5 Modern Python Tools Every Developer Should Master in 2025

STAY AHEAD WITH THESE GAME-CHANGING PYTHON TOOLS!

5 Modern Python Tools Every Developer Should Master in 2025

The Python ecosystem keeps evolving, and new tools introduced to make development faster, cleaner, and more efficient. If you’re still relying on outdated workflows, it’s time to upgrade your toolkit!

Here are 5 more cutting-edge Python tools that will boost your productivity in 2025!

1. Supabase-Py — The Firebase Alternative for Python

Supabase is a best alternative of Firebase provides a seamless way to interact with PostgreSQL database, authentication system, and storage services using Python.

GitHub - supabase/supabase-py: Python Client for Supabase. Query Postgres from Flask, Django…

Python Client for Supabase. Query Postgres from Flask, Django, FastAPI. Python user authentication, security policies…

Installation:

pip install supabase

Example:

Setup a Supabase client:

import os 
from supabase import create_client, Client 
 
url: str = os.environ.get("SUPABASE_URL") # Add SUPABASE_URL in .env file 
key: str = os.environ.get("SUPABASE_KEY") # Add SUPABASE_KEY in .env file 
supabase: Client = create_client(url, key)

Use the supabase client to interface with your database.

Sign-up

user = supabase.auth.sign_up({ "email": users_email, "password": users_password })

Sign-in

user = supabase.auth.sign_in_with_password({ "email": users_email, "password": users_password })

Insert Data

data = supabase.table("countries").insert({"name":"Germany"}).execute() 
 
# Assert we pulled real data. 
assert len(data.data) > 0

2. Modin — Pandas on Steroids

Modin is a drop-in replacement for pandas that scales across all CPU cores.

GitHub - modin-project/modin: Modin: Scale your Pandas workflows by changing a single line of code

Modin: Scale your Pandas workflows by changing a single line of code - modin-project/modin

Installation:

pip install "modin[all]" # (Recommended) Install Modin with Ray and Dask engines.

Example:

import modin.pandas as pd 
 
df = pd.read_csv("large_file.csv")  # Much faster than pandas!

3. CuPy — GPU-Powered NumPy & SciPy

CuPy accelerates NumPy and SciPy operations using NVIDIA GPUs.

GitHub - cupy/cupy: NumPy & SciPy for GPU

NumPy & SciPy for GPU. Contribute to cupy/cupy development by creating an account on GitHub.

Installation:

pip install cupy

Example:

import cupy as cp 
 
arr = cp.array([1, 2, 3, 4]) 
print(arr**2)  # Computed on GPU!

4. Promptify — OpenAI-Like AI Prompt Engineering

A powerful LLM orchestration framework like LangChain.

GitHub - promptslab/Promptify: Prompt Engineering | Prompt Versioning | Use GPT or other prompt…

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our…

Installation:

pip install promptify

Example:

from promptify import Prompter,OpenAI, Pipeline 
 
sentence     =  """The patient is a 93-year-old female with a medical        
                history of chronic right hip pain, osteoporosis,      
                hypertension, depression, and chronic atrial       
                fibrillation admitted for evaluation and management     
                of severe nausea and vomiting and urinary tract     
                infection""" 
model        = OpenAI(api_key) # or `HubModel()` for Huggingface-based inference or 'Azure' etc 
prompter     = Prompter('ner.jinja') # select a template or provide custom template 
pipe         = Pipeline(prompter , model) 
 
result = pipe.fit(sentence, domain="medical", labels=None) 
 
### Output 
[ 
    {"E": "93-year-old", "T": "Age"}, 
    {"E": "chronic right hip pain", "T": "Medical Condition"}, 
    {"E": "osteoporosis", "T": "Medical Condition"}, 
    {"E": "hypertension", "T": "Medical Condition"}, 
    {"E": "depression", "T": "Medical Condition"}, 
    {"E": "chronic atrial fibrillation", "T": "Medical Condition"}, 
    {"E": "severe nausea and vomiting", "T": "Symptom"}, 
    {"E": "urinary tract infection", "T": "Medical Condition"}, 
    {"Branch": "Internal Medicine", "Group": "Geriatrics"}, 
]

5. SQLModel — The Best Way to Work with SQL in Python

Combines SQLAlchemy + Pydantic for a seamless ORM experience. SQL databases in Python, designed for simplicity, compatibility, and robustness.

GitHub - fastapi/sqlmodel: SQL databases in Python, designed for simplicity, compatibility, and…

SQL databases in Python, designed for simplicity, compatibility, and robustness. - fastapi/sqlmodel

Installation:

pip install sqlmodel

Example:

from typing import Optional 
from sqlmodel import Field, Session, SQLModel, create_engine 
 
class Hero(SQLModel, table=True): 
    id: Optional[int] = Field(default=None, primary_key=True) 
    name: str 
    secret_name: str 
    age: Optional[int] = None 
 
hero_1 = Hero(name="Deadpond", secret_name="Dive Wilson") 
hero_2 = Hero(name="Spider-Boy", secret_name="Pedro Parqueador") 
hero_3 = Hero(name="Rusty-Man", secret_name="Tommy Sharp", age=48) 
 
engine = create_engine("sqlite:///database.db") 
 
SQLModel.metadata.create_all(engine) 
with Session(engine) as session: 
    session.add(hero_1) 
    session.add(hero_2) 
    session.add(hero_3) 
    session.commit()

Final Thoughts

These 5 modern Python tools will help you build faster, smarter, and more scalable applications in 2025.

Which library are you most excited to try? Let me know in the comments!