Migrating from Python to Rust? Here's how to map your packages

Porting Python code to Rust is one of the most common performance optimization plays in modern software engineering.

Whether you are rebuilding a bottlenecked web service (moving from FastAPI to Actix), accelerating a data pipeline (moving from Pandas to Polars), or rewriting a CLI utility, the performance gains are massive. Rust services routinely run 10x to 100x faster while consuming a fraction of the RAM.

But once you install Rust and set up your Cargo.toml, you hit the first roadblock: dependency mapping.

Python's PyPI ecosystem and Rust's Crates.io ecosystem look completely different. Python code relies on dynamic runtime patterns and heavy frameworks, whereas Rust prioritizes compiled type-safety, explicit memory management, and modular crates.

To save you hours of browsing crates.io, here is the ultimate cheat sheet for mapping common Python packages to their Rust equivalents, followed by a way to automate this directly inside your editor.

📊 Python ➡️ Rust Crate Mapping Cheat Sheet

🔍 In-Depth Mappings & Code Examples

1. HTTP Requests: requests ➡️ reqwest

In Python, fetching data from an API is famously simple:

import requests



response = requests.get('https://api.github.com/users/octocat')

data = response.json()

print(data['name'])

In Rust, Reqwest handles this asynchronously (using Tokio as the runtime). We pair it with Serde to safely parse the JSON into a strongly-typed struct:

use serde::Deserialize;



#[derive(Deserialize, Debug)]

struct GithubUser {

    name: String,

}



#[tokio::main]

async fn main() -> Result<(), reqwest::Error> {

    let user: GithubUser = reqwest::Client::new()

        .get("https://api.github.com/users/octocat")

        .header("User-Agent", "rust-app")

        .send()

        .await?

        .json()

        .await?;



    println!("User Name: {}", user.name);

    Ok(())

}

2. DataFrames: pandas ➡️ polars

If you are processing millions of rows of data, Rust's Polars will feel like moving from a bicycle to a rocket ship:

use polars::prelude::*;



fn main() -> Result<()> {

    // Read a CSV and filter rows where age > 30

    let df = CsvReader::from_path("users.csv")?

        .has_header(true)

        .finish()?

        .lazy()

        .filter(col("age").gt(lit(30)))

        .collect()?;



    println!("{}", df);

    Ok(())

}

3. Web Frameworks: FastAPI ➡️ Axum

FastAPI is loved for its automatic type validation and clean path routing. In Rust, Axum uses a declarative handler system that feels very familiar to FastAPI developers, but runs with near-zero latency overhead:

use axum::{routing::get, Json, Router};

use serde::Serialize;



#[derive(Serialize)]

struct Status {

    status: String,

}



#[tokio::main]

async fn main() {

    let app = Router::new().route("/status", get(handler));



    let listener = tokio::net::TcpListener::bind("0.0.0.0:3000").await.unwrap();

    axum::serve(listener, app).await.unwrap();

}



async fn handler() -> Json<Status> {

    Json(Status {

        status: "ok".to_string(),

    })

}

🤖 How to automate this in VS Code

Instead of context-switching to browser tabs to find crate names and boilerplate code, you can use ** and give the project a star on GitHub!*