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Adopt-A-Widget (The Boring Flutter Development Show, Ep. 41)

Have you ever wanted to contribute to Flutter? Flutter is an open source project and now is your chance to shine!

This month, we are also asking you to “Adopt a Widget” and help fix up our docs! Just look for issues with the “adopt a widget” label, leave a comment, and send a pull request!

Contributing to Flutter on GitHub→ https://goo.gle/contributing-to-flutter
Learn more about Adopt-A-Widget → https://goo.gle/2HAbMAE

#flutter #dart #mobile-apps #programming #developer

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Buddha Community

Adopt-A-Widget (The Boring Flutter Development Show, Ep. 41)

Google's Flutter 1.20 stable announced with new features - Navoki

Flutter Google cross-platform UI framework has released a new version 1.20 stable.

Flutter is Google’s UI framework to make apps for Android, iOS, Web, Windows, Mac, Linux, and Fuchsia OS. Since the last 2 years, the flutter Framework has already achieved popularity among mobile developers to develop Android and iOS apps. In the last few releases, Flutter also added the support of making web applications and desktop applications.

Last month they introduced the support of the Linux desktop app that can be distributed through Canonical Snap Store(Snapcraft), this enables the developers to publish there Linux desktop app for their users and publish on Snap Store.  If you want to learn how to Publish Flutter Desktop app in Snap Store that here is the tutorial.

Flutter 1.20 Framework is built on Google’s made Dart programming language that is a cross-platform language providing native performance, new UI widgets, and other more features for the developer usage.

Here are the few key points of this release:

Performance improvements for Flutter and Dart

In this release, they have got multiple performance improvements in the Dart language itself. A new improvement is to reduce the app size in the release versions of the app. Another performance improvement is to reduce junk in the display of app animation by using the warm-up phase.

sksl_warm-up

If your app is junk information during the first run then the Skia Shading Language shader provides for pre-compilation as part of your app’s build. This can speed it up by more than 2x.

Added a better support of mouse cursors for web and desktop flutter app,. Now many widgets will show cursor on top of them or you can specify the type of supported cursor you want.

Autofill for mobile text fields

Autofill was already supported in native applications now its been added to the Flutter SDK. Now prefilled information stored by your OS can be used for autofill in the application. This feature will be available soon on the flutter web.

flutter_autofill

A new widget for interaction

InteractiveViewer is a new widget design for common interactions in your app like pan, zoom drag and drop for resizing the widget. Informations on this you can check more on this API documentation where you can try this widget on the DartPad. In this release, drag-drop has more features added like you can know precisely where the drop happened and get the position.

Updated Material Slider, RangeSlider, TimePicker, and DatePicker

In this new release, there are many pre-existing widgets that were updated to match the latest material guidelines, these updates include better interaction with Slider and RangeSliderDatePicker with support for date range and time picker with the new style.

flutter_DatePicker

New pubspec.yaml format

Other than these widget updates there is some update within the project also like in pubspec.yaml file format. If you are a flutter plugin publisher then your old pubspec.yaml  is no longer supported to publish a plugin as the older format does not specify for which platform plugin you are making. All existing plugin will continue to work with flutter apps but you should make a plugin update as soon as possible.

Preview of embedded Dart DevTools in Visual Studio Code

Visual Studio code flutter extension got an update in this release. You get a preview of new features where you can analyze that Dev tools in your coding workspace. Enable this feature in your vs code by _dart.previewEmbeddedDevTools_setting. Dart DevTools menu you can choose your favorite page embed on your code workspace.

Network tracking

The updated the Dev tools comes with the network page that enables network profiling. You can track the timings and other information like status and content type of your** network calls** within your app. You can also monitor gRPC traffic.

Generate type-safe platform channels for platform interop

Pigeon is a command-line tool that will generate types of safe platform channels without adding additional dependencies. With this instead of manually matching method strings on platform channel and serializing arguments, you can invoke native class and pass nonprimitive data objects by directly calling the Dartmethod.

There is still a long list of updates in the new version of Flutter 1.2 that we cannot cover in this blog. You can get more details you can visit the official site to know more. Also, you can subscribe to the Navoki newsletter to get updates on these features and upcoming new updates and lessons. In upcoming new versions, we might see more new features and improvements.

You can get more free Flutter tutorials you can follow these courses:

#dart #developers #flutter #app developed #dart devtools in visual studio code #firebase local emulator suite in flutter #flutter autofill #flutter date picker #flutter desktop linux app build and publish on snapcraft store #flutter pigeon #flutter range slider #flutter slider #flutter time picker #flutter tutorial #flutter widget #google flutter #linux #navoki #pubspec format #setup flutter desktop on windows

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

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