Zara  Bryant

Zara Bryant

1622251124

AI Show Live - Microsoft Build Recap - Episode 15

On this special edition of the AI Show, Seth welcomes exciting guests and friends of the show to talk about all the AI news & announcements from Microsoft Build Conference 2021 https://aka.ms/learnatbuild.

Jump to:

  • 00:00 Livestream begins
  • 05:30 Seth joins
  • 15:07 Reduce time to value with Azure Applied AI Services w/Jeff Mendenhall
  • 26:56 Q&A w/Jeff
  • 39:22 What’s new in Text Analytics for Health w/Ashly Yeo
  • 52:04 Q&A w/Ashly
  • 57:47 Translate documents at scale and preserve formatting with Azure Translator
  • w/Krishna Doss
  • 01:08:41 Q&A w/Krishna
  • 01:22:01 Learn how to build bots with Azure Bot Service’s comprehensive development experience w/Gary Pretty
  • 1:42:18 Q&A w/Gary
  • 01:47:46 Managed Endpoints w/Sethu Raman
  • 02:01:08 Q&A w/Sethu

Learn more:
Reduce time to value with Azure Applied AI Services
https://aka.ms/AIShow/AppliedAIServices
https://aka.ms/AIShow/AppliedAIDoc
https://twitter.com/JeffLMendenhall

What’s new in Text Analytics for Health
https://aka.ms/AIShow/TextAnalyticsforHealth/HowTo
https://aka.ms/AIShow/TextAnalytics/Sample
https://aka.ms/AIShow/TechCommunity/TextAnalytics

Translate documents at scale and preserve formatting with Azure Translator
https://aka.ms/AIShow/Translation/Doc
https://aka.ms/AIShow/DocTranslationInTranslator
https://aka.ms/AIShow/NetKit
https://aka.ms/AIShow/PythonKit
https://twitter.com/Krishna_Doss

Learn how to build bots with Azure Bot Service’s comprehensive development experience
https://aka.ms/AIShow/ConversationalAI/Build2021
https://twitter.com/GaryPretty

Managed Endpoints
https://aka.ms/AIShow/HowToDeploy/ManagedEndpoints
https://twitter.com/Sethu20

#ai #artificial-intelligence #developer #microsoft

What is GEEK

Buddha Community

AI Show Live - Microsoft Build Recap - Episode 15
Callum Slater

Callum Slater

1653465344

PySpark Cheat Sheet: Spark DataFrames in Python

This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples.

You'll probably already know about Apache Spark, the fast, general and open-source engine for big data processing; It has built-in modules for streaming, SQL, machine learning and graph processing. Spark allows you to speed analytic applications up to 100 times faster compared to other technologies on the market today. Interfacing Spark with Python is easy with PySpark: this Spark Python API exposes the Spark programming model to Python. 

Now, it's time to tackle the Spark SQL module, which is meant for structured data processing, and the DataFrame API, which is not only available in Python, but also in Scala, Java, and R.

Without further ado, here's the cheat sheet:

PySpark SQL cheat sheet

This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'll also see that this cheat sheet also on how to run SQL Queries programmatically, how to save your data to parquet and JSON files, and how to stop your SparkSession.

Spark SGlL is Apache Spark's module for working with structured data.

Initializing SparkSession 
 

A SparkSession can be used create DataFrame, register DataFrame as tables, execute SGL over tables, cache tables, and read parquet files.

>>> from pyspark.sql import SparkSession
>>> spark a SparkSession \
     .builder\
     .appName("Python Spark SQL basic example") \
     .config("spark.some.config.option", "some-value") \
     .getOrCreate()

Creating DataFrames
 

Fromm RDDs

>>> from pyspark.sql.types import*

Infer Schema

>>> sc = spark.sparkContext
>>> lines = sc.textFile(''people.txt'')
>>> parts = lines.map(lambda l: l.split(","))
>>> people = parts.map(lambda p: Row(nameap[0],ageaint(p[l])))
>>> peopledf = spark.createDataFrame(people)

