Jeetendra Jaiswal

Python datetime operations

noreply@blogger.com (Anonymous) — Thu, 21 Mar 2019 22:38:00 +0000

Python has a module named datetime to work with date and time.

Below are few examples of various datetime operations in python.

import time

import datetime

print("Time in seconds: %s" %time.time())

print("Current date_time1: " , datetime.datetime.now()

print("Current date_time2: " ,datetime.datetime.now().strftime("%y-%m-%d-%H-%M"))

print("Current year: ", datetime.date.today().strftime("%Y"))

print("Current month: ", datetime.date.today().strftime("%B"))

print("Current week number: ", datetime.date.today().strftime("%W"))

print("Weekday of the week: ", datetime.date.today().strftime("%w"))

print("Day of year: ", datetime.date.today().strftime("%j"))

print("Day of the month: ", datetime.date.today().strftime("%d"))

print("Day of week: ", datetime.date.today().strftime("%A"))

Output:

Time in seconds: 1553207598.3700132

Current date_time1: 2019-03-21 22:33:18.370013

Current date_time2: 19-03-21-22-33

Current year: 2019

Current Month: March

Week number of the year: 11

Weekday of the week: 4

Day of year: 080

Day of the month: 21

Day of week: Thursday

File operations in Python

noreply@blogger.com (Anonymous) — Thu, 21 Mar 2019 12:28:00 +0000

The different file operation in Python are

Open a file
Read or write
Close the file

Open()

Python has a built-in function open() to open a file. The open() function is used to open files in our system, the filename is the name of the file to be opened.

f = open("<Filename>")

We can specify the mode while opening a file. In mode, we specify whether we want to read 'r', write 'w' or append 'a' to the file.

The default is reading in text mode. In this mode, we get strings when reading from the file.

Example

filename = "hello.txt"

file = open(filename, "r")

for line in file:

print line,

Read ()

The read functions contains following methods

read() #return one big string
readline #return one line at a time
readlines #returns a list of lines

Write ()

This method writes a sequence of strings to the file.

write () #Used to write a fixed sequence of characters to a file
writelines() #writelines can write a list of strings.

Append ()

The append function is used to append to the file instead of overwriting it.

To append to an existing file, simply open the file in append mode ("a")

Close()

When you’re done with a file, use close() to close it and free up any system

resources taken up by the open file

File Handling Examples

To open a text file, use:

f = open("hello.txt", "r")

To read a text file, use:

f = open("hello.txt","r")

print(f.read())

To read one line at a time, use:

f = open("hello".txt", "r")

print(f.readline())

To read a list of lines use:

f = open("hello.txt.", "r")

print(f.readlines())

To write to a file, use:

f = open("hello.txt","w")

write("Hello World")

f.close()

To write to a file, use:

f = open("hello.txt", "w")

text_lines = ["text line 1", "text line 2", "text line 3"]

f.writelines(text_lines)

f.close()

To append to file, use:

f = open("hello.txt", "a")

write("Hello World again")

f.close()

To close a file, use

f = open("hello.txt", "r")

print(f.read())

f.close()

Python Basics for beginners

noreply@blogger.com (Anonymous) — Thu, 21 Mar 2019 11:52:00 +0000

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

First Python Program

The simplest command in python is the "print" command. The print command prints the output.

In Python 2, the "print" statement is not a function, and therefore it is invoked without parentheses.

However, in Python 3, it is a function, and must be invoked with parentheses.

Below is a simple syntax to print "Hello World!"

print("Hello World!")

Lines and Indentation

Python provides no braces to indicate blocks of code for class and function definitions or flow control. Blocks of code are denoted by line indentation, which is rigidly enforced.

The number of spaces in the indentation is variable, but all statements within the block must be indented the same amount. For example −

if True:

print "True"

else:

print "False"

However, the following block generates an error −

if True:

print "Answer"

print "True"

else:

print "Answer"

print "False"

Thus, in Python all the continuous lines indented with same number of spaces would form a block.

Standard Data Types

Python has various standard data types that are used to define the operations possible on them and the storage method for each of them.

Python has five standard data types −

Numbers
String
List
Tuple
Dictionary

Basic Operators

Operators are the constructs which can manipulate the value of operands.

