It’s tempting to turn to Google and ChatGPT every time you need a Python function. However, toggling back and forth is time-consuming and energy-draining. According to Professors Meyer, Evans, and Rubinstein, each “task switch” results in a 40% loss in productivity. After experiencing a lot of brain drain from toggling, I decided to memorize these ten Python functions, and my programming skills have increased.

In this article, you’ll learn ten functions that have improved my coding skills. I’ve included examples for each to help you navigate some of the nuances. The examples are based on randomly generated sales data.

1. len() function

• purpose : Get the length of an iterable (e.g., list, string, or DataFrame rows/columns).
• Example :

python 

data = [10, 20, 30, 40]
print(len(data))  # Output: 4

2. sum() function

• Purpose : Calculate the sum of an iterable (e.g., list or array).
• Example :

python 

numbers = [1, 2, 3, 4, 5]
print(sum(numbers))  # Output: 15

3.Map() function

• Purpose : Apply a function to each repeating item.
• Example :

python 

numbers = [1, 2, 3, 4]
squared = list(map(lambda x: x**2, numbers))
print(squared)  # Output: [1, 4, 9, 16]

4. filter() function

• Purpose : Filter elements in a return based on a condition.
• Example :

python 

numbers = [1, 2, 3, 4, 5, 6]
evens = list(filter(lambda x: x % 2 == 0, numbers))
print(evens)  # Output: [2, 4, 6]

5.zip() function

• Purpose: Combine multiple iterables element-wise.
• Example :

python 

names = ["Alice", "Bob", "Charlie"]
scores = [85, 90, 95]
combined = list(zip(names, scores))
print(combined)  # Output: [('Alice', 85), ('Bob', 90), ('Charlie', 95)]

6.sorted() function

• Purpose : To return a sorted list from an iterable.
• Example :

python 

data = [7, 1, 5, 3]
print(sorted(data))  # Output: [1, 3, 5, 7]

7.round() function

• Purpose: Round the number to the nearest integer or to the specified precision.
• Example :

python 

pi = 3.14159
print(round(pi, 2))  # Output: 3.14

8.enumerate() function

• Purpose : Get the index and value when making a return.
• Example :

python 

items = ['a', 'b', 'c']
for idx, value in enumerate(items):
    print(idx, value)
# Output:
# 0 a
# 1 b
# 2 c

9.any() / all() function

• Purpose : Check if any or all repeatable elements meet a condition.
• Example :

python 

values = [True, False, True]
print(any(values))  # Output: True
print(all(values))  # Output: False

10.set() function

• Purpose : To remove duplicates from an iterable and create a set.
• Example :

python 

data = [1, 2, 2, 3, 4, 4, 5]
unique_data = set(data)
print(unique_data)  # Output: {1, 2, 3, 4, 5}

These functions are foundational and highly versatile, saving time and improving code efficiency for data scientists.