1600273740
A sample showing bootstrap 5 with VueJs. But as of 15 Sept 2020 bootstrap 5 is still in alpha
npm install
npm run serve
npm run build
npm run lint
Author: apgapg
Source Code: https://github.com/apgapg/vue_bootstrap_5_sample
#vuejs #vue #javascript
1653465344
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:
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.
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()
>>> 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 entries of age, only keep those records of which the values are >24
>>> df.filter(df["age"]>24).show()
>>> df = df.dropDuplicates()
>>> 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()
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)
>>> 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()
>>> df.groupBy("age")\ #Group by age, count the members in the groups
.count() \
.show()
>>> peopledf.sort(peopledf.age.desc()).collect()
>>> df.sort("age", ascending=False).collect()
>>> df.orderBy(["age","city"],ascending=[0,1])\
.collect()
>>> df.repartition(10)\ #df with 10 partitions
.rdd \
.getNumPartitions()
>>> df.coalesce(1).rdd.getNumPartitions() #df with 1 partition
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()
>>> 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
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")
>>> spark.stop()
Have this Cheat Sheet at your fingertips
Original article source at https://www.datacamp.com
#pyspark #cheatsheet #spark #dataframes #python #bigdata
1642496884
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.
1598221151
Volt is a free and open source Bootstrap 5 Admin Dashboard featuring over 100 components, 11 example pages and 3 customized plugins. Volt does not require jQuery as a dependency meaning that every library and script’s are jQuery free.
Because it is created using the latest version of Bootstrap 5, every components and element is based on the latest Bootstrap 5 Sass variables and HTML markup. Check out the documentation of the components here.
We created no less than 11 advanced example pages such as the overview page, transactions, user settings, sign in and sign up and many more.
Every component, plugin and getting started is thoroughly documented on our online documentation.
This product is built using the following widely used technologies:
Dashboard | Transactions | Settings | Forms |
---|---|---|---|
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Sign in | Sign up | Forgot password | Reset password |
---|---|---|---|
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Lock Profile | 404 Not Found | 500 Server Error | Documentation |
---|---|---|---|
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npm install gulp-cli -g
volt/
folder to download all the project dependencies. You’ll find them in the node_modules/
folder.npm install
volt/
folder to serve the project files using BrowserSync. Running gulp will compile the theme and open /index.html
in your main browser.gulp
While the gulp command is running, files in the assets/scss/
, assets/js/
and components/
folders will be monitored for changes. Files from the assets/scss/
folder will generate injected CSS.
Hit CTRL+C
to terminate the gulp command. This will stop the local server from running.
If you’d like to get a version of our theme without Sass, Gulp or Npm, we’ve got you covered. Run the following command:
gulp build:dev
This will generate a folder html&css
which will have unminified CSS, Html and Javascript.
If you’d like to compile the code and get a minified version of the HTML and CSS just run the following Gulp command:
gulp build:dist
This will generate a folder dist
which will have minified CSS, Html and Javascript.
The documentation for Volt is hosted on our website.
Within the download you’ll find the following directories and files:
Volt Bootstrap 5 Admin Dashboard
.
├── README.md
├── gulpfile.js
├── package-lock.json
├── package.json
└── src
├── assets
│ ├── img
│ └── js
├── index.html
├── pages
│ ├── components
│ ├── dashboard
│ ├── examples
│ ├── settings.html
│ ├── tables
│ └── transactions.html
├── partials
│ ├── _analytics.html
│ ├── _footer.html
│ ├── _head.html
│ ├── _navigation.html
│ ├── _pages-preview.html
│ ├── _preloader.html
│ ├── _scripts.html
│ └── dashboard
└── scss
├── volt
└── volt.scss
At present, we officially aim to support the last two versions of the following browsers:
We use GitHub Issues as the official bug tracker for Volt Bootstrap 5 Admin Dashboard. Here are some advices for our users that want to report an issue:
If you have questions or need help integrating the product please contact us instead of opening an issue.
Twitter: https://twitter.com/themesberg
Facebook: https://www.facebook.com/themesberg/
Dribbble: https://dribbble.com/themesberg
Instagram: https://www.instagram.com/themesberg/
#bootstrap #bootstrap5 #bootstrap-5 #bootstrap-5-dashboard #bootstrap-5-admin-dashboard #themesberg
1598883697
Almost 2 months ago Bootstrap 5 alpha has been launched and there are two big news: jQuery has been dropped as a dependency and there is no more direct support for IE 10/11. We’ve been playing around with the new version of the framework and it is exciting to see some features such as the Utility API and working with vanilla JS.
In today’s tutorial, I would like to show you guys how to create a really simple admin dashboard interface using Bootstrap 5. Here’s an online demo if you want to check it out before building it. You’ll create the following sections:
Read more at Themesberg Blog - Tutorial: How to Build a Simple Admin Dashboard Interface using Bootstrap 5
#tutorial #bootstrap5 #bootstrap-5 #bootstrap-5-dashboard #bootstrap-5-tutorial
1605183428
Just about a day ago, I saw on my Twitter feed that Bootstrap 5 Alpha 3 has officially arrived bringing some new features, such as a brand new accordion component, floating labels for input form elements, improvements for the block button component, and a few more utility classes and icons.
As a refresher, let me remind you that the first version of Bootstrap 5 was launched on the 26th of June, 2020 bringing drastic changes, such as removing jQuery as a dependency and dropping support for Internet Explorer 10 and 11.
Without further ado, let’s see what changes the new Alpha 3 version brings to Bootstrap 5.
Read more about Bootstrap 5 Alpha 3 Changes on Themesberg.
#bootstrap #bootstrap5 #bootstrap-5 #bootstrap-5-alpha- #themesberg