Specify Schema

>>> people = parts.map(lambda p: Row(name=p[0],
               age=int(p[1].strip())))
>>>  schemaString = "name age"
>>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
>>> schema = StructType(fields)
>>> spark.createDataFrame(people, schema).show()

 

From Spark Data Sources
JSON

>>>  df = spark.read.json("customer.json")
>>> df.show()

>>>  df2 = spark.read.load("people.json", format="json")

Parquet files

>>> df3 = spark.read.load("users.parquet")

TXT files

>>> df4 = spark.read.text("people.txt")

Filter 

#Filter entries of age, only keep those records of which the values are >24
>>> df.filter(df["age"]>24).show()

Duplicate Values 

>>> df = df.dropDuplicates()

Queries 
 

>>> from pyspark.sql import functions as F

Select

>>> df.select("firstName").show() #Show all entries in firstName column
>>> df.select("firstName","lastName") \
      .show()
>>> df.select("firstName", #Show all entries in firstName, age and type
              "age",
              explode("phoneNumber") \
              .alias("contactInfo")) \
      .select("contactInfo.type",
              "firstName",
              "age") \
      .show()
>>> df.select(df["firstName"],df["age"]+ 1) #Show all entries in firstName and age, .show() add 1 to the entries of age
>>> df.select(df['age'] > 24).show() #Show all entries where age >24

When

>>> df.select("firstName", #Show firstName and 0 or 1 depending on age >30
               F.when(df.age > 30, 1) \
              .otherwise(0)) \
      .show()
>>> df[df.firstName.isin("Jane","Boris")] #Show firstName if in the given options
.collect()

Like 

>>> df.select("firstName", #Show firstName, and lastName is TRUE if lastName is like Smith
              df.lastName.like("Smith")) \
     .show()

Startswith - Endswith 

>>> df.select("firstName", #Show firstName, and TRUE if lastName starts with Sm
              df.lastName \
                .startswith("Sm")) \
      .show()
>>> df.select(df.lastName.endswith("th"))\ #Show last names ending in th
      .show()

Substring 

>>> df.select(df.firstName.substr(1, 3) \ #Return substrings of firstName
                          .alias("name")) \
        .collect()

Between 

>>> df.select(df.age.between(22, 24)) \ #Show age: values are TRUE if between 22 and 24
          .show()

Add, Update & Remove Columns 

Adding Columns

 >>> df = df.withColumn('city',df.address.city) \
            .withColumn('postalCode',df.address.postalCode) \
            .withColumn('state',df.address.state) \
            .withColumn('streetAddress',df.address.streetAddress) \
            .withColumn('telePhoneNumber', explode(df.phoneNumber.number)) \
            .withColumn('telePhoneType', explode(df.phoneNumber.type)) 

Updating Columns

>>> df = df.withColumnRenamed('telePhoneNumber', 'phoneNumber')

Removing Columns

  >>> df = df.drop("address", "phoneNumber")
 >>> df = df.drop(df.address).drop(df.phoneNumber)
 

Missing & Replacing Values 
 

>>> df.na.fill(50).show() #Replace null values
 >>> df.na.drop().show() #Return new df omitting rows with null values
 >>> df.na \ #Return new df replacing one value with another
       .replace(10, 20) \
       .show()

GroupBy 

>>> df.groupBy("age")\ #Group by age, count the members in the groups
      .count() \
      .show()

Sort 
 

>>> peopledf.sort(peopledf.age.desc()).collect()
>>> df.sort("age", ascending=False).collect()
>>> df.orderBy(["age","city"],ascending=[0,1])\
     .collect()

Repartitioning 

>>> df.repartition(10)\ #df with 10 partitions
      .rdd \
      .getNumPartitions()
>>> df.coalesce(1).rdd.getNumPartitions() #df with 1 partition

Running Queries Programmatically 
 

Registering DataFrames as Views

>>> peopledf.createGlobalTempView("people")
>>> df.createTempView("customer")
>>> df.createOrReplaceTempView("customer")

Query Views

>>> df5 = spark.sql("SELECT * FROM customer").show()
>>> peopledf2 = spark.sql("SELECT * FROM global_temp.people")\
               .show()

