JavaScript Best Practices — Exceptions, Loops, and Returns

Like any kind of apps, JavaScript apps also have to be written well. Otherwise, we run into all kinds of issues later on.

In this article, we’ll look at some best practices we should follow when writing JavaScript code.

No Multiple Spaces Inside Regex Literals

We shouldn’t have multiple spaces inside regex literals.

For instance, we shouldn’t write:

const regex = /foo  bar/;

Instead, we write:

const regexp = /foo bar/;

Assignment in return Statements Must be Surrounded by Parentheses

We should surround return statements in parentheses so we can be sure that the assignment is being done before returning the value.

For instance, instead of writing:

function add (a, b) {
  return result = a + b;
}

We write:

function add (a, b) {
  return (result = a + b);
}

Don’t Assign a Variable to Itself

Assigning in a variable to itself is a useless statement, so we shouldn’t write it.

For instance, we shouldn’t write things like:

num = num;

Don’t Compare a Variable to Itself

Since comparing a variable to itself is always true , it’s useless to have a variable compared to itself.

For instance, we shouldn’t write:

if (score === score) {}

Don’t Use the Comma Operator

The comma operator always returns the last expression in the sequence, so it’s pretty useless.

Therefore, we shouldn’t have code like:

const foo = (doSomething(), !!test);

Don’t Assign Things to Restricted Names

We shouldn’t create variables with restricted names and assign values to them.

It doesn’t work and it just causes confusion.

For example, we shouldn’t have code like:

let undefined = 'value';

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PySpark Cheat Sheet: Spark in Python

This PySpark cheat sheet with code samples covers the basics like initializing Spark in Python, loading data, sorting, and repartitioning.

Apache Spark is generally known as a fast, general and open-source engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It allows you to speed analytic applications up to 100 times faster compared to technologies on the market today. You can interface Spark with Python through "PySpark". This is the Spark Python API exposes the Spark programming model to Python.

Even though working with Spark will remind you in many ways of working with Pandas DataFrames, you'll also see that it can be tough getting familiar with all the functions that you can use to query, transform, inspect, ... your data. What's more, if you've never worked with any other programming language or if you're new to the field, it might be hard to distinguish between RDD operations.

Let's face it, map() and flatMap() are different enough, but it might still come as a challenge to decide which one you really need when you're faced with them in your analysis. Or what about other functions, like reduce() and reduceByKey()?

PySpark cheat sheet

Even though the documentation is very elaborate, it never hurts to have a cheat sheet by your side, especially when you're just getting into it.

This PySpark cheat sheet covers the basics, from initializing Spark and loading your data, to retrieving RDD information, sorting, filtering and sampling your data. But that's not all. You'll also see that topics such as repartitioning, iterating, merging, saving your data and stopping the SparkContext are included in the cheat sheet.

Note that the examples in the document take small data sets to illustrate the effect of specific functions on your data. In real life data analysis, you'll be using Spark to analyze big data.

PySpark is the Spark Python API that exposes the Spark programming model to Python.

Initializing Spark

SparkContext

>>> from pyspark import SparkContext
>>> sc = SparkContext(master = 'local[2]')

Inspect SparkContext

>>> sc.version #Retrieve SparkContext version
>>> sc.pythonVer #Retrieve Python version
>>> sc.master #Master URL to connect to
>>> str(sc.sparkHome) #Path where Spark is installed on worker nodes
>>> str(sc.sparkUser()) #Retrieve name of the Spark User running SparkContext
>>> sc.appName #Return application name
>>> sc.applicationld #Retrieve application ID
>>> sc.defaultParallelism #Return default level of parallelism
>>> sc.defaultMinPartitions #Default minimum number of partitions for RDDs

Configuration

>>> from pyspark import SparkConf, SparkContext
>>> conf = (SparkConf()
     .setMaster("local")
     .setAppName("My app")
     . set   ("spark. executor.memory",   "lg"))
>>> sc = SparkContext(conf = conf)

Using the Shell

In the PySpark shell, a special interpreter-aware SparkContext is already created in the variable called sc.

$ ./bin/spark-shell --master local[2]
$ ./bin/pyspark --master local[s] --py-files code.py

Set which master the context connects to with the --master argument, and add Python .zip..egg or.py files to the

runtime path by passing a comma-separated list to --py-files.

Loading Data

Parallelized Collections

>>> rdd = sc.parallelize([('a',7),('a',2),('b',2)])
>>> rdd2 = sc.parallelize([('a',2),('d',1),('b',1)])
>>> rdd3 = sc.parallelize(range(100))
>>> rdd = sc.parallelize([("a",["x","y","z"]),
               ("b" ["p","r,"])])

External Data

Read either one text file from HDFS, a local file system or any Hadoop-supported file system URI with textFile(), or read in a directory of text files with wholeTextFiles().