Consider the expression 4 + 5 = 9. Here, 4 and 5 are called operands and + is called operator.

Types of Operator

Python language supports the following types of operators.

Arithmetic Operators
Comparison (Relational) Operators
Assignment Operators
Logical Operators
Bitwise Operators
Membership Operators
Identity Operators

Functions

A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.

Defining a Function

You can define functions to provide the required functionality. Here are simple rules to define a function in Python.
Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ).
Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
The first statement of a function can be an optional statement - the documentation string of the function or docstring.
The code block within every function starts with a colon (:) and is indented.
The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.

Syntax

def functionname( parameters ):

"function_docstring"

function_suite

return [expression]

Machine Learning

noreply@blogger.com (Anonymous) — Thu, 21 Mar 2019 10:41:00 +0000

What is Machine Learning (ML)?

Machine Learning is programming machines to make it capable of taking decision itself based on their experience.

Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results.

Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to make actionable insights. Machine learning is closely related to data mining and Bayesian predictive modelling. The machine receives data as input, use an algorithm to formulate answers.

Machine Learning v/s Traditional programming

Traditional programming differs significantly from machine learning.

In traditional programming, data and program are run on the computer to produce the output.

In Machine Learning, data and output are run on the computer to create a program. This program can be used in traditional programming.

Types of Learning

Learning is the process of converting experience into expertise or knowledge.

Learning can be broadly classified into categories, as mentioned below, based on the nature of the learning data and interaction between the learner and the environment.

Supervised learning:

o The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.

o Supervised learning can be further classified into two types:

Ø Regression

Ø Classification

o Supervised learning is commonly used in real world applications, such as face and speech recognition, products or movie recommendations, and sales forecasting.

Unsupervised learning:

o The program is given a bunch of data and must find patterns and relationships therein.

o It is the opposite of supervised learning.

o Unsupervised learning is used to detect anomalies, outliers, such as fraud or defective equipment, or to group customers with similar behaviours for a sales campaign.

Semi-supervised Learning:

o If some learning samples are labelled, but some other are not labelled, then it is semi-supervised learning.

o It makes use of a large amount of unlabelled data for training and a small amount of labelled data for testing.

o Semi-supervised learning is applied in cases where it is expensive to acquire a fully labelled dataset while more practical to label a small subset.

Reinforcement Learning:

o Learning data gives feedback so that the system adjusts to dynamic conditions to achieve a certain objective.

o The system evaluates its performance based on the feedback responses and reacts accordingly.

o AI types like it, it is the most ambitious type of learning.

Similarly, there are four categories of machine learning algorithms as shown below −

Supervised learning algorithm
Unsupervised learning algorithm
Semi-supervised learning algorithm
Reinforcement learning algorithm

However, the most commonly used ones are supervised and unsupervised learning.

Machine Learning Process

Purpose of Machine Learning

Machine learning can be seen as a branch of Artificial Intelligence (AI), since, the ability to change experience into expertise or to detect patterns in complex data is a mark of human or animal intelligence.

As a field of science, machine learning shares common concepts with other disciplines such as statistics, information theory, game theory, and optimization.

As a subfield of information technology, its objective is to program machines so that they will learn.

However, it is to be seen that, the purpose of machine learning is not building an automated duplication of intelligent behaviour but using the power of computers to complement and supplement human intelligence. For example, machine learning programs can scan and process huge databases detecting patterns that are beyond the scope of human perception.

Applications

Below are some of the applications of Machine Learning:

Web search: ranking page based on what you are most likely to click on.
Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How to decide where to invest money.
E-commerce: Predicting customer churn. Whether or not a transaction is fraudulent.
Space exploration: space probes and radio astronomy.
Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
Social networks: Data on relationships and preferences. Machine learning to extract value from data.
Debugging: Use in computer science problems like debugging. Labour intensive process. Could suggest where the bug could be.

Challenges

The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.

Conclusion

Machine learning is used by many known organizations in many different fields. However, this technology is quite new and not very mature, so it contains many security and ethical problems. However, this technology can help us in dealing with a lot of problems such as security. Machine learning has a lot of scope and with advancement in technology, it will get better n better.

Basics of Data Analytics

noreply@blogger.com (Anonymous) — Tue, 30 Jan 2018 14:36:00 +0000

Data vs Information

Data usually refers to raw data, or unprocessed data. It is the basic form of data, data that hasn’t been analysed or processed in any manner.

For example: Jeetendra,Jaiswal,36,Gamdevi,Road,Bhan,dup,West,78,Mum

Information is data that has been interpreted so that it has meaning for the user. Once the data is analysed, it is considered as information.

For example: Jeetendra Jaiswal

365, Gamdevi Road

Bhandup (West),

Mumbai-400078

What is Data Analytics?

Data Analytics is the science of using various techniques and processes for examining raw data for extracting information from them. Data is extracted and processed using various techniques based on organizational requirements with the purpose of finding patterns and drawing conclusions about that information.

Organizations collect and analyse both real time and historic data related to their customers.

Using data analytics, organizations can identify and optimize their operations, identify the needs of their customers and provide them the best possible service, proactively identify the future trend and respond to it leading to a gain in this competitive market over others.

All these can help the organizations to optimize costs and increase their revenues.

Types of data analytics applications

Data Analytics can be broadly classified under 2 categories

1) Exploratory Data Analysis (EDA)

An approach to analysing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modelling or hypothesis testing task.

2) Confirmatory Data Analysis (CDA)

An approach where you evaluate your evidence using traditional statistical tools such as significance, inference, and confidence.

Data analytics can also be classified as

I. Quantitative Data Analysis (QtDA)

An approach where numerical data is analysed with quantifiable variables that can be compared or measured statistically.

II. Qualitative Data Analysis (QlDA)

An approach where we understand the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.

More advanced types of data analytics

A. Data mining

Data Mining is the analysis of large quantities of data to extract previously unknown, interesting patterns of data, unusual data and the dependencies.

B. Business Intelligence

Business Intelligence techniques and tools are for acquisition and transformation of large amounts of unstructured business data to help identify, develop and create new strategic business opportunities.

C. Statistical Analysis

Statistics is the study of collection, analysis, interpretation, presentation, and organization of data. The 2 main types of Statistical Analysis are:

1) Descriptive statistics - data from the entire population or a sample is summarized with numerical descriptors such as mean, frequency, percentage, etc.

2) Inferential statistics − It uses patterns in the sample data to draw inferences about the represented population or accounting for randomness. These inferences can be −

· answering yes/no questions about the data (hypothesis testing)

· estimating numerical characteristics of the data (estimation)

· describing associations within the data (correlation)

· modelling relationships within the data (E.g. regression analysis)

D. Predictive Analytics

Predictive Analytics use statistical models to analyse current and historical data for forecasting (predictions) about future or otherwise unknown events.

E. Text Analytics

Text Analytics, also referred to as Text mining provides a means of analysing documents, emails and other text-based content.

Use of Data Analytics

Data analytics initiatives support a wide variety of business uses. Below are few examples

1) Banks and credit card companies analyse withdrawal and spending patterns to prevent fraud and identity theft.

2) E-commerce companies and marketing services providers do clickstream analysis to identify website visitors who are more likely to buy a product or service based on navigation and page-viewing patterns.

3) Mobile network operators examine customer data to forecast churn so they can take steps to prevent defections to business rivals; to boost customer relationship management efforts, they and other companies also engage in CRM analytics to segment customers for marketing campaigns and equip call centre workers with up-to-date information about callers.

4) Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.

Fundamentals to Data Analytics

1) Identifying the Data

It is important for a data analytics project to identify where the valuable information resides and map it based on the 4V framework, defined by Volume, Variety, Velocity and Veracity.

2) Data Quality

Data quality is the core aspect in data analytics that decides its intended use in business operations and decision making. The correctness and consistency of the data demonstrates its quality and fitment for use.

3) Business Objectives

Clarity in defining your goals and objectives are essential for achieving success through analytics. Analysts need to have in mind the big-picture while building the conceptual framework and process useful in data analytics.

4) Data Availability & Access

Data availability and access is the fundamental requirement to data analytics. Authorized personnel should be able to access the internal organizational data, and, the information external to the organization must be collected from reliable resources.

5) Insight

The success of a data analytics project depends on the quantifiable insight it generates. The derived system should be able to provide timely and accurate answer to the business questions. This presents a valuable actionable insight that marks a way to tread and adds up to the value chain.