Inspect Data 
 

>>> df.dtypes #Return df column names and data types
>>> df.show() #Display the content of df
>>> df.head() #Return first n rows
>>> df.first() #Return first row
>>> df.take(2) #Return the first n rows >>> df.schema Return the schema of df
>>> df.describe().show() #Compute summary statistics >>> df.columns Return the columns of df
>>> df.count() #Count the number of rows in df
>>> df.distinct().count() #Count the number of distinct rows in df
>>> df.printSchema() #Print the schema of df
>>> df.explain() #Print the (logical and physical) plans

Output

Data Structures 
 

 >>> rdd1 = df.rdd #Convert df into an RDD
 >>> df.toJSON().first() #Convert df into a RDD of string
 >>> df.toPandas() #Return the contents of df as Pandas DataFrame

Write & Save to Files 

>>> df.select("firstName", "city")\
       .write \
       .save("nameAndCity.parquet")
 >>> df.select("firstName", "age") \
       .write \
       .save("namesAndAges.json",format="json")

Stopping SparkSession 

>>> spark.stop()

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #dataframes #python #bigdata

How to Create a Responsive Dropdown Menu Bar with Search Field using HTML & CSS

In this guide you’ll learn how to create a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

To create a responsive dropdown menu bar with search field using only HTML & CSS . First, you need to create two Files one HTML File and another one is CSS File.

1: First, create an HTML file with the name of index.html

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <title>Dropdown Menu with Search Box | Codequs</title>
  <link rel="stylesheet" href="style.css">
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"/>
</head>
<body>
  <div class="wrapper">
    <nav>
      <input type="checkbox" id="show-search">
      <input type="checkbox" id="show-menu">
      <label for="show-menu" class="menu-icon"><i class="fas fa-bars"></i></label>
      <div class="content">
      <div class="logo"><a href="#">CodingNepal</a></div>
        <ul class="links">
          <li><a href="#">Home</a></li>
          <li><a href="#">About</a></li>
          <li>
            <a href="#" class="desktop-link">Features</a>
            <input type="checkbox" id="show-features">
            <label for="show-features">Features</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li><a href="#">Drop Menu 4</a></li>
            </ul>
          </li>
          <li>
            <a href="#" class="desktop-link">Services</a>
            <input type="checkbox" id="show-services">
            <label for="show-services">Services</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li>
                <a href="#" class="desktop-link">More Items</a>
                <input type="checkbox" id="show-items">
                <label for="show-items">More Items</label>
                <ul>
                  <li><a href="#">Sub Menu 1</a></li>
                  <li><a href="#">Sub Menu 2</a></li>
                  <li><a href="#">Sub Menu 3</a></li>
                </ul>
              </li>
            </ul>
          </li>
          <li><a href="#">Feedback</a></li>
        </ul>
      </div>
      <label for="show-search" class="search-icon"><i class="fas fa-search"></i></label>
      <form action="#" class="search-box">
        <input type="text" placeholder="Type Something to Search..." required>
        <button type="submit" class="go-icon"><i class="fas fa-long-arrow-alt-right"></i></button>
      </form>
    </nav>
  </div>
  <div class="dummy-text">
    <h2>Responsive Dropdown Menu Bar with Searchbox</h2>
    <h2>using only HTML & CSS - Flexbox</h2>
  </div>
</body>
</html>