>>> textFile = sc.textFile("/my/directory/•.txt")
>>> textFile2 = sc.wholeTextFiles("/my/directory/")

Retrieving RDD Information

Basic Information

>>> rdd.getNumPartitions() #List the number of partitions
>>> rdd.count() #Count RDD instances 3
>>> rdd.countByKey() #Count RDD instances by key
defaultdict(<type 'int'>,{'a':2,'b':1})
>>> rdd.countByValue() #Count RDD instances by value
defaultdict(<type 'int'>,{('b',2):1,('a',2):1,('a',7):1})
>>> rdd.collectAsMap() #Return (key,value) pairs as a dictionary
   {'a': 2, 'b': 2}
>>> rdd3.sum() #Sum of RDD elements 4950
>>> sc.parallelize([]).isEmpty() #Check whether RDD is empty
True

Summary

>>> rdd3.max() #Maximum value of RDD elements 
99
>>> rdd3.min() #Minimum value of RDD elements
0
>>> rdd3.mean() #Mean value of RDD elements 
49.5
>>> rdd3.stdev() #Standard deviation of RDD elements 
28.866070047722118
>>> rdd3.variance() #Compute variance of RDD elements 
833.25
>>> rdd3.histogram(3) #Compute histogram by bins
([0,33,66,99],[33,33,34])
>>> rdd3.stats() #Summary statistics (count, mean, stdev, max & min)

Applying Functions

#Apply a function to each RFD element
>>> rdd.map(lambda x: x+(x[1],x[0])).collect()
[('a' ,7,7, 'a'),('a' ,2,2, 'a'), ('b' ,2,2, 'b')]
#Apply a function to each RDD element and flatten the result
>>> rdd5 = rdd.flatMap(lambda x: x+(x[1],x[0]))
>>> rdd5.collect()
['a',7 , 7 ,  'a' , 'a' , 2,  2,  'a', 'b', 2 , 2, 'b']
#Apply a flatMap function to each (key,value) pair of rdd4 without changing the keys
>>> rdds.flatMapValues(lambda x: x).collect()
[('a', 'x'), ('a', 'y'), ('a', 'z'),('b', 'p'),('b', 'r')]

Selecting Data

Getting

>>> rdd.collect() #Return a list with all RDD elements 
[('a', 7), ('a', 2), ('b', 2)]
>>> rdd.take(2) #Take first 2 RDD elements 
[('a', 7),  ('a', 2)]
>>> rdd.first() #Take first RDD element
('a', 7)
>>> rdd.top(2) #Take top 2 RDD elements 
[('b', 2), ('a', 7)]

Sampling

>>> rdd3.sample(False, 0.15, 81).collect() #Return sampled subset of rdd3
     [3,4,27,31,40,41,42,43,60,76,79,80,86,97]

Filtering

>>> rdd.filter(lambda x: "a" in x).collect() #Filter the RDD
[('a',7),('a',2)]
>>> rdd5.distinct().collect() #Return distinct RDD values
['a' ,2, 'b',7]
>>> rdd.keys().collect() #Return (key,value) RDD's keys
['a',  'a',  'b']

Iterating

>>> def g (x): print(x)
>>> rdd.foreach(g) #Apply a function to all RDD elements
('a', 7)
('b', 2)
('a', 2)

Reshaping Data

Reducing

>>> rdd.reduceByKey(lambda x,y : x+y).collect() #Merge the rdd values for each key
[('a',9),('b',2)]
>>> rdd.reduce(lambda a, b: a+ b) #Merge the rdd values
('a', 7, 'a' , 2 , 'b' , 2)

Grouping by

>>> rdd3.groupBy(lambda x: x % 2) #Return RDD of grouped values
          .mapValues(list)
          .collect()
>>> rdd.groupByKey() #Group rdd by key
          .mapValues(list)
          .collect() 
[('a',[7,2]),('b',[2])]

Aggregating

>> seqOp = (lambda x,y: (x[0]+y,x[1]+1))
>>> combOp = (lambda x,y:(x[0]+y[0],x[1]+y[1]))
#Aggregate RDD elements of each partition and then the results
>>> rdd3.aggregate((0,0),seqOp,combOp) 
(4950,100)
#Aggregate values of each RDD key
>>> rdd.aggregateByKey((0,0),seqop,combop).collect() 
     [('a',(9,2)), ('b',(2,1))]
#Aggregate the elements of each partition, and then the results
>>> rdd3.fold(0,add)
     4950
#Merge the values for each key
>>> rdd.foldByKey(0, add).collect()
[('a' ,9), ('b' ,2)]
#Create tuples of RDD elements by applying a function
>>> rdd3.keyBy(lambda x: x+x).collect()

