1597343340
Iam a new Linux user and created a couple of groups on the server. I need to find out all members of a group called “ftponly”. How do I list all members of a group on Linux or Unix-like systems?
The /etc/group file is a text file that defines the groups on the Linux and Unix based systems. You can simply query this file to find and list all members of a group.
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There are two types of groups in Linux:
Use the grep command as follows:
$ grep 'grpup-name-here' /etc/group
$ grep 'ftponly' /etc/group
$ grep -i --color 'ftponly' /etc/group
ftponly:x:1001:raj,vivek,archana,sai,sayali
To get just a list of all members of a group called ftponly, type the following awk command:
awk -F':' '/ftponly/{print $4}' /etc/group
## list all members of sudo group in linux #
awk -F':' '/sudo/{print $4}' /etc/group
Want to see group memberships for each given USERNAME under Linux? The syntax is as follows for the groups command:
groups
groups {USERNAME}
groups vivek
The following outputs indicates that the user named ‘vivek’ is part of four groups including ‘vivek’ primary group:
vivek : vivek wheel lxd vboxusers
Warning: members command is not installed on most Linux distros. Use yum command or apt-get command/apt command to install the same:
$ sudo apt-get install members
To outputs members of a group called ftponly, enter:
$ members {GROUPNAME}
$ members ftponly
Fig. 01: members command in action to list members in a group
You can displays information about groups containing user name, or users contained in group name using lid command as follows.
Warning: lid command is not installed on most distros. Use yum command or apt-get command to install the same:
$ sudo apt-get install libuser
#linux
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.
1625631509
Dig Command Line Options and Examples
Here is the frequently used command line options and example’s of dig command.
1. Basic Dig Command
A basic dig command accept domain name as command line parameter and prints Address record.
2. Query With Specific DNS Server
The default dig command queries to dns server configured on your system. For example, the Linux systems keep default DNS entry in /etc/resolv.conf.
3. Print Short Answer
Use +short command line option to print result in short form. This is basically useful with the shell scripting and other automation tasks.
4. Print Detailed but Specific Result
Use +noall with +answer to print detailed information but specific. This will print only answer section including few more details as a result.
#linux commands #command #dig #dig command #useful examples #linux
1625625674
mv Command Examples
Below is the basic mv command examples on Linux terminal.
#linux commands #command #mv #useful example #mv command #linux
1625562321
date Command Examples
Show date time in UTC/GMT
View past dates on specific days
View future dates
View date in other timezone
Print date in specific format
View file modification time
#linux commands #command #date #linux