Obie  Rowe

Obie Rowe

1598928180

6 Hacks to Make a Programmer’s Life Easier

Whether you are a novice or experienced programmer, following these helpful professional tips will save you time and greatly improve the quality of your code.

1) Make Code For Humans, Not just Machines

When you are writing code, you must always think: Will another programmer understand what you have written? That other programmer may very well be your future self. To ensure readability, it’s essential to make your identifiers (i.e. variable and function names) consistent throughout your code. Additionally, designing a similar length, grammatical structure, and explanatory style when naming your functions will provide uniformity and help anyone who is reading the code.

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Martin Fowler

2) Make Code You will Remember

This won’t be the last code you write, so write code you will remember six months or a year from now. Name your methods with short but descriptive verbs representing the action they perform. When coding with numbers and strings, assign meaningful variables that will be recognizable later on. Using meaningless numbers, signs and symbols will only create frustration in the future. Keep it simple and straightforward.

3) Use the Right Language to Solve the Problem

When it comes to deciding which programming language to use, choose the right language for the project. Often new programmers try to cut corners and code with a language they already know, even if a different language is better suited for the task. Finding and using the correct language or tool for the project might increase the work but putting in the extra time will be worth it in the long run. Remember, it’s better to take the extra time and do a job right the first time, than to take shortcuts and have to go back and fix the loopholes you missed initially.

4) If You Get Stuck, Take a Break

Everyone has those moments when you get so frustrated with coding you start yelling and blaming your troubles on your computer. If or when this happens, take a step back from your computer and do something to take your mind off coding. Perhaps a bathroom break or a walk around the block will be sufficient. But often it’s better to work on another project or take a half hour break from work completely and do something relaxing. Once you feel refreshed and energized, take another stab at the code.

5) Keep Methods Maintainable

Complicated monster methods are a common error seen among novice programmers. To save time and headaches, limit your methods to a manageable size making your code easier and quicker to read and maintain. When you have lengthy methods, break it up into smaller descriptive components, each portion representing a well-abstracted action. If you don’t do this proactively, the monster method will win. With maintainable methods, not only will the code be more readable, it makes debugging the code simpler and less time consuming.

#programming-tips #python3 #programming #python #python-programming

What is GEEK

Buddha Community

6 Hacks to Make a Programmer’s Life Easier
Mike  Kozey

Mike Kozey

1656151740

Test_cov_console: Flutter Console Coverage Test

Flutter Console Coverage Test

This small dart tools is used to generate Flutter Coverage Test report to console

How to install

Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dev_dependencies:
  test_cov_console: ^0.2.2

How to run

run the following command to make sure all flutter library is up-to-date

flutter pub get
Running "flutter pub get" in coverage...                            0.5s

run the following command to generate lcov.info on coverage directory

flutter test --coverage
00:02 +1: All tests passed!

run the tool to generate report from lcov.info

flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
 print_cov_constants.dart                    |    0.00 |    0.00 |    0.00 |    no unit testing|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Optional parameter

If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE>                      The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...>    A list of contains string for files without unit testing
                                       to be excluded from report
-l, --line                             It will print Lines & Uncovered Lines only
                                       Branch & Functions coverage percentage will not be printed
-i, --ignore                           It will not print any file without unit testing
-m, --multi                            Report from multiple lcov.info files
-c, --csv                              Output to CSV file
-o, --output=<CSV-FILE>                Full path of output CSV file
                                       If not given, "coverage/test_cov_console.csv" will be used
-t, --total                            Print only the total coverage
                                       Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM>                   Print only the whether total coverage is passed MINIMUM value or not
                                       If the value >= MINIMUM, it will print PASSED, otherwise FAILED
                                       Note: it will ignore all other option (if any), except -m
-h, --help                             Show this help

example run the tool with parameters

flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

report for multiple lcov.info files (-m, --multi)

It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File                                         |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b -                            |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/                                     |         |         |         |                   |
 print_cov.dart                              |  100.00 |  100.00 |   88.37 |...,149,205,206,207|
lib/                                         |         |         |         |                   |
 test_cov_console.dart                       |    0.00 |    0.00 |    0.00 |    no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
 All files with unit testing                 |  100.00 |  100.00 |   88.37 |                   |
---------------------------------------------|---------|---------|---------|-------------------|

Output to CSV file (-c, --csv, -o, --output)

flutter pub run test_cov_console -c --output=coverage/test_coverage.csv

#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""

Installing

Use this package as an executable

Install it

You can install the package from the command line:

dart pub global activate test_cov_console

Use it

The package has the following executables:

$ test_cov_console

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add test_cov_console

With Flutter:

 $ flutter pub add test_cov_console

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

dependencies:
  test_cov_console: ^0.2.2

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:test_cov_console/test_cov_console.dart';

example/lib/main.dart

import 'package:flutter/material.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        // This is the theme of your application.
        //
        // Try running your application with "flutter run". You'll see the
        // application has a blue toolbar. Then, without quitting the app, try
        // changing the primarySwatch below to Colors.green and then invoke
        // "hot reload" (press "r" in the console where you ran "flutter run",
        // or simply save your changes to "hot reload" in a Flutter IDE).
        // Notice that the counter didn't reset back to zero; the application
        // is not restarted.
        primarySwatch: Colors.blue,
        // This makes the visual density adapt to the platform that you run
        // the app on. For desktop platforms, the controls will be smaller and
        // closer together (more dense) than on mobile platforms.
        visualDensity: VisualDensity.adaptivePlatformDensity,
      ),
      home: MyHomePage(title: 'Flutter Demo Home Page'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  MyHomePage({Key? key, required this.title}) : super(key: key);

  // This widget is the home page of your application. It is stateful, meaning
  // that it has a State object (defined below) that contains fields that affect
  // how it looks.

  // This class is the configuration for the state. It holds the values (in this
  // case the title) provided by the parent (in this case the App widget) and
  // used by the build method of the State. Fields in a Widget subclass are
  // always marked "final".

  final String title;

  @override
  _MyHomePageState createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  int _counter = 0;

  void _incrementCounter() {
    setState(() {
      // This call to setState tells the Flutter framework that something has
      // changed in this State, which causes it to rerun the build method below
      // so that the display can reflect the updated values. If we changed
      // _counter without calling setState(), then the build method would not be
      // called again, and so nothing would appear to happen.
      _counter++;
    });
  }

  @override
  Widget build(BuildContext context) {
    // This method is rerun every time setState is called, for instance as done
    // by the _incrementCounter method above.
    //
    // The Flutter framework has been optimized to make rerunning build methods
    // fast, so that you can just rebuild anything that needs updating rather
    // than having to individually change instances of widgets.
    return Scaffold(
      appBar: AppBar(
        // Here we take the value from the MyHomePage object that was created by
        // the App.build method, and use it to set our appbar title.
        title: Text(widget.title),
      ),
      body: Center(
        // Center is a layout widget. It takes a single child and positions it
        // in the middle of the parent.
        child: Column(
          // Column is also a layout widget. It takes a list of children and
          // arranges them vertically. By default, it sizes itself to fit its
          // children horizontally, and tries to be as tall as its parent.
          //
          // Invoke "debug painting" (press "p" in the console, choose the
          // "Toggle Debug Paint" action from the Flutter Inspector in Android
          // Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
          // to see the wireframe for each widget.
          //
          // Column has various properties to control how it sizes itself and
          // how it positions its children. Here we use mainAxisAlignment to
          // center the children vertically; the main axis here is the vertical
          // axis because Columns are vertical (the cross axis would be
          // horizontal).
          mainAxisAlignment: MainAxisAlignment.center,
          children: <Widget>[
            Text(
              'You have pushed the button this many times:',
            ),
            Text(
              '$_counter',
              style: Theme.of(context).textTheme.headline4,
            ),
          ],
        ),
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: Icon(Icons.add),
      ), // This trailing comma makes auto-formatting nicer for build methods.
    );
  }
}

Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console 
License: BSD-3-Clause license

#flutter #dart #test 

渚  直樹

渚 直樹

1635917640

ループを使用して、Rustのデータを反復処理します

このモジュールでは、Rustでハッシュマップ複合データ型を操作する方法について説明します。ハッシュマップのようなコレクション内のデータを反復処理するループ式を実装する方法を学びます。演習として、要求された注文をループし、条件をテストし、さまざまなタイプのデータを処理することによって車を作成するRustプログラムを作成します。

さび遊び場

錆遊び場は錆コンパイラにブラウザインタフェースです。言語をローカルにインストールする前、またはコンパイラが利用できない場合は、Playgroundを使用してRustコードの記述を試すことができます。このコース全体を通して、サンプルコードと演習へのPlaygroundリンクを提供します。現時点でRustツールチェーンを使用できない場合でも、コードを操作できます。

Rust Playgroundで実行されるすべてのコードは、ローカルの開発環境でコンパイルして実行することもできます。コンピューターからRustコンパイラーと対話することを躊躇しないでください。Rust Playgroundの詳細については、What isRust?をご覧ください。モジュール。

学習目標

このモジュールでは、次のことを行います。

  • Rustのハッシュマップデータ型、およびキーと値にアクセスする方法を確認してください
  • ループ式を使用してRustプログラムのデータを反復処理する方法を探る
  • Rustプログラムを作成、コンパイル、実行して、ループを使用してハッシュマップデータを反復処理します

Rustのもう1つの一般的なコレクションの種類は、ハッシュマップです。このHashMap<K, V>型は、各キーKをその値にマッピングすることによってデータを格納しますV。ベクトル内のデータは整数インデックスを使用してアクセスされますが、ハッシュマップ内のデータはキーを使用してアクセスされます。

ハッシュマップタイプは、オブジェクト、ハッシュテーブル、辞書などのデータ項目の多くのプログラミング言語で使用されます。

ベクトルのように、ハッシュマップは拡張可能です。データはヒープに格納され、ハッシュマップアイテムへのアクセスは実行時にチェックされます。

ハッシュマップを定義する

次の例では、書評を追跡するためのハッシュマップを定義しています。ハッシュマップキーは本の名前であり、値は読者のレビューです。

use std::collections::HashMap;
let mut reviews: HashMap<String, String> = HashMap::new();

reviews.insert(String::from("Ancient Roman History"), String::from("Very accurate."));
reviews.insert(String::from("Cooking with Rhubarb"), String::from("Sweet recipes."));
reviews.insert(String::from("Programming in Rust"), String::from("Great examples."));

このコードをさらに詳しく調べてみましょう。最初の行に、新しいタイプの構文が表示されます。

use std::collections::HashMap;

このuseコマンドは、Rust標準ライブラリの一部HashMapからの定義をcollectionsプログラムのスコープに取り込みます。この構文は、他のプログラミング言語がインポートと呼ぶものと似ています。

HashMap::newメソッドを使用して空のハッシュマップを作成します。reviews必要に応じてキーと値を追加または削除できるように、変数を可変として宣言します。この例では、ハッシュマップのキーと値の両方がStringタイプを使用しています。

let mut reviews: HashMap<String, String> = HashMap::new();

キーと値のペアを追加します

このinsert(<key>, <value>)メソッドを使用して、ハッシュマップに要素を追加します。コードでは、構文は<hash_map_name>.insert()次のとおりです。

reviews.insert(String::from("Ancient Roman History"), String::from("Very accurate."));

キー値を取得する

ハッシュマップにデータを追加した後、get(<key>)メソッドを使用してキーの特定の値を取得できます。

// Look for a specific review
let book: &str = "Programming in Rust";
println!("\nReview for \'{}\': {:?}", book, reviews.get(book));

出力は次のとおりです。

Review for 'Programming in Rust': Some("Great examples.")