Mathematical Operations

>>>> rdd.subtract(rdd2).collect() #Return each rdd value not contained in rdd2
[('b' ,2), ('a' ,7)]
#Return each (key,value) pair of rdd2 with no matching key in rdd
>>> rdd2.subtractByKey(rdd).collect()
[('d', 1)1
>>>rdd.cartesian(rdd2).collect() #Return the Cartesian product of rdd and rdd2

Sort

>>> rdd2.sortBy(lambda x: x[1]).collect() #Sort RDD by given function
[('d',1),('b',1),('a',2)]
>>> rdd2.sortByKey().collect() #Sort (key, value) ROD by key
[('a' ,2), ('b' ,1), ('d' ,1)]

Repartitioning

>>> rdd.repartition(4) #New RDD with 4 partitions
>>> rdd.coalesce(1) #Decrease the number of partitions in the RDD to 1

Saving

>>> rdd.saveAsTextFile("rdd.txt")
>>> rdd.saveAsHadoopFile("hdfs:// namenodehost/parent/child",
               'org.apache.hadoop.mapred.TextOutputFormat')

Stopping SparkContext

>>> sc.stop()

Execution

$ ./bin/spark-submit examples/src/main/python/pi.py

Have this Cheat Sheet at your fingertips

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

#pyspark #cheatsheet #spark #python

bindu singh

1647351133

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

Physical and Medical standards –

Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
The candidate's body weight should be proportional to his or her height.
Candidates with blemish-free skin will have an advantage.
Physical fitness is required of the candidate.
Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
There should be no history of mental disease in the candidate's past.
The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

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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()

>>> 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

Royce Reinger

1659330128

Calculates Edit Distance using Damerau-Levenshtein Algorithm

damerau-levenshtein

The damerau-levenshtein gem allows to find edit distance between two UTF-8 or ASCII encoded strings with O(N*M) efficiency.

This gem implements pure Levenshtein algorithm, Damerau modification of it (where 2 character transposition counts as 1 edit distance). It also includes Boehmer & Rees 2008 modification of Damerau algorithm, where transposition of bigger than 1 character blocks is taken in account as well (Rees 2014).

require "damerau-levenshtein"
DamerauLevenshtein.distance("Something", "Smoething") #returns 1

It also returns a diff between two strings according to Levenshtein alrorithm. The diff is expressed by tags <ins>, <del>, and <subst>. Such tags make it possible to highlight differnce between strings in a flexible way.

require "damerau-levenshtein"
differ = DamerauLevenshtein::Differ.new
differ.run("corn", "cron")
# output: ["c<subst>or</subst>n", "c<subst>ro</subst>n"]

Dependencies

sudo apt-get install build-essential libgmp3-dev

Installation

gem install damerau-levenshtein

Examples

require "damerau-levenshtein"
dl = DamerauLevenshtein

compare using Damerau Levenshtein algorithm

dl.distance("Something", "Smoething") #returns 1

compare using Levensthein algorithm

dl.distance("Something", "Smoething", 0) #returns 2

compare using Boehmer & Rees modification

dl.distance("Something", "meSothing", 2) #returns 2 instead of 4

comparison of words with UTF-8 characters should work fine:

dl.distance("Sjöstedt", "Sjostedt") #returns 1

compare two arrays

dl.array_distance([1,2,3,5], [1,2,3,4]) #returns 1

return diff between two strings

differ = DamerauLevenshtein::Differ.new
differ.run("Something", "smthg")

return diff between two strings in raw format

differ = DamerauLevenshtein::Differ.new
differ.format = :raw
differ.run("Something", "smthg")

API Description

Methods

DamerauLevenshtein.version

DamerauLevenshtein.version
#returns version number of the gem

DamerauLevenshtein.distance

DamerauLevenshtein.distance(string1, string2, block_size, max_distance)
#returns edit distance between 2 strings

DamerauLevenshtein.string_distance(string1, string2, block_size, max_distance)
# an alias for .distance

DamerauLevenshtein.array_distance(array1, array2, block_size, max_distance)
# returns edit distance between 2 arrays of integers

DamerauLevenshtein.distance and .array_distance take 4 arguments:

string1 (array1 for .array_distance)
string2 (array2 for .array_distance)
block_size (default is 1)
max_distance (default is 10)

block_size determines maximum number of characters in a transposition block:

block_size = 0
(transposition does not count -- it is a pure Levenshtein algorithm)

block_size = 1
(transposition between 2 adjustent characters --
it is pure Damerau-Levenshtein algorithm)

block_size = 2
(transposition between blocks as big as 2 characters -- so abcd and cdab
counts as edit distance 2, not 4)

block_size = 3
(transposition between blocks as big as 3 characters --
so abcdef and defabc counts as edit distance 3, not 6)

etc.

max_distance -- is a threshold after which algorithm gives up and returns max_distance instead of real edit distance.

