Python

One diagram per command.

Author

James Balamuta

Every cheatsheet below pairs each command with a small diagram of what it does, laid out as purpose, command, then result. Each post carries a full composite image, one diagram per section, and a print-ready PDF, in both light and dark variants.

For the story behind the series, see Python Cheatsheets, One Diagram Per Command.

Anthropic Claude SDK Cheatsheet
The Anthropic Python SDK at a glance, from a single Claude message to streaming, tool use, images, and prompt caching.

Beautiful Soup Cheatsheet
A quick reference for pulling data out of messy HTML with Beautiful Soup, parsing a document and searching it by tag and CSS selector.

DuckDB Cheatsheet
DuckDB runs SQL right on your DataFrames and files with no server, here from a first query to joins, exports, and EXPLAIN.

FastAPI Cheatsheet
A quick reference for FastAPI, building typed web APIs where your function signatures define the routes, validation, and docs.

GeoPandas Cheatsheet
GeoPandas extends pandas to geospatial data, covered here from reading and reprojecting to spatial joins and choropleth maps.

Gradio Cheatsheet
Gradio at a glance, wrapping any Python function in a shareable web UI from simple inputs to full chat apps.

JAX Cheatsheet
The core of JAX in brief, NumPy that runs on GPUs and TPUs with composable transforms for autodiff, compilation, and vmap.

Keras Cheatsheet
The essentials of Keras, the high-level way to build and train neural networks, from stacking layers to callbacks and transfer learning.

Matplotlib Cheatsheet
A tour of Matplotlib, from a first Figure and Axes to fully styled, multi-panel figures.

NumPy Cheatsheet
An overview of NumPy, the array that nearly every scientific Python library builds on, with vectorized math, broadcasting, and linear algebra.

Optuna Cheatsheet
Optuna searches for better hyperparameters from a plain Python objective, covered here from running a study to pruning and visualizing results.

Plotly Cheatsheet
A quick reference for Plotly’s interactive charts you can zoom, pan, and hover, from express one-liners to composed figures and dashboards.

PyArrow Cheatsheet
The essentials of PyArrow, Python’s gateway to the Arrow columnar format, moving data between pandas, Polars, and Parquet at zero-copy speed.

PyTorch Cheatsheet
The everyday PyTorch loop, from tensors and autograd to building models, training them, and running on a GPU.

PyTorch Lightning Cheatsheet
The key pieces of PyTorch Lightning, which strips away training boilerplate and organizes your model, loops, and logging around a Trainer.

Pydantic Cheatsheet
Pydantic makes type hints validate your data at runtime, covered here from coercion and constraints to validators and serialization.

Requests Cheatsheet
HTTP for humans, with Requests’ verbs, headers, auth, sessions, and responses at a glance.

Rich Cheatsheet
A field guide to Rich, which brings color and structure to terminal output with tables, progress bars, highlighting, and clean tracebacks.

SQLAlchemy Cheatsheet
A quick reference for SQLAlchemy, Python’s SQL toolkit and ORM, from raw queries and connections to declarative models and relationships.

SciPy Cheatsheet
A field guide to SciPy, the toolbox built on NumPy that spans optimization, integration, statistics, and signal processing.

Streamlit Cheatsheet
Streamlit turns a plain Python script into an interactive data app, covered here through widgets, layout, caching, and the rerun model.

Transformers Cheatsheet
The essentials of Hugging Face Transformers, from one-call pipelines to tokenization, text generation, and fine-tuning with Trainer.

Typer Cheatsheet
A field guide to Typer, building command-line apps from type hints where function arguments become options, help, and validation.

XGBoost Cheatsheet
XGBoost is the gradient-boosting library behind countless winning models, covered here from training and tuning to early stopping.

conda / mamba Cheatsheet
The everyday conda and mamba commands, for creating environments, managing packages, and sharing your setup.

httpx Cheatsheet
An overview of httpx, the modern client that does sync and async in one API, with HTTP/2, streaming, and connection reuse.

instructor Cheatsheet
A look at instructor, which hands back clean, typed Pydantic objects from an LLM instead of raw text, with automatic retries.

narwhals Cheatsheet
narwhals in brief, writing dataframe code once and running it on pandas, Polars, PyArrow, and more.

pandas Cheatsheet
The everyday pandas moves, from building and cleaning frames to grouping, reshaping, and reading or writing them.

pathlib Cheatsheet
A field guide to pathlib, which treats filesystem paths as objects you join with a slash and read, glob, and reshape cleanly.

pip Cheatsheet
The pip commands you reach for, from simple installs to editable projects, requirements files, and the cache.

plotnine Cheatsheet
A tour of the grammar of graphics in Python, building plots one layer at a time with plotnine the way ggplot2 does in R.

polars Cheatsheet
A quick reference for Polars, the fast DataFrame library with a lazy engine, built around expressions for selecting, filtering, grouping, and joining.

pytest Cheatsheet
A quick reference for pytest, from plain assert tests to fixtures, parametrization, and markers that keep a suite fast and readable.

scikit-learn Cheatsheet
The scikit-learn workflow, from splitting data and building pipelines to tuning, scoring, and saving a model.

seaborn Cheatsheet
An overview of seaborn’s statistical plots, with sensible defaults for distributions, relationships, categories, and faceting.

sentence-transformers Cheatsheet
sentence-transformers turns sentences into embeddings for semantic search, covered here through similarity, retrieval, and reranking for RAG.

spaCy Cheatsheet
An overview of spaCy, the industrial-strength NLP library that turns raw text into tokens, entities, and parses at scale.

statsmodels Cheatsheet
The statsmodels essentials, covering classical statistics with readable summaries across regression, GLMs, time series, and tests.

uv Cheatsheet
uv at a glance, the all-in-one, fast Python project manager for versions, environments, dependencies, and publishing.
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