ノート

出力には、書評が単なる「すばらしい例」ではなく「Some( "すばらしい例。")」として表示されていることに注意してください。getメソッドはOption<&Value>型を返すため、Rustはメソッド呼び出しの結果を「Some()」表記でラップします。

キーと値のペアを削除します

この.remove()メソッドを使用して、ハッシュマップからエントリを削除できます。get無効なハッシュマップキーに対してメソッドを使用すると、getメソッドは「なし」を返します。

// Remove book review
let obsolete: &str = "Ancient Roman History";
println!("\n'{}\' removed.", obsolete);
reviews.remove(obsolete);

// Confirm book review removed
println!("\nReview for \'{}\': {:?}", obsolete, reviews.get(obsolete));

出力は次のとおりです。

'Ancient Roman History' removed.
Review for 'Ancient Roman History': None

このコードを試して、このRustPlaygroundでハッシュマップを操作できます。

演習:ハッシュマップを使用して注文を追跡する
この演習では、ハッシュマップを使用するように自動車工場のプログラムを変更します。

ハッシュマップキーと値のペアを使用して、車の注文に関する詳細を追跡し、出力を表示します。繰り返しになりますが、あなたの課題は、サンプルコードを完成させてコンパイルして実行することです。

この演習のサンプルコードで作業するには、次の2つのオプションがあります。

  • コードをコピーして、ローカル開発環境で編集します。
  • 準備されたRustPlaygroundでコードを開きます。

ノート

サンプルコードで、todo!マクロを探します。このマクロは、完了するか更新する必要があるコードを示します。

現在のプログラムをロードする

最初のステップは、既存のプログラムコードを取得することです。

  1. 編集のために既存のプログラムコードを開きます。コードは、データ型宣言、および定義のため含みcar_qualitycar_factoryおよびmain機能を。

次のコードをコピーしてローカル開発環境で編集する
か、この準備されたRustPlaygroundでコードを開きます。

#[derive(PartialEq, Debug)]
struct Car { color: String, motor: Transmission, roof: bool, age: (Age, u32) }

#[derive(PartialEq, Debug)]
enum Transmission { Manual, SemiAuto, Automatic }

#[derive(PartialEq, Debug)]
enum Age { New, Used }

// Get the car quality by testing the value of the input argument
// - miles (u32)
// Return tuple with car age ("New" or "Used") and mileage
fn car_quality (miles: u32) -> (Age, u32) {

    // Check if car has accumulated miles
    // Return tuple early for Used car
    if miles > 0 {
        return (Age::Used, miles);
    }

    // Return tuple for New car, no need for "return" keyword or semicolon
    (Age::New, miles)
}

// Build "Car" using input arguments
fn car_factory(order: i32, miles: u32) -> Car {
    let colors = ["Blue", "Green", "Red", "Silver"];

    // Prevent panic: Check color index for colors array, reset as needed
    // Valid color = 1, 2, 3, or 4
    // If color > 4, reduce color to valid index
    let mut color = order as usize;
    if color > 4 {        
        // color = 5 --> index 1, 6 --> 2, 7 --> 3, 8 --> 4
        color = color - 4;
    }

    // Add variety to orders for motor type and roof type
    let mut motor = Transmission::Manual;
    let mut roof = true;
    if order % 3 == 0 {          // 3, 6, 9
        motor = Transmission::Automatic;
    } else if order % 2 == 0 {   // 2, 4, 8, 10
        motor = Transmission::SemiAuto;
        roof = false;
    }                            // 1, 5, 7, 11

    // Return requested "Car"
    Car {
        color: String::from(colors[(color-1) as usize]),
        motor: motor,
        roof: roof,
        age: car_quality(miles)
    }
}

fn main() {
    // Initialize counter variable
    let mut order = 1;
    // Declare a car as mutable "Car" struct
    let mut car: Car;

    // Order 6 cars, increment "order" for each request
    // Car order #1: Used, Hard top
    car = car_factory(order, 1000);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    // Car order #2: Used, Convertible
    order = order + 1;
    car = car_factory(order, 2000);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);    

    // Car order #3: New, Hard top
    order = order + 1;
    car = car_factory(order, 0);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    // Car order #4: New, Convertible
    order = order + 1;
    car = car_factory(order, 0);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    // Car order #5: Used, Hard top
    order = order + 1;
    car = car_factory(order, 3000);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    // Car order #6: Used, Hard top
    order = order + 1;
    car = car_factory(order, 4000);
    println!("{}: {:?}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);
}

2. プログラムをビルドします。次のセクションに進む前に、コードがコンパイルされて実行されることを確認してください。

次の出力が表示されます。

1: Used, Hard top = true, Manual, Blue, 1000 miles
2: Used, Hard top = false, SemiAuto, Green, 2000 miles
3: New, Hard top = true, Automatic, Red, 0 miles
4: New, Hard top = false, SemiAuto, Silver, 0 miles
5: Used, Hard top = true, Manual, Blue, 3000 miles
6: Used, Hard top = true, Automatic, Green, 4000 miles

注文の詳細を追跡するためのハッシュマップを追加する

現在のプログラムは、各車の注文を処理し、各注文が完了した後に要約を印刷します。car_factory関数を呼び出すたびにCar、注文の詳細を含む構造体が返され、注文が実行されます。結果はcar変数に格納されます。

お気づきかもしれませんが、このプログラムにはいくつかの重要な機能がありません。すべての注文を追跡しているわけではありません。car変数は、現在の注文の詳細のみを保持しています。関数carの結果で変数が更新されるたびcar_factoryに、前の順序の詳細が上書きされます。

ファイリングシステムのようにすべての注文を追跡するために、プログラムを更新する必要があります。この目的のために、<K、V>ペアでハッシュマップを定義します。ハッシュマップキーは、車の注文番号に対応します。ハッシュマップ値は、Car構造体で定義されているそれぞれの注文の詳細になります。

  1. ハッシュマップを定義するには、main関数の先頭、最初の中括弧の直後に次のコードを追加します{
// Initialize a hash map for the car orders
    // - Key: Car order number, i32
    // - Value: Car order details, Car struct
    use std::collections::HashMap;
    let mut orders: HashMap<i32, Car> = HashMap;

2. ordersハッシュマップを作成するステートメントの構文の問題を修正します。

ヒント

ハッシュマップを最初から作成しているので、おそらくこのnew()メソッドを使用することをお勧めします。

3. プログラムをビルドします。次のセクションに進む前に、コードがコンパイルされていることを確認してください。コンパイラからの警告メッセージは無視してかまいません。

ハッシュマップに値を追加する

次のステップは、履行された各自動車注文をハッシュマップに追加することです。

このmain関数では、car_factory車の注文ごとに関数を呼び出します。注文が履行された後、println!マクロを呼び出して、car変数に格納されている注文の詳細を表示します。

// Car order #1: Used, Hard top
    car = car_factory(order, 1000);
    println!("{}: {}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    ...

    // Car order #6: Used, Hard top
    order = order + 1;
    car = car_factory(order, 4000);
    println!("{}: {}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

新しいハッシュマップで機能するように、これらのコードステートメントを修正します。

  • car_factory関数の呼び出しは保持します。返された各Car構造体は、ハッシュマップの<K、V>ペアの一部として格納されます。
  • println!マクロの呼び出しを更新して、ハッシュマップに保存されている注文の詳細を表示します。
  1. main関数で、関数の呼び出しcar_factoryとそれに伴うprintln!マクロの呼び出しを見つけます。
// Car order #1: Used, Hard top
    car = car_factory(order, 1000);
    println!("{}: {}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

    ...

    // Car order #6: Used, Hard top
    order = order + 1;
    car = car_factory(order, 4000);
    println!("{}: {}, Hard top = {}, {:?}, {}, {} miles", order, car.age.0, car.roof, car.motor, car.color, car.age.1);

2. すべての自動車注文のステートメントの完全なセットを次の改訂されたコードに置き換えます。

// Car order #1: Used, Hard top
    car = car_factory(order, 1000);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    // Car order #2: Used, Convertible
    order = order + 1;
    car = car_factory(order, 2000);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    // Car order #3: New, Hard top
    order = order + 1;
    car = car_factory(order, 0);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    // Car order #4: New, Convertible
    order = order + 1;
    car = car_factory(order, 0);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    // Car order #5: Used, Hard top
    order = order + 1;
    car = car_factory(order, 3000);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    // Car order #6: Used, Hard top
    order = order + 1;
    car = car_factory(order, 4000);
    orders(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

3. 今すぐプログラムをビルドしようとすると、コンパイルエラーが表示されます。<K、V>ペアをordersハッシュマップに追加するステートメントに構文上の問題があります。問題がありますか?先に進んで、ハッシュマップに順序を追加する各ステートメントの問題を修正してください。

ヒント

ordersハッシュマップに直接値を割り当てることはできません。挿入を行うにはメソッドを使用する必要があります。

プログラムを実行する

プログラムが正常にビルドされると、次の出力が表示されます。

Car order 1: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("Used", 1000) })
Car order 2: Some(Car { color: "Green", motor: SemiAuto, roof: false, age: ("Used", 2000) })
Car order 3: Some(Car { color: "Red", motor: Automatic, roof: true, age: ("New", 0) })
Car order 4: Some(Car { color: "Silver", motor: SemiAuto, roof: false, age: ("New", 0) })
Car order 5: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("Used", 3000) })
Car order 6: Some(Car { color: "Green", motor: Automatic, roof: true, age: ("Used", 4000) })

改訂されたコードの出力が異なることに注意してください。println!マクロディスプレイの内容Car各値を示すことによって、構造体と対応するフィールド名。

次の演習では、ループ式を使用してコードの冗長性を減らします。

for、while、およびloop式を使用します


多くの場合、プログラムには、その場で繰り返す必要のあるコードのブロックがあります。ループ式を使用して、繰り返しの実行方法をプログラムに指示できます。電話帳のすべてのエントリを印刷するには、ループ式を使用して、最初のエントリから最後のエントリまで印刷する方法をプログラムに指示できます。

Rustは、プログラムにコードのブロックを繰り返させるための3つのループ式を提供します。

  • loop:手動停止が発生しない限り、繰り返します。
  • while:条件が真のままで繰り返します。
  • for:コレクション内のすべての値に対して繰り返します。

この単元では、これらの各ループ式を見ていきます。

ループし続けるだけ

loop式は、無限ループを作成します。このキーワードを使用すると、式の本文でアクションを継続的に繰り返すことができます。ループを停止させるための直接アクションを実行するまで、アクションが繰り返されます。

次の例では、「We loopforever!」というテキストを出力します。そしてそれはそれ自体で止まりません。println!アクションは繰り返し続けます。

loop {
    println!("We loop forever!");
}

loop式を使用する場合、ループを停止する唯一の方法は、プログラマーとして直接介入する場合です。特定のコードを追加してループを停止したり、Ctrl + Cなどのキーボード命令を入力してプログラムの実行を停止したりできます。

loop式を停止する最も一般的な方法は、breakキーワードを使用してブレークポイントを設定することです。

loop {
    // Keep printing, printing, printing...
    println!("We loop forever!");
    // On the other hand, maybe we should stop!
    break;                            
}

プログラムがbreakキーワードを検出すると、loop式の本体でアクションの実行を停止し、次のコードステートメントに進みます。

breakキーワードは、特別な機能を明らかにするloop表現を。breakキーワードを使用すると、式本体でのアクションの繰り返しを停止することも、ブレークポイントで値を返すこともできます。

次の例はbreakloop式でキーワードを使用して値も返す方法を示しています。

let mut counter = 1;
// stop_loop is set when loop stops
let stop_loop = loop {
    counter *= 2;
    if counter > 100 {
        // Stop loop, return counter value
        break counter;
    }
};
// Loop should break when counter = 128
println!("Break the loop at counter = {}.", stop_loop);

出力は次のとおりです。

Break the loop at counter = 128.

私たちのloop表現の本体は、これらの連続したアクションを実行します。

  1. stop_loop変数を宣言します。
  2. 変数値をloop式の結果にバインドするようにプログラムに指示します。
  3. ループを開始します。loop式の本体でアクションを実行します:
    ループ本体
    1. counter値を現在の値の2倍にインクリメントします。
    2. counter値を確認してください。
    3. もしcounter値が100以上です。

ループから抜け出し、counter値を返します。

4. もしcounter値が100以上ではありません。

ループ本体でアクションを繰り返します。

5. stop_loop値を式のcounter結果である値に設定しますloop

loop式本体は、複数のブレークポイントを持つことができます。式に複数のブレークポイントがある場合、すべてのブレークポイントは同じタイプの値を返す必要があります。すべての値は、整数型、文字列型、ブール型などである必要があります。ブレークポイントが明示的に値を返さない場合、プログラムは式の結果を空のタプルとして解釈します()

しばらくループする

whileループは、条件式を使用しています。条件式が真である限り、ループが繰り返されます。このキーワードを使用すると、条件式がfalseになるまで、式本体のアクションを実行できます。

whileループは、ブール条件式を評価することから始まります。条件式がと評価されるtrueと、本体のアクションが実行されます。アクションが完了すると、制御は条件式に戻ります。条件式がと評価されるfalseと、while式は停止します。

次の例では、「しばらくループします...」というテキストを出力します。ループを繰り返すたびに、「カウントが5未満である」という条件がテストされます。条件が真のままである間、式本体のアクションが実行されます。条件が真でなくなった後、whileループは停止し、プログラムは次のコードステートメントに進みます。

while counter < 5 {
    println!("We loop a while...");
    counter = counter + 1;
}

これらの値のループ

forループは、項目のコレクションを処理するためにイテレータを使用しています。ループは、コレクション内の各アイテムの式本体のアクションを繰り返します。このタイプのループの繰り返しは、反復と呼ばれます。すべての反復が完了すると、ループは停止します。

Rustでは、配列、ベクトル、ハッシュマップなど、任意のコレクションタイプを反復処理できます。Rustはイテレータを使用して、コレクション内の各アイテムを最初から最後まで移動します

forループはイテレータとして一時変数を使用しています。変数はループ式の開始時に暗黙的に宣言され、現在の値は反復ごとに設定されます。

次のコードでは、コレクションはbig_birds配列であり、イテレーターの名前はbirdです。

let big_birds = ["ostrich", "peacock", "stork"];
for bird in big_birds

iter()メソッドを使用して、コレクション内のアイテムにアクセスします。for式は結果にイテレータの現在の値をバインドするiter()方法。式本体では、イテレータ値を操作できます。

let big_birds = ["ostrich", "peacock", "stork"];
for bird in big_birds.iter() {
    println!("The {} is a big bird.", bird);
}

出力は次のとおりです。

The ostrich is a big bird.
The peacock is a big bird.
The stork is a big bird.

イテレータを作成するもう1つの簡単な方法は、範囲表記を使用することですa..b。イテレータはa値から始まりb、1ステップずつ続きますが、値を使用しませんb

for number in 0..5 {
    println!("{}", number * 2);
}

このコードは、0、1、2、3、および4の数値をnumber繰り返し処理します。ループの繰り返しごとに、値を変数にバインドします。

出力は次のとおりです。

0
2
4
6
8

このコードを実行して、このRustPlaygroundでループを探索できます。

演習:ループを使用してデータを反復処理する


この演習では、自動車工場のプログラムを変更して、ループを使用して自動車の注文を反復処理します。

main関数を更新して、注文の完全なセットを処理するためのループ式を追加します。ループ構造は、コードの冗長性を減らすのに役立ちます。コードを簡素化することで、注文量を簡単に増やすことができます。

このcar_factory関数では、範囲外の値での実行時のパニックを回避するために、別のループを追加します。

課題は、サンプルコードを完成させて、コンパイルして実行することです。

この演習のサンプルコードで作業するには、次の2つのオプションがあります。

  • コードをコピーして、ローカル開発環境で編集します。
  • 準備されたRustPlaygroundでコードを開きます。

ノート

サンプルコードで、todo!マクロを探します。このマクロは、完了するか更新する必要があるコードを示します。

プログラムをロードする

前回の演習でプログラムコードを閉じた場合は、この準備されたRustPlaygroundでコードを再度開くことができます。

必ずプログラムを再構築し、コンパイラエラーなしで実行されることを確認してください。

ループ式でアクションを繰り返す

より多くの注文をサポートするには、プログラムを更新する必要があります。現在のコード構造では、冗長ステートメントを使用して6つの注文をサポートしています。冗長性は扱いにくく、維持するのが困難です。

ループ式を使用してアクションを繰り返し、各注文を作成することで、構造を単純化できます。簡略化されたコードを使用すると、多数の注文をすばやく作成できます。

  1. ではmain機能、削除次の文を。このコードブロックは、order変数を定義および設定し、自動車の注文のcar_factory関数とprintln!マクロを呼び出し、各注文をordersハッシュマップに挿入します。
// Order 6 cars
    // - Increment "order" after each request
    // - Add each order <K, V> pair to "orders" hash map
    // - Call println! to show order details from the hash map

    // Initialize order variable
    let mut order = 1;

    // Car order #1: Used, Hard top
    car = car_factory(order, 1000);
    orders.insert(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

    ...

    // Car order #6: Used, Hard top
    order = order + 1;
    car = car_factory(order, 4000);
    orders.insert(order, car);
    println!("Car order {}: {:?}", order, orders.get(&order));

2. 削除されたステートメントを次のコードブロックに置き換えます。

// Start with zero miles
    let mut miles = 0;

    todo!("Add a loop expression to fulfill orders for 6 cars, initialize `order` variable to 1") {

        // Call car_factory to fulfill order
        // Add order <K, V> pair to "orders" hash map
        // Call println! to show order details from the hash map        
        car = car_factory(order, miles);
        orders.insert(order, car);
        println!("Car order {}: {:?}", order, orders.get(&order));

        // Reset miles for order variety
        if miles == 2100 {
            miles = 0;
        } else {
            miles = miles + 700;
        }
    }

3. アクションを繰り返すループ式を追加して、6台の車の注文を作成します。order1に初期化された変数が必要です。

4. プログラムをビルドします。コードがエラーなしでコンパイルされることを確認してください。

次の例のような出力が表示されます。

Car order 1: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("New", 0) })
Car order 2: Some(Car { color: "Green", motor: SemiAuto, roof: false, age: ("Used", 700) })
Car order 3: Some(Car { color: "Red", motor: Automatic, roof: true, age: ("Used", 1400) })
Car order 4: Some(Car { color: "Silver", motor: SemiAuto, roof: false, age: ("Used", 2100) })
Car order 5: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("New", 0) })
Car order 6: Some(Car { color: "Green", motor: Automatic, roof: true, age: ("Used", 700) })

車の注文を11に増やす

 プログラムは現在、ループを使用して6台の車の注文を処理しています。6台以上注文するとどうなりますか?

  1. main関数のループ式を更新して、11台の車を注文します。
    todo!("Update the loop expression to create 11 cars");

2. プログラムを再構築します。実行時に、プログラムはパニックになります!

Compiling playground v0.0.1 (/playground)
    Finished dev [unoptimized + debuginfo] target(s) in 1.26s
    Running `target/debug/playground`
thread 'main' panicked at 'index out of bounds: the len is 4 but the index is 4', src/main.rs:34:29

この問題を解決する方法を見てみましょう。

ループ式で実行時のパニックを防ぐ

このcar_factory関数では、if / else式を使用colorして、colors配列のインデックスの値を確認します。

// Prevent panic: Check color index for colors array, reset as needed
    // Valid color = 1, 2, 3, or 4
    // If color > 4, reduce color to valid index
    let mut color = order as usize;
    if color > 4 {        
        // color = 5 --> index 1, 6 --> 2, 7 --> 3, 8 --> 4
        color = color - 4;
    }

colors配列には4つの要素を持ち、かつ有効なcolor場合は、インデックスの範囲は0〜3の条件式をチェックしているcolor私たちはをチェックしません(インデックスが4よりも大きい場合color、その後の関数で4に等しいインデックスへのときに我々のインデックスを車の色を割り当てる配列では、インデックス値から1を減算しますcolor - 1color値4はcolors[3]、配列と同様に処理されます。)

現在のif / else式は、8台以下の車を注文するときの実行時のパニックを防ぐためにうまく機能します。しかし、11台の車を注文すると、プログラムは9番目の注文でパニックになります。より堅牢になるように式を調整する必要があります。この改善を行うために、別のループ式を使用します。

  1. ではcar_factory機能、ループ式であれば/他の条件文を交換してください。colorインデックス値が4より大きい場合に実行時のパニックを防ぐために、次の擬似コードステートメントを修正してください。
// Prevent panic: Check color index, reset as needed
    // If color = 1, 2, 3, or 4 - no change needed
    // If color > 4, reduce to color to a valid index
    let mut color = order as usize;
    todo!("Replace `if/else` condition with a loop to prevent run-time panic for color > 4");

ヒント

この場合、if / else条件からループ式への変更は実際には非常に簡単です。

2. プログラムをビルドします。コードがエラーなしでコンパイルされることを確認してください。

次の出力が表示されます。

Car order 1: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("New", 0) })
Car order 2: Some(Car { color: "Green", motor: SemiAuto, roof: false, age: ("Used", 700) })
Car order 3: Some(Car { color: "Red", motor: Automatic, roof: true, age: ("Used", 1400) })
Car order 4: Some(Car { color: "Silver", motor: SemiAuto, roof: false, age: ("Used", 2100) })
Car order 5: Some(Car { color: "Blue", motor: Manual, roof: true, age: ("New", 0) })
Car order 6: Some(Car { color: "Green", motor: Automatic, roof: true, age: ("Used", 700) })
Car order 7: Some(Car { color: "Red", motor: Manual, roof: true, age: ("Used", 1400) })
Car order 8: Some(Car { color: "Silver", motor: SemiAuto, roof: false, age: ("Used", 2100) })
Car order 9: Some(Car { color: "Blue", motor: Automatic, roof: true, age: ("New", 0) })
Car order 10: Some(Car { color: "Green", motor: SemiAuto, roof: false, age: ("Used", 700) })
Car order 11: Some(Car { color: "Red", motor: Manual, roof: true, age: ("Used", 1400) })

概要

このモジュールでは、Rustで使用できるさまざまなループ式を調べ、ハッシュマップの操作方法を発見しました。データは、キーと値のペアとしてハッシュマップに保存されます。ハッシュマップは拡張可能です。

loop手動でプロセスを停止するまでの式は、アクションを繰り返します。while式をループして、条件が真である限りアクションを繰り返すことができます。このfor式は、データ収集を反復処理するために使用されます。

この演習では、自動車プログラムを拡張して、繰り返されるアクションをループし、すべての注文を処理しました。注文を追跡するためにハッシュマップを実装しました。

このラーニングパスの次のモジュールでは、Rustコードでエラーと障害がどのように処理されるかについて詳しく説明します。

 リンク: https://docs.microsoft.com/en-us/learn/modules/rust-loop-expressions/

#rust #Beginners 

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How to find WiFi Passwords using Python 2021|Hack WiFi Passwords|Python Script to find WiFi Password

Hack Wifi Passwords easily..

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Elian  Harber

Elian Harber

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Bokeh Plotting Backend for Pandas and GeoPandas

Pandas-Bokeh provides a Bokeh plotting backend for Pandas, GeoPandas and Pyspark DataFrames, similar to the already existing Visualization feature of Pandas. Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series.

With Pandas-Bokeh, creating stunning, interactive, HTML-based visualization is as easy as calling:

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Pandas-Bokeh also provides native support as a Pandas Plotting backend for Pandas >= 0.25. When Pandas-Bokeh is installed, switchting the default Pandas plotting backend to Bokeh can be done via:

pd.set_option('plotting.backend', 'pandas_bokeh')

More details about the new Pandas backend can be found below.


Interactive Documentation

Please visit:

https://patrikhlobil.github.io/Pandas-Bokeh/

for an interactive version of the documentation below, where you can play with the dynamic Bokeh plots.


For more information have a look at the Examples below or at notebooks on the Github Repository of this project.

Startimage


 

Installation

You can install Pandas-Bokeh from PyPI via pip

pip install pandas-bokeh

or conda:

conda install -c patrikhlobil pandas-bokeh

With the current release 0.5.5, Pandas-Bokeh officially supports Python 3.6 and newer. For more details, see Release Notes.

How To Use

Classical Use

The Pandas-Bokeh library should be imported after Pandas, GeoPandas and/or Pyspark. After the import, one should define the plotting output, which can be:

pandas_bokeh.output_notebook(): Embeds the Plots in the cell outputs of the notebook. Ideal when working in Jupyter Notebooks.

pandas_bokeh.output_file(filename): Exports the plot to the provided filename as an HTML.

For more details about the plotting outputs, see the reference here or the Bokeh documentation.

Notebook output (see also bokeh.io.output_notebook)

import pandas as pd import pandas_bokeh pandas_bokeh.output_notebook()

File output to "Interactive Plot.html" (see also bokeh.io.output_file)

import pandas as pd import pandas_bokeh pandas_bokeh.output_file("Interactive Plot.html")

Pandas-Bokeh as native Pandas plotting backend

For pandas >= 0.25, a plotting backend switch is natively supported. It can be achievied by calling:

import pandas as pd
pd.set_option('plotting.backend', 'pandas_bokeh')

Now, the plotting API is accessible for a Pandas DataFrame via:

df.plot(...)

All additional functionalities of Pandas-Bokeh are then accessible at pd.plotting. So, setting the output to notebook is:

pd.plotting.output_notebook()

or calling the grid layout functionality:

pd.plotting.plot_grid(...)

Note: Backwards compatibility is kept since there will still be the df.plot_bokeh(...) methods for a DataFrame.


Plot types

Supported plottypes are at the moment:

Also, check out the complementary chapter Outputs, Formatting & Layouts about:


Lineplot

Basic Lineplot

This simple lineplot in Pandas-Bokeh already contains various interactive elements:

  • a pannable and zoomable (zoom in plotarea and zoom on axis) plot
  • by clicking on the legend elements, one can hide and show the individual lines
  • a Hovertool for the plotted lines

Consider the following simple example:

import numpy as np

np.random.seed(42)
df = pd.DataFrame({"Google": np.random.randn(1000)+0.2, 
                   "Apple": np.random.randn(1000)+0.17}, 
                   index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
df.plot_bokeh(kind="line")       #equivalent to df.plot_bokeh.line()

ApplevsGoogle_1

Note, that similar to the regular pandas.DataFrame.plot method, there are also additional accessors to directly access the different plotting types like:

  • df.plot_bokeh(kind="line", ...)df.plot_bokeh.line(...)
  • df.plot_bokeh(kind="bar", ...)df.plot_bokeh.bar(...)
  • df.plot_bokeh(kind="hist", ...)df.plot_bokeh.hist(...)
  • ...

Advanced Lineplot

There are various optional parameters to tune the plots, for example:

kind: Which kind of plot should be produced. Currently supported are: "line", "point", "scatter", "bar" and "histogram". In the near future many more will be implemented as horizontal barplot, boxplots, pie-charts, etc.

x: Name of the column to use for the horizontal x-axis. If the x parameter is not specified, the index is used for the x-values of the plot. Alternative, also an array of values can be passed that has the same number of elements as the DataFrame.

y: Name of column or list of names of columns to use for the vertical y-axis.

figsize: Choose width & height of the plot

title: Sets title of the plot

xlim/ylim: Set visibler range of plot for x- and y-axis (also works for datetime x-axis)

xlabel/ylabel: Set x- and y-labels

logx/logy: Set log-scale on x-/y-axis

xticks/yticks: Explicitly set the ticks on the axes

color: Defines a single color for a plot.

colormap: Can be used to specify multiple colors to plot. Can be either a list of colors or the name of a Bokeh color palette

hovertool: If True a Hovertool is active, else if False no Hovertool is drawn.

hovertool_string: If specified, this string will be used for the hovertool (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation and here)

toolbar_location: Specify the position of the toolbar location (None, "above", "below", "left" or "right"). Default: "right"

zooming: Enables/Disables zooming. Default: True

panning: Enables/Disables panning. Default: True

fontsize_label/fontsize_ticks/fontsize_title/fontsize_legend: Set fontsize of labels, ticks, title or legend (int or string of form "15pt")

rangetool Enables a range tool scroller. Default False

kwargs**: Optional keyword arguments of bokeh.plotting.figure.line

Try them out to get a feeling for the effects. Let us consider now:

df.plot_bokeh.line(
    figsize=(800, 450),
    y="Apple",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    toolbar_location=None,
    colormap=["red", "blue"],
    hovertool_string=r"""<img
                        src='https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png' 
                        height="42" alt="@imgs" width="42"
                        style="float: left; margin: 0px 15px 15px 0px;"
                        border="2"></img> Apple 
                        
                        <h4> Stock Price: </h4> @{Apple}""",
    panning=False,
    zooming=False)

ApplevsGoogle_2

Lineplot with data points

For lineplots, as for many other plot-kinds, there are some special keyword arguments that only work for this plotting type. For lineplots, these are:

plot_data_points: Plot also the data points on the lines

plot_data_points_size: Determines the size of the data points

marker: Defines the point type (Default: "circle"). Possible values are: 'circle', 'square', 'triangle', 'asterisk', 'circle_x', 'square_x', 'inverted_triangle', 'x', 'circle_cross', 'square_cross', 'diamond', 'cross'

kwargs**: Optional keyword arguments of bokeh.plotting.figure.line```

Let us use this information to have another version of the same plot:

df.plot_bokeh.line(
    figsize=(800, 450),
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(100, 200),
    xlim=("2001-01-01", "2001-02-01"),
    colormap=["red", "blue"],
    plot_data_points=True,
    plot_data_points_size=10,
    marker="asterisk")

ApplevsGoogle_3

Lineplot with rangetool

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()

df.plot_bokeh(rangetool=True)

rangetool

Pointplot

If you just wish to draw the date points for curves, the pointplot option is the right choice. It also accepts the kwargs of bokeh.plotting.figure.scatter like marker or size:

import numpy as np

x = np.arange(-3, 3, 0.1)
y2 = x**2
y3 = x**3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
df.plot_bokeh.point(
    x="x",
    xticks=range(-3, 4),
    size=5,
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    marker="x")

Pointplot

Stepplot

With a similar API as the line- & pointplots, one can generate a stepplot. Additional keyword arguments for this plot type are passes to bokeh.plotting.figure.step, e.g. mode (before, after, center), see the following example

import numpy as np

x = np.arange(-3, 3, 1)
y2 = x**2
y3 = x**3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
df.plot_bokeh.step(
    x="x",
    xticks=range(-1, 1),
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    figsize=(800,300),
    fontsize_title=30,
    fontsize_label=25,
    fontsize_ticks=15,
    fontsize_legend=5,
    )

df.plot_bokeh.step(
    x="x",
    xticks=range(-1, 1),
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    mode="after",
    figsize=(800,300)
    )

Stepplot

Note that the step-plot API of Bokeh does so far not support a hovertool functionality.

