This article will give you an introduction to installing PostgreSql modules.
- Learn how to query the key-value pairs with hstore
- Store and validate ISBN numbers with isn
- Store encrypted data with chkpass
- Do partial keyword match (fuzzy string matching) with fuzzystrmatch
Cassandra uses a distributed architecture with consistent hashing to distribute data across nodes in a cluster. It provides high availability and partition tolerance by replicating data across multiple nodes and data centers. The coordinator node handles read and write requests from clients by interacting with the necessary replica nodes to satisfy the requested consistency level. Cassandra stores data both in memory and on disk for high performance and durability. It uses commit logs, memtables, and SSTables to manage data writes, caches for efficient reads, and a gossip protocol to detect node failures.
This document discusses indexing and query optimization in MongoDB. It provides an overview of how indexes work, how to create and manage indexes, and how the query optimizer uses indexes to efficiently search for documents. It explains that compound indexes can help queries that search on multiple fields and that the explain() method and database profiler can help analyze query performance and determine if an index is being used effectively.
The document provides an introduction to basic MySQL commands for logging in, creating and modifying database structure (DDL commands), retrieving and modifying data (DML commands), managing transactions (TCL commands), controlling access (DCL commands), and other common commands like SET, DESCRIBE, SHOW, and SHUTDOWN. It explains what each type of command is used for and provides examples.
The document discusses structures in C++. It defines a structure called Employee that contains data members for an employee's first name, last name, and date of birth. It shows how to declare a structure variable Emp initialized with values, and print out the employee's details using dot operators to access individual data members like Emp.dob.month. Structures allow storing different data types together and can be nested, like including a date structure within the Employee structure.
This document provides instructions and examples for using the MySQL database system. It discusses MySQL concepts like database, tables, rows, and columns. It also demonstrates common SQL commands like CREATE, SELECT, INSERT, UPDATE, DROP. Examples show how to create databases and tables, insert and query data, use functions, conditions and wildcards. Script files demonstrate populating tables with sample data.
This document provides instructions on installing and configuring MySQL on Linux. It discusses downloading and installing the MySQL RPM package, setting the root password for security, starting the MySQL server and client, and running basic queries to test the installation. It also covers additional MySQL commands and configurations including user privileges, database design, backups, and restoring data.
The document provides an overview of accessing and using MySQL with PHP. It discusses MySQL database structure and syntax, common MySQL commands, data types in MySQL, and how PHP fits with MySQL. It also covers topics like connecting to a MySQL database with PHP, creating and manipulating database tables, inserting and retrieving data, and maintaining state with cookies and sessions.
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
Cassandra uses a distributed architecture with consistent hashing to distribute data across nodes in a cluster. It provides high availability and partition tolerance by replicating data across multiple nodes and data centers. The coordinator node handles read and write requests from clients by interacting with the necessary replica nodes to satisfy the requested consistency level. Cassandra stores data both in memory and on disk for high performance and durability. It uses commit logs, memtables, and SSTables to manage data writes, caches for efficient reads, and a gossip protocol to detect node failures.
This document discusses indexing and query optimization in MongoDB. It provides an overview of how indexes work, how to create and manage indexes, and how the query optimizer uses indexes to efficiently search for documents. It explains that compound indexes can help queries that search on multiple fields and that the explain() method and database profiler can help analyze query performance and determine if an index is being used effectively.
The document provides an introduction to basic MySQL commands for logging in, creating and modifying database structure (DDL commands), retrieving and modifying data (DML commands), managing transactions (TCL commands), controlling access (DCL commands), and other common commands like SET, DESCRIBE, SHOW, and SHUTDOWN. It explains what each type of command is used for and provides examples.
The document discusses structures in C++. It defines a structure called Employee that contains data members for an employee's first name, last name, and date of birth. It shows how to declare a structure variable Emp initialized with values, and print out the employee's details using dot operators to access individual data members like Emp.dob.month. Structures allow storing different data types together and can be nested, like including a date structure within the Employee structure.
This document provides instructions and examples for using the MySQL database system. It discusses MySQL concepts like database, tables, rows, and columns. It also demonstrates common SQL commands like CREATE, SELECT, INSERT, UPDATE, DROP. Examples show how to create databases and tables, insert and query data, use functions, conditions and wildcards. Script files demonstrate populating tables with sample data.
This document provides instructions on installing and configuring MySQL on Linux. It discusses downloading and installing the MySQL RPM package, setting the root password for security, starting the MySQL server and client, and running basic queries to test the installation. It also covers additional MySQL commands and configurations including user privileges, database design, backups, and restoring data.
The document provides an overview of accessing and using MySQL with PHP. It discusses MySQL database structure and syntax, common MySQL commands, data types in MySQL, and how PHP fits with MySQL. It also covers topics like connecting to a MySQL database with PHP, creating and manipulating database tables, inserting and retrieving data, and maintaining state with cookies and sessions.
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
A database is a collection of data organized in tables that can be queried and manipulated. A database management system (DBMS) allows users to add, insert, retrieve, change, and delete data from the database. The relational model represents data in tables with rows and columns and allows queries using SQL. The Perl DBI provides a standardized interface to connect to and interact with different database systems from Perl code.
This document discusses various Python data structures including lists, tuples, dictionaries, and sets. It provides examples of how to create, access, update, and delete elements in each data structure. For lists, examples demonstrate how to create and modify lists, access elements, and use common list methods. Tuples are explained as immutable lists that cannot be modified. Dictionaries are described as storing elements through key-value pairs. Sets are defined as unordered collections of unique elements.
MySQL is an open-source relational database management system that can be installed on Linux and Windows. The document provides step-by-step instructions for installing and configuring MySQL and describes common SQL commands for creating and managing databases, tables, and data. Key MySQL features and administration tasks such as backup, restoration, user and privilege management are also overviewed.
elrang, a general-purpose, concurrent, functional programming language. (https://en.wikipedia.org/wiki/Erlang_(programming_language)
this slide describe the language, based on this book.
learn you some erlang - (http://learnyousomeerlang.com/)
this slide covers -
9. A short visit to common data structures
10. the hitchhiker's guide to concurrency
Indexing and Query Optimizer (Richard Kreuter)MongoDB
The document discusses indexing and query optimization in MongoDB. It covers indexing basics, when indexes can and cannot be used, creating and maintaining indexes, and using explain() to understand query plans. The query optimizer is empirical rather than cost-based, and hint() can be used to force a specific query plan.
This document discusses building price models using data mining techniques. It describes creating a wine price dataset based on wine rating and age, with price determined by a wineprice function. The dataset is then used to test k-nearest neighbors (k-NN) algorithms and weighted k-NN algorithms for price estimation. Cross-validation and handling of non-homogeneous variables like bottle size and aisle location are also covered. Optimization techniques like hill climbing, simulated annealing, and genetic algorithms are applied to find optimal weight values for variables in the weighted k-NN algorithm.
The document discusses various Python standard library modules. It introduces string formatting methods like format() and regular expressions. It also covers datetime and calendar modules for working with dates and times, collections for specialized container datatypes, and numeric and math modules. Functions like map(), filter() and modules like functools, itertools, os, sys are also mentioned. The document provides examples of using these modules.
This document provides an overview and instructions for installing and using the MySQL database system. It describes MySQL's client-server architecture, how to connect to the MySQL server using the command line client, and provides examples of common SQL commands for creating databases and tables, inserting, selecting, updating, and deleting rows of data. It also introduces some basic SQL functions and provides SQL scripts as examples to create tables and insert data.
The document discusses indexing and query optimization in MongoDB. It covers indexing basics, when indexes can and cannot be used, creating and maintaining indexes, and using explain() to understand query plans. The query optimizer is empirical and tries different plans to select the fastest, and hint() can be used to force a specific plan.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
This document provides an overview of SQL analytic queries and tips and tricks, mostly related to PostgreSQL. It begins with an introduction on the topics to be covered, including SQL basics, advanced topics, and a conclusion. It then shares some lesser known facts about SQL, including that it is standardized, turing complete, and the only successful 4th generation programming language. The document reviews the revision history of SQL standards from 1986 to the present. It provides examples of common table expressions, temporary tables, unnesting and aggregation, subqueries, and lateral joins in SQL.
The document discusses the EXPLAIN statement in MySQL. It provides examples of using the traditional EXPLAIN output and the new JSON format for EXPLAIN. The JSON format provides more detailed information about the query plan and execution in a structured format. It allows seeing things like how conditions are split and when subqueries are evaluated.
This document provides an overview of topics covered on Day 1 of a Python training, including strings, control flow, and data structures. Strings topics include methods, formatting, and Unicode. Control flow covers conditions, loops (for and while), and range. Data structures discussed are tuples, lists, dictionaries, and sorting. The document concludes with an overview of topics for the next session, including functions, object-oriented programming, and Python packaging.
The document discusses Relational Database Management Systems (RDBMS). It defines key concepts such as data, database, DBMS, RDBMS and provides examples of how data is structured in tables with rows and columns. It also summarizes common RDBMS features like SQL queries, data types, integrity constraints, functions and joins. Overall, the document provides a high-level overview of RDBMS components and functionality.
SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.
Sexy JavaScript with lodash and ES6
Declarative programing is all about intent. It's about writing code that focuses on what we want to do, not how we do it. Declarative code is elegant and easy to change and maintain. Functional programing is a paradigm that helps writing such code, and libraries such as underscore.js and lodash are a huge help. This talk is about sprinkling ES6 syntax on top of lodash in order to write sexy, compact and declarative code.
Developing Applications with MySQL and Java for beginnersSaeid Zebardast
A presentation about Developing Applications with MySQL and Java for beginners. It includes the following topics:
- Requirements
- MySQL Data Definitions
- Java Classes
- MySQL Connector (JDBC)
- Define Methods
- Compile and Run
Thomas Rückstieß gave a presentation on indexing and query optimization in MongoDB. He discussed what indexes are, why they are needed, how to create and manage indexes, and how to optimize queries. He emphasized that absent or suboptimal indexes are a common performance problem and outlined some common indexing mistakes to avoid, such as trying to use multiple indexes per query, low selectivity indexes, and queries that cannot use indexes like regular expressions and negation.
Reducing Development Time with MongoDB vs. SQLMongoDB
Buzz Moschetti compares the development time and effort required to save and fetch contact data using MongoDB versus SQL over the course of two weeks. With SQL, each time a new field is added or the data structure changes, the SQL schema must be altered and code updated in multiple places. With MongoDB, the data structure can evolve freely without changes to the data access code - it remains a simple insert and find. By day 14, representing the more complex data structure in SQL would require flattening some data and storing it in non-ideal ways, while MongoDB continues to require no changes to the simple data access code.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. It also covers modifying the database using INSERT, DELETE, ALTER and DROP statements.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the history and standards of SQL, the data definition language for creating tables with attributes and constraints, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
A database is a collection of data organized in tables that can be queried and manipulated. A database management system (DBMS) allows users to add, insert, retrieve, change, and delete data from the database. The relational model represents data in tables with rows and columns and allows queries using SQL. The Perl DBI provides a standardized interface to connect to and interact with different database systems from Perl code.
This document discusses various Python data structures including lists, tuples, dictionaries, and sets. It provides examples of how to create, access, update, and delete elements in each data structure. For lists, examples demonstrate how to create and modify lists, access elements, and use common list methods. Tuples are explained as immutable lists that cannot be modified. Dictionaries are described as storing elements through key-value pairs. Sets are defined as unordered collections of unique elements.
