1578456538
We here at Tableau are very proud of how easy it is to see and understand data with Tableau. Once you get started, it’s intuitive to dive deeper by adding more and more fields, formulae, and calculations to a simple visualization—until it becomes slower and slower to render. In a world where two-second response times can lose an audience, performance is crucial.
So where do I start?
So how can you make your dashboards run faster? Your first step is to identify the problem spots by running and interpreting your performance recording. The performance recorder is every Tableau speed demon’s ticket to the fast lane. The performance recorder can pinpoint slow worksheets, slow queries, and long render-times on a dashboard. It even shows the query text, allowing you to work with your database team on optimizing at the database level.
Now that you know which views or data connections are slowing you down, below are six tips to make those dashboards more performant. For each tip, we’ve listed the most common causes of performance degradation as well as some quick solutions.
To get in-Depth knowledge on Tableau Dashboard you can enroll for live Tableau Online Training
1. Your data strategy drives performance
Extracts are typically much faster to work with than a live data source, and are especially great for prototyping. The key is to use domain-specific cuts of your data. The Data Engine is not intended to be a replacement for a data warehouse. Rather, it’s meant to be a supplement for fast prototyping and data discovery.
Keep in mind: Extracts are not always the long-term solution. The typical extent of an extract is between 500 million to one billion rows; mileage will vary. When querying against constantly-refreshing data, a live connection often makes more sense when operationalizing the view.
2. Reduce the marks (data points) in your view
When data is highly granular, Tableau must render and precisely place each element. Each mark represents a batch that Tableau must parse. More marks create more batches; drawing 1,000 points on a graph is more difficult than drawing three bars in a chart.
Large crosstabs with a bevy of quick filters can cause increased load times when you try to view all the rows and dimensions on a Tableau view.
Excessive marks (think: data points) on a view also reduce the visual analytics value. Large, slow, manual table scans can cause information overload and make it harder to see and understand your data.
Here’s how you can avoid this trap:
3. Limit your filters by number and type
Filtering in Tableau is extremely powerful and expressive. However, inefficient and excessive filters are one of the most common causes of poorly performing workbooks and dashboards. Note: Showing the filter dialog requires Tableau to load its members and may create extra queries, especially if the filtered dimension is not in the view.
4. Optimize and materialize your calculations
Perform calculations in the database. Wherever possible, especially on production views, perform calculations in the database to reduce overhead in Tableau. Aggregate calculations are great for calculated fields in Tableau. Perform row-level calculations in the database when you can.
Reduce the number of nested calculations. Just like Russian nesting dolls, unpacking a calculation and then building it takes longer for each extra layer.
Reduce the granularity of LOD or table calculations in the view. The more granular the calculation, the longer it takes.
LODs - Look at the number of unique dimension members in the calculation.
Table Calculations - the more marks in the view, the longer it will take to calculate.
Where possible, use MIN or MAX instead of AVG. AVG requires more processing than MIN or MAX. Often rows will be duplicated and display the same result with MIN, MAX, or AVG.
Make groups with calculations. Like include filters, calculated groups load only named members of the domain, whereas Tableau’s group function loads the entire domain.
Use Booleans or numeric calculations instead of string calculations. Computers can process integers and Booleans (t/f) much faster than strings. Boolean>Int>Float>Date>DateTime>String
5. Take advantage of Tableau’s query optimization
6. Clean up your workbooks!
Take your career to new heights of success with a tableau training
#tableau #tableu #dashboard #technology #businessintelligence
1578456538
We here at Tableau are very proud of how easy it is to see and understand data with Tableau. Once you get started, it’s intuitive to dive deeper by adding more and more fields, formulae, and calculations to a simple visualization—until it becomes slower and slower to render. In a world where two-second response times can lose an audience, performance is crucial.
So where do I start?
So how can you make your dashboards run faster? Your first step is to identify the problem spots by running and interpreting your performance recording. The performance recorder is every Tableau speed demon’s ticket to the fast lane. The performance recorder can pinpoint slow worksheets, slow queries, and long render-times on a dashboard. It even shows the query text, allowing you to work with your database team on optimizing at the database level.
Now that you know which views or data connections are slowing you down, below are six tips to make those dashboards more performant. For each tip, we’ve listed the most common causes of performance degradation as well as some quick solutions.
To get in-Depth knowledge on Tableau Dashboard you can enroll for live Tableau Online Training
1. Your data strategy drives performance
Extracts are typically much faster to work with than a live data source, and are especially great for prototyping. The key is to use domain-specific cuts of your data. The Data Engine is not intended to be a replacement for a data warehouse. Rather, it’s meant to be a supplement for fast prototyping and data discovery.
Keep in mind: Extracts are not always the long-term solution. The typical extent of an extract is between 500 million to one billion rows; mileage will vary. When querying against constantly-refreshing data, a live connection often makes more sense when operationalizing the view.
