SQLAlchemy
Pandas
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SQLAlchemy | Pandas | |
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122 | 393 | |
8,611 | 41,678 | |
3.2% | 1.6% | |
9.8 | 10.0 | |
5 days ago | 4 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SQLAlchemy
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Introducing Flama for Robust Machine Learning APIs
Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.
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Unlocking efficient authZ with Cerbos’ Query Plan
To simplify this process, Cerbos developers have come up with adapters for popular Object-Relational Mapping (ORM) frameworks. You can check out for more details on the query plan repo - which also contains adapters for Prisma and SQLAlchemy - as well as a fully functioning application using Mongoose as its ORM.
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Python: Just Write SQL
That above pattern is one I've seen people do even recently, using the "select().c" attribute which from very early versions of SQLAlchemy is defined as "the columns from a subquery of the SELECT" ; this usage began raising deprecation warnings in 1.4 and is fully removed in 2.0 as it was a remnant of a much earlier version of SQLAlchemy. it will do exactly as you say, "make a subquery for each filter condition".
the moment you see SQLAlchemy doing something you see that seems "asinine", send an example to https://github.com/sqlalchemy/sqlalchemy/discussions and I will clarify what's going on, correct the usage so that the query you have is what you expect, and quite often we will add new warnings or documentation when we see people doing things we didn't anticipate.
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A steering council note about making the global
The creator and lead maintainer of SQLAlchemy, one of the most popular and most used Python library for accessing databases (who doesn't?) gave a rather interesting response to PEP703.
If this doesn't ring any alarm bells I don't know what will.
> Basically for the moment the GIL-less idea would likely be burdensome for us and the fact that it's only an "option" seems to strongly imply major compatibility issues that we would not prefer.
https://github.com/sqlalchemy/sqlalchemy/discussions/10002#d...
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More public SQL-queryable databases?
Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.
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Day 46-47: Beginner FastAPI Series - Part 3
Our tool we're going to be using for interfacing with the SQLite database is SQLAlchemy, a SQL toolkit that provides a unified API for various relational databases. If you installed FastAPI with pip install "fastapi[all]", SQLAlchemy is already part of your setup. but if you opted for FastAPI alone, you would need to install SQLAlchemy separately with pip install sqlalchemy.
- Is there a Python module that can store data between runs?
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Starlite updates March '22 | 2.0 is coming
This feature is yet to be released, but it will allow you to seamlessly use data modelled with for example Pydantic, SQLAlchemy, msgspec or dataclasses in your route handlers, without the need for an intermediary model; The conversion will be handled by the specific DTO "backend" implementation. This new paradigm also makes it trivial to add support for any such modelling library, by simply implementing an appropriate backend.
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Reddit Recap Series: Backend Performance Tuning
The second problem was caused by the pgBouncer setup. pgBouncer is an impostor that owns several dozen of real PostgreSQL connections, but pretends that it has thousands of them available for the backend services. Similar to fractional-reserve banking. So, it needs a way to find out when the real DB connection becomes free and can be used by another service. Our pgBouncer was configured as pool_mode=transaction. I.e., it detected when the current transaction was over, and returned the PostgreSQL connection into the pool, making it available to other users. However, this mode was found to not work well with the code that was using SQLAlchemy: committing the current transaction immediately started a new one. So, the expensive connection between pgBouncer and PostgreSQL remained checked out as long as the connection from service to pgBouncer remained open (forever, or close to that).
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Domain model with SQLAlchemy
In this blog post, we will explore the power of SQLAlchemy, a popular ORM library in Python, to model our domain objects.
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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How to Build and Deploy a Machine Learning model using Docker
Pandas
What are some alternatives?
tortoise-orm - Familiar asyncio ORM for python, built with relations in mind
PonyORM - Pony Object Relational Mapper
Peewee - a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Orator - The Orator ORM provides a simple yet beautiful ActiveRecord implementation.
prisma-client-py - Prisma Client Python is an auto-generated and fully type-safe database client designed for ease of use
tensorflow - An Open Source Machine Learning Framework for Everyone
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
pyDAL - A pure Python Database Abstraction Layer
GINO - GINO Is Not ORM - a Python asyncio ORM on SQLAlchemy core.
psycopg2 - PostgreSQL database adapter for the Python programming language
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows