Naret  Suthala

Naret Suthala

1625689980

How To Show String And Numbers Separately Into An Array in Javascript

In this video I have shown how to show separately string and numbers into an array. Javascript tutorial. Javascript course. Es6.

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How To Show String And Numbers Separately Into An Array in Javascript

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:  
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
        - 10:33  Finding the length of an array
        - 11:44  Adding Elements
        - 15:06  Removing elements
        - 18:32  Array concatenation
       - 20:59  Slicing
       - 23:26  Looping  


Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  • By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
array.array()
  • Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
arr.array()
  • Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODEC TYPEPYTHON TYPESIZE
'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8
'f'floatfloat4
'd'doublefloat8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming 

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.

Brook  Legros

Brook Legros

1659199883

String Pattern: Generate Strings Supplying A Simple Pattern in Ruby

StringPattern

With this gem, you can easily generate strings supplying a very simple pattern. Even generate random words in English or Spanish. Also, you can validate if a text fulfills a specific pattern or even generate a string following a pattern and returning the wrong length, value... for testing your applications. Perfect to be used in test data factories.

Also you can use regular expressions (Regexp) to generate strings: /[a-z0-9]{2,5}\w+/.gen

To do even more take a look at nice_hash gem

Installation

Add this line to your application's Gemfile:

gem 'string_pattern'

And then execute:

$ bundle

Or install it yourself as:

$ gem install string_pattern

Usage

What is a string pattern?

A pattern is a string where we supply these elements "a-b:c" where a is min_length, b is max_length (optional) and c is a set of symbol_type

min_length: minimum length of the string

max_length (optional): maximum length of the string. If not provided, the result will be with the min_length provided

symbol_type: The type of the string we want.
    x: from a to z (lowercase)
    X: A to Z (capital letters)
    L: A to Z and a to z
    T: National characters defined on StringPattern.national_chars
    n or N: for numbers. 0 to 9
    $: special characters, $%&#...  (includes blank space)
    _: blank space
    *: all characters
    0: empty string will be accepted.  It needs to be at the beginning of the symbol_type string
        @: It will generate a valid email following the official algorithm. It cannot be used with other symbol_type
        W: for English words, capital and lower. It cannot be used with other symbol_type
        w: for English words only lower and words separated by underscore. It cannot be used with other symbol_type
        P: for Spanish words, capital and lower. It cannot be used with other symbol_type
        p: for Spanish words only lower and words separated by underscore. It cannot be used with other symbol_type
    

How to generate a string following a pattern

To generate a string following a pattern you can do it using directly the StringPattern class or the generate method in the class, be aware you can always use also the alias method: gen

require 'string_pattern'

#StringPattern class
p StringPattern.generate "10:N"
#>3448910834
p StringPattern.gen "5:X"
#>JDDDK

#String class
p "4:Nx".gen
#>xaa3

#Symbol class
p :"10:T".generate
#>AccBdjklñD

#Array class
p [:"3:N", "fixed", :"3:N"].gen
#>334fixed920
p "(,3:N,) ,3:N,-,2:N,-,2:N".split(',').generate 
#>(937) 980-65-05

#Kernel
p gen "3:N"
#>443

Generating unique strings

If you want to generate for example 1000 strings and be sure all those strings are different you can use:

StringPattern.dont_repeat = true #default: false
1000.times {
    puts :"6-20:L/N/".gen
}
StringPattern.cache_values = Hash.new() #to clean the generated values from memory

Using dont_repeat all the generated string during the current run will be unique.

In case you just want one particular string to be unique but not the rest then add to the pattern just in the end the symbol: &

The pattern needs to be a symbol object.

1000.times {
    puts :"6-20:L/N/&".gen #will be unique
    puts :"10:N".gen
}

Generate words randomly in English or Spanish

To generate a string of the length you want that will include only real words, use the symbol types:

  • W: generates English words following CamelCase ('ExampleOutput')
  • w: generates English words following snake_case ('example_output')
  • P: generates Spanish words following CamelCase ('EjemploSalida')
  • p: generates Spanish words following snake_case ('ejemplo_salida')
require 'string_pattern'

puts '10-30:W'.gen
#> FirstLieutenant
puts '10-30:w'.gen
#> paris_university
puts '10-30:P'.gen
#> SillaMetalizada
puts '10-30:p'.gen
#> despacho_grande

If you want to use a different word separator than "_" when using 'w' or 'p':

# blank space for example
require 'string_pattern'

StringPattern.word_separator = ' '

puts '10-30:w'.gen
#> paris university
puts '10-30:p'.gen
#> despacho grande

The word list is loaded on the first request to generate words, after that the speed to generate words increases amazingly. 85000 English words and 250000 Spanish words. The vocabularies are a sample of public open sources.