2. Reduce the marks (data points) in your view
When data is highly granular, Tableau must render and precisely place each element. Each mark represents a batch that Tableau must parse. More marks create more batches; drawing 1,000 points on a graph is more difficult than drawing three bars in a chart.
Large crosstabs with a bevy of quick filters can cause increased load times when you try to view all the rows and dimensions on a Tableau view.
Excessive marks (think: data points) on a view also reduce the visual analytics value. Large, slow, manual table scans can cause information overload and make it harder to see and understand your data.
Here’s how you can avoid this trap:
3. Limit your filters by number and type
Filtering in Tableau is extremely powerful and expressive. However, inefficient and excessive filters are one of the most common causes of poorly performing workbooks and dashboards. Note: Showing the filter dialog requires Tableau to load its members and may create extra queries, especially if the filtered dimension is not in the view.
4. Optimize and materialize your calculations
Perform calculations in the database. Wherever possible, especially on production views, perform calculations in the database to reduce overhead in Tableau. Aggregate calculations are great for calculated fields in Tableau. Perform row-level calculations in the database when you can.
Reduce the number of nested calculations. Just like Russian nesting dolls, unpacking a calculation and then building it takes longer for each extra layer.
Reduce the granularity of LOD or table calculations in the view. The more granular the calculation, the longer it takes.
LODs - Look at the number of unique dimension members in the calculation.
Table Calculations - the more marks in the view, the longer it will take to calculate.
Where possible, use MIN or MAX instead of AVG. AVG requires more processing than MIN or MAX. Often rows will be duplicated and display the same result with MIN, MAX, or AVG.
Make groups with calculations. Like include filters, calculated groups load only named members of the domain, whereas Tableau’s group function loads the entire domain.
Use Booleans or numeric calculations instead of string calculations. Computers can process integers and Booleans (t/f) much faster than strings. Boolean>Int>Float>Date>DateTime>String
5. Take advantage of Tableau’s query optimization
6. Clean up your workbooks!
Take your career to new heights of success with a tableau training
#tableau #tableu #dashboard #technology #businessintelligence
1635917640
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。
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。
このモジュールでは、次のことを行います。
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。
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();
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."));
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。
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。
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。
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。
car_quality
、car_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
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。
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。
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。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。
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break
、loop
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。
stop_loop
変数を宣言します。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。loop
式の本体でアクションを実行します:counter
値を現在の値の2倍にインクリメントします。counter
値を確認してください。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()
。
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;
}
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。
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。
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。
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。
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。order
1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) })
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?
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。
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 - 1
。color
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。
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) })
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/
1561523460
This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.
Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.
For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.
However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.
DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.
You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.
Check out the infographic by clicking on the button below:
With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.
What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.
Matplotlib
Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)
>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))
>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)
>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png', transparent=True) #Save transparent figures
>>> plt.show()
>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0
>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
cmap= 'gist_earth',
interpolation= 'nearest',
vmin=-2,
vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot
>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows
>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z) #Make a violin plot
y-axis
x-axis
The basic steps to creating plots with matplotlib are:
1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot
>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4] #Step 1
>>> y = [10,20,25,30]
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3) #Step 3, 4
>>> ax.scatter([2,4,6],
[5,15,25],
color= 'darkgreen',
marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6
>>> plt.cla() #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window
>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
cmap= 'seismic' )
>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")
>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid')
>>> plt.plot(x,y,ls= '--')
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' )
>>> plt.setp(lines,color= 'r',linewidth=4.0)
>>> ax.text(1,
-2.1,
'Example Graph',
style= 'italic' )
>>> ax.annotate("Sine",
xy=(8, 0),
xycoords= 'data',
xytext=(10.5, 0),
textcoords= 'data',
arrowprops=dict(arrowstyle= "->",
connectionstyle="arc3"),)
>>> plt.title(r '$sigma_i=15$', fontsize=20)
Limits & Autoscaling
>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal') #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5]) #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis
Legends
>>> ax.set(title= 'An Example Axes', #Set a title and x-and y-axis labels
ylabel= 'Y-Axis',
xlabel= 'X-Axis')
>>> ax.legend(loc= 'best') #No overlapping plot elements
Ticks
>>> ax.xaxis.set(ticks=range(1,5), #Manually set x-ticks
ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
direction= 'inout',
length=10)
Subplot Spacing
>>> fig3.subplots_adjust(wspace=0.5, #Adjust the spacing between subplots
hspace=0.3,
left=0.125,
right=0.9,
top=0.9,
bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area
Axis Spines
>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10)) #Move the bottom axis line outward
Have this Cheat Sheet at your fingertips
Original article source at https://www.datacamp.com
#matplotlib #cheatsheet #python
1642995900
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:
df.plot_bokeh()
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.
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.
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.
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:
For more details about the plotting outputs, see the reference here or the Bokeh documentation.