Levenshtein algorithm is expensive, so it makes sense to give up when edit distance is becoming too big. The argument max_distance does just that.


DamerauLevenshtein.distance("abcdefg", "1234567", 0, 3)
# output: 4 -- it gave up when edit distance exceeded 3

DamerauLevenshtein::Differ

differ = DamerauLevenshtein::Differ.new creates an instance of new differ class to return difference between two strings

differ.format shows current format for diff. Default is :tag format

differ.format = :raw changes current format for diffs. Possible values are :tag and :raw

differ.run("String1", "String2") returns difference between two strings.

For example:

differ = DamerauLevenshtein::Differ.new
differ.run("Something", "smthng")
# output: ["<ins>S</ins><subst>o</subst>m<ins>e</ins>th<ins>i</ins>ng",
#          "<del>S</del><subst>s</subst>m<del>e</del>th<del>i</del>ng"]

Or with parsing:

require "damerau-levenshtein"
require "nokogiri"

differ = DamerauLevenshtein::Differ.new
res = differ.run("Something", "Smothing!")
nodes = Nokogiri::XML("<root>#{res.first}</root>")

markup = nodes.root.children.map do |n|
  case n.name
  when "text"
    n.text
  when "del"
    "~~#{n.children.first.text}~~"
  when "ins"
    "*#{n.children.first.text}*"
  when "subst"
    "**#{n.children.first.text}**"
  end
end.join("")

puts markup

Output

S*o*m**e**thing~~!~~

Contributing to damerau-levenshtein

Check out the latest master to make sure the feature hasn't been implemented or the bug hasn't been fixed yet
Check out the issue tracker to make sure someone already hasn't requested it and/or contributed it
Fork the project
Start a feature/bugfix branch
Commit and push until you are happy with your contribution
Make sure to add tests for it. This is important so I don't break it in a future version unintentionally.
Please try not to mess with the Rakefile, version, or history. If you want to have your own version, or is otherwise necessary, that is fine, but please isolate to its own commit so I can cherry-pick around it.

Versioning

This gem is following practices of Semantic Versioning

Download Details:

Author: GlobalNamesArchitecture
Source Code: https://github.com/GlobalNamesArchitecture/damerau-levenshtein
License: MIT license

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Lowa Alice

1624406400

JavaScript Loops Tutorial

JavaScript loops made simple.

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The origin of the article: https://www.youtube.com/watch?v=s9wW2PpJsmQ&list=PLTjRvDozrdlxEIuOBZkMAK5uiqp8rHUax&index=8
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JavaScript Best Practices — Exceptions, Loops, and Returns

No Multiple Spaces Inside Regex Literals

Assignment in return Statements Must be Surrounded by Parentheses

Don’t Assign a Variable to Itself

Don’t Compare a Variable to Itself

Don’t Use the Comma Operator

Don’t Assign Things to Restricted Names

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PySpark Cheat Sheet: Spark in Python

Initializing Spark

SparkContext

Inspect SparkContext

Configuration

Using the Shell

Loading Data

Parallelized Collections

External Data

Retrieving RDD Information

Basic Information

Summary

Applying Functions

Selecting Data

Iterating

Reshaping Data

Mathematical Operations

Sort

Repartitioning

Saving

Stopping SparkContext

Execution

Procedure To Become An Air Hostess/Cabin Crew

PySpark Cheat Sheet: Spark DataFrames in Python

Initializing SparkSession

Creating DataFrames

Fromm RDDs

Filter

Duplicate Values

Queries

Add, Update & Remove Columns

Missing & Replacing Values

GroupBy

Sort

Repartitioning

Running Queries Programmatically

Inspect Data

Output

Stopping SparkSession

Calculates Edit Distance using Damerau-Levenshtein Algorithm

damerau-levenshtein

Dependencies

Installation

Examples

API Description

Methods

DamerauLevenshtein.version

DamerauLevenshtein.distance

DamerauLevenshtein::Differ

Contributing to damerau-levenshtein

Versioning

Download Details:

JavaScript Loops Tutorial