6) Data Visualization

For a meaningful insight, it is a must to present the information in an appealing and insightful manner to the intended audiences. The business story and the user story should be represented with advanced visualization techniques for better clarity, with scope for interactive exploration.

7) Data Practices

The right data analysis framework, a standard architecture for data interoperability, and strict compliance to data security & privacy norms creates a trusted environment for data analysis. These enablers help organizations to undertake projects that assure high data security for a project’s success and stakeholders’ buy-in.

Challenges in Data Analytics

1) Handling Enormous Data in Less Time

Handling the data of any business or industry is itself a significant challenge, but when it comes to handling enormous data, the task gets much more difficult. Critical business decisions should be taken effectively, but we need to have strong IT infrastructure which can read the data faster and delivering real-time insights. To overcome this challenge, you can use Apache Hadoop’s MapReduce that helps in splitting the data of the application in small fragments. This process makes the data measurable.

2) Visual Representation of Data

Another important task is the visual representation of data. You need to represent the data in an easy format that makes it readable and understandable to the audience. Handling an unstructured data and then representing in a visually attractive manner could be a difficult task. To recover this issue, the data analyst can utilize different types of graphs or tables to represent the data.

3) Application Should Be Scalable

The major factor to consider is the scalability factor of the of the applications. Several organizations are facing the same issue where the volume of data has been increasing each passing day. Due to the multiple layers between the database and front-end, the data traversal takes time. To overcome this issue, the organizations should take care of the application’s architecture and technology to reduce performance issues and enhance scalability.

Guidelines for effective use of Data Analytics

1) Define the Questions

Your questions will define your work process. So, define your questions and ask measurable and clear questions. Define your problem clearly and design the question in such a way that it either qualify or disqualify potential solutions.

2) Set Appropriate Measurement Priorities

This point covers two different scenarios, i.e. decide what to measure and how to measure. You need to think about these situations. Deciding on how to measure the data is important before the data collection phase as it also has its own set of questions.

3) Collect Data

After defining the questions and setting up the measurement priorities, now you need to collect the data. Try to keep your collected data in an organized way.

4) Analyse and Make Data Useful

Now is the time to analyse the data. You can manipulate the data in multiple ways by plotting and searching correlations or by building a pivot table. The pivot table will help in sorting and filtering data and calculate the maximum, minimum, mean and standard deviation of your data.

5) Interpret Results

Data Analytics is incomplete without compelling visualization. This is the time to interpret your data. Interpreting the data will answer all the data-related questions.

Conclusion

Data analytics is helping businesses to consider hundreds of parameters to predict with reasonable accuracy what will happen. Both structured and unstructured data are growing exponentially. With proper use of technology, this huge data can be used to make more accurate decisions which can help the organizations improve its operations, reduce costs, improve sales, provide better service to customers and improve its efficiency.

Difference between a structure and a class

noreply@blogger.com (Anonymous) — Mon, 16 Jun 2014 18:30:00 +0000

Structures are value type and Classes are reference type.
Structures can not have constructors or destructors. Classes can have both constructors and destructors.
Structures do not support Inheritance, while Classes support Inheritance.
Classes are usually used for large amounts of data, whereas structs are usually used for smaller amounts of data.
A structure couldn't be null whereas a class can be null.
A structure can't be abstract whereas a class can be abstract.

Difference between Dataset.clone and Dataset.copy in C#

noreply@blogger.com (Anonymous) — Sun, 15 Jun 2014 18:38:00 +0000

Dataset.clone copies just the structure of dataset (including all the datatables, schemas, relations and constraints). It doesn't copy the data.

Dataset.copy copies both the dataset structure and data.

Difference between Constant and ReadOnly in C#

noreply@blogger.com (Anonymous) — Sun, 15 Jun 2014 18:25:00 +0000

Constant:
--------

Constant value is declared using "const" keyword.
A const needs to be declared and initialized at declaration only.
A const’s value is evaluated at design time.
A const can not be static.

const string name = "Jeetendra";

ReadOnly:
--------

ReadOnly value is declared using "readonly" keyword.
A readonly can be initialized at declaration or by the code in the constructor.
A readonly’s value is evaluated at runtime.
A readonly can be static.

readonly string name = "Jaiswal";

Sealed class in C#

noreply@blogger.com (Anonymous) — Sun, 15 Jun 2014 11:38:00 +0000

A sealed class is a class which cannot be inherited by any class.
In C#, we can use the "sealed" keyword to define a class as sealed.
Sealed class is used when we don't want our class to be inherited by any other class.

SealedExample.cs

----------------

public sealed class SealedExample

{

public int Result(int num1, int num2)

{

return num1 * num2;

}

Program.cs

----------

class Program

{

static void Main(string[] args)

{

SealedExample se = new SealedExample();

Console.WriteLine("Result: {0}", se.Result(7, 3));

}

Set maximum limit to the number of objects that can be created for a class

noreply@blogger.com (Anonymous) — Sun, 15 Jun 2014 11:13:00 +0000

Employee.cs
-----------------
public class Employee
{
//Variable to maintain the total number of objects created
private static int numberOfObjects = 0;

protected Employee()
{
Console.WriteLine("Employee {0} created", numberOfObjects);
}

//Check the total number of objects created.
//If count is <5, then create an object
//Else return null.
public static Employee createEmployee()
{
if (numberOfObjects++ < 5)
{
return new Employee();
}
else
{
return null;
}
}
}

Program.cs
----------------
class Program
{
static void Main(string[] args)
{ //Output
Employee emp1 = Employee.createEmployee();//Employee 1 created
Employee emp2 = Employee.createEmployee();//Employee 2 created
Employee emp3 = Employee.createEmployee();//Employee 3 created
Employee emp4 = Employee.createEmployee();//Employee 4 created
Employee emp5 = Employee.createEmployee();//Employee 5 created
Employee emp6 = Employee.createEmployee();//null
}
}

Object Oriented Programming (OOPS) Concepts in C#

noreply@blogger.com (Anonymous) — Fri, 13 Jun 2014 19:12:00 +0000

Class:

A grouping of related objects.
A blueprint used for producing objects having a similar set of attributes & behavior, but which can be uniquely identified.
A user defined data type.

public class Employee

{

protected int empID;

protected string name;

protected string address;

public void display()

{

Console.WriteLine("Employee Class");

}

Object:

A real world entity that is identified by simulating the human thought process.
A Real world entity that has

State
Identity &
Behavior

Employee emp = new Employee();

"emp" is the object of "Employee" class.

OOPS concepts:

1) Abstraction:

Abstraction is

What you expose to the end user
What an Object does

Abstraction refers to the description of externally visible behavior of an Object.

Abstraction is used to describe what an Object does rather than how it does it.

Abstraction is a process of hiding the implementation details and displaying the essential features.

Example: Consider an ATM machine.
We are exposed only the screen with various options through which we can do many kind of transactions like check balance, withdraw amount, etc.
We don't need to know how it internally processes this complex transactions by checking the Total amount available, withdrawal amount not more than available amount, Net amount left is not less than minimum balance limit, etc.
So, the user friendly screen with various options is exposed to the end user and this refers to abstraction.

2) Encapsulation:

Encapsulation is

What you hide from the end user
How an Object does what it does

Encapsulation is a description of the internal data structure & implementation that gives rise to the externally visible behaviour.

Encapsulation hides the details of the implementation of an Object.

Example: Consider an ATM machine.
We are exposed only the screen with various options through which we can do many kind of transactions like check balance, withdraw amount, etc.
We don't need to know how it internally processes this complex transactions by checking the Total amount available, withdrawal amount not more than available amount, Net amount left is not less than minimum balance limit, etc.
So, the complex transactions done by the ATM internally is hidden from the end user and this refers to encapsulation.

3) Inheritance:

Extend rather than modifying
Do not break what is already working
Reuse rather then re-invent
Inheritance enables you to create a new class that reuses, extends, and modifies the behavior that is defined in another class.