Scatterplot

A basic scatterplot can be created using the kind="scatter" option. For scatterplots, the x and y parameters have to be specified and the following optional keyword argument is allowed:

category: Determines the category column to use for coloring the scatter points

kwargs**: Optional keyword arguments of bokeh.plotting.figure.scatter

Note, that the pandas.DataFrame.plot_bokeh() method return per default a Bokeh figure, which can be embedded in Dashboard layouts with other figures and Bokeh objects (for more details about (sub)plot layouts and embedding the resulting Bokeh plots as HTML click here).

In the example below, we use the building grid layout support of Pandas-Bokeh to display both the DataFrame (using a Bokeh DataTable) and the resulting scatterplot:

# Load Iris Dataset:
df = pd.read_csv(
    r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/iris/iris.csv"
)
df = df.sample(frac=1)

# Create Bokeh-Table with DataFrame:
from bokeh.models.widgets import DataTable, TableColumn
from bokeh.models import ColumnDataSource

data_table = DataTable(
    columns=[TableColumn(field=Ci, title=Ci) for Ci in df.columns],
    source=ColumnDataSource(df),
    height=300,
)

# Create Scatterplot:
p_scatter = df.plot_bokeh.scatter(
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization",
    show_figure=False,
)

# Combine Table and Scatterplot via grid layout:
pandas_bokeh.plot_grid([[data_table, p_scatter]], plot_width=400, plot_height=350)

 

Scatterplot

A possible optional keyword parameters that can be passed to bokeh.plotting.figure.scatter is size. Below, we use the sepal length of the Iris data as reference for the size:

#Change one value to clearly see the effect of the size keyword
df.loc[13, "sepal length (cm)"] = 15

#Make scatterplot:
p_scatter = df.plot_bokeh.scatter(
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization with Size Keyword",
    size="sepal length (cm)")

Scatterplot2

In this example you can see, that the additional dimension sepal length cannot be used to clearly differentiate between the virginica and versicolor species.

Barplot

The barplot API has no special keyword arguments, but accepts optional kwargs of bokeh.plotting.figure.vbar like alpha. It uses per default the index for the bar categories (however, also columns can be used as x-axis category using the x argument).

data = {
    'fruits':
    ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
    '2015': [2, 1, 4, 3, 2, 4],
    '2016': [5, 3, 3, 2, 4, 6],
    '2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")

p_bar = df.plot_bokeh.bar(
    ylabel="Price per Unit [€]", 
    title="Fruit prices per Year", 
    alpha=0.6)

Barplot

Using the stacked keyword argument you also maked stacked barplots:

p_stacked_bar = df.plot_bokeh.bar(
    ylabel="Price per Unit [€]",
    title="Fruit prices per Year",
    stacked=True,
    alpha=0.6)

Barplot2

Also horizontal versions of the above barplot are supported with the keyword kind="barh" or the accessor plot_bokeh.barh. You can still specify a column of the DataFrame as the bar category via the x argument if you do not wish to use the index.

#Reset index, such that "fruits" is now a column of the DataFrame:
df.reset_index(inplace=True)

#Create horizontal bar (via kind keyword):
p_hbar = df.plot_bokeh(
    kind="barh",
    x="fruits",
    xlabel="Price per Unit [€]",
    title="Fruit prices per Year",
    alpha=0.6,
    legend = "bottom_right",
    show_figure=False)

#Create stacked horizontal bar (via barh accessor):
p_stacked_hbar = df.plot_bokeh.barh(
    x="fruits",
    stacked=True,
    xlabel="Price per Unit [€]",
    title="Fruit prices per Year",
    alpha=0.6,
    legend = "bottom_right",
    show_figure=False)

#Plot all barplot examples in a grid:
pandas_bokeh.plot_grid([[p_bar, p_stacked_bar],
                        [p_hbar, p_stacked_hbar]], 
                       plot_width=450)

Barplot3

Histogram

For drawing histograms (kind="hist"), Pandas-Bokeh has a lot of customization features. Optional keyword arguments for histogram plots are:

bins: Determines bins to use for the histogram. If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If bins is a string, it defines the method used to calculate the optimal bin width, as defined by histogram_bin_edges.

histogram_type: Either "sidebyside", "topontop" or "stacked". Default: "topontop"

stacked: Boolean that overrides the histogram_type as "stacked" if given. Default: False

kwargs**: Optional keyword arguments of bokeh.plotting.figure.quad

Below examples of the different histogram types:

import numpy as np

df_hist = pd.DataFrame({
    'a': np.random.randn(1000) + 1,
    'b': np.random.randn(1000),
    'c': np.random.randn(1000) - 1
    },
    columns=['a', 'b', 'c'])

#Top-on-Top Histogram (Default):
df_hist.plot_bokeh.hist(
    bins=np.linspace(-5, 5, 41),
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Top-on-Top)",
    line_color="black")

#Side-by-Side Histogram (multiple bars share bin side-by-side) also accessible via
#kind="hist":
df_hist.plot_bokeh(
    kind="hist",
    bins=np.linspace(-5, 5, 41),
    histogram_type="sidebyside",
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Side-by-Side)",
    line_color="black")

#Stacked histogram:
df_hist.plot_bokeh.hist(
    bins=np.linspace(-5, 5, 41),
    histogram_type="stacked",
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Stacked)",
    line_color="black")

Histogram

Further, advanced keyword arguments for histograms are:

  • weights: A column of the DataFrame that is used as weight for the histogramm aggregation (see also numpy.histogram)
  • normed: If True, histogram values are normed to 1 (sum of histogram values=1). It is also possible to pass an integer, e.g. normed=100 would result in a histogram with percentage y-axis (sum of histogram values=100). Default: False
  • cumulative: If True, a cumulative histogram is shown. Default: False
  • show_average: If True, the average of the histogram is also shown. Default: False

Their usage is shown in these examples:

p_hist = df_hist.plot_bokeh.hist(
    y=["a", "b"],
    bins=np.arange(-4, 6.5, 0.5),
    normed=100,
    vertical_xlabel=True,
    ylabel="Share[%]",
    title="Normal distributions (normed)",
    show_average=True,
    xlim=(-4, 6),
    ylim=(0, 30),
    show_figure=False)

p_hist_cum = df_hist.plot_bokeh.hist(
    y=["a", "b"],
    bins=np.arange(-4, 6.5, 0.5),
    normed=100,
    cumulative=True,
    vertical_xlabel=True,
    ylabel="Share[%]",
    title="Normal distributions (normed & cumulative)",
    show_figure=False)

pandas_bokeh.plot_grid([[p_hist, p_hist_cum]], plot_width=450, plot_height=300)

Histogram2


 

Areaplot

Areaplot (kind="area") can be either drawn on top of each other or stacked. The important parameters are:

stacked: If True, the areaplots are stacked. If False, plots are drawn on top of each other. Default: False

kwargs**: Optional keyword arguments of bokeh.plotting.figure.patch


Let us consider the energy consumption split by source that can be downloaded as DataFrame via:

df_energy = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/energy/energy.csv", 
parse_dates=["Year"])
df_energy.head()
YearOilGasCoalNuclear EnergyHydroelectricityOther Renewable
1970-01-012291.5826.71467.317.7265.85.8
1971-01-012427.7884.81459.224.9276.46.3
1972-01-012613.9933.71475.734.1288.96.8
1973-01-012818.1978.01519.645.9292.57.3
1974-01-012777.31001.91520.959.6321.17.7


Creating the Areaplot can be achieved via:

df_energy.plot_bokeh.area(
    x="Year",
    stacked=True,
    legend="top_left",
    colormap=["brown", "orange", "black", "grey", "blue", "green"],
    title="Worldwide energy consumption split by energy source",
    ylabel="Million tonnes oil equivalent",
    ylim=(0, 16000))

areaplot

Note that the energy consumption of fossile energy is still increasing and renewable energy sources are still small in comparison 😢!!! However, when we norm the plot using the normed keyword, there is a clear trend towards renewable energies in the last decade:

df_energy.plot_bokeh.area(
    x="Year",
    stacked=True,
    normed=100,
    legend="bottom_left",
    colormap=["brown", "orange", "black", "grey", "blue", "green"],
    title="Worldwide energy consumption split by energy source",
    ylabel="Million tonnes oil equivalent")

areaplot2

Pieplot

For Pieplots, let us consider a dataset showing the results of all Bundestags elections in Germany since 2002:

df_pie = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/Bundestagswahl/Bundestagswahl.csv")
df_pie
Partei20022005200920132017
CDU/CSU38.535.233.841.532.9
SPD38.534.223.025.720.5
FDP7.49.814.64.810.7
Grünen8.68.110.78.48.9
Linke/PDS4.08.711.98.69.2
AfD0.00.00.00.012.6
Sonstige3.04.06.011.05.0

We can create a Pieplot of the last election in 2017 by specifying the "Partei" (german for party) column as the x column and the "2017" column as the y column for values:

df_pie.plot_bokeh.pie(
    x="Partei",
    y="2017",
    colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
    title="Results of German Bundestag Election 2017",
    )

pieplot

When you pass several columns to the y parameter (not providing the y-parameter assumes you plot all columns), multiple nested pieplots will be shown in one plot:

df_pie.plot_bokeh.pie(
    x="Partei",
    colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
    title="Results of German Bundestag Elections [2002-2017]",
    line_color="grey")

pieplot2

Mapplot

The mapplot method of Pandas-Bokeh allows for plotting geographic points stored in a Pandas DataFrame on an interactive map. For more advanced Geoplots for line and polygon shapes have a look at the Geoplots examples for the GeoPandas API of Pandas-Bokeh.

For mapplots, only (latitude, longitude) pairs in geographic projection (WGS84) can be plotted on a map. The basic API has the following 2 base parameters:

  • x: name of the longitude column of the DataFrame
  • y: name of the latitude column of the DataFrame

The other optional keyword arguments are discussed in the section about the GeoPandas API, e.g. category for coloring the points.

