🐍 Why Python List is Faster Than You Think (and Sometimes Slower)

Photo by Markus Spiske on Unsplash

Have you ever wondered why list.append() in Python feels super fast… until suddenly it doesn’t? 🤔
Today, let’s uncover the secret life of Python lists in under 2 minutes.

🔧 Python Lists Are NOT Arrays

In many languages (like C/Java), arrays are fixed-size. But in Python, a list is actually a dynamic array — more like C++’s vector.

That means:

✅ You can keep appending elements without worrying about size.

✅ Most appends are O(1) (constant time).

⚠️ But sometimes, an append suddenly becomes O(n) because Python needs to resize the underlying memory.

📈 The Trick: Over-Allocation

When you create a list, Python allocates extra hidden space for future growth. So, if you append, it usually just places the new element in the reserved slot.

When it runs out of slots, Python allocates a bigger block and copies everything.

🧪 Let’s See It in Action

Try this code 👇

import sys

lst = []
for i in range(40):
    lst.append(i)
    print(f"Length: {len(lst)}, Memory: {sys.getsizeof(lst)} bytes")

Output

👉 Notice how memory doesn’t increase for every append. It increases only when resizing kicks in.

📊 Visualizing List Growth

Let’s track how Python list memory changes as we keep appending:

import sys
import matplotlib.pyplot as plt

sizes, mem = [], []
lst = []

for i in range(1000):
    lst.append(i)
    sizes.append(len(lst))
    mem.append(sys.getsizeof(lst))

plt.step(sizes, mem, where='post')
plt.xlabel("List Length")
plt.ylabel("Memory (bytes)")
plt.title(" Python List Memory Growth")
plt.show()

• The graph grows in steps 📈, not smoothly.
• Each flat part means Python is just filling up the already allocated memory.
• Each jump means Python had to resize and allocate more memory.

⚡ When Lists Feel Slow

Most of the time, appends are lightning fast 🚀.
But if you’re doing millions of inserts at the front, or need fixed numeric arrayslists are inefficient.

Better alternatives:

• collections.deque:fast appends/pops from both ends.
• array.array or NumPy: compact and efficient for numbers.

🎯 Takeaway

Python lists are smart dynamic arrays:

• Most appends are cheap (O(1) amortized).
• Occasional resizes are expensive (O(n)).
• Memory jumps explain why lists feel “sometimes fast, sometimes slow.”

So next time your list lags, remember: it’s just Python reallocating behind the scenes 🐍✨