MySQL is an open-source relational database management system that can be installed on Linux and Windows. The document provides step-by-step instructions for installing and configuring MySQL and describes common SQL commands for creating and managing databases, tables, and data. Key MySQL features and administration tasks such as backup, restoration, user and privilege management are also overviewed.
elrang, a general-purpose, concurrent, functional programming language. (https://en.wikipedia.org/wiki/Erlang_(programming_language)
this slide describe the language, based on this book.
learn you some erlang - (http://learnyousomeerlang.com/)
this slide covers -
9. A short visit to common data structures
10. the hitchhiker's guide to concurrency
Indexing and Query Optimizer (Richard Kreuter)MongoDB
The document discusses indexing and query optimization in MongoDB. It covers indexing basics, when indexes can and cannot be used, creating and maintaining indexes, and using explain() to understand query plans. The query optimizer is empirical rather than cost-based, and hint() can be used to force a specific query plan.
This document discusses building price models using data mining techniques. It describes creating a wine price dataset based on wine rating and age, with price determined by a wineprice function. The dataset is then used to test k-nearest neighbors (k-NN) algorithms and weighted k-NN algorithms for price estimation. Cross-validation and handling of non-homogeneous variables like bottle size and aisle location are also covered. Optimization techniques like hill climbing, simulated annealing, and genetic algorithms are applied to find optimal weight values for variables in the weighted k-NN algorithm.
The document discusses various Python standard library modules. It introduces string formatting methods like format() and regular expressions. It also covers datetime and calendar modules for working with dates and times, collections for specialized container datatypes, and numeric and math modules. Functions like map(), filter() and modules like functools, itertools, os, sys are also mentioned. The document provides examples of using these modules.
This document provides an overview and instructions for installing and using the MySQL database system. It describes MySQL's client-server architecture, how to connect to the MySQL server using the command line client, and provides examples of common SQL commands for creating databases and tables, inserting, selecting, updating, and deleting rows of data. It also introduces some basic SQL functions and provides SQL scripts as examples to create tables and insert data.
The document discusses indexing and query optimization in MongoDB. It covers indexing basics, when indexes can and cannot be used, creating and maintaining indexes, and using explain() to understand query plans. The query optimizer is empirical and tries different plans to select the fastest, and hint() can be used to force a specific plan.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
This document provides an overview of SQL analytic queries and tips and tricks, mostly related to PostgreSQL. It begins with an introduction on the topics to be covered, including SQL basics, advanced topics, and a conclusion. It then shares some lesser known facts about SQL, including that it is standardized, turing complete, and the only successful 4th generation programming language. The document reviews the revision history of SQL standards from 1986 to the present. It provides examples of common table expressions, temporary tables, unnesting and aggregation, subqueries, and lateral joins in SQL.
The document discusses the EXPLAIN statement in MySQL. It provides examples of using the traditional EXPLAIN output and the new JSON format for EXPLAIN. The JSON format provides more detailed information about the query plan and execution in a structured format. It allows seeing things like how conditions are split and when subqueries are evaluated.
This document provides an overview of topics covered on Day 1 of a Python training, including strings, control flow, and data structures. Strings topics include methods, formatting, and Unicode. Control flow covers conditions, loops (for and while), and range. Data structures discussed are tuples, lists, dictionaries, and sorting. The document concludes with an overview of topics for the next session, including functions, object-oriented programming, and Python packaging.
The document discusses Relational Database Management Systems (RDBMS). It defines key concepts such as data, database, DBMS, RDBMS and provides examples of how data is structured in tables with rows and columns. It also summarizes common RDBMS features like SQL queries, data types, integrity constraints, functions and joins. Overall, the document provides a high-level overview of RDBMS components and functionality.
SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.