Below an example of plotting all cities for more than 1 million inhabitants:

df_mapplot = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/populated_places.csv")
df_mapplot.head()
namepop_maxlatitudelongitudesize
Mesa108539433.423915-111.7360841.085394
Sharjah110302725.37138355.4064781.103027
Changwon108149935.219102128.5835621.081499
Sheffield129290053.366677-1.4999971.292900
Abbottabad118364734.14950373.1995011.183647
df_mapplot["size"] = df_mapplot["pop_max"] / 1000000
df_mapplot.plot_bokeh.map(
    x="longitude",
    y="latitude",
    hovertool_string="""<h2> @{name} </h2> 
    
                        <h3> Population: @{pop_max} </h3>""",
    tile_provider="STAMEN_TERRAIN_RETINA",
    size="size", 
    figsize=(900, 600),
    title="World cities with more than 1.000.000 inhabitants")

 

Mapplot

Geoplots

Pandas-Bokeh also allows for interactive plotting of Maps using GeoPandas by providing a geopandas.GeoDataFrame.plot_bokeh() method. It allows to plot the following geodata on a map :

  • Points/MultiPoints
  • Lines/MultiLines
  • Polygons/MultiPolygons

Note: t is not possible to mix up the objects types, i.e. a GeoDataFrame with Points and Lines is for example not allowed.

Les us start with a simple example using the "World Borders Dataset" . Let us first import all neccessary libraries and read the shapefile:

import geopandas as gpd
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()

#Read in GeoJSON from URL:
df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")
df_states.head()
STATE_NAMEREGIONPOPESTIMATE2010POPESTIMATE2011POPESTIMATE2012POPESTIMATE2013POPESTIMATE2014POPESTIMATE2015POPESTIMATE2016POPESTIMATE2017geometry
Hawaii413638171378323139277214080381417710142632014286831427538(POLYGON ((-160.0738033454681 22.0041773479577...
Washington467413866819155689089969634107046931715281872809347405743(POLYGON ((-122.4020153103835 48.2252163723779...
Montana4990507996866100352210119211019931102831710386561050493POLYGON ((-111.4754253002074 44.70216236909688...
Maine113275681327968132810113279751328903132778713302321335907(POLYGON ((-69.77727626137293 44.0741483685119...
North Dakota2674518684830701380722908738658754859755548755393POLYGON ((-98.73043728833767 45.93827137024809...

Plotting the data on a map is as simple as calling:

df_states.plot_bokeh(simplify_shapes=10000)

US_States_1

We also passed the optional parameter simplify_shapes (~meter) to improve plotting performance (for a reference see shapely.object.simplify). The above geolayer thus has an accuracy of about 10km.

Many keyword arguments like xlabel, ylabel, xlim, ylim, title, colormap, hovertool, zooming, panning, ... for costumizing the plot are also available for the geoplotting API and can be uses as in the examples shown above. There are however also many other options especially for plotting geodata:

  • geometry_column: Specify the column that stores the geometry-information (default: "geometry")
  • hovertool_columns: Specify column names, for which values should be shown in hovertool
  • hovertool_string: If specified, this string will be used for the hovertool (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation)
  • colormap_uselog: If set True, the colormapper is using a logscale. Default: False
  • colormap_range: Specify the value range of the colormapper via (min, max) tuple
  • tile_provider: Define build-in tile provider for background maps. Possible values: None, 'CARTODBPOSITRON', 'CARTODBPOSITRON_RETINA', 'STAMEN_TERRAIN', 'STAMEN_TERRAIN_RETINA', 'STAMEN_TONER', 'STAMEN_TONER_BACKGROUND', 'STAMEN_TONER_LABELS'. Default: CARTODBPOSITRON_RETINA
  • tile_provider_url: An arbitraty tile_provider_url of the form '/{Z}/{X}/{Y}*.png' can be passed to be used as background map.
  • tile_attribution: String (also HTML accepted) for showing attribution for tile source in the lower right corner
  • tile_alpha: Sets the alpha value of the background tile between [0, 1]. Default: 1

One of the most common usage of map plots are choropleth maps, where the color of a the objects is determined by the property of the object itself. There are 3 ways of drawing choropleth maps using Pandas-Bokeh, which are described below.

Categories

This is the simplest way. Just provide the category keyword for the selection of the property column:

  • category: Specifies the column of the GeoDataFrame that should be used to draw a choropleth map
  • show_colorbar: Whether or not to show a colorbar for categorical plots. Default: True

Let us now draw the regions as a choropleth plot using the category keyword (at the moment, only numerical columns are supported for choropleth plots):

df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    category="REGION",
    show_colorbar=False,
    colormap=["blue", "yellow", "green", "red"],
    hovertool_columns=["STATE_NAME", "REGION"],
    tile_provider="STAMEN_TERRAIN_RETINA")

When hovering over the states, the state-name and the region are shown as specified in the hovertool_columns argument.

US_States_2

 

Dropdown

By passing a list of column names of the GeoDataFrame as the dropdown keyword argument, a dropdown menu is shown above the map. This dropdown menu can be used to select the choropleth layer by the user. :

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    dropdown=["POPESTIMATE2010", "POPESTIMATE2017"],
    colormap="Viridis",
    hovertool_string="""
                        <img
                        src="https://www.states101.com/img/flags/gif/small/@STATE_NAME_SMALL.gif" 
                        height="42" alt="@imgs" width="42"
                        style="float: left; margin: 0px 15px 15px 0px;"
                        border="2"></img>
                
                        <h2>  @STATE_NAME </h2>
                        <h3> 2010: @POPESTIMATE2010 </h3>
                        <h3> 2017: @POPESTIMATE2017 </h3>""",
    tile_provider_url=r"http://c.tile.stamen.com/watercolor/{Z}/{X}/{Y}.jpg",
    tile_attribution='Map tiles by <a href="http://stamen.com">Stamen Design</a>, under <a href="http://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>. Data by <a href="http://openstreetmap.org">OpenStreetMap</a>, under <a href="http://www.openstreetmap.org/copyright">ODbL</a>.'
    )

US_States_3

Using hovertool_string, one can pass a string that can contain arbitrary HTML elements (including divs, images, ...) that is shown when hovering over the geographies (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation).

Here, we also used an OSM tile server with watercolor style via tile_provider_url and added the attribution via tile_attribution.

Sliders

Another option for interactive choropleth maps is the slider implementation of Pandas-Bokeh. The possible keyword arguments are here:

  • slider: By passing a list of column names of the GeoDataFrame, a slider can be used to . This dropdown menu can be used to select the choropleth layer by the user.
  • slider_range: Pass a range (or numpy.arange) of numbers object to relate the sliders values with the slider columns. By passing range(0,10), the slider will have values [0, 1, 2, ..., 9], when passing numpy.arange(3,5,0.5), the slider will have values [3, 3.5, 4, 4.5]. Default: range(0, len(slider))
  • slider_name: Specifies the title of the slider. Default is an empty string.

This can be used to display the change in population relative to the year 2010:


#Calculate change of population relative to 2010:
for i in range(8):
    df_states["Delta_Population_201%d"%i] = ((df_states["POPESTIMATE201%d"%i] / df_states["POPESTIMATE2010"]) -1 ) * 100

#Specify slider columns:
slider_columns = ["Delta_Population_201%d"%i for i in range(8)]

#Specify slider-range (Maps "Delta_Population_2010" -> 2010, 
#                           "Delta_Population_2011" -> 2011, ...):
slider_range = range(2010, 2018)

#Make slider plot:
df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    slider=slider_columns,
    slider_range=slider_range,
    slider_name="Year", 
    colormap="Inferno",
    hovertool_columns=["STATE_NAME"] + slider_columns,
    title="Change of Population [%]")

US_States_4



 

Plot multiple geolayers

If you wish to display multiple geolayers, you can pass the Bokeh figure of a Pandas-Bokeh plot via the figure keyword to the next plot_bokeh() call:

import geopandas as gpd
import pandas_bokeh
pandas_bokeh.output_notebook()

# Read in GeoJSONs from URL:
df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")
df_cities = gpd.read_file(
    r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/ne_10m_populated_places_simple_bigcities.geojson"
)
df_cities["size"] = df_cities.pop_max / 400000

#Plot shapes of US states (pass figure options to this initial plot):
figure = df_states.plot_bokeh(
    figsize=(800, 450),
    simplify_shapes=10000,
    show_figure=False,
    xlim=[-170, -80],
    ylim=[10, 70],
    category="REGION",
    colormap="Dark2",
    legend="States",
    show_colorbar=False,
)

#Plot cities as points on top of the US states layer by passing the figure:
df_cities.plot_bokeh(
    figure=figure,         # <== pass figure here!
    category="pop_max",
    colormap="Viridis",
    colormap_uselog=True,
    size="size",
    hovertool_string="""<h1>@name</h1>
                        <h3>Population: @pop_max </h3>""",
    marker="inverted_triangle",
    legend="Cities",
)

Multiple Geolayers


Point & Line plots:

Below, you can see an example that use Pandas-Bokeh to plot point data on a map. The plot shows all cities with a population larger than 1.000.000. For point plots, you can select the marker as keyword argument (since it is passed to bokeh.plotting.figure.scatter). Here an overview of all available marker types:

gdf = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/ne_10m_populated_places_simple_bigcities.geojson")
gdf["size"] = gdf.pop_max / 400000

gdf.plot_bokeh(
    category="pop_max",
    colormap="Viridis",
    colormap_uselog=True,
    size="size",
    hovertool_string="""<h1>@name</h1>
                        <h3>Population: @pop_max </h3>""",
    xlim=[-15, 35],
    ylim=[30,60],
    marker="inverted_triangle");

Pointmap

In a similar way, also GeoDataFrames with (multi)line shapes can be drawn using Pandas-Bokeh.


