PyDbLite (LifeTime) Activation Code [Mac/Win] - Python 2.3 and above compatible - Pure Python engine - Pythonic API (names and syntax of the interface) - Fully documentation - Very small and fast (even faster than SQLite in most cases) - No migration of data (you add your data in memory) - Can be used as a ORM (Python-Oriented-Relational-Mapping) engine - Can be used as a DB interface library - Strongly Pythonic interface (and language independent) - Tons of supported database backends: SQLite (the most used backend on a platform), MySQL, PostgreSQL, Oracle (with specific version of database setup required) or for the DB2 backend, specific MySQL, PostgreSQL or SQLite connector developed by the author PyDbLite Features: - Full documentation - Very small and fast - Well thought interface - Robust API - Tons of databases supported - Configurable timeout (exception : 10s by default) PyDbLite backends Description: - SQLite - MySQL - PostgreSQL - DB2 - Oracle - Oracle 9i or Oracle 10g is required for the Oracle backend (if Oracle runs on an older linux version). Installation: 1) 'pip install pyblitz' 2) 'pip install pydblite' (for a interactive DB-Interface) If you don't have pip, you can directly download pydblite from github - git clone - Then follow the installation process from the documentation - Or if you want to use the SQLite backend and the sqlite3 module, you can directly download pyblitz-sqlite, a pure-Python implementation of a sqlite3-DB interface. PYDBLITE API - PYDBLITE OPERATIONS ----- - sql_query (sqlite-query) (use the pydblite-sqlite version (if you use SQLite backend) or the pydblite-mysql if you want to use the database mysql interface) - sql_query_timeout (in seconds) - sql_dump (in file path) - sql_create_table (in table name) - sql_drop_table (in table name) - sql_insert_into (in table name, columns) PyDbLite PyDbLite Torrent Download is a light version of PySqlite3. It is an in-memory database engine that can be used as a pure-Python persistence backend for Python (and compatible with Python 2.3+). It supports SQL-like queries and DML (INSERT, UPDATE, DELETE) statements. Some features currently available: In-memory database engine Fast, pure Python Query Language Compatible with Python 2.3+ Supports SQLite, MySQL and PostgreSQL back-end PyDB class for SQL and string expressions PyDB class for databases Asynchronous connection to a database server Can use SQLite as a database server Fully tested and documented Our PySqlite engine is actually an enhanced version of sqlite3 and probably a better choice than anything else if you want a pure Python engine. A: Yogi has already mentioned SQLite but here is a high level overview of why SQLite is "the" database for Python. SQLite is not a SQL database (or relational database in general) and does not require that you learn a special language in order to work with a SQL database. SQLite is a standalone database engine that can run on many platforms, including Windows, Linux, Mac OS X, and others. SQLite requires no operating system and does not depend on a server operating system. Therefore, SQLite does not require a connection to a web server in order to function. SQLite is designed to run in a memory-restricted environment, such as embedded systems (PDAs, cellphones, etc.) and where memory is severely limited. If you're writing an app for a paltry RAM-constrained device, you'll want a lightweight database engine. SQLite is not an embedded database, but it will do for those limited environments. SQLite does not support storage of relational data. SQLite was designed for writing apps that simply need to store and retrieve data. SQLite is not a data mapper or a data access framework. SQLite is not a desktop database. It is a special case of a special case. However, it does serve many roles. In addition to acting as a database engine, SQLite can be used as a file system, a command-line executable, or a one-to-one object-relational mapping. SQLite is also faster than many other database engines for storing small amounts of data. SQLite does 6a5afdab4c PyDbLite Torrent X64 PyDbLite is a Python database engine that supports Python 2.3 and above. It is designed to be a pure-python engine - It does not use any C library (thread-unsafe, with the limitations of the Windows platform) - It is simple to use - It is compatible with Python 2.5 and above - It is compatible with Python 2.2 and above - It is compatible with Python 2.3 and above - It is a pure-python engine, using CPython C-API for database queries - It has no limitations from the database system (you can use db1 and db2 at the same time) - Its version is maintained to be compatible with the last Python - It is pure-python, so it is really fast (but not designed to be the fastest) - It is compatible with SQLite - It is compatible with MySQL - It is compatible with sqlalchemy (but it's not the best) - It is designed to be a fast in-memory engine - It is easy to write queries - It allows fast code to be developed (a lot of pure-python tasks) - It allows fast executions (easy to execute queries in Python lists) - It is very simple and easy to use * Pick a folder and a file extension * Click on "Open" to import all files from that folder In this tutorial, you will learn how to use the Python Data Framework (PyDF) and how to build a database application with PyDF. You will create a database application called "SnowFlakes" which will allow you to store your snowflakes' data and query them. You will create the design of the database and the schema for each table and column using PyDF. You will create the database using PyDF. Finally, you will end up with a database application with your snowflakes. A database application can be created with PyDF in two ways: - you can use the command line to create a database application with PyDF - you can use the editor of PyDF to create a database application In the next tutorial, you will use the editor of PyDF to create a database application. But for now, let's start by creating the database schema. Step 1 : Create a database application with PyDF We create a new database application using PyDF. We use PyDF command line to create What's New in the? ============================================== PyDbLite is a pure-Python database engine which, unlike SQLite, offers : * in-memory data storage * no language parsing overhead * Pythonic syntax * Uses list comprehensions as a query language, instead of SQL. * SQLite and MySQL compatible * Local-stored cache * SQLite emulation * Extensions support for PyDbLite, such as a better query language * Built-in brand of fault tolerance PyDbLite Features: ============================================== * Pure-Python * In-memory * Supports Python 2.3+ * Runs on any platform where Python is present * No language parsing overhead * Uses Python list comprehensions as a query language, instead of SQL * SQLite and MySQL compatible * Local-stored cache * No SQL parsing overhead * Supports SQLite extensions * SQLite and MySQL compatible * Built-in fault tolerance PyDbLite Query Language: ============================================== List comprehension is a Python 2.x feature that was added in Python 2.5. It allows composing and executing multiple Python code-blocks as a unit of code and with very little cost. The syntax is very simple: >>> query = "( select sum(amount) as _sum from transactions " >>> where date_time = "5/3/2007 5:12:00 PM" and location = "NY" )" >>> list(cursor.execute(query)) [34] >>> The result of the above query is being obtained through list comprehension and if we need more than one query to produce the results, we will add a "list com" part like the following: >>> query = "( select sum(amount) as _sum from transactions " >>> where date_time = "5/3/2007 5:12:00 PM" and location = "NY" )" >>> print query ( select sum(amount) as _sum from transactions where date_time = '5/3/2007 5:12:00 PM' and location = 'NY' ) >>> list(cursor.execute(query)) [34] >>> print query ( select sum(amount) as _sum from transactions where date_time = '5/3/2007 5:12:00 PM' and location = 'NY' ) >>> Using list comprehension we are able to run System Requirements For PyDbLite: Windows 7 Processor: 1.6 GHz Dual Core Memory: 4 GB RAM Hard Disk: 6 GB available space Mac OS X 10.8 LinuxQ: RSpec require "x" should_not raise "y" if "x" is not present Using RSpec, I'm trying to write a test that requires the presence of a file and a
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