2: Second, create a CSS file with the name of style.css

@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap');
*{
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  text-decoration: none;
  font-family: 'Poppins', sans-serif;
}
.wrapper{
  background: #171c24;
  position: fixed;
  width: 100%;
}
.wrapper nav{
  position: relative;
  display: flex;
  max-width: calc(100% - 200px);
  margin: 0 auto;
  height: 70px;
  align-items: center;
  justify-content: space-between;
}
nav .content{
  display: flex;
  align-items: center;
}
nav .content .links{
  margin-left: 80px;
  display: flex;
}
.content .logo a{
  color: #fff;
  font-size: 30px;
  font-weight: 600;
}
.content .links li{
  list-style: none;
  line-height: 70px;
}
.content .links li a,
.content .links li label{
  color: #fff;
  font-size: 18px;
  font-weight: 500;
  padding: 9px 17px;
  border-radius: 5px;
  transition: all 0.3s ease;
}
.content .links li label{
  display: none;
}
.content .links li a:hover,
.content .links li label:hover{
  background: #323c4e;
}
.wrapper .search-icon,
.wrapper .menu-icon{
  color: #fff;
  font-size: 18px;
  cursor: pointer;
  line-height: 70px;
  width: 70px;
  text-align: center;
}
.wrapper .menu-icon{
  display: none;
}
.wrapper #show-search:checked ~ .search-icon i::before{
  content: "\f00d";
}
.wrapper .search-box{
  position: absolute;
  height: 100%;
  max-width: calc(100% - 50px);
  width: 100%;
  opacity: 0;
  pointer-events: none;
  transition: all 0.3s ease;
}
.wrapper #show-search:checked ~ .search-box{
  opacity: 1;
  pointer-events: auto;
}
.search-box input{
  width: 100%;
  height: 100%;
  border: none;
  outline: none;
  font-size: 17px;
  color: #fff;
  background: #171c24;
  padding: 0 100px 0 15px;
}
.search-box input::placeholder{
  color: #f2f2f2;
}
.search-box .go-icon{
  position: absolute;
  right: 10px;
  top: 50%;
  transform: translateY(-50%);
  line-height: 60px;
  width: 70px;
  background: #171c24;
  border: none;
  outline: none;
  color: #fff;
  font-size: 20px;
  cursor: pointer;
}
.wrapper input[type="checkbox"]{
  display: none;
}
/* Dropdown Menu code start */
.content .links ul{
  position: absolute;
  background: #171c24;
  top: 80px;
  z-index: -1;
  opacity: 0;
  visibility: hidden;
}
.content .links li:hover > ul{
  top: 70px;
  opacity: 1;
  visibility: visible;
  transition: all 0.3s ease;
}
.content .links ul li a{
  display: block;
  width: 100%;
  line-height: 30px;
  border-radius: 0px!important;
}
.content .links ul ul{
  position: absolute;
  top: 0;
  right: calc(-100% + 8px);
}
.content .links ul li{
  position: relative;
}
.content .links ul li:hover ul{
  top: 0;
}
/* Responsive code start */
@media screen and (max-width: 1250px){
  .wrapper nav{
    max-width: 100%;
    padding: 0 20px;
  }
  nav .content .links{
    margin-left: 30px;
  }
  .content .links li a{
    padding: 8px 13px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 100px);
  }
  .wrapper .search-box input{
    padding: 0 100px 0 15px;
  }
}
@media screen and (max-width: 900px){
  .wrapper .menu-icon{
    display: block;
  }
  .wrapper #show-menu:checked ~ .menu-icon i::before{
    content: "\f00d";
  }
  nav .content .links{
    display: block;
    position: fixed;
    background: #14181f;
    height: 100%;
    width: 100%;
    top: 70px;
    left: -100%;
    margin-left: 0;
    max-width: 350px;
    overflow-y: auto;
    padding-bottom: 100px;
    transition: all 0.3s ease;
  }
  nav #show-menu:checked ~ .content .links{
    left: 0%;
  }
  .content .links li{
    margin: 15px 20px;
  }
  .content .links li a,
  .content .links li label{
    line-height: 40px;
    font-size: 20px;
    display: block;
    padding: 8px 18px;
    cursor: pointer;
  }
  .content .links li a.desktop-link{
    display: none;
  }
  /* dropdown responsive code start */
  .content .links ul,
  .content .links ul ul{
    position: static;
    opacity: 1;
    visibility: visible;
    background: none;
    max-height: 0px;
    overflow: hidden;
  }
  .content .links #show-features:checked ~ ul,
  .content .links #show-services:checked ~ ul,
  .content .links #show-items:checked ~ ul{
    max-height: 100vh;
  }
  .content .links ul li{
    margin: 7px 20px;
  }
  .content .links ul li a{
    font-size: 18px;
    line-height: 30px;
    border-radius: 5px!important;
  }
}
@media screen and (max-width: 400px){
  .wrapper nav{
    padding: 0 10px;
  }
  .content .logo a{
    font-size: 27px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 70px);
  }
  .wrapper .search-box .go-icon{
    width: 30px;
    right: 0;
  }
  .wrapper .search-box input{
    padding-right: 30px;
  }
}
.dummy-text{
  position: absolute;
  top: 50%;
  left: 50%;
  width: 100%;
  z-index: -1;
  padding: 0 20px;
  text-align: center;
  transform: translate(-50%, -50%);
}
.dummy-text h2{
  font-size: 45px;
  margin: 5px 0;
}

