The Full Python Cheatsheet: From Basics to Data Science

Python has become one of the most popular programming languages in the world—and for good reason. Whether you're automating tasks, building web apps, or diving deep into data science, Python’s simplicity and versatility make it a top choice.

To help beginners and professionals alike, we are sharing a comprehensive Python cheatsheet in a downloadable PDF format that covers everything from the basic syntax to powerful data science libraries. Bookmark it, print it, share it—this is the companion you didn’t know you needed.

🔹 Why a Python Cheatsheet?

A cheatsheet is a compact reference guide that helps you:

  • Recall syntax quickly without diving into documentation.
  • Speed up your workflow.
  • Understand how different tools fit together.
  • Stay productive while learning or working.

🐍 1. Python Basics

Variables & Data Types:

x = 10          # Integer
pi = 3.14       # Float
name = "Alice"  # String
is_valid = True # Boolean

Lists, Tuples, and Dictionaries:

my_list = [1, 2, 3]
my_tuple = (4, 5, 6)
my_dict = {'name': 'Bob', 'age': 25}

Control Flow:

if x > 0:
    print("Positive")
elif x == 0:
    print("Zero")
else:
    print("Negative")

Loops:

for i in range(5):
    print(i)

while x > 0:
    x -= 1

Functions:

def greet(name):
    return f"Hello, {name}"

📦 2. Working with Libraries

Python shines because of its massive ecosystem of libraries. Here are some essential ones for data science:

📊 3. Numpy: Numerical Computing

import numpy as np

arr = np.array([1, 2, 3])
arr.shape
arr.mean()
np.zeros((2, 3))

🧹 4. Pandas: Data Manipulation

import pandas as pd

df = pd.read_csv("data.csv")
df.head()
df['column'].mean()
df.groupby('category').sum()

📈 5. Matplotlib & Seaborn: Visualization

import matplotlib.pyplot as plt
import seaborn as sns

plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

sns.histplot(df['column'])

🧠 6. Scikit-learn: Machine Learning

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X = df[['feature1', 'feature2']]
y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X, y)

model = LinearRegression()
model.fit(X_train, y_train)
model.predict(X_test)

🚀 Wrapping Up

Whether you're just getting started or working on complex machine learning models, having a quick reference is a game changer. This cheatsheet is designed to grow with you—so feel free to add your own notes as you go.

👉 Download the full printable cheatsheet here

Tip: Save this post and revisit it whenever you feel stuck or need a quick refresher. Python’s ecosystem is vast, but with a solid cheatsheet by your side, nothing’s out of reach.