Sexy JavaScript with lodash and ES6
Declarative programing is all about intent. It's about writing code that focuses on what we want to do, not how we do it. Declarative code is elegant and easy to change and maintain. Functional programing is a paradigm that helps writing such code, and libraries such as underscore.js and lodash are a huge help. This talk is about sprinkling ES6 syntax on top of lodash in order to write sexy, compact and declarative code.
Developing Applications with MySQL and Java for beginnersSaeid Zebardast
A presentation about Developing Applications with MySQL and Java for beginners. It includes the following topics:
- Requirements
- MySQL Data Definitions
- Java Classes
- MySQL Connector (JDBC)
- Define Methods
- Compile and Run
Thomas Rückstieß gave a presentation on indexing and query optimization in MongoDB. He discussed what indexes are, why they are needed, how to create and manage indexes, and how to optimize queries. He emphasized that absent or suboptimal indexes are a common performance problem and outlined some common indexing mistakes to avoid, such as trying to use multiple indexes per query, low selectivity indexes, and queries that cannot use indexes like regular expressions and negation.
Reducing Development Time with MongoDB vs. SQLMongoDB
Buzz Moschetti compares the development time and effort required to save and fetch contact data using MongoDB versus SQL over the course of two weeks. With SQL, each time a new field is added or the data structure changes, the SQL schema must be altered and code updated in multiple places. With MongoDB, the data structure can evolve freely without changes to the data access code - it remains a simple insert and find. By day 14, representing the more complex data structure in SQL would require flattening some data and storing it in non-ideal ways, while MongoDB continues to require no changes to the simple data access code.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. It also covers modifying the database using INSERT, DELETE, ALTER and DROP statements.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the history and standards of SQL, the data definition language for creating tables with attributes and constraints, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries, string operations and more. Examples are provided throughout to illustrate SQL concepts and syntax.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. The document is made up of multiple slides that cover these SQL topics at a high-level.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of SQL and relational database concepts. It describes the history and standards of SQL, data definition and domain types in SQL, basic query structure including the SELECT, FROM, and WHERE clauses, and DML operations like INSERT, DELETE, and ALTER TABLE. Examples of table schemas and queries involving joins, aggregation, and renaming are provided to illustrate SQL syntax and capabilities.
The document discusses various methods for reading data into R from different sources:
- CSV files can be read using read.csv()
- Excel files can be read using the readxl package
- SAS, Stata, and SPSS files can be imported using the haven package functions read_sas(), read_dta(), and read_sav() respectively
- SAS files with the .sas7bdat extension can also be read using the sas7bdat package
This document provides an overview of Python libraries for data analysis and data science. It discusses popular Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn and visualization libraries like matplotlib and Seaborn. It describes the functionality of these libraries for tasks like reading and manipulating data, descriptive statistics, inferential statistics, machine learning and data visualization. It also provides examples of using these libraries to explore a sample dataset and perform operations like data filtering, aggregation, grouping and missing value handling.
This document discusses various data structures in C#, including arrays, lists, queues, stacks, hash tables, and more. It provides code examples and explains the time complexity of common operations for each data structure. Asymptotic analysis and big-O notation are introduced for analyzing how efficiently a data structure handles operations as its size increases.
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docxcarliotwaycave
INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a job or task that you know how to do and then write a minimum of 2 full pages, maximum of 3 full pages, Informative Essay teaching the reader how to do that job or task. You will follow the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the information in Chapter 10.5 in our text on Process Analysis helpful. The lecture notes will really be the most important to read in writing this assignment. However, here is a link to that chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach. As the notes explain, this should be a job or task that you already know how to do, and it should be something you can do well. At this point, think about your audience (reader). Will your reader need any knowledge or experience to do this job or task, or will you write these instructions for a general reader where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer advice on this organization process. Be sure to include an introductory paragraph that has the four main points presented in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages long, maximum of 3 full pages long. You will use the MLA formatting that you used in previous essays from Units 3, 4, and 5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several times for revision and editing. It would be helpful to have at least one other person proofread it as well before submitting the assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1 through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE, na="",col.names=TRUE, sep=",")
head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
This document provides an overview of SQL (Structured Query Language) and how it can be used to access and manipulate data within relational database management systems (RDBMS). It describes what SQL is, common SQL commands like SELECT, INSERT, UPDATE and DELETE, SQL data types, database tables, and key clauses like WHERE that are used to filter SQL queries. Examples are provided throughout to illustrate SQL syntax and usage.
