Whether you’re new to Python or have been around it for a while, there are a few libraries that show up again and again. These are the “everyday essentials” — the ones that quietly power web requests, data work, and number crunching behind the scenes.

Think of them as the salt, pepper, and olive oil of Python: simple, reliable, and used everywhere.

1. requests — Making the Web Talk Back

If you ever need to interact with an API, download a webpage, or send data across the internet, requests is usually the first tool you reach for.

It turns complicated networking code into something easy to read and understand.

Example:

import requests

response = requests.get("https://api.github.com")
print(response.json())

2. pandas — Data’s Best Friend

When working with tables, CSV exports, logs, or anything that looks like a spreadsheet, pandas is the go-to library.
It makes sorting, filtering, grouping, and cleaning data feel natural.

Example:

import pandas as pd

df = pd.read_csv("sales.csv")
print(df.head())

If you work in operations, support, observability, or analytics, you will likely touch pandas sooner or later.

3. numpy — Fast Math Under the Hood

numpy focuses on numerical operations. It handles large datasets faster than plain Python lists and is foundational for machine learning, scientific computing, and data analysis.

Even libraries like TensorFlow and PyTorch rely on numpy.

Example:

import numpy as np

numbers = np.array([1, 2, 3])
print(numbers * 10)

Why These Three Matter

If you’re learning Python for support engineering, DevOps, SRE, data workflows, or automation, these three libraries build the foundation for almost anything you’ll do.

How to Get Started

Install them directly:

pip install requests pandas numpy

Then start experimenting — small scripts go a long way.

Use this file to create your basic python script.