Public class Parent

{

Public Parent()

{

Console.WriteLine ("Parent class");

}

Public void Display()

{

Console.WriteLine ("Parent method");

}

Public class Child: Parent

{

Public Child()

{

Console.WriteLine("Child Class");

}

Public static void Main ()

{

Child obj = new Child();

obj.Display();

}

Output:

Base Class
Child Class
Parent method

3) Polymorphism:

One form – multiple behaviour.
Enabling the client to select which code block to execute dynamically without breaking the client.
Enables the client to dynamically replace the object to be operated upon without the breaking the client.
Classes provide different implementations of methods having the same name.
Polymorphism is of two types:
- Method Overloading (Compile time polymorphism)
- Method Overriding (Runtime polymorphism)

Difference between Abstract Class and Interface

noreply@blogger.com (Anonymous) — Thu, 12 Jun 2014 19:22:00 +0000

SrNo	Abstract Class	Interface
1	Abstract class is a class that contain complete and abstract (incomplete) both types of members	Interface is a type which contains only the signatures of methods, delegates or events
2	Abstract class can be inherited by other classes but cannot be instantiated.	Interfaces are implemented by a class
3	A class can inherit only one abstract class	A class can implement multiple interfaces
4	Abstract class can contain data members, constructors	Interface doesn't contain data members, constructors
5	We can not achieve multiple inheritance	We can achieve multiple inheritance
6	Can contain access modifiers like public, private, protected, etc. for functions, snubs, properties.	Doesn't contain access modifies. By default, everything is public.
7	Members of abstract class can be static.	Members of interface cannot be static.

Serialization and Deserialization in c#

noreply@blogger.com (Anonymous) — Wed, 11 Jun 2014 19:05:00 +0000

Serialization is the process of converting an object into stream of bytes for storage and data transmission.
Deserialization is the reverse process of converting stream of bytes to their original form.

Advantages of Serialization:

Facilitate the transportation of an object through a network
Create a clone of an object

Disadvantage of Serialization:

Resource overhead that is involved in serializing and deserializing the data.
Latency issues that are involved for transmitting the data over the network.
Serialization is a slow process.

Restore backup in SQL Server using SQL Scripts

noreply@blogger.com (Anonymous) — Mon, 02 Jun 2014 18:48:00 +0000

1) Place the backup file <SampleBackupFile.bak> to an accessible location <E:\SampleFolder>.

2) Retrieve the mdf and ldf file names of the database from the backup file using the below query.

RESTORE FILELISTONLY
FROM DISK = 'E:\SampleFolder\SampleBackupFile.bak'

3) Now restore the database <SampleDB> using the below query.

RESTORE DATABASE <SampleDB>
FROM DISK = 'E:\SampleFolder\SampleBackupFile.bak'
WITH MOVE 'mdf_filename'
TO 'E:\SampleFolder\SampleMDFFile.mdf',
MOVE 'ldf_filename'
TO 'E:\SampleFolder\SampleLDFFile.ldf'

TRIM function in SQL Server

noreply@blogger.com (Anonymous) — Sun, 01 Jun 2014 10:58:00 +0000

The TRIM function in SQL Server is used to remove white spaces.

There are 3 types of TRIM:

1)TRIM(<InputString>) - Removes white spaces from both left and right side of the input string.

2)LTRIM(<InputString>) - Remove white spaces from left side of the input string.

3)RTRIM(<InputString>) - Removes white spaces from right side of the input string.

Type	Query	Output
TRIM	SELECT TRIM(' Hello ')	Hello
LTRIM	SELECT TRIM(' Hello')	Hello
RTRIM	SELECT TRIM('Hello ')	Hello

Search any SP/Table/View/Function in SQL Server

noreply@blogger.com (Anonymous) — Sun, 01 Jun 2014 06:59:00 +0000

SELECT DISTINCT so.name [Name], so.xtype [Type]
FROM sysobjects so INNER JOIN syscomments sc
on so.id = sc.id
WHERE sc.text like '%<SearchText>%'

Reverse a string without using string function in C#

noreply@blogger.com (Anonymous) — Sat, 31 May 2014 18:34:00 +0000

------------------------------------------
Input String -> "Hello"
Expected Output -> "olleH"
------------------------------------------

string inputString = "Hello";
string outputString = "";

char[] array = inputString.ToCharArray();
Array.Reverse(array);
outputString  = new string(array);

------------------------------------------

Materialized Views in SQL Server

noreply@blogger.com (Anonymous) — Fri, 30 Nov 2012 18:43:00 +0000

Materialized Views

In SQL, a view is a virtual table based on the result-set of an SQL statement.
A view contains rows and columns, just like a real table. The fields in a view are fields from one or more actual tables in the database.
You can add SQL functions, WHERE and JOIN statements to a view and present the data as if the data were coming from one single table.
But if a view performs an operation on millions of records, performance of the view will decrease dramatically and if this operation is performed frequently then the view will access millions of records repeatedly.
To resolve this issue, materialized view comes into picture.
A materialized view is defined same as a regular view but the output is stored in a physical table which is updated frequently whenever the actual tables change.
They are useful to aggregate data in business intelligence applications with complex queries.