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_file("Interactive Plot.html")
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.
Supported plottypes are at the moment:
Also, check out the complementary chapter Outputs, Formatting & Layouts about:
This simple lineplot in Pandas-Bokeh already contains various interactive elements:
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()
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(...)
There are various optional parameters to tune the plots, for example:
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 [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(0, 400),
toolbar_location=None,
colormap=["red", "blue"],
hovertool_string=r"""<img
src='https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png'
height="42" alt="@imgs" width="42"
style="float: left; margin: 0px 15px 15px 0px;"
border="2"></img> Apple
<h4> Stock Price: </h4> @{Apple}""",
panning=False,
zooming=False)
For lineplots, as for many other plot-kinds, there are some special keyword arguments that only work for this plotting type. For lineplots, these are:
Let us use this information to have another version of the same plot:
df.plot_bokeh.line(
figsize=(800, 450),
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
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")
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)
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")
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)
)
Note that the step-plot API of Bokeh does so far not support a hovertool functionality.
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)
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)")
In this example you can see, that the additional dimension sepal length cannot be used to clearly differentiate between the virginica and versicolor species.
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)
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)
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)
For drawing histograms (kind="hist"), Pandas-Bokeh has a lot of customization features. Optional keyword arguments for histogram plots are:
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")
Further, advanced keyword arguments for histograms are:
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)
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()
Year | Oil | Gas | Coal | Nuclear Energy | Hydroelectricity | Other Renewable |
---|---|---|---|---|---|---|
1970-01-01 | 2291.5 | 826.7 | 1467.3 | 17.7 | 265.8 | 5.8 |
1971-01-01 | 2427.7 | 884.8 | 1459.2 | 24.9 | 276.4 | 6.3 |
1972-01-01 | 2613.9 | 933.7 | 1475.7 | 34.1 | 288.9 | 6.8 |
1973-01-01 | 2818.1 | 978.0 | 1519.6 | 45.9 | 292.5 | 7.3 |
1974-01-01 | 2777.3 | 1001.9 | 1520.9 | 59.6 | 321.1 | 7.7 |
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))
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")
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
Partei | 2002 | 2005 | 2009 | 2013 | 2017 |
---|---|---|---|---|---|
CDU/CSU | 38.5 | 35.2 | 33.8 | 41.5 | 32.9 |
SPD | 38.5 | 34.2 | 23.0 | 25.7 | 20.5 |
FDP | 7.4 | 9.8 | 14.6 | 4.8 | 10.7 |
Grünen | 8.6 | 8.1 | 10.7 | 8.4 | 8.9 |
Linke/PDS | 4.0 | 8.7 | 11.9 | 8.6 | 9.2 |
AfD | 0.0 | 0.0 | 0.0 | 0.0 | 12.6 |
Sonstige | 3.0 | 4.0 | 6.0 | 11.0 | 5.0 |
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",
)
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")
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:
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()
name | pop_max | latitude | longitude | size |
---|---|---|---|---|
Mesa | 1085394 | 33.423915 | -111.736084 | 1.085394 |
Sharjah | 1103027 | 25.371383 | 55.406478 | 1.103027 |
Changwon | 1081499 | 35.219102 | 128.583562 | 1.081499 |
Sheffield | 1292900 | 53.366677 | -1.499997 | 1.292900 |
Abbottabad | 1183647 | 34.149503 | 73.199501 | 1.183647 |
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")
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 :
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_NAME | REGION | POPESTIMATE2010 | POPESTIMATE2011 | POPESTIMATE2012 | POPESTIMATE2013 | POPESTIMATE2014 | POPESTIMATE2015 | POPESTIMATE2016 | POPESTIMATE2017 | geometry |
---|---|---|---|---|---|---|---|---|---|---|
Hawaii | 4 | 1363817 | 1378323 | 1392772 | 1408038 | 1417710 | 1426320 | 1428683 | 1427538 | (POLYGON ((-160.0738033454681 22.0041773479577... |
Washington | 4 | 6741386 | 6819155 | 6890899 | 6963410 | 7046931 | 7152818 | 7280934 | 7405743 | (POLYGON ((-122.4020153103835 48.2252163723779... |
Montana | 4 | 990507 | 996866 | 1003522 | 1011921 | 1019931 | 1028317 | 1038656 | 1050493 | POLYGON ((-111.4754253002074 44.70216236909688... |
Maine | 1 | 1327568 | 1327968 | 1328101 | 1327975 | 1328903 | 1327787 | 1330232 | 1335907 | (POLYGON ((-69.77727626137293 44.0741483685119... |
North Dakota | 2 | 674518 | 684830 | 701380 | 722908 | 738658 | 754859 | 755548 | 755393 | POLYGON ((-98.73043728833767 45.93827137024809... |
Plotting the data on a map is as simple as calling:
df_states.plot_bokeh(simplify_shapes=10000)
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:
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.