Data Manipulation with Numpy and Pandas in PythonStarting with NOllieShoresna
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
Interface Python with MySQL connectivity.pptxBEENAHASSINA1
The document discusses connecting a Python application to a MySQL database. It provides steps to install the mysql.connector package to bridge the connection between Python and MySQL. It explains how to open a connection, create a cursor, execute queries to retrieve and manipulate data, and extract results. Methods shown include using cursors to fetch data row by row, parameterized queries using placeholders, and performing INSERT, UPDATE and DELETE operations with commit.
This document provides an introduction and overview of Cassandra and NoSQL databases. It discusses the challenges faced by modern web applications that led to the development of NoSQL databases. It then describes Cassandra's data model, API, consistency model, and architecture including write path, read path, compactions, and more. Key features of Cassandra like tunable consistency levels and high availability are also highlighted.
This document provides an overview and tutorial for using SQLite database tools in Android application development. It discusses the SQLiteOpenHelper and SQLiteDatabase classes, which are used to create, open, and manage an app's database. It then demonstrates how to create a database manager class that extends SQLiteOpenHelper to define the database structure, and includes methods like addRow(), deleteRow(), and updateRow() to interact with the database. The goal is to build reusable database functionality that can be included in most Android apps.
The document provides information about MySQL including:
1. MySQL is an open source relational database management system based on SQL that is used to add, remove, and modify information in databases.
2. It describes basic MySQL commands like CREATE TABLE, DROP TABLE, SELECT, INSERT, UPDATE, and provides syntax examples.
3. It also covers advanced commands, functions in MySQL like aggregate functions, numeric functions and string functions as well as stored procedures.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
Similar to PostgreSQL Modules Tutorial - chkpass, hstore, fuzzystrmach, isn (20)
In Part I of this tutorial (http://scr.bi/L412S2), we looked at how to visualise a shapefile, add styles and query the attributes table. In this part, let us explore how to perform geoprocessing, plot custom data and prepare a map for publishing.
By - Sagar Arlekar, Niket Narang
This document provides an introduction to making maps using the open source desktop tool QGIS. It explains how to visualize shapefiles in QGIS, style layers by adjusting symbols, colors, and labels. Attributes for each feature can be viewed and queried in the attributes table. Selections can be saved as new shapefiles. The tutorial uses Alaska data and shapefiles of airports to demonstrate these mapping functions in QGIS.
The document outlines a 5 phase business plan for Foodlets, a service that shares information about food items including pictures, names, locations, and prices. Phase I involves building the Foodlets.in website. Phase II collects data on the best food in the local area. Phase III allows food makers to gain business and food lovers to find good options. Phase IV provides a service to humanity. Phase V has the team cashing in brownie points for loads of money. The document asks what April Fool's prank readers played and provides social media links to share stories.
Rails Plugins - Linux For You, March 2011 IssueSagar Arlekar
'Linux For You' article by http://foodlets.in founders Govind Naroji and Sagar Arlekar.
This is a tutorial on will_paginate (pagination), authlogic + omniauth (authentication) and paperclip (file attachments) plugins.
Getting Started - Creating products and services that make life betterSagar Arlekar
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An interactive session where we shared the Foodlets story and engaged with the students on different aspects of Entrepreneurship and how to look for opportunities around.
