It's the presentation slides I prepared for my college workshop. This demonstrates how you can talk with PostgreSql db using python scripting.For queries, mail at dipeshsuwal@gmail.com
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
This developer-focused webinar will explain how to use the Cypher graph query language. Cypher, a query language designed specifically for graphs, allows for expressing complex graph patterns using simple ASCII art-like notation and offers a simple but expressive approach for working with graph data.
During this webinar you'll learn:
-Basic Cypher syntax
-How to construct graph patterns using Cypher
-Querying existing data
-Data import with Cypher
-Using aggregations such as statistical functions
-Extending the power of Cypher using procedures and functions
An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
This is a introduction to PostgreSQL that provides a brief overview of PostgreSQL's architecture, features and ecosystem. It was delivered at NYLUG on Nov 24, 2014.
http://www.meetup.com/nylug-meetings/events/180533472/
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Introduction To Python' will help you establish a strong hold on all the fundamentals in the Python programming language. Below are the topics covered in this PPT:
Introduction To Python
Keywords And Identifiers
Variables And Data Types
Operators
Loops In Python
Functions
Classes And Objects
OOPS Concepts
File Handling
YouTube Video: https://youtu.be/uYjRzbP5aZs
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Some parts of our applications don't need to be asynchronous or interact with the outside world: it's enough that they are stateful, possibly with the ability to handle failure, context, and logging. Although you can use ZIO 2 or monad transformers for this task, both come with drawbacks. In this presentation, Jorge Vásquez will introduce you to ZPure, a data type from ZIO Prelude, which lets you scale back on the power of ZIO 2, but with the same high-performance, type-inference, and ergonomics you expect from ZIO 2 libraries.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
The goal of this presentation is to broaden your knowledge of Python, exploring some concepts and techniques you might have never heard about. I won't go into too much detail, the goal is only to inspire you to research those features and patterns.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
This presentation focuses on optimization of queries in MySQL from developer’s perspective. Developers should care about the performance of the application, which includes optimizing SQL queries. It shows the execution plan in MySQL and explain its different formats - tabular, TREE and JSON/visual explain plans. Optimizer features like optimizer hints and histograms as well as newer features like HASH joins, TREE explain plan and EXPLAIN ANALYZE from latest releases are covered. Some real examples of slow queries are included and their optimization explained.
This presentation is a great resource for zero-based Python programmers who wants to learn Python 3. This course includes brief history of Python and familiarity of its basic syntax.
A comparison of different solutions for full-text search in web applications using PostgreSQL and other technology. Presented at the PostgreSQL Conference West, in Seattle, October 2009.
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
This is a introduction to PostgreSQL that provides a brief overview of PostgreSQL's architecture, features and ecosystem. It was delivered at NYLUG on Nov 24, 2014.
http://www.meetup.com/nylug-meetings/events/180533472/
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Introduction To Python' will help you establish a strong hold on all the fundamentals in the Python programming language. Below are the topics covered in this PPT:
Introduction To Python
Keywords And Identifiers
Variables And Data Types
Operators
Loops In Python
Functions
Classes And Objects
OOPS Concepts
File Handling
YouTube Video: https://youtu.be/uYjRzbP5aZs
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Some parts of our applications don't need to be asynchronous or interact with the outside world: it's enough that they are stateful, possibly with the ability to handle failure, context, and logging. Although you can use ZIO 2 or monad transformers for this task, both come with drawbacks. In this presentation, Jorge Vásquez will introduce you to ZPure, a data type from ZIO Prelude, which lets you scale back on the power of ZIO 2, but with the same high-performance, type-inference, and ergonomics you expect from ZIO 2 libraries.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
The goal of this presentation is to broaden your knowledge of Python, exploring some concepts and techniques you might have never heard about. I won't go into too much detail, the goal is only to inspire you to research those features and patterns.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
This presentation focuses on optimization of queries in MySQL from developer’s perspective. Developers should care about the performance of the application, which includes optimizing SQL queries. It shows the execution plan in MySQL and explain its different formats - tabular, TREE and JSON/visual explain plans. Optimizer features like optimizer hints and histograms as well as newer features like HASH joins, TREE explain plan and EXPLAIN ANALYZE from latest releases are covered. Some real examples of slow queries are included and their optimization explained.
