This sample code shows how to connect to the AIQUM Database and pull userquota details from it. AIQUM Requirements: 1. AIQUM 9.7 or higher. 2. An AIQUM “Database User” account with the “Report Schema” role. Python Requirements: 1. The mysql-connector-python module must be installed. AIQUM Database Schema documentation is on the NetApp Support Site: https://mysupport.netapp.com/documentation/docweb/index.html?productID=63834 Using aiqum_user_quota_report.py

sqlmodel SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python type annotations, and powered by Pydantic and SQLAlchemy. The key features are: Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. […]

Mini-Twitter-Database This was done as a database design course project at Amirkabir university of technology. This is a relational database management system that is built using MySQL and works with CLI in python. This project contains the following files: tables.sql: A DDL script to generate relational tables for our mini-twitter schema including relevant constraints. procedures.sql: Several procedures to perform complex queries. triggers.sql: Some triggers that are sensitive to a particular event and perform specific tasks as they occur. Relational_schema.png: Relational […]

Datargsing Datargsing is a data management and manipulation Python library which is currently in deving Why this library is good? This Python library has classes and methods with a detailled documentation directly in you ide (if it supports documentation) and this library is really simple and logical GitHub https://github.com/LTHCTheMaster/Datargsing    

montydb Monty, Mongo tinified. MongoDB implemented in Python! What is it? A pure Python-implemented database that looks and works like MongoDB. >>> from montydb import MontyClient >>> col = MontyClient(“:memory:”).db.test >>> col.insert_many( [{“stock”: “A”, “qty”: 6}, {“stock”: “A”, “qty”: 2}] ) >>> cur = col.find( {“stock”: “A”, “qty”: {“$gt”: 4}} ) >>> next(cur) {‘_id’: ObjectId(‘5ad34e537e8dd45d9c61a456’), ‘stock’: ‘A’, ‘qty’: 6} Most of the CRUD operators have been implemented. You can visit issue #14 to see the full list. This project is […]

sql2nosql Migrate data from SQL to NoSQL easily. Installation 💯 pip install sql2nosql –upgrade Dependencies 📢 For the package to work, it first needs “clients”, which are other packages that are in charge of managing the data in the database. Most of them work very similar, as in the case of ‘mysql-connector’ and ‘pymysql’ for MySQL databases, and ‘PyMongo’ for MongoDB databases. For example, the parameter ‘sql_client’ of the Migrator() class, receives by parameter a string where it is indicated […]

psycopg2 Psycopg is the most popular PostgreSQL database adapter for the Python programming language. Its main features are the complete implementation of the Python DB API 2.0 specification and the thread safety (several threads can share the same connection). It was designed for heavily multi-threaded applications that create and destroy lots of cursors and make a large number of concurrent “INSERT”s or “UPDATE”s. Psycopg 2 is mostly implemented in C as a libpq wrapper, resulting in being both efficient and […]

MPContribs MPContribs enables materials researchers to contribute experimental and theoretical materials data to Materials Project (MP). Data on MPContribs is collectively maintained as annotations to existing MP materials and automatically exposed to over 130,000 MP users. MPContribs disseminates contributions as preview cards on MP’s materials details pages, in form of domain-specific interactive landing pages on its portal, and programmatically through its API. MPContribs deployments dedicated to datasets for Machine Learning and from DOE Light Sources are currently being built up. […]

Random database/dataframe generator Often, beginners in SQL or data science struggle with the matter of easy access to a large sample database file (.DB or .sqlite) for practicing SQL commands. Would it not be great to have a simple tool or library to generate a large database with multiple tables, filled with data of one’s own choice? After all, databases break every now and then and it is safest to practice with a randomly generated one 🙂 While it is […]

shillelagh Shillelagh is a Python DB API and SQLAlchemy dialect for querying non-SQL resources like APIs and files. You can use it to write queries like this: INSERT INTO “csv:///path/to/file.csv” SELECT time, chance_of_rain FROM “https://api.weatherapi.com/v1/history.json?key=XXX&q=London” WHERE strftime(‘%Y-%m-%d’, time) IN ( SELECT day FROM “https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=2064361835” ) The query above reads holidays from a Google Sheet, uses the days to get weather data from WeatherAPI, and writes the change of rain at each hour of the holidays into a (pre-existing) CSV file. […]