Getting Started - Going out and creating a changeSagar Arlekar
This document discusses starting a food sharing social network called Foodlets. It encourages the reader to go out and create change by sharing food photos and reviews on Foodlets. It then provides some facts about how things are easier when you actually do them and how finding help from others. Finally, it suggests some initial steps one can take like academic projects, internships, building a personal brand, and finding a mentor to help take their first steps towards entrepreneurship and changing the world.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
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- Practical examples and best practices to implement right away
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
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Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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1. Admin How To
Installing
and Using
PostgreSQL
Modules
In this article, we will learn how to install and use the PostgreSQL modules chkpass,
fuzzystrmatch, isn and hstore. Modules add different capabilities to a database, like
admin and monitoring tools, new data types, operators, functions and algorithms.
Let’s look at modules that add new data types and algorithms, which will help us to
push some of the application logic to the database.
P
ostgreSQL has been called the ‘most advanced open su postgres
source database’. I have been using it for the last four createdb module_test
years as an RDBMS for Foodlets.in, and as a spatial
data store at CSTEP (Center for Study of Science, Technology Apply the chkpass, fuzzystrmatch, isn and hstore modules
and Policy). PostgreSQL is one piece of software that doesn’t to the module_test database by running the following
fail to impress me every now and then. commands:
Installing the modules psql -d module_test -f chkpass.sql
psql -d module_test -f fuzzystrmatch.sql
Note: I am running Ubuntu 10.04 and PostgreSQL 8.4. psql -d module_test -f isn.sql
psql -d module_test -f hstore.sql
Install the postgresql-contrib package and restart the
database server, then check the contrib directory for the list of Let us now look at an example of how each of the
available modules: modules is used.
sudo apt-get install postgresql-contrib Using chkpass
sudo /etc/init.d/postgresql-8.4 restart The chkpass module will introduce a new data type,
cd /usr/share/postgresql/8.4/contrib/ ‘chkpass’, in the database. This type is used to store an
ls encrypted field, e.g., a password. Let’s see how chkpass
works for a user account table that we create and insert
Create a test database called module_test: two rows into:
88 | March 2012 | LINUX For You | www.LinuxForU.com
2. How To Admin
CREATE TABLE accounts (username varchar(100), password Using isn
chkpass); This module will introduce data types to store
INSERT INTO accounts(username, “password”) VALUES (‘user1’, international standard numbers like International Standard
‘pass1’); Book Numbers (ISBN), International Standard Music
INSERT INTO accounts(username, “password”) VALUES (‘user2’, Numbers (ISMN), International Standard Serial Numbers
‘pass2’); (ISSN), Universal Product Codes (UPC), etc. It will also
add functions to validate data, type-cast numbers from
We can authenticate users with a query like the one older formats to the newer 13-digit formats, and vice-
that follows: versa. Let’s test this module for storing book information:
SELECT count(*) from accounts where username=’user1’ and CREATE TABLE books(number isbn13, title varchar(100))
password = ‘pass1’ INSERT INTO books(“number”, title) VALUES (‘978-03’,
‘Rework’);
The ‘=’ operator uses the eq(column_name, text) in
the module to test for equality. Chkpass uses the Unix The INSERT statement throws an error: Invalid
crypt() function, and hence it is weak; only the first eight input syntax for ISBN number: “978-03”. However, this
characters of the text are used in the algorithm. Chkpass works just fine:
has limited practical use; the pgcrypto module is an
effective alternative. INSERT INTO books(“number”, title) VALUES (‘978-0307463746’,
‘Rework’)
Using fuzzystrmatch
This module installs the soundx(), difference(), To convert a 10-digit ISBN to 13 digits, use the
levenshtein() and metaphone() functions. Soundx() and isbn13() function:
metaphone() are phonetic algorithms—they convert a
text string to a code string based on its pronunciation. INSERT INTO books(“number”, title) VALUES
Difference() and levenshtein() return a numeric value (isbn13(‘0307463745’), ‘Rework’)
based on the similarity of the two input strings. Let’s
now look into the levenshtein() and metaphone() (Actually, the name of the book mentioned here,
functions. The Levenshtein distance between two 'Rework' by Jason Fried, happens to be my favourite
strings is the minimum number of insertions, deletions book on product/project management! I have prescribed
or substitutions required to convert one string to it to all my team-mates.)
another.
