How to query pandas DataFrames with SQL

This page summarizes the projects mentioned and recommended in the original post on dev.to

Scout Monitoring - Free Django app performance insights with Scout Monitoring
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InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
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  • jinjasql

    Template Language for SQL with Automatic Bind Parameter Extraction

  • Since Deepnote uses jinjasql templating, you can pass Python variables, functions, and control structures (e.g., "if" statements and "for" loops) into your SQL queries.

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

    Scout Monitoring logo
  • SQLAlchemy

    The Database Toolkit for Python

  • There are multiple ways to run SQL queries in a Jupyter notebook, but this tutorial will focus on using SQLAlchemy --- a Python library that provides an API for connecting to and interacting with different relational databases, including SQLite, MySQL, and PostgreSQL.

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  • Pandas is a go-to tool for tabular data management, processing, and analysis in Python, but sometimes you may want to go from pandas to SQL.

  • cheatsheets

    Official Matplotlib cheat sheets (by matplotlib)

  • Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more.

  • Keras

    Deep Learning for humans

  • Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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