Now you’ve successfully created a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

Microsoft Reveals Need To Prioritise Skills To Maximise Value From AI

Microsoft India today released new research revealing that organisations that combine the deployment of AI with skilling initiatives are generating most value from AI. The topline findings of the research underscore that mature AI firms are more confident about the return on AI and skills.

The tech giant recently conducted a global survey with approximately 12,000 people working with enterprise companies. The research surveyed employees and leaders within large enterprises across industry verticals in India, and 19 other countries, to look at the skills needed to thrive as AI becomes increasingly adopted by businesses, as well as the key learnings from early AI adopters.

The survey found a direct link between having the skills needed to thrive in an AI world and the value organisations gain from their AI implementations. The research further reveals that employees are keen to acquire AI relevant skills that are growing in importance and are of value to them personally and to the business. The organisation leaders surveyed predicted that half of all employees will be equipped with AI skills in the next 6-10 years, which is nearly one-and-a-half times more than the present estimations.

#news #ai research for businesses #ai survey #microsoft #microsoft ai for business survey #microsoft ai research #microsoft survey

Zara  Bryant

Zara Bryant

1622251124

AI Show Live - Microsoft Build Recap - Episode 15

On this special edition of the AI Show, Seth welcomes exciting guests and friends of the show to talk about all the AI news & announcements from Microsoft Build Conference 2021 https://aka.ms/learnatbuild.

Jump to:

  • 00:00 Livestream begins
  • 05:30 Seth joins
  • 15:07 Reduce time to value with Azure Applied AI Services w/Jeff Mendenhall
  • 26:56 Q&A w/Jeff
  • 39:22 What’s new in Text Analytics for Health w/Ashly Yeo
  • 52:04 Q&A w/Ashly
  • 57:47 Translate documents at scale and preserve formatting with Azure Translator
  • w/Krishna Doss
  • 01:08:41 Q&A w/Krishna
  • 01:22:01 Learn how to build bots with Azure Bot Service’s comprehensive development experience w/Gary Pretty
  • 1:42:18 Q&A w/Gary
  • 01:47:46 Managed Endpoints w/Sethu Raman
  • 02:01:08 Q&A w/Sethu

Learn more:
Reduce time to value with Azure Applied AI Services
https://aka.ms/AIShow/AppliedAIServices
https://aka.ms/AIShow/AppliedAIDoc
https://twitter.com/JeffLMendenhall

What’s new in Text Analytics for Health
https://aka.ms/AIShow/TextAnalyticsforHealth/HowTo
https://aka.ms/AIShow/TextAnalytics/Sample
https://aka.ms/AIShow/TechCommunity/TextAnalytics

Translate documents at scale and preserve formatting with Azure Translator
https://aka.ms/AIShow/Translation/Doc
https://aka.ms/AIShow/DocTranslationInTranslator
https://aka.ms/AIShow/NetKit
https://aka.ms/AIShow/PythonKit
https://twitter.com/Krishna_Doss

Learn how to build bots with Azure Bot Service’s comprehensive development experience
https://aka.ms/AIShow/ConversationalAI/Build2021
https://twitter.com/GaryPretty

Managed Endpoints
https://aka.ms/AIShow/HowToDeploy/ManagedEndpoints
https://twitter.com/Sethu20

#ai #artificial-intelligence #developer #microsoft

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution