Python for Data Science Cheat Sheet | NumPy Pandas Sklearn | Quick Reference

Stop Googling the same Python syntax over and over. Keep this on your desk instead.

This one-page Python for Data Science Cheat Sheet covers everything from core Python syntax to the key functions in NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn — all on a single clean, printable reference sheet built for data scientists, analysts, and ML engineers.

What's included:
Core Python Syntax:
Assignment, unpacking, f-strings, list and dict comprehensions
Lambda functions, *args/**kwargs, conditionals, for loops
Error handling (try/except) and context managers (with/open)

Built-in Data Structures:
Lists, tuples, sets, and dicts with common operations
Indexing, slicing, sorted(), zip(), enumerate(), and key builtins

NumPy Essentials:
Array creation, zeros, arange, shape, dtype
Indexing, element-wise math, mean/std, reshape

Pandas — DataFrames (15 operations):
Read CSV and Excel, head/info/describe
Select columns, filter rows, sort, groupby, merge
Handle NULLs, count unique values, rename columns, export to CSV

Matplotlib & Seaborn:

Line, scatter, histogram, bar charts
Labels, titles, plt.show(), heatmaps, box plots

Scikit-learn — ML Workflow:
train_test_split, StandardScaler, fit_transform
Model init, fit, predict, accuracy_score, cross_val_score

Bonus: Standard imports tip box — the five import lines every data science notebook starts with

Format: 8.5 x 11 inch (US Letter) — also prints cleanly on A4
File type: PDF — instant digital download, no waiting

Perfect for:
Data scientists and analysts who switch between projects and contexts
Bootcamp students learning Python for data science
Anyone studying for a data science interview or technical screen
ML engineers who work primarily in Spark or SQL and need a Python refresher
Anyone building their first scikit-learn model and always forgetting the workflow order

This is a DIGITAL DOWNLOAD. No physical item will be shipped. You'll receive instant access to your PDF after purchase.