This is the simplest way. Just provide the category keyword for the selection of the property column:
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.
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>.'
)
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.
Another option for interactive choropleth maps is the slider implementation of Pandas-Bokeh. The possible keyword arguments are here:
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 [%]")
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",
)
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");
In a similar way, also GeoDataFrames with (multi)line shapes can be drawn using Pandas-Bokeh.
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")
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"))
The pandas.DataFrame.plot_bokeh API has the following additional keyword arguments:
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.
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")
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.
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:
Format | Output |
---|---|
0 | 3 |
0.000 | 3.141 |
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 $")
If you want to suppress the scientific notation for axes, you can use the disable_scientific_axes parameter, which accepts one of "x", "y", "xy":
df = pd.DataFrame({"Animal": ["Mouse", "Rabbit", "Dog", "Tiger", "Elefant", "Wale"],
"Weight [g]": [19, 3000, 40000, 200000, 6000000, 50000000]})
p_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", show_figure=False)
p_non_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", disable_scientific_axes="y", show_figure=False,)
pandas_bokeh.plot_grid([[p_scientific, p_non_scientific]], plot_width = 450)
As shown in the Scatterplot Example, combining plots with plots or other HTML elements is straighforward in Pandas-Bokeh due to the layout capabilities of Bokeh. The easiest way to generate a dashboard layout is using the pandas_bokeh.plot_grid method (which is an extension of bokeh.layouts.gridplot):
import pandas as pd
import numpy as np
import pandas_bokeh
pandas_bokeh.output_notebook()
#Barplot:
data = {
'fruits':
['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 3, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")
p_bar = df.plot_bokeh(
kind="bar",
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
show_figure=False)
#Lineplot:
np.random.seed(42)
df = pd.DataFrame({
"Google": np.random.randn(1000) + 0.2,
"Apple": np.random.randn(1000) + 0.17
},
index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
p_line = df.plot_bokeh(
kind="line",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
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)
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)
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.
Download Details:
Author: PatrikHlobil
Source Code: https://github.com/PatrikHlobil/Pandas-Bokeh
License: MIT License
1667904060
A Django plugin for creating AJAX driven forms in Bootstrap modal.
This repository includes Dockerfile
and docker-compose.yml
files so you can easily setup and start to experiment with django-bootstrap-modal-forms
running inside of a container on your local machine. Any changes you make in bootstrap_modal_forms
, examples
and test
folders are reflected in the container (see docker-compose.yml) and the data stored in sqlite3 database are persistent even if you remove stopped container. Follow the steps below to run the app:
$ clone repository
$ cd django-bootstrap-modal-forms
$ docker compose up (use -d flag to run app in detached mode in the background)
$ visit 0.0.0.0:8000
Install django-bootstrap-modal-forms
:
$ pip install django-bootstrap-modal-forms
Add bootstrap_modal_forms
to your INSTALLED_APPS in settings.py:
INSTALLED_APPS = [
...
'bootstrap_modal_forms',
...
]
Include Bootstrap, jQuery and jquery.bootstrap.modal.forms.js
on every page where you would like to set up the AJAX driven Django forms in Bootstrap modal.
IMPORTANT: Adjust Bootstrap and jQuery file paths to match yours, but include jquery.bootstrap.modal.forms.js
exactly as in code bellow.
<head>
<link rel="stylesheet" href="{% static 'assets/css/bootstrap.css' %}">
</head>
<body>
<script src="{% static 'assets/js/bootstrap.js' %}"></script>
<script src="{% static 'assets/js/jquery.js' %}"></script>
<script src="{% static 'js/jquery.bootstrap.modal.forms.js' %}"></script>
<!-- You can alternatively load the minified version -->
<script src="{% static 'js/jquery.bootstrap.modal.forms.min.js' %}"></script>
</body>
index.html
<script type="text/javascript">
$(document).ready(function() {
$("#create-book").modalForm({
formURL: "{% url 'create_book' %}"
});
});
</script>
modalForm
opens modalformURL
is appended to the modalformURL
success_url
and shows success_message
, which are both defined in related Django viewDefine BookModelForm and inherit built-in form BSModalModelForm
.
forms.py
from .models import Book
from bootstrap_modal_forms.forms import BSModalModelForm
class BookModelForm(BSModalModelForm):
class Meta:
model = Book
fields = ['title', 'author', 'price']
Define form's html and save it as Django template.
formURL
defined in #6.class="invalid"
or custom errorClass
(see paragraph Options) to the elements that wrap the fields.class="invalid"
acts as a flag for the fields having errors after the form has been POSTed.book/create_book.html
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h5 class="modal-title">Create new Book</h5>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
{% for field in form %}
<div class="form-group{% if field.errors %} invalid{% endif %}">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{{ field }}
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
{% endfor %}
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
<button type="submit" class="btn btn-primary">Create</button>
</div>
</form>
Define a class-based view BookCreateView and inherit from built-in generic view BSModalCreateView
. BookCreateView processes the form defined in #1, uses the template defined in #2 and redirects to success_url
showing success_message
.