 


Colorbar formatting:

If you want to display the numerical labels on your colorbar with an alternative to the scientific format, you can pass in a one of the bokeh number string formats or an instance of one of the bokeh.models.formatters to the colorbar_tick_format argument in the geoplot

An example of using the string format argument:

df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

# pass in a string format to colorbar_tick_format to display the ticks as 10m rather than 1e7
df_states.plot_bokeh(
    figsize=(900, 600),
    category="POPESTIMATE2017",
    simplify_shapes=5000,    
    colormap="Inferno",
    colormap_uselog=True,
    colorbar_tick_format="0.0a")

colorbar_tick_format with string argument

An example of using the bokeh PrintfTickFormatter:

df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

for i in range(8):
    df_states["Delta_Population_201%d"%i] = ((df_states["POPESTIMATE201%d"%i] / df_states["POPESTIMATE2010"]) -1 ) * 100

# pass in a PrintfTickFormatter instance colorbar_tick_format to display the ticks with 2 decimal places  
df_states.plot_bokeh(
    figsize=(900, 600),
    category="Delta_Population_2017",
    simplify_shapes=5000,    
    colormap="Inferno",
    colorbar_tick_format=PrintfTickFormatter(format="%4.2f"))

colorbar_tick_format with bokeh.models.formatter_instance


Outputs, Formatting & Layouts

Output options

The pandas.DataFrame.plot_bokeh API has the following additional keyword arguments:

  • show_figure: If True, the resulting figure is shown (either in the notebook or exported and shown as HTML file, see Basics. If False, None is returned. Default: True
  • return_html: If True, the method call returns an HTML string that contains all Bokeh CSS&JS resources and the figure embedded in a div. This HTML representation of the plot can be used for embedding the plot in an HTML document. Default: False

If you have a Bokeh figure or layout, you can also use the pandas_bokeh.embedded_html function to generate an embeddable HTML representation of the plot. This can be included into any valid HTML (note that this is not possible directly with the HTML generated by the pandas_bokeh.output_file output option, because it includes an HTML header). Let us consider the following simple example:

#Import Pandas and Pandas-Bokeh (if you do not specify an output option, the standard is
#output_file):
import pandas as pd
import pandas_bokeh

#Create DataFrame to Plot:
import numpy as np
x = np.arange(-10, 10, 0.1)
sin = np.sin(x)
cos = np.cos(x)
tan = np.tan(x)
df = pd.DataFrame({"x": x, "sin(x)": sin, "cos(x)": cos, "tan(x)": tan})

#Make Bokeh plot from DataFrame using Pandas-Bokeh. Do not show the plot, but export
#it to an embeddable HTML string:
html_plot = df.plot_bokeh(
    kind="line",
    x="x",
    y=["sin(x)", "cos(x)", "tan(x)"],
    xticks=range(-20, 20),
    title="Trigonometric functions",
    show_figure=False,
    return_html=True,
    ylim=(-1.5, 1.5))

#Write some HTML and embed the HTML plot below it. For production use, please use
#Templates and the awesome Jinja library.
html = r"""
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}});
</script>
<script type="text/javascript"
  src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>

<h1> Trigonometric functions </h1>

<p> The basic trigonometric functions are:</p>

<p>$ sin(x) $</p>
<p>$ cos(x) $</p>
<p>$ tan(x) = \frac{sin(x)}{cos(x)}$</p>

<p>Below is a plot that shows them</p>

""" + html_plot

#Export the HTML string to an external HTML file and show it:
with open("test.html" , "w") as f:
    f.write(html)
    
import webbrowser
webbrowser.open("test.html")

This code will open up a webbrowser and show the following page. As you can see, the interactive Bokeh plot is embedded nicely into the HTML layout. The return_html option is ideal for the use in a templating engine like Jinja.

Embedded HTML

Auto Scaling Plots

For single plots that have a number of x axis values or for larger monitors, you can auto scale the figure to the width of the entire jupyter cell by setting the sizing_mode parameter.

df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd']) df.plot_bokeh(kind="bar", figsize=(500, 200), sizing_mode="scale_width")

Scaled Plot

The figsize parameter can be used to change the height and width as well as act as a scaling multiplier against the axis that is not being scaled.

 

Number formats

To change the formats of numbers in the hovertool, use the number_format keyword argument. For a documentation about the format to pass, have a look at the Bokeh documentation.Let us consider some examples for the number 3.141592653589793:

FormatOutput
03
0.0003.141
0.00 $3.14 $

This number format will be applied to all numeric columns of the hovertool. If you want to make a very custom or complicated hovertool, you should probably use the hovertool_string keyword argument, see e.g. this example. Below, we use the number_format parameter to specify the "Stock Price" format to 2 decimal digits and an additional $ sign.

import numpy as np

#Lineplot:
np.random.seed(42)
df = pd.DataFrame({
    "Google": np.random.randn(1000) + 0.2,
    "Apple": np.random.randn(1000) + 0.17
},
                  index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
df.plot_bokeh(
    kind="line",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    colormap=["red", "blue"],
    number_format="1.00 $")

Number format

Suppress scientific notation for axes

If you want to suppress the scientific notation for axes, you can use the disable_scientific_axes parameter, which accepts one of "x", "y", "xy":

df = pd.DataFrame({"Animal": ["Mouse", "Rabbit", "Dog", "Tiger", "Elefant", "Wale"],
                   "Weight [g]": [19, 3000, 40000, 200000, 6000000, 50000000]})
p_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", show_figure=False)
p_non_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", disable_scientific_axes="y", show_figure=False,)
pandas_bokeh.plot_grid([[p_scientific, p_non_scientific]], plot_width = 450)

Number format

 

Dashboard Layouts

As shown in the Scatterplot Example, combining plots with plots or other HTML elements is straighforward in Pandas-Bokeh due to the layout capabilities of Bokeh. The easiest way to generate a dashboard layout is using the pandas_bokeh.plot_grid method (which is an extension of bokeh.layouts.gridplot):

import pandas as pd
import numpy as np
import pandas_bokeh
pandas_bokeh.output_notebook()

#Barplot:
data = {
    'fruits':
    ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
    '2015': [2, 1, 4, 3, 2, 4],
    '2016': [5, 3, 3, 2, 4, 6],
    '2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")
p_bar = df.plot_bokeh(
    kind="bar",
    ylabel="Price per Unit [€]",
    title="Fruit prices per Year",
    show_figure=False)

#Lineplot:
np.random.seed(42)
df = pd.DataFrame({
    "Google": np.random.randn(1000) + 0.2,
    "Apple": np.random.randn(1000) + 0.17
},
                  index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
p_line = df.plot_bokeh(
    kind="line",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    colormap=["red", "blue"],
    show_figure=False)

#Scatterplot:
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris["data"])
df.columns = iris["feature_names"]
df["species"] = iris["target"]
df["species"] = df["species"].map(dict(zip(range(3), iris["target_names"])))
p_scatter = df.plot_bokeh(
    kind="scatter",
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization",
    show_figure=False)

#Histogram:
df_hist = pd.DataFrame({
    'a': np.random.randn(1000) + 1,
    'b': np.random.randn(1000),
    'c': np.random.randn(1000) - 1
},
                       columns=['a', 'b', 'c'])

p_hist = df_hist.plot_bokeh(
    kind="hist",
    bins=np.arange(-6, 6.5, 0.5),
    vertical_xlabel=True,
    normed=100,
    hovertool=False,
    title="Normal distributions",
    show_figure=False)

#Make Dashboard with Grid Layout:
pandas_bokeh.plot_grid([[p_line, p_bar], 
                        [p_scatter, p_hist]], plot_width=450)

Dashboard Layout

Using a combination of row and column elements (see also Bokeh Layouts) allow for a very easy general arrangement of elements. An alternative layout to the one above is:

p_line.plot_width = 900
p_hist.plot_width = 900

layout = pandas_bokeh.column(p_line,
                pandas_bokeh.row(p_scatter, p_bar),
                p_hist)

pandas_bokeh.show(layout)

Alternative Dashboard Layout


 



 

 

Release Notes

Release Notes can be found here.

Contributing to Pandas-Bokeh

If you wish to contribute to the development of Pandas-Bokeh you can follow the instructions on the CONTRIBUTING.md.

 

Author: PatrikHlobil
Source Code: https://github.com/PatrikHlobil/Pandas-Bokeh 
License: MIT License

#machine-learning  #datavisualizations #python 

ERIC  MACUS

ERIC MACUS

1647540000

Substrate Knowledge Map For Hackathon Participants

Substrate Knowledge Map for Hackathon Participants

The Substrate Knowledge Map provides information that you—as a Substrate hackathon participant—need to know to develop a non-trivial application for your hackathon submission.

The map covers 6 main sections:

  1. Introduction
  2. Basics
  3. Preliminaries
  4. Runtime Development
  5. Polkadot JS API
  6. Smart Contracts

Each section contains basic information on each topic, with links to additional documentation for you to dig deeper. Within each section, you'll find a mix of quizzes and labs to test your knowledge as your progress through the map. The goal of the labs and quizzes is to help you consolidate what you've learned and put it to practice with some hands-on activities.

Introduction

One question we often get is why learn the Substrate framework when we can write smart contracts to build decentralized applications?

The short answer is that using the Substrate framework and writing smart contracts are two different approaches.

Smart contract development

Traditional smart contract platforms allow users to publish additional logic on top of some core blockchain logic. Since smart contract logic can be published by anyone, including malicious actors and inexperienced developers, there are a number of intentional safeguards and restrictions built around these public smart contract platforms. For example:

Fees: Smart contract developers must ensure that contract users are charged for the computation and storage they impose on the computers running their contract. With fees, block creators are protected from abuse of the network.

Sandboxed: A contract is not able to modify core blockchain storage or storage items of other contracts directly. Its power is limited to only modifying its own state, and the ability to make outside calls to other contracts or runtime functions.

Reversion: Contracts can be prone to undesirable situations that lead to logical errors when wanting to revert or upgrade them. Developers need to learn additional patterns such as splitting their contract's logic and data to ensure seamless upgrades.

These safeguards and restrictions make running smart contracts slower and more costly. However, it's important to consider the different developer audiences for contract development versus Substrate runtime development.

Building decentralized applications with smart contracts allows your community to extend and develop on top of your runtime logic without worrying about proposals, runtime upgrades, and so on. You can also use smart contracts as a testing ground for future runtime changes, but done in an isolated way that protects your network from any errors the changes might introduce.

In summary, smart contract development:

  • Is inherently safer to the network.
  • Provides economic incentives and transaction fee mechanisms that can't be directly controlled by the smart contract author.
  • Provides computational overhead to support graceful logical failures.
  • Has a low barrier to entry for developers and enables a faster pace of community interaction.

Substrate runtime development

Unlike traditional smart contract development, Substrate runtime development offers none of the network protections or safeguards. Instead, as a runtime developer, you have total control over how the blockchain behaves. However, this level of control also means that there is a higher barrier to entry.

Substrate is a framework for building blockchains, which almost makes comparing it to smart contract development like comparing apples and oranges. With the Substrate framework, developers can build smart contracts but that is only a fraction of using Substrate to its full potential.

With Substrate, you have full control over the underlying logic that your network's nodes will run. You also have full access for modifying and controlling each and every storage item across your runtime modules. As you progress through this map, you'll discover concepts and techniques that will help you to unlock the potential of the Substrate framework, giving you the freedom to build the blockchain that best suits the needs of your application.

You'll also discover how you can upgrade the Substrate runtime with a single transaction instead of having to organize a community hard-fork. Upgradeability is one of the primary design features of the Substrate framework.

