it doesn't work with all data types ✓ Lists - it works reversed([1, 2, 3]) returns an iterator list(reversed([1, 2, 3])) → [3, 2, 1] ✓ Tuples - it also works reversed((1, 2, 3)) can be easily iterated ✗ Sets - not supported reversed({1, 2, 3}) → TypeError Why? Sets don't have a fixed order, so they can't be "reversed" If you need to reverse a set: list(reversed(list({1, 2, 3})))

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Личная жизнь почти миллионера в 35, пока мне 22 https://t.me/bozhehraninas

⚡️ Python code that works, but does extra work 100 times over This Python code looks normal. It works. It passes the tests. But it does extra work dozens, and sometimes hundreds of times. The most common reason is that you accidentally turn a linear algorithm into a quadratic one. A typical scenario: - there's a list - inside the loop, you repeatedly do in, count, index - everything works quickly with small data - on real data, the application starts to "slow down for no reason" The problem is that: - list is O(n) for searching - searching inside the loop = O(n²) - Python honestly does the work you asked it to do Pros don't think about "whether it works or not", but how many extra operations are being performed. The correct approach: - if you need membership checks, use set - if you're counting elements, use dict or Counter - if the data doesn't change, pre-calculate it once This technique is one of the most common sources of hidden performance bugs in Python code.

# ❌ Bad: O(n²)
users = ["alice", "bob", "carol", "dave"]

for u in users:
    if u in users:   # full list traversal every time
        process(u)


# ✅ Good: O(n)
users = ["alice", "bob", "carol", "dave"]
users_set = set(users)

for u in users:
    if u in users_set:
        process(u)

A bit of Python basics. Day 7. Counting the number of occurrences of an element If you need to find out how many times an element appears in an iterable collection, you can use the Counter class from the collections module. Counter() returns a dictionary with the number of times each element appears in the sequence. Let's say we want to find out how many times the name Peter appears in the following list. We can use Counter(). See below:

from collections import Counter

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']

count_peter = Counter(list1).get("Peter")

print(f'The name "Peter" appears in the list '
      f'{count_peter} times.')

Output: The name "Peter" appears in the list 2 times. Another way to do this is with a regular for loop. We create a count variable and increase it by 1 each time we find the name Peter in the sequence. This is a naive approach. See below:

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']
# Create a count variable
count = 0
for name in list1:
    if name == 'Peter':
        count +=1
print(f'The name "Peter" appears in the list'
      f' {count} times.')

Output: The name "Peter" appears in the list 2 times. Lists and other iterable data structures in Python have a built-in count() method, which allows us to count the number of occurrences of a specific element. We can use count() to count how many times Peter appears in the list.

list1 = ['John', 'Kelly', 'Peter', 'Moses', 'Peter']

print(f'The name "Peter" appears in the list '
      f'{list1.count("Peter")} times.')

Output: The name "Peter" appears in the list 2 times. 👉 https://t.me/DataScience4

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A bit of Python basics. Day 6 - Exchanging variable values In Python, you can swap variables after they have already been assigned objects. Below, we first assign 20 to the variable x and 30 to the variable y, and then swap them: x becomes 30, and y becomes 20. This method is called tuple packing/unpacking.

x, y = 20, 30
x, y = y, x

print('x is: ', x)
print('y is: ', y)

x is 30 y is 20 You can also use the XOR (exclusive or) operator to swap variables. This is a three-step method. In the example below, we swap the values of x and y.

x = 20
y = 30

# step one
x ^= y
# step two
y ^= x
# step three
x ^= y

print(f'x is: {x}')
print(f'y is: {y}')

x is: 30 y is: 20 You can also use arithmetic operations (addition and subtraction) to swap variables without a temporary variable. However, this method is recommended for swapping numeric data types. Here's an example:

# Use arithmetic operations
x = 5
y = 10

x = x + y
y = x - y
x = x - y

print("After swapping:")
print("x =", x)
print("y =", y)

After swapping: x = 10 y = 5 As a result of these arithmetic operations, the values of x and y have actually been swapped. After the swap, x contains the original value of y (10), and y contains the original value of x (5). This method of swapping variables without a temporary variable is based on the fact that when you add or subtract the value of one variable from another, you can effectively swap their values without the need for additional storage. 👉 https://t.me/DataScience4

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