views.py
from django.urls import reverse_lazy
from .forms import BookModelForm
from .models import Book
from bootstrap_modal_forms.generic import BSModalCreateView
class BookCreateView(BSModalCreateView):
template_name = 'examples/create_book.html'
form_class = BookModelForm
success_message = 'Success: Book was created.'
success_url = reverse_lazy('index')
Define URL for the view in #3.
from django.urls import path
from books import views
urlpatterns = [
path('', views.Index.as_view(), name='index'),
path('create/', views.BookCreateView.as_view(), name='create_book'),
]
Define the Bootstrap modal window and html element triggering modal opening.
modalForms
in single template (see #6).id
and the same value should also be set as modalID
option when instantiating modalForm
on trigger element.id="create-book"
) is used for instantiation of modalForm
in #6.modalForm
is bound to it.<div class="modal-content"></div>
and sets action attribute of the form to formURL
set in #6.index.html
<div class="modal fade" tabindex="-1" role="dialog" id="modal">
<div class="modal-dialog" role="document">
<div class="modal-content"></div>
</div>
</div>
<!-- Create book button -->
<button id="create-book" class="btn btn-primary" type="button" name="button">Create book</button>
Add script to the template from #5 and bind the modalForm
to the trigger element. Set BookCreateView URL defined in #4 as formURL
property of modalForm
.
modalForm
with unique URL to it.modalID
, modalContent
, modalForm
and errorClass
are used in this example, while formURL
is customized. If you customize any other option adjust the code of the above examples accordingly.index.html
<script type="text/javascript">
$(document).ready(function() {
$("#create-book").modalForm({
formURL: "{% url 'create_book' %}"
});
});
</script>
Set asyncUpdate and asyncSettings settings to create or update objects without page redirection to successUrl and define whether a modal should close or stay opened after form submission. See comments in example below and paragraph modalForm options for explanation of asyncSettings. See examples on how to properly reinstantiate modal forms for all CRUD buttons when using async options.
index.html
<!-- asyncSettings.dataElementId -->
<table id="books-table" class="table">
<thead>
...
</thead>
<tbody>
{% for book in books %}
<tr>
...
<!-- Update book buttons -->
<button type="button" class="update-book btn btn-sm btn-primary" data-form-url="{% url 'update_book' book.pk %}">
<span class="fa fa-pencil"></span>
</button>
...
</td>
</tr>
{% endfor %}
</tbody>
</table>
<script type="text/javascript">
$(function () {
...
# asyncSettings.successMessage
var asyncSuccessMessage = [
"<div ",
"style='position:fixed;top:0;z-index:10000;width:100%;border-radius:0;' ",
"class='alert alert-icon alert-success alert-dismissible fade show mb-0' role='alert'>",
"Success: Book was updated.",
"<button type='button' class='close' data-dismiss='alert' aria-label='Close'>",
"<span aria-hidden='true'>×</span>",
"</button>",
"</div>",
"<script>",
"$('.alert').fadeTo(2000, 500).slideUp(500, function () {$('.alert').slideUp(500).remove();});",
"<\/script>"
].join();
# asyncSettings.addModalFormFunction
function updateBookModalForm() {
$(".update-book").each(function () {
$(this).modalForm({
formURL: $(this).data("form-url"),
asyncUpdate: true,
asyncSettings: {
closeOnSubmit: false,
successMessage: asyncSuccessMessage
dataUrl: "books/",
dataElementId: "#books-table",
dataKey: "table",
addModalFormFunction: updateBookModalForm
}
});
});
}
updateBookModalForm();
...
});
</script>
urls.py
from django.urls import path
from . import views
urlpatterns = [
...
# asyncSettings.dataUrl
path('books/', views.books, name='books'),
...