This presentation is a great resource for zero-based Python programmers who wants to learn Python 3. This course includes brief history of Python and familiarity of its basic syntax.
A comparison of different solutions for full-text search in web applications using PostgreSQL and other technology. Presented at the PostgreSQL Conference West, in Seattle, October 2009.
Connect python application with postgreSQL database using psycopg2.
Perform DDL & DML operations
Create table , Insert/update/delete and select records
Understand connection & cursor class
This is an quick introduction to Scalding and Monoids. Scalding is a Scala library that makes writing MapReduce jobs very easy. Monoids on the other hand promise parallelism and quality and they make some more challenging algorithms look very easy.
The talk was held at the Helsinki Data Science meetup on January 9th 2014.
Introduction to source{d} Engine and source{d} Lookout source{d}
Join us for a presentation and demo of source{d} Engine and source{d} Lookout. Combining code retrieval, language agnostic parsing, and git management tools with familiar APIs parsing, source{d} Engine simplifies code analysis. source{d} Lookout, a service for assisted code review that enables running custom code analyzers on GitHub pull requests.
Dr. Hsieh is teaching how to use the state-of-the-art libraries, Spark by Apache, to conduct data analysis on hadoop platform in ISSNIP 2015, Singapore. He started with teaching the basic operations like “map, reduce, flatten, and more,” followed by explaining the extension of Spark, including MLib, GraphX, and SparkSQL.
Connecting and using PostgreSQL database with psycopg2 [Python 2.7]Dinesh Neupane
This presentation covers the basic idea of connecting postgresql database with python and psycopg2 module.
Covered Topics:
1. Psycopg2 Installation
2. Connecting to PostgreSQL Database
3. Connection Parameters
4. Create and Drop Table
5. Adaptation of Python Values to SQL Types
6. SQL Transactions
7. DML
Advance Map reduce - Apache hadoop Bigdata training by Design PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the Advance Map reduce concepts of Hadoop and Big Data.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Twitter Author Prediction from Tweets using Bayesian NetworkHendy Irawan
Can We Predict the Author from a Tweet?
Most authors have a distinct writing style
... And unique topics to talk about
... And signature distribution of words used to tweet
Can we train Bayesian Network so that occurrence of words in a tweet can be used to infer the author of that tweet?
In summary: YES!
Disclaimer: Accuracy varies
In a test suite with @dakwatuna vs @farhatabbaslaw (very different tweet topics) – 100% prediction accuracy is achieved
Wprowadzenie do technologii Big Data / Intro to Big Data EcosystemSages
Introduction to Hadoop Map Reduce, Pig, Hive and Ambari technologies.
Workshop deck prepared and presented on September 5th 2015 by Radosław Stankiewicz.
During that the day participants had also the possibility to go through prepared tutorials and test their analysis on real cluster.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
2. psycopg is a PostgreSQL database adapter for
the Python_ programming language. This is
version 2, a complete rewrite of the original
code to provide new-style classes for
connection and cursor objects and other sweet
candies.
Homepage:
http://initd.org/projects/psycopg2
3. PyGreSQL – PostgreSQL module for Python
PyGreSQL is an open-source Python module
that interfaces to a PostgreSQL database. It
embeds the PostgreSQL query library to allow
easy use of the powerful PostgreSQL features
from a Python script.
5. Linux
Look for a package such as python-psycopg2
sudo apt-get install python-psycopg2 - to install
the package with all its dependencies.
Windows
http://www.stickpeople.com/projects/python/win-psycopg/
http://initd.org/psycopg/
6. Binary(...) Binary(buffer) -> new binary object
Build an object capable to hold a binary string value.
