Python - Count elements in list

Count `em all with len()

To get a round total count of elements in a list, Python's len() function is your dedicated sidekick. It counts swiftly, works flawlessly for lists, strings, tuples, and dictionaries:

MyList = ["a", "b", "c"]
print(len(MyList))  # Output: 3, "Takes three to tango!" - Dancing Python Snake
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Whether you're a Python newbie or a seasoned pro, len() is a tool that you'd never want to miss out on your Pythonic adventures.

Uniqueness counts!

To count elements sans duplicates, send the list through the unique filter of a set first, and then let len() do the counting:

unique_count = len(set(['apple', 'banana', 'apple', 'banana'])) # "No apples and bananas were cloned in this operation!" - Non-GMO Python
print(unique_count)  # Output: 2
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Ready to handle data where uniqueness matters? This simple counting pattern will have you covered!

The art of specific counting with list.count()

So, you are interested in how many times a certain shady character (element) appears in your list? Use your list's built-in .count() method to keep tabs:

fruits = ['apple', 'banana', 'apple']
apple_count = fruits.count('apple') # "Did somebody say, 'apple'? Count me in." - Hungry Python
print(apple_count)  # Output: 2
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While Counter rolls out an all-encompassing tally, .count() just quietly focuses on one element -- often all you need (winks).

Performance - Time is of the essence

When it comes to performance-critical applications, time complexity is king. Here, len() stands tall with an O(1) complexity – it's oblivious to the size of the list. On the downside, Counter and list’s .count() method may consume more time depending on the length and contents of the list. Choose wisely!

Optimize, learn, repeat

Is Python new territory for you? The "Byte of Python" ebook can be your guiding beacon, introducing Python fundamentals including counting operations. For the speed-freaks, optimizing Counter has been discussed in considerable depth in articles such as "Making Counter in Python Faster" on Towards Data Science.