Search Results for "sql sorting queries"

Welcome to the project page for pyDaSSQLiteMan (Python Data and SQLite Manager). This application is a Python Tkinter application designed to enable easy SQLite database file manipulation, and an easy method to run SQL styled queries on CSV files. If you're not downloading the pre-compiled executable you will need Python to run this app. You can download it from http://www.python.org/ . I'd recommend something after version 3. Testing has been Windows based. SQLite is a free database format released into the public domain. See http://www.sqlite.org/ Please check out the Wiki Home page https://sourceforge.net/p/pycsvdb/wiki/Home/ and the General Help page (https://sourceforge.net/p/pycsvdb/wiki/General%20Help/) to find extra information and help. ...

SQL with Python in the middle. SPyQL is a query language that combines the simplicity and structure of SQL with the power and readability of Python. SPyQL offers a command-line interface that allows running SPyQL queries on top of text data (e.g. CSV, JSON). Data can come from files but also from data streams, such as as Kafka, or from databases such as PostgreSQL.

Project has moved to: https://github.com/OptimalBPM/qal QAL is a collection of libraries for mining, transforming and writing data from and to a number of places. Sources and destinations include different SQL and NoSQL backends, file formats like .csv, XML and excel. Even untidy HTML web pages. It has a database abstraction layer that supports connectivity to Postgres, MySQL, DB2, Oracle, MS SQL server. JSON and MongoDB is coming. It uses XML/JSON formats(self-generated SQL schemas) for representing queries, transformation and merging, making it scriptable. ...

Python library provides the briefest way to construct SQL queries in JSON form. Simple example: ('*','table',id:[1,3]) is Select * from table where id in [1,3]. Sqlalchemy patch module is provided to extend sqlalchemy with convenient jsonSQL meth

A cgi program written in Python that turns HTTP GET and POST queries into SQL queries, runs them, turns the results into XML, and then returns the XML as the response to the HTTP query. It also supports returning a single BLOB as the whole response.

For 2D data objects in Python, you have 3 main options: - Numpy Array - Pandas DataFrame (built on np.array) - SQL table Numpy and Pandas are great for working with a complete set of data, but not very efficient for building up row by row. SQL is good for building up the object row by row, but you have to write SQL and leave the world of Python objects. PySimpleTable tries to find the middle ground with more flexibility than Numpy or Pandas for incrementally building a 2D object without needing to use SQL. ...