Date(...) - Date(year, month, day) -> new date
Build an object holding a date value.
Time(...) - Time(hour, minutes, seconds, tzinfo=None) –> new time
Build an object holding a time value.
Timestamp(...) -
Timestamp(year, month, day, hour, minutes, seconds, tzinfo=None) -
> new timestamp
Build an object holding a timestamp value
7. Create a new database connection and returns a new connection instance.
connect(dsn=None, database=None, user=None, password=None, host=No
ne, port=None, connection_factory=None, cursor_factory=None, async=Fal
se).
Parameters:
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created.
8. It allows to:
create new cursors using the cursor() method to execute
database commands and queries,
commit() - The changes are committed to the database.
rollback() In case of an error, we roll back any possible
changes to our database table.
9. The class cursor allows interaction with the database:
send commands to the database using methods such as execute() and
executemany(),
retrieve data from the database by iteration or using methods such as
fetchone(), fetchmany(), fetchall().
cur.description – to get metadata
copy-to – to copy db tables to a file
copy-from – to copy files to a db
scrollable()
mogrify()
10. import psycopg2
Connect to an existing database
conn=psycopg2.connect("dbname=test user=postgres")
- as a libpq connection string
conn=psycopg2.connect(database=“test” user=“postgres")
- as a set of keyword arguments
Open a cursor to perform database operations
cur = conn.cursor()
11. Psycopg cursor usually fetches all the record from database
during a query, usually which is a large dataset to be handled
in the client side, hence to do controlled transfer of data to the
client,we use server side cursors.
Psycopg wraps the database server side cursor in named
cursors. A named cursor is created using the cursor() method
specifying the name parameter.
To move in the dataset we use scroll() method and scrollable()
method to move backward as well
12. Make the changes to the database persistent
conn.commit()
Close communication with the database
cur.close()
conn.close()
15. cur.execute("DROP TABLE IF EXISTS tablename")
cur.execute("CREATE TABLE tablename (id serial
PRIMARY KEY, num integer, data varchar)“)
cur.executemany() ???????
Create and drop table
16. cur.execute("SELECT * FROM tablename")
cur.execute("INSERT INTO tablename
(num, data) VALUES (%s, %s)", ...
(100, “apple"))
insert
insertmany
17. Obtain data as Python objects
cur.fetchone(),cur.fetchall(),cur.fetchmany(5)
fetchone
fetchall
18. Always be a %s, even if a different placeholder (such as a %d for
integers or %f for floats) may look more appropriate:
cur.execute("INSERT INTO tablename VALUES (%d)", (42,)) # WRONG
cur.execute("INSERT INTO tablenameVALUES (%s)", (42,)) # correct
Named arguments are supported too using %(name)s placeholders.
cur.execute( """INSERT INTO tablename (an_int, a_date,
another_date, a_string) VALUES (%(int)s, %(date)s, %(date)s,
%(str)s);""", {'int': 10, 'str': "O'Reilly", 'date': datetime.date(2005,
11, 18)})
19. The Python string operator % is not used: the execute() method accepts a
tuple or dictionary of values as second parameter.
For positional variables binding, the second argument must always be a
sequence, even if it contains a single variable:
cur.execute("INSERT INTO foo VALUES (%s)", "bar") # WRONG
cur.execute("INSERT INTO foo VALUES (%s)", ("bar")) # WRONG
cur.execute("INSERT INTO foo VALUES (%s)", ("bar",)) # correct
cur.execute("INSERT INTO foo VALUES (%s)", ["bar"]) # correct
20. Python PostgreSQL
None NULL
Bool Bool
Float Real,double
Int,long Smallint,integer,bigint
Decimal Numeric
Str Varchar
Date Date
Datetime Timestamp
List array
Many standard Python types are adapted into SQL and returned as Python objects
when a query is executed.
Psycopg casts Python variables to SQL literals by type.