Design Patterns

noreply@blogger.com (Anonymous) — Mon, 19 Nov 2012 19:28:00 +0000

Design patterns are recurring solutions to software design problems found during application development.Design patterns are important building blocks for designing and modelling applications on all platforms.

Design patterns was first presented by Erich Gamma, Richard Helm,

Ralph Johnson and John Vlisside. They are also called as “Gang

Of Four”(GoF).

They wrote a book on design patterns titled

"Design Patterns:Elements of Reusable Object-Oriented Software"

Most of the design patterns are grouped into the following three

types:

1) Creational Patterns

2) Structural Patterns

3) Behavioral Patterns

1) Creational Patterns:

Creation Patterns abstracts the object creation process from the

client. It creates objects for the client rather than having

clients instantiate objects directly.

Some of the design patterns under this group are:

a) Factory Method

b) Singleton

c) Microsoft Factory

d) Abstract Factory

2) Structural Patterns:

Structural Patterns are concerned with class and object

composition. They compose interfaces using inheritance and define

ways to compose objects to achieve a new functionality.

Some of the design patterns under this group are:

a) Decorator

b) Facade

c) Adapter

d) Bridge

3) Behavioral Patterns:

Behavioral Patterns are basically concerned with communication

between objects.

Some of the design patterns under this group are:

a)Memento

b)Strategy

c)Template Method

d)Command

Difference between Delete and Truncate commands in SQL Server

noreply@blogger.com (Anonymous) — Thu, 11 Oct 2012 18:28:00 +0000

Delete	Truncate
DELETE is a DML command	TRUNCATE is a DDL command
We can delete all records of the table or only specific records based on filter criteria	Truncate deletes all records of the table and there is no provision to delete specific records
A log is maintained in case of delete and hence it is possible to roll back the transaction	No log is maintained in case of truncate and hence it is not possible to roll back the transaction
We can execute a trigger in case of delete	A trigger cannot be executed in case of truncate
Indexes like identity are not initialized in case of Delete	Indexes like identity are initialized in case of Truncate
Example: Delete from where	Example: Truncate table

Get month list at runtime in SQL Server

noreply@blogger.com (Anonymous) — Fri, 14 Sep 2012 12:08:00 +0000

WITH CTE
AS
( SELECT 1 AS MonthNumber
UNION ALL
SELECT MonthNumber + 1 -- add month number to 1 recursively
FROM CTE
WHERE MonthNumber < 12 -- just to restrict the month number upto 12
)
SELECT
MonthNumber,
DATENAME(MONTH,DATEADD(MONTH, MonthNumber ,0)- 1)[MonthName] -- function to list the monthname.
FROM CTE

Difference between Layer and Tier

noreply@blogger.com (Anonymous) — Thu, 02 Feb 2012 18:03:00 +0000

Layer corresponds to logical separation of code. A layer typically live within the same process and machine.
Typical Layers include:
1) Presentation Layer
2) Business Layer
3) Data Access Layer

Tier corresponds to physical separation of components i.e, different components like exe, dll, etc are deployed on the same server or multiple servers. Hence it can be said that Tiers are physical deployment of Layers.
Typical Tiers include:
1) Presentation Tier
2) Business Logic Tier
3) Data Tier

Remove time part from date in SQL Server

noreply@blogger.com (Anonymous) — Thu, 26 Jan 2012 18:13:00 +0000

If we want to remove time part from the date without converting the date to varchar.

Example:

SELECT GETDATE() gives the Current Date.
i.e, 2012-01-26 23:38:21.983

Now f I want only the date part i.e, 2012-01-26 00:00:00.000.
But I don't want to convert it to varchar.

Below is the code.

SELECT DATEADD(DAY,DATEDIFF(DAY,0,GETDATE()),0)

Output:
2012-01-26 00:00:00.000