]
views.py
from django.http import JsonResponse
from django.template.loader import render_to_string
from .models import Book
def books(request):
data = dict()
if request.method == 'GET':
books = Book.objects.all()
# asyncSettings.dataKey = 'table'
data['table'] = render_to_string(
'_books_table.html',
{'books': books},
request=request
)
return JsonResponse(data)
modalID
Sets the custom id of the modal. Default: "#modal"
modalContent
Sets the custom class of the element to which the form's html is appended. If you change modalContent
to the custom class, you should also change modalForm
accordingly. To keep Bootstrap's modal style you should than copy Bootstrap's style for modal-content
and set it to your new modalContent class. Default: ".modal-content"
modalForm
Sets the custom form selector. Default: ".modal-content form"
formURL
Sets the url of the form's view and html. Default: null
isDeleteForm
Defines if form is used for deletion. Should be set to true
for deletion forms. Default: false
errorClass
Sets the custom class for the form fields having errors. Default: ".invalid"
asyncUpdate
Sets asynchronous content update after form submission. Default: false
asyncSettings.closeOnSubmit
Sets whether modal closes or not after form submission. Default: false
asyncSettings.successMessage
Sets successMessage shown after succesful for submission. Should be set to string defining message element. See asyncSuccessMessage
example above. Default: null
asyncSettings.dataUrl
Sets url of the view returning new queryset = all of the objects plus newly created or updated one after asynchronous update. Default: null
asyncSettings.dataElementId
Sets the id
of the element which rerenders asynchronously updated queryset. Default: null
asyncSettings.dataKey
Sets the key containing asynchronously updated queryset in the data dictionary returned from the view providing updated queryset. Default: null
asyncSettings.addModalFormFunction
Sets the method needed for reinstantiation of event listeners on buttons (single or all CRUD buttons) after asynchronous update. Default: null
triggerElement.modalForm({
modalID: "#modal",
modalContent: ".modal-content",
modalForm: ".modal-content form",
formURL: null,
isDeleteForm: false,
errorClass: ".invalid",
asyncUpdate: false,
asyncSettings: {
closeOnSubmit: false,
successMessage: null,
dataUrl: null,
dataElementId: null,
dataKey: null,
addModalFormFunction: null
}
});
Import forms with from bootstrap_modal_forms.forms import BSModalForm
.
BSModalForm
Inherits PopRequestMixin and Django's forms.Form.
BSModalModelForm
Inherits PopRequestMixin, CreateUpdateAjaxMixin and Django's forms.ModelForm.
Import mixins with from bootstrap_modal_forms.mixins import PassRequestMixin
.
PassRequestMixin
Puts the request into the form's kwargs.
PopRequestMixin
Pops request out of the kwargs and attaches it to the form's instance.
CreateUpdateAjaxMixin
Saves or doesn't save the object based on the request type.
DeleteMessageMixin
Deletes object if request is not ajax request.
LoginAjaxMixin
Authenticates user if request is not ajax request.
Import generic views with from bootstrap_modal_forms.generic import BSModalFormView
.
BSModalFormView
Inherits PassRequestMixin and Django's generic.FormView.
BSModalCreateView
Inherits PassRequestMixin and Django's SuccessMessageMixin and generic.CreateView.
BSModalUpdateView
Inherits PassRequestMixin and Django's SuccessMessageMixin and generic.UpdateView.
BSModalReadView
Inherits Django's generic.DetailView.
BSModalDeleteView
Inherits DeleteMessageMixin and Django's generic.DeleteView.
To see django-bootstrap-modal-forms
in action clone the repository and run the examples locally:
$ git clone https://github.com/trco/django-bootstrap-modal-forms.git
$ cd django-bootstrap-modal-forms
$ pip install -r requirements.txt
$ python manage.py migrate
$ python manage.py runserver
Run unit and functional tests inside of project folder:
$ python manage.py test
For explanation how all the parts of the code work together see paragraph Usage. To test the working solution presented here clone and run Examples.
forms.py
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from bootstrap_modal_forms.mixins import PopRequestMixin, CreateUpdateAjaxMixin
class CustomUserCreationForm(PopRequestMixin, CreateUpdateAjaxMixin,
UserCreationForm):
class Meta:
model = User
fields = ['username', 'password1', 'password2']
signup.html
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Sign up</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="{% if form.non_field_errors %}invalid{% endif %} mb-2">
{% for error in form.non_field_errors %}
{{ error }}
{% endfor %}
</div>
{% for field in form %}
<div class="form-group">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{% render_field field class="form-control" placeholder=field.label %}
<div class="{% if field.errors %} invalid{% endif %}">
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
</div>
{% endfor %}
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-primary">Sign up</button>
</div>
</form>
views.py
from django.urls import reverse_lazy
from bootstrap_modal_forms.generic import BSModalCreateView
from .forms import CustomUserCreationForm
class SignUpView(BSModalCreateView):
form_class = CustomUserCreationForm
template_name = 'examples/signup.html'
success_message = 'Success: Sign up succeeded. You can now Log in.'
success_url = reverse_lazy('index')
urls.py
from django.urls import path
from . import views
app_name = 'accounts'
urlpatterns = [
path('signup/', views.SignUpView.as_view(), name='signup')
]
.html file containing modal, trigger element and script instantiating modalForm
<div class="modal fade" tabindex="-1" role="dialog" id="modal">
<div class="modal-dialog" role="document">
<div class="modal-content"></div>
</div>
</div>
<button id="signup-btn" class="btn btn-primary" type="button" name="button">Sign up</button>
<script type="text/javascript">
$(function () {
// Sign up button
$("#signup-btn").modalForm({
formURL: "{% url 'signup' %}"
});
});
</script>
For explanation how all the parts of the code work together see paragraph Usage. To test the working solution presented here clone and run Examples.