Generate strings using Regular Expressions (Regexp)

Take in consideration this feature is not supporting all possibilities for Regular expressions but it is fully functional. If you find any bug or limitation please add it to issues: https://github.com/MarioRuiz/string_pattern/issues

In case you want to change the default maximum for repetitions when using * or +: StringPattern.default_infinite = 30 . By default is 10.

If you want to translate a regular expression into an StringPattern use the method we added to Regexp class: to_sp

Examples:

/[a-z0-9]{2-5}\w+/.to_sp
#> ["2-5:nx", "1-10:Ln_"]

#regular expression for UUID v4
/[0-9A-F]{8}-[0-9A-F]{4}-4[0-9A-F]{3}-[89AB][0-9A-F]{3}-[0-9A-F]{12}/.to_sp
#> ["8:n[ABCDEF]", "-", "4:n[ABCDEF]", "-4", "3:n[ABCDEF]", "-", "1:[89AB]", "3:n[ABCDEF]", "-", "12:n[ABCDEF]"]

If you want to generate a random string following the regular expression, you can do it like a normal string pattern:


regexp = /[0-9A-F]{8}-[0-9A-F]{4}-4[0-9A-F]{3}-[89AB][0-9A-F]{3}-[0-9A-F]{12}/

# using StringPattern class
puts StringPattern.generate(regexp)

# using Kernel
puts generate(regexp)

# using generate method added to Regexp class
puts regexp.generate

#using the alias 'gen'
puts regexp.gen 

# output:
#>7009574B-6F2F-436E-BB7A-EA5FDA6B4E47
#>5FB1718F-108A-4F62-8170-33C43FD86B1D
#>05745B6F-93BA-475F-8118-DD56E5EAC4D1
#>2D6FC189-8D50-45A8-B182-780193838502

String patterns

How to generate one or another string

In case you need to specify that the string is generated selecting one or another fixed string or pattern, you can do it by using Array of patterns and in the position you want you can add an array with the possible values

p ["uno:", :"5:N", ['.red','.green', :'3:L'] ].gen

# first position a fixed string: "uno:"
# second position 5 random numbers
# third position one of these values: '.red', '.green' or 3 letters

# example output: 
# 'uno:34322.red'
# 'uno:44432.green'
# 'uno:34322.red'
# 'uno:28795xAB'

Take in consideration that this is only available to generate successful strings but not for validation

Custom characters

Also, it's possible to provide the characters we want. To do that we'll use the symbol_type [characters]

If we want to add the character ] we have to write ]]

Examples

# four chars from the ones provided: asDF9
p "4:[asDF9]".gen    #> aaaa, asFF, 9sFD

# from 2 to 20 chars, capital and lower chars (Xx) and also valid the characters $#6
p "2-20:[$#6]Xx".gen    #> aaaa, asFF, 66, B$DkKL#9aDD
 
# four chars from these: asDF]9
p "4:[asDF]]9]".gen    #> aa]a, asFF, 9s]D

Required characters or symbol types

We'll use the symbol / to specify which characters or symbols we want to be included on the resulting string as required values /symbols or characters/

If we need to add the character / we'll use //

Examples:

# four characters. optional: capitals and numbers, required: lower
"4:XN/x/".gen    # aaaa, FF9b, j4em, asdf, ADFt

# from 6 to 15 chars. optional: numbers, capitals and the chars $ and Æ. required the chars: 23abCD
"6-15:[/23abCD/$Æ]NX".gen    # bCa$D32, 32DJIOKLaCb, b23aD568C
 
# from 4 to 9 chars. optional: numbers and capitals. required: lowers and the characters $ and 5
"4-9:[/$5/]XN/x/".generate    # aa5$, F5$F9b, j$4em5, a5sdf$, $ADFt5 

Excluded characters

If we want to exclude a few characters in the result, we'll use the symbol %characters%

If you need to exclude the character %, you should use %%

Examples:

# from 2 to 20 characters. optional: Numbers and characters A, B and C. excluded: the characters 8 and 3
"2-20:[%83%ABC]N".gen    # B49, 22900, 9CAB, 22, 11CB6270C26C4572A50C

# 10 chars. optional: Letters (capital and lower). required: numbers. excluded: the characters 0 and WXYzZ
"10:L/n/[%0WXYzZ%]".gen    # GoO2ukCt4l, Q1Je2remFL, qPg1T92T2H, 4445556781

Not fulfilling a pattern

If we want our resulting string doesn't fulfill the pattern we supply, then we'll use the symbol ! at the beginning

Examples:

"!4:XN/x/".gen    # a$aaa, FF9B, j4DDDem, as, 2345

"!10:N".gen     # 123, 34899Add34, 3434234234234008, AAFj#kd2x

Generate a string with specific expected errors

Usually, for testing purposes you need to generate strings that don't fulfill a specific pattern, then you can supply as a parameter expected_errors (alias: errors)

The possible values you can specify is one or more of these ones: :length, :min_length, :max_length, :value, :required_data, :excluded_data, :string_set_not_allowed

:length: wrong length, minimum or maximum
:min_length: wrong minimum length
:max_length: wrong maximum length
:value: wrong resultant value
:required_data: the output string won't include all necessary required data. It works only if required data supplied on the pattern.
:excluded_data: the resultant string will include one or more characters that should be excluded. It works only if excluded data supplied on the pattern.
:string_set_not_allowed: it will include one or more characters that are not supposed to be on the string.

