I Forced Myself to Use Only Built-In Functions in Python for a Week

I Thought Built-In Functions Were Boring — I Was So Wrong

I Forced Myself to Use Only Built-In Functions in Python for a Week

The Challenge That Started It All

As a Python developer, it’s easy to get addicted to the shiny new toys — Pandas, NumPy, Requests, or even full-blown frameworks. Need to manipulate data? There’s a library for that. Want to format output? Grab a utility package. But one day, I asked myself:

What if I stripped all of that away and used only what Python gave me out of the box?

So I took on a week-long challenge:

• No pip installs
• Only built-in Python modules and functions
• No cheating by copying helpers from StackOverflow

Here’s how it went — and what I discovered along the way.

Why Even Do This?

Before we dive in, let me clarify why this challenge matters:

Deeper language mastery: Using only built-ins forces you to explore corners of Python you might ignore.

Better debugging skills: When you can’t rely on wrapper libraries, you understand the internals better.

Minimalist mindset: Sometimes, less truly is more.

What “Built-In” Actually Means

For this challenge, I allowed myself only two things:

• Built-in functions like map(), filter(), sorted(), zip(), enumerate(), etc.
• Standard library modules like math, datetime, itertools, collections, etc.

No pandas. No requests. No numpy. No exceptions.

Day 1–2: Data Manipulation Without Pandas

Scenario: I had to parse a CSV, group rows, and calculate averages.

What I normally would do:

import pandas as pd   
df = pd.read_csv("data.csv")   
grouped = df.groupby("category").mean()

What I had to do:

import csv 
from collections import defaultdict 
 
data = defaultdict(list) 
 
with open("data.csv", newline='') as f: 
    reader = csv.DictReader(f) 
    for row in reader: 
        data[row['category']].append(float(row['value'])) 
 
averages = {k: sum(v)/len(v) for k, v in data.items()}

What I learned:

csv module is surprisingly powerful.

defaultdict + dictionary comprehension is your best friend.

You don’t need Pandas for every data task.

Day 3: List Transformations with map() and filter()

Scenario: I needed to clean and transform user data from a form submission.

What I normally would do:
Write a for loop or use list comprehensions.

What I did instead:

names = [" Alice ", "BOB", "   Charlie  "] 
 
cleaned = list(map(lambda name: name.strip().title(), names))

Lesson:
map() and filter() aren't just academic relics. Used well, they’re elegant and readable.

Day 4: Date Formatting Without datetime Libraries

I needed to calculate due dates and format them nicely.

from datetime import datetime, timedelta 
 
due_date = datetime.now() + timedelta(days=5) 
print("Due on:", due_date.strftime("%A, %d %B %Y"))

Discovery: strftime() is criminally underused. It gives you human-readable dates without needing arrow or pendulum.

Day 5–6: API Calls Without Requests

This was where it got tricky.

import urllib.request 
import json 
 
with urllib.request.urlopen("https://api.github.com") as response: 
    data = json.load(response) 
    print(data["current_user_url"])

It’s clunkier than requests, sure. But it works. You start appreciating what’s happening under the hood.

What This Week Taught Me

1. Built-ins are better than you think
   You can do 80–90% of tasks without third-party dependencies.
2. Less code, fewer bugs
   Standard modules are well-tested and maintained — no version hell.
3. New respect for the standard library
   From itertools to functools, Python's toolbox is deep.
4. Mindset shift
   I stopped looking for plugins and started solving problems.

My Favorite Built-Ins I Re-Discovered

any(), all()

enumerate()

zip()

collections.Counter

itertools.groupby

functools.reduce

These functions feel like “cheat codes” once you know how and when to use them.

Final Thoughts: Should You Try This?

Absolutely. Here’s why:

• You’ll become a better, more resourceful Python developer.
• You’ll learn to write leaner, faster, and more reliable code.
• You’ll gain confidence in your core skills instead of defaulting to packages.

You don’t need more libraries. You need deeper understanding.

Try it for a day. Then a week. You’ll come out the other side sharper and more Pythonic than ever.

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