You can set the login redirection by setting the LOGIN_REDIRECT_URL
in settings.py
.
You can also set the custom login redirection by:
success_url
to the extra_context
of CustomLoginView
success_url
variable as a value of the hidden input field
with name="next"
within the Login form htmlforms.py
from django.contrib.auth.forms import AuthenticationForm
from django.contrib.auth.models import User
class CustomAuthenticationForm(AuthenticationForm):
class Meta:
model = User
fields = ['username', 'password']
login.html
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Log in</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="{% if form.non_field_errors %}invalid{% endif %} mb-2">
{% for error in form.non_field_errors %}
{{ error }}
{% endfor %}
</div>
{% for field in form %}
<div class="form-group">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{% render_field field class="form-control" placeholder=field.label %}
<div class="{% if field.errors %} invalid{% endif %}">
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
</div>
{% endfor %}
<!-- Hidden input field for custom redirection after successful login -->
<input type="hidden" name="next" value="{{ success_url }}">
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-primary">Log in</button>
</div>
</form>
views.py
from django.urls import reverse_lazy
from bootstrap_modal_forms.generic import BSModalLoginView
from .forms import CustomAuthenticationForm
class CustomLoginView(BSModalLoginView):
authentication_form = CustomAuthenticationForm
template_name = 'examples/login.html'
success_message = 'Success: You were successfully logged in.'
extra_context = dict(success_url=reverse_lazy('index'))
urls.py
from django.urls import path
from . import views
app_name = 'accounts'
urlpatterns = [
path('login/', views.CustomLoginView.as_view(), name='login')
]
.html file containing modal, trigger element and script instantiating modalForm
<div class="modal fade" tabindex="-1" role="dialog" id="modal">
<div class="modal-dialog" role="document">
<div class="modal-content"></div>
</div>
</div>
<button id="login-btn" class="btn btn-primary" type="button" name="button">Sign up</button>
<script type="text/javascript">
$(function () {
// Log in button
$("#login-btn").modalForm({
formURL: "{% url 'login' %}"
});
});
</script>
For explanation how all the parts of the code work together see paragraph Usage. To test the working solution presented here clone and run Examples.
forms.py
from .models import Book
from bootstrap_modal_forms.forms import BSModalModelForm
class BookModelForm(BSModalModelForm):
class Meta:
model = Book
exclude = ['timestamp']
create_book.html
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Create Book</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="{% if form.non_field_errors %}invalid{% endif %} mb-2">
{% for error in form.non_field_errors %}
{{ error }}
{% endfor %}
</div>
{% for field in form %}
<div class="form-group">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{% render_field field class="form-control" placeholder=field.label %}
<div class="{% if field.errors %} invalid{% endif %}">
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
</div>
{% endfor %}
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-primary">Create</button>
</div>
</form>
update_book.html
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Update Book</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="{% if form.non_field_errors %}invalid{% endif %} mb-2">
{% for error in form.non_field_errors %}
{{ error }}
{% endfor %}
</div>
{% for field in form %}
<div class="form-group">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{% render_field field class="form-control" placeholder=field.label %}
<div class="{% if field.errors %} invalid{% endif %}">
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
</div>
{% endfor %}
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-primary">Update</button>
</div>
</form>
read_book.html
{% load widget_tweaks %}
<div class="modal-header">
<h3 class="modal-title">Book details</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="">
Title: {{ book.title }}
</div>
<div class="">
Author: {{ book.author }}
</div>
<div class="">
Price: {{ book.price }} €
</div>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
</div>
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Delete Book</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<p>Are you sure you want to delete book with title
<strong>{{ book.title }}</strong>?</p>
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-danger">Delete</button>
</div>
</form>
views.py
from django.urls import reverse_lazy
from django.views import generic
from .forms import BookModelForm
from .models import Book
from bootstrap_modal_forms.generic import (
BSModalCreateView,
BSModalUpdateView,
BSModalReadView,
BSModalDeleteView
)
class Index(generic.ListView):
model = Book
context_object_name = 'books'
template_name = 'index.html'
# Create
class BookCreateView(BSModalCreateView):
template_name = 'examples/create_book.html'
form_class = BookModelForm
success_message = 'Success: Book was created.'
success_url = reverse_lazy('index')
# Update
class BookUpdateView(BSModalUpdateView):
model = Book
template_name = 'examples/update_book.html'
form_class = BookModelForm
success_message = 'Success: Book was updated.'
success_url = reverse_lazy('index')
# Read
class BookReadView(BSModalReadView):
model = Book
template_name = 'examples/read_book.html'
# Delete
class BookDeleteView(BSModalDeleteView):
model = Book
template_name = 'examples/delete_book.html'
success_message = 'Success: Book was deleted.'