Examples:

"10-20:N".gen errors: [:min_length]
#> 627, 098262, 3408

"20:N".gen errors: [:length, :value]
#> |13, tS1b)r-1)<RT65202eTo6bV0g~, 021400323<2ahL0NP86a698063*56076

"10:L/n/".gen errors: [:value]
#> 1hwIw;v{KQ, mpk*l]!7:!, wocipgZt8@

Validate if a string is following a pattern

If you need to validate if a specific text is fulfilling the pattern you can use the validate method.

If a string pattern supplied and no other parameters supplied the output will be an array with the errors detected.

Possible output values, empty array (validation without errors detected) or one or more of: :min_length, :max_length, :length, :value, :string_set_not_allowed, :required_data, :excluded_data

In case an array of patterns supplied it will return only true or false

Examples:

#StringPattern class
StringPattern.validate((text: "This text will be validated", pattern: :"10-20:Xn")
#> [:max_length, :length, :value, :string_set_not_allowed]

#String class
"10:N".validate "333444"
#> [:min_length, :length]

#Symbol class
:"10:N".validate("333444")
#> [:min_length, :length]

#Array class
["5:L","3:xn","4-10:n"].validate "DjkljFFc343444390"
#> false

If we want to validate a string with a pattern and we are expecting to get specific errors, you can supply the parameter expected_errors (alias: errors) or not_expected_errors (aliases: non_expected_errors, not_errors).

In this case, the validate method will return true or false.

Examples:

"10:N".val "3445", errors: [:min_length]
#> true

"10:N/[09]/".validate "4434039440", errors: [:value]
#> false

"10-12:XN/x/".validate "FDDDDDAA343434", errors: [:max_length, :required_data]
#> true

Configure

SP_ADD_TO_RUBY

This gem adds the methods generate (alias: gen) and validate (alias: val) to the Ruby classes: String, Array, and Symbol.

Also adds the method generate (alias: gen) to Kernel. By default (true) it is always added.

In case you don't want to be added, just before requiring the library set:

SP_ADD_TO_RUBY = false
require 'string_pattern'

In case it is set to true (default) then you will be able to use:

require 'string_pattern'

#String object
"20-30:@".gen 
#>dkj34MljjJD-df@jfdluul.dfu

"10:L/N/[/-./%d%]".validate("12ds6f--.s") 
#>[:value, :string_set_not_allowed]

"20-40:@".validate(my_email)

#Kernel
gen "10:N"
#>3433409877

#Array object
"(,3:N,) ,3:N,-,2:N,-,2:N".split(",").generate 
#>(937) 980-65-05

%w{( 3:N ) 1:_ 3:N - 2:N - 2:N}.gen 
#>(045) 448-63-09

["1:L", "5-10:LN", "-", "3:N"].gen 
#>zqWihV-746

national_chars

To specify which national characters will be used when using the symbol type: T, you use StringPattern.national_chars, by default is the English alphabet

StringPattern.national_chars = (('a'..'z').to_a + ('A'..'Z').to_a).join + "áéíóúÁÉÍÓÚüÜñÑ"
"10-20:Tn".gen #>AAñ34Ef99éNOP

optimistic

If true it will check on the strings of the array positions supplied if they have the pattern format and assume in that case that is a pattern. If not it will assume the patterns on the array will be supplied as symbols. By default is set to true.

StringPattern.optimistic = false
["5:X","fixedtext", "3:N"].generate
#>5:Xfixedtext3:N
[:"5:X","fixedtext", :"3:N"].generate
#>AUJKJfixedtext454

StringPattern.optimistic = true
["5:X","fixedtext", "3:N"].generate
#>KKDMEfixedtext344
[:"5:X","fixedtext", :"3:N"].generate
#>SAAERfixedtext988

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/marioruiz/string_pattern.

License

The gem is available as open source under the terms of the MIT License.


Author: MarioRuiz
Source code: https://github.com/MarioRuiz/string_pattern
License: MIT license

#ruby  #ruby-on-rails 

Madilyn  Kihn

Madilyn Kihn

1590478483

Convert String To Array Using Javascript Split Method

The easiest approach to use javascript built-in method String.split().

#javascript #javascript string #string to array #morioh #array