In summary, runtime development:

  • Provides low level access to your entire blockchain.
  • Removes the overhead of built-in safety for performance.
  • Has a higher barrier of entry for developers.
  • Provides flexibility to customize full-stack application logic.

To learn more about using smart contracts within Substrate, refer to the Smart Contract - Overview page as well as the Polkadot Builders Guide.

Navigating the documentation

If you need any community support, please join the following channels based on the area where you need help:

Alternatively, also look for support on Stackoverflow where questions are tagged with "substrate" or on the Parity Subport repo.

Use the following links to explore the sites and resources available on each:

Substrate Developer Hub has the most comprehensive all-round coverage about Substrate, from a "big picture" explanation of architecture to specific technical concepts. The site also provides tutorials to guide you as your learn the Substrate framework and the API reference documentation. You should check this site first if you want to look up information about Substrate runtime development. The site consists of:

Knowledge Base: Explaining the foundational concepts of building blockchain runtimes using Substrate.

Tutorials: Hand-on tutorials for developers to follow. The first SIX tutorials show the fundamentals in Substrate and are recommended for every Substrate learner to go through.

How-to Guides: These resources are like the O'Reilly cookbook series written in a task-oriented way for readers to get the job done. Some examples of the topics overed include:

  • Setting up proper weight functions for extrinsic calls.
  • Using off-chain workers to fetch HTTP requests.
  • Writing tests for your pallets It can also be read from

API docs: Substrate API reference documentation.

Substrate Node Template provides a light weight, minimal Substrate blockchain node that you can set up as a local development environment.

Substrate Front-end template provides a front-end interface built with React using Polkadot-JS API to connect to any Substrate node. Developers are encouraged to start new Substrate projects based on these templates.

If you face any technical difficulties and need support, feel free to join the Substrate Technical matrix channel and ask your questions there.

Additional resources

Polkadot Wiki documents the specific behavior and mechanisms of the Polkadot network. The Polkadot network allows multiple blockchains to connect and pass messages to each other. On the wiki, you can learn about how Polkadot—built using Substrate—is customized to support inter-blockchain message passing.

Polkadot JS API doc: documents how to use the Polkadot-JS API. This JavaScript-based API allows developers to build custom front-ends for their blockchains and applications. Polkadot JS API provides a way to connect to Substrate-based blockchains to query runtime metadata and send transactions.

Quiz #1

👉 Submit your answers to Quiz #1

Basics

Set up your local development environment

Here you will set up your local machine to install the Rust compiler—ensuring that you have both stable and nightly versions installed. Both stable and nightly versions are required because currently a Substrate runtime is compiled to a native binary using the stable Rust compiler, then compiled to a WebAssembly (WASM) binary, which only the nightly Rust compiler can do.

Also refer to:

Lab #1

👉 Complete Lab #1: Run a Substrate node

Interact with a Substrate network using Polkadot-JS apps

Polkadot JS Apps is the canonical front-end to interact with any Substrate-based chain.

You can configure whichever endpoint you want it to connected to, even to your localhost running node. Refer to the following two diagrams.

  1. Click on the top left side showing your currently connected network:

assets/01-polkadot-app-endpoint.png

  1. Scroll to the bottom of the menu, open DEVELOPMENT, and choose either Local Node or Custom to specify your own endpoint.

assets/02-polkadot-app-select-endpoint.png

Quiz #2

👉 Complete Quiz #2

Lab #2

👉 Complete Lab #2: Using Polkadot-JS Apps

Notes: If you are connecting Apps to a custom chain (or your locally-running node), you may need to specify your chain's custom data types in JSON under Settings > Developer.

Polkadot-JS Apps only receives a series of bytes from the blockchain. It is up to the developer to tell it how to decode and interpret these custom data type. To learn more on this, refer to:

You will also need to create an account. To do so, follow these steps on account generation. You'll learn that you can also use the Polkadot-JS Browser Plugin (a Metamask-like browser extension to manage your Substrate accounts) and it will automatically be imported into Polkadot-JS Apps.

Notes: When you run a Substrate chain in development mode (with the --dev flag), well-known accounts (Alice, Bob, Charlie, etc.) are always created for you.

Lab #3

👉 Complete Lab #3: Create an Account

Preliminaries

You need to know some Rust programming concepts and have a good understanding on how blockchain technology works in order to make the most of developing with Substrate. The following resources will help you brush up in these areas.

Rust

You will need familiarize yourself with Rust to understand how Substrate is built and how to make the most of its capabilities.

If you are new to Rust, or need a brush up on your Rust knowledge, please refer to The Rust Book. You could still continue learning about Substrate without knowing Rust, but we recommend you come back to this section whenever in doubt about what any of the Rust syntax you're looking at means. Here are the parts of the Rust book we recommend you familiarize yourself with:

  • ch 1 - 10: These chapters cover the foundational knowledge of programming in Rust
  • ch 13: On iterators and closures
  • ch 18 - 19: On advanced traits and advanced types. Learn a bit about macros as well. You will not necessarily be writing your own macros, but you'll be using a lot of Substrate and FRAME's built-in macros to write your blockchain runtime.

How blockchains work

Given that you'll be writing a blockchain runtime, you need to know what a blockchain is, and how it works. The **Web3 Blockchain Fundamental MOOC Youtube video series provides a good basis for understanding key blockchain concepts and how blockchains work.

The lectures we recommend you watch are: lectures 1 - 7 and lecture 10. That's 8 lectures, or about 4 hours of video.

Quiz #3

👉 Complete Quiz #3

Substrate runtime development

High level architecture

To know more about the high level architecture of Substrate, please go through the Knowledge Base articles on Getting Started: Overview and Getting Started: Architecture.

In this document, we assume you will develop a Substrate runtime with FRAME (v2). This is what a Substrate node consists of.

assets/03-substrate-architecture.png

Each node has many components that manage things like the transaction queue, communicating over a P2P network, reaching consensus on the state of the blockchain, and the chain's actual runtime logic (aka the blockchain runtime). Each aspect of the node is interesting in its own right, and the runtime is particularly interesting because it contains the business logic (aka "state transition function") that codifies the chain's functionality. The runtime contains a collection of pallets that are configured to work together.

On the node level, Substrate leverages libp2p for the p2p networking layer and puts the transaction pool, consensus mechanism, and underlying data storage (a key-value database) on the node level. These components all work "under the hood", and in this knowledge map we won't cover them in detail except for mentioning their existence.

Quiz #4

👉 Complete Quiz #4

Runtime development topics

In our Developer Hub, we have a thorough coverage on various subjects you need to know to develop with Substrate. So here we just list out the key topics and reference back to Developer Hub. Please go through the following key concepts and the directed resources to know the fundamentals of runtime development.

Key Concept: Runtime, this is where the blockchain state transition function (the blockchain application-specific logic) is defined. It is about composing multiple pallets (can be understood as Rust modules) together in the runtime and hooking them up together.

Runtime Development: Execution, this article describes how a block is produced, and how transactions are selected and executed to reach the next "stage" in the blockchain.

Runtime Develpment: Pallets, this article describes what the basic structure of a Substrate pallet is consists of.

Runtime Development: FRAME, this article gives a high level overview of the system pallets Substrate already implements to help you quickly develop as a runtime engineer. Have a quick skim so you have a basic idea of the different pallets Substrate is made of.

Lab #4

👉 Complete Lab #4: Adding a Pallet into a Runtime

Runtime Development: Storage, this article describes how data is stored on-chain and how you could access them.

Runtime Development: Events & Errors, this page describe how external parties know what has happened in the blockchain, via the emitted events and errors when executing transactions.

Notes: All of the above concepts we leverage on the #[pallet::*] macro to define them in the code. If you are interested to learn more about what other types of pallet macros exist go to the FRAME macro API documentation and this doc on some frequently used Substrate macros.

Lab #5

👉 Complete Lab #5: Building a Proof-of-Existence dApp

Lab #6

👉 Complete Lab #6: Building a Substrate Kitties dApp

Quiz #5

👉 Complete Quiz #5

Polkadot JS API

Polkadot JS API is the javascript API for Substrate. By using it you can build a javascript front end or utility and interact with any Substrate-based blockchain.

The Substrate Front-end Template is an example of using Polkadot JS API in a React front-end.

  • Runtime Development: Metadata, this article describes the API allowing external parties to query what API is open for the chain. Polkadot JS API makes use of a chain's metadata to know what queries and functions are available from a chain to call.

Lab #7

👉 Complete Lab #7: Using Polkadot-JS API

Quiz #6

👉 Complete Quiz #6: Using Polkadot-JS API

Smart contracts

Learn about the difference between smart contract development vs Substrate runtime development, and when to use each here.

In Substrate, you can program smart contracts using ink!.

Quiz #7

👉 Complete Quiz #7: Using ink!

What we do not cover

A lot 😄

On-chain runtime upgrades. We have a tutorial on On-chain (forkless) Runtime Upgrade. This tutorial introduces how to perform and schedule a runtime upgrade as an on-chain transaction.

About transaction weight and fee, and benchmarking your runtime to determine the proper transaction cost.

Off-chain Features

There are certain limits to on-chain logic. For instance, computation cannot be too intensive that it affects the block output time, and computation must be deterministic. This means that computation that relies on external data fetching cannot be done on-chain. In Substrate, developers can run these types of computation off-chain and have the result sent back on-chain via extrinsics.

Tightly- and Loosely-coupled pallets, calling one pallet's functions from another pallet via trait specification.

Blockchain Consensus Mechansim, and a guide on customizing it to proof-of-work here.

Parachains: one key feature of Substrate is the capability of becoming a parachain for relay chains like Polkadot. You can develop your own application-specific logic in your chain and rely on the validator community of the relay chain to secure your network, instead of building another validator community yourself. Learn more with the following resources:

Terms clarification

  • Substrate: the blockchain development framework built for writing highly customized, domain-specific blockchains.
  • Polkadot: Polkadot is the relay chain blockchain, built with Substrate.
  • Kusama: Kusama is Polkadot's canary network, used to launch features before these features are launched on Polkadot. You could view it as a beta-network with real economic value where the state of the blockchain is never reset.
  • Web 3.0: is the decentralized internet ecosystem that, instead of apps being centrally stored in a few servers and managed by a sovereign party, it is an open, trustless, and permissionless network when apps are not controlled by a centralized entity.
  • Web3 Foundation: A foundation setup to support the development of decentralized web software protocols. Learn more about what they do on thier website.

Others


Author: substrate-developer-hub
Source Code: https://github.com/substrate-developer-hub/hackathon-knowledge-map
License: 

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