Danfo.js专题 - 附：Dnotebook（Danfo Notebook）单机资源与汉化文档

var df2
load_csv("//localhost:4400/javascripts/datas/train.csv").then((df)=>{
  df2 = df
})

table(df2.head())

table(df2.describe())

//Display the number of rows and columns
console.log(df2.shape)

console.log(df2.dtypes)

let dtypes = new dfd.Series(df2.dtypes,{index: df2.columns})
table(dtypes)

//Remove Id columns
let cols_2_remove = ['Product_Identifier', 'Supermarket_Identifier', 'Product_Supermarket_Identifier']
var new_df = df2.drop({columns:cols_2_remove,axis:1}) // drop the columns
table(new_df.head())

console.log(df2.dtypes)

[string,string,string,string,string,float32,string,float32,int32,string....3 more]

let dtypes = new dfd.Series(df2.dtypes,{index: df2.columns})
table(dtypes)

//Remove Id columns
let cols_2_remove = ['Product_Identifier', 'Supermarket_Identifier', 'Product_Supermarket_Identifier']
var new_df = df2.drop({columns:cols_2_remove,axis:1}) // drop the columns

table(new_df.head())

//print the shape
console.log(new_df.shape)

[4990,10]

//list out the categorical and numerical variable
var cat_cols = ['Product_Fat_Content','Product_Type',
                        'Supermarket _Size', 'Supermarket_Location_Type',
                    'Supermarket_Type' ]
var num_cols = ['Product_Weight', 'Product_Shelf_Visibility',
                        'Product_Price', 'Supermarket_Opening_Year', 'Product_Supermarket_Sales']

for(let i in cat_cols){
  let col = cat_cols[i]
  let counts = new_df[col].value_counts()
  var layout = {
    title: `Bar Plot for ${col}`
  }
  viz(`cat_viz${i}`, x =>{
    counts.plot(x).bar({layout:layout})
  })
}

for(let i in num_cols){
  let col = num_cols[i]
  let layout ={
    xaxis:{
      title: col
    },
    yaxis: {
      title:'Product_Supermarket_Sales'
    }
  }
  viz(`num_col${i}`, x=>{
      new_df.plot(x).scatter({x:col,y:'Product_Supermarket_Sales'})
  })
}

for(let i in cat_cols){
  let col = cat_cols[i]
  let layout ={
    xaxis:{
      title: col
    },
    yaxis: {
      title:'Product_Supermarket_Sales'
    }
  }
  viz(`cat_cols${i}`, x=>{
    new_df.plot(x).box({x:col, y:'Product_Supermarket_Sales'})
  })
}

npm install -g dnotebook

dnotebook

# This is a level 1 heading
## This is a level 2 heading
This is some plain text that forms a paragraph.
Add emphasis via **bold** and __bold__, or *italic* and _italic_.
Paragraphs must be separated by an empty line.
* Sometimes we want to include lists.
* Which can be indented.
1. Lists can also be numbered.
2. For ordered lists.
[It is possible to include hyperlinks](https://www.example.com)
Inline code uses single backticks: `foo()`, and code blocks use triple backticks:
```
bar()
```
Or can be indented by 4 spaces:
    foo()
And finally, adding images is easy: ![Alt text](https://www.example.com/image.jpg)

//load package downloads and install any JavaScript package via its CDN
load_package(["https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest",
              "https://cdn.jsdelivr.net/npm/danfojs@0.2.4/dist/index.min.js"])

json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
            { A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
            { A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
            { A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
df = new dfd.DataFrame(json_data)
table(df)

var xs = tf.tensor2d([[0,0],[0,1],[1,0],[1,1]])
var ys = tf.tensor2d([[0],[1],[1],[0]])
function getModel(){
  var model = tf.sequential()
  model.add(tf.layers.dense({units:8, inputShape:2, activation: 'tanh'}))
  model.add(tf.layers.dense({units:1, activation: 'sigmoid'}))
  model.compile({optimizer: 'sgd', loss: 'binaryCrossentropy', lr:0.1})
  return model
}

console.log(xs)

var loss = []
var epochs = []
const model = getModel()
model.fit(xs, ys, {
  batchSize: 1,
  epochs: 50,
  callbacks: {
    onEpochEnd: async(epoch, logs)=>{epochs.push(epoch); loss.push((logs.loss).toFixed(4))}
  }
})

s = new dfd.Series(loss)
s.plot(this_div()).line()

load_package(["https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest", 
             "https://cdn.plot.ly/plotly-latest.min.js"])

var trace1 = {
  x: [1, 2, 3, 4],
  y: [10, 11, 12, 13],
  mode: 'markers',
  marker: {
    size: [40, 60, 80, 100]
  }
};
var data = [trace1];
var layout = {
  title: 'Marker Size',
  showlegend: false,
  height: 600,
  width: 600
};
Plotly.newPlot(this_div(), data, layout); //this_div is a function that returns the current output div name

var df
load_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
.then((d)=>{
  df = d
})

table(df) //displays object as a formated table

var layout = {
    title: 'A financial charts',
    xaxis: {
      title: 'Date',
    },
    yaxis: {
      title: 'Count',
    }
}
new_df = df.set_index({ key: "Date" })
new_df.plot(this_div()).line({ columns: ["AAPL.Open", "AAPL.High"], layout: layout })

for(let i=0; i<10; i++){
  console.log(i)
}

for(let i=0; i<10; i++){
  console.forlog(i)
}