Using hstore
SELECT levenshtein(‘foodlets’, ‘booklets’); You must have heard enough about NoSQL and key-
value databases. It’s not always NoSQL vs relational
This query returns 2, as is obvious. databases—with the hstore module, PostgreSQL
The metaphone() function takes a text string and allows you to store data in the form of key-value pairs,
the maximum length of the output code as its two input within a column of a table. Imagine you are processing
parameters. These examples return FTLTS: spreadsheets and you have no idea about the column
headers and the data type of the data in the sheets.
SELECT metaphone(‘foodlets’, 6); That’s when hstore comes to your rescue! Incidentally,
SELECT metaphone(‘fudlets’, 6); hstore takes keys and values as text; the value can
be NULL, but not the key. Let’s create a table with a
If we try to get the Levenshtein distance between the column of type hstore and insert some rows:
returned strings, this returns 0:
CREATE TABLE kv_data( id integer, data hstore)
SELECT levenshtein(‘FTLTS’,’FTLTS’); INSERT into kv_data values
(1, hstore(‘name’, ‘amit’) || hstore(‘city’, ‘bangalore’)),
This means that the two words sound similar. (2, hstore(‘name’, ‘raghu’) || hstore(‘age’, ‘26’)),
Fuzzystrmatch is very helpful in implementing the (3, hstore(‘name’, ‘ram’) || hstore(‘age’, ‘28’));
search feature for a website. Now the search can work with
alternate spellings and misspelled keywords. Reminds you You can create your own keys like ‘height’,
of the ‘Did you mean...’ feature on Google Search, right? ‘favourite_book,’ etc. The ‘||’ operator is used for
www.LinuxForU.com | LINUX For You | March 2012 | 89
3. Admin How To
concatenation. Now that we have a table and a few
rows of data, let’s look at some SELECT, UPDATE and
DELETE queries. To select rows with the value for ‘city’ as
‘bangalore’, use the following query:
SELECT * from kv_data where data->’city’ = ‘bangalore’
To get the average age across the table (returns 27.0), use
the query given below:
SELECT avg((data->’age’)::integer) age from kv_data;
Here, ::integer is used to type-cast the text value to an
integer, so that math operations can be performed on it.
To select and sort rows by ‘name’ values, use:
SELECT * from kv_data order by data->’name’ desc
Update the ‘city’ value to ‘delhi’ for all rows, as follows:
UPDATE kv_data SET data = data || (‘city’ => ‘delhi’);
Then, delete the ‘age’ key (and values) from all rows, as
shown below:
UPDATE kv_data set data = delete(data, ‘age’)
Next, delete rows with the ‘name’ as ‘amit’:
DELETE from kv_data where data->’name’ = ‘amit’
Although not a full-fledged key-value storage, hstore does
provide us with the flexibility of a key-value database and the
power of SQL queries.
Other useful modules
Here are some other modules you may find useful:
• Pgcrypto provides functions for hashing and
encryption. It supports SHA, MD5, Blowfish, AES
and other algorithms.
• Citext adds a case-insensitive text data type, which
stores text in lower-case form.
• Uuid-ossp provides functions to generate universally
unique identifiers.
• Pg_trgm adds functions to find text similarity based
on trigram matching.
By: Sagar Arlekar
The author is a research engineer at CSTEP, Bengaluru. He
works in the domains of GIS and agent-based simulations. He
co-founded Foodlets.in, a visual food guide built entirely on
open source technologies.
90 | March 2012 | LINUX For You | www.LinuxForU.com