success_url = reverse_lazy('index')
urls.py
from django.urls import path
from books import views
urlpatterns = [
path('', views.Index.as_view(), name='index'),
path('create/', views.BookCreateView.as_view(), name='create_book'),
path('update/<int:pk>', views.BookUpdateView.as_view(), name='update_book'),
path('read/<int:pk>', views.BookReadView.as_view(), name='read_book'),
path('delete/<int:pk>', views.BookDeleteView.as_view(), name='delete_book')
]
.html file containing modal, trigger elements and script instantiating modalForms
<!-- Modal 1 with id="create-book"-->
<div class="modal fade" id="create-modal" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog">
<div class="modal-content">
</div>
</div>
</div>
<!-- Modal 2 with id="modal" -->
<div class="modal fade" tabindex="-1" role="dialog" id="modal">
<div class="modal-dialog" role="document">
<div class="modal-content"></div>
</div>
</div>
<!-- Create book button -->
<button id="create-book" class="btn btn-primary" type="button" name="button">Create book</button>
{% for book in books %}
<div class="text-center">
<!-- Read book buttons -->
<button type="button" class="read-book bs-modal btn btn-sm btn-primary" data-form-url="{% url 'read_book' book.pk %}">
<span class="fa fa-eye"></span>
</button>
<!-- Update book buttons -->
<button type="button" class="update-book bs-modal btn btn-sm btn-primary" data-form-url="{% url 'update_book' book.pk %}">
<span class="fa fa-pencil"></span>
</button>
<!-- Delete book buttons -->
<button type="button" class="delete-book bs-modal btn btn-sm btn-danger" data-form-url="{% url 'delete_book' book.pk %}">
<span class="fa fa-trash"></span>
</button>
</div>
{% endfor %}
<script type="text/javascript">
$(function () {
// Read book buttons
$(".read-book").each(function () {
$(this).modalForm({formURL: $(this).data("form-url")});
});
// Delete book buttons - formURL is retrieved from the data of the element
$(".delete-book").each(function () {
$(this).modalForm({formURL: $(this).data("form-url"), isDeleteForm: true});
});
// Create book button opens form in modal with id="create-modal"
$("#create-book").modalForm({
formURL: "{% url 'create_book' %}",
modalID: "#create-modal"
});
});
</script>
data-form-url
attribute of each Update, Read and Delete button should be set to relevant URL with pk argument of the object to be updated, read or deleted.data-form-url
URLs should than be set as formURLs
for modalForms
bound to the buttons.For explanation how all the parts of the code work together see paragraph Usage. To test the working solution presented here clone and run Examples.
forms.py
from bootstrap_modal_forms.forms import BSModalForm
class BookFilterForm(BSModalForm):
type = forms.ChoiceField(choices=Book.BOOK_TYPES)
class Meta:
fields = ['type']
filter_book.html
{% load widget_tweaks %}
<form method="post" action="">
{% csrf_token %}
<div class="modal-header">
<h3 class="modal-title">Filter Books</h3>
<button type="button" class="close" data-dismiss="modal" aria-label="Close">
<span aria-hidden="true">×</span>
</button>
</div>
<div class="modal-body">
<div class="{% if form.non_field_errors %}invalid{% endif %} mb-2">
{% for error in form.non_field_errors %}
{{ error }}
{% endfor %}
</div>
{% for field in form %}
<div class="form-group">
<label for="{{ field.id_for_label }}">{{ field.label }}</label>
{% render_field field class="form-control" placeholder=field.label %}
<div class="{% if field.errors %} invalid{% endif %}">
{% for error in field.errors %}
<p class="help-block">{{ error }}</p>
{% endfor %}
</div>
</div>
{% endfor %}
</div>
<div class="modal-footer">
<button type="submit" class="btn btn-primary">Filter</button>
</div>
</form>
views.py
class BookFilterView(BSModalFormView):
template_name = 'examples/filter_book.html'
form_class = BookFilterForm
def form_valid(self, form):
self.filter = '?type=' + form.cleaned_data['type']
response = super().form_valid(form)
return response
def get_success_url(self):
return reverse_lazy('index') + self.filter
urls.py
from django.urls import path
from . import views
app_name = 'accounts'
urlpatterns = [
path('filter/', views.BookFilterView.as_view(), name='filter_book'),
]
index.html
...
<button id="filter-book" class="filter-book btn btn-primary" type="button" name="button" data-form-url="{% url 'filter_book' %}">
<span class="fa fa-filter mr-2"></span>Filter books
</button>
...
<script type="text/javascript">
$(function () {
...
$("#filter-book").each(function () {
$(this).modalForm({formURL: $(this).data('form-url')});
});
...
});
</script>
This is an Open Source project and any contribution is appreciated.
Author: trco
Source Code: https://github.com/trco/django-bootstrap-modal-forms
License: MIT license