Billy Chandler

Billy Chandler

1614304872

Web API for Xamarin Developers | The Xamarin Show

This week, James is joined Brady Gaster who is here to talk to use all ASP.NET Core Web API and why it is absolutely awesome for mobile development with Xamarin. He walks us through some best practices, new features, and awesome libraries that we can use to create powerful backends for our apps.

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#xamarin #api #web-development #mobile-apps

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Web API for Xamarin Developers | The Xamarin Show
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

Rahim Makhani

Rahim Makhani

1619150053

Get your own Mobile App with the latest Xamarin app development service

Xamarin is the best fit for mobile app developers who work on cross-platform applications such as various languages for coding and UI paradigms. It also allows developers to use C# for Android, iOS, and Windows apps.

Xamarin app development services are the latest app development services. Xamarin is built on an open-source platform built-in .Net framework. It is used for mobile app development. Xamarin is a cross-platform implementation of the common language infrastructure and common language specification.

Nevina Infotech will help you to develop your own mobile app with Xamarin app development services. We have the best mobile app developers who will help you to develop your mobile app.

#xamarin app development #xamarin development company #xamarin application development services #xamarin mobile app development #xamarin app development services #xamarin mobile development

Rahim Makhani

Rahim Makhani

1627274472

Develop An Unique Web App for your Firm

Web app represents the particular firm or organization for which it is developed. With the help of a web app, the firm owner can promote and increase their business by reaching more and more customers for their website or web app.

Every firm or organization must have its own web app to represent their company, what they do, what they provide users feedback, and a lot more. If you have your web app then users can know your company deeply and they can also show interest in your company.

To develop a unique web app contact Nevina Infotech that is the best web application development services provider company, that can help you to develop the web app for your firm as per your requirement.

#web application development company #web application development services #web app development company #custom web application development company #web app development services #web application development agency

Rahim Makhani

Rahim Makhani

1626238039

Find the best web app development company for your Startup

A web app is the best way to promote their business for startups. You can’t verbally go and tell everyone to visit your company, but your website or web app can do that. A web app can represent your company, and the visitors who are visiting your website or web app will get knowledge about your firm. Doing this can help you to increase your customer rate.

Nevina Infotech is the best web app development company to choose for developing your web app for your startup. We have a great team of web developers to work with. Our developers are dedicated and enthusiastic in their work.

#web application development company #web application development services #web app development company #custom web application development company #web app development services #custom web application development services

Marcelle  Smith

Marcelle Smith

1598437740

A Simple Guide to API Development Tools

APIs can be as simple as 1 endpoint for use by 100s of users or as complex as the AWS APIs with 1000s of endpoints and 100s of thousands of users. Building them can mean spending a couple of hours using a low-code platform or months of work using a multitude of tools. Hosting them can be as simple as using one platform that does everything we need or as complex as setting up and managing ingress control, security, caching, failover, metrics, scaling etc.

What they all have in common are three basic steps to go from nothing to a running API.

Each of these steps has its own set of tools. Here are some I’ve used and popular alternatives.

Design

REST is the most popular API interface and has the best tooling. Our design output for REST services always includes an OpenAPI specification. The specification language can be tricky to get right in JSON (how many curly brackets?) or YAML (how many spaces?) so a good editor saves a lot of time.

Four popular ones are:

I’ve only used Swagger and Postman but both Insomnia and Stoplight look interesting. All of them offer additional functionality like documentation, testing and collaboration so are much more than just specification generators.

#api #apis #api-development #restful-api #rest-api #development-tools #app-development-tools #developer-tools