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‎README.md‎

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@@ -47,6 +47,60 @@ easy and intuitive. It is heavily inspired by [Pandas](https://pandas.pydata.org
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- Robust data preprocessing functions like [OneHotEncoders](https://jsdata.gitbook.io/danfojs/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://jsdata.gitbook.io/danfojs/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardSaler](https://jsdata.gitbook.io/danfojs/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://jsdata.gitbook.io/danfojs/api-reference/general-functions/danfo.minmaxscaler) are supported on DataFrame and Series
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To use danfo.js via script tags, copy and paste the CDN below to your HTML file
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```html
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<script src="https://cdn.jsdelivr.net/npm/danfojs@0.1.0/dist/index.min.js"></script>
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```
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### Example Usage in the Browser
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```html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<script src="https://cdn.jsdelivr.net/npm/danfojs@0.1.0/dist/index.min.js"></script>
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<title>Document</title>
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</head>
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<body>
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<div id="div1"></div>
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<div id="div2"></div>
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<div id="div3"></div>
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<script>
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dfd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
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.then(df => {
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df['AAPL.Open'].plot("div1").box() //makes a box plot
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df.plot("div2").table() //display csv as table
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new_df = df.set_index({ key: "Date" }) //resets the index to Date column
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new_df.plot("div3").line({ columns: ["AAPL.Open", "AAPL.High"] }) //makes a timeseries plot
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}).catch(err => {
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console.log(err);
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})
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</script>
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</body>
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</html>
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```
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Output in Browser:
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![](assets/browser-out.gif)
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## How to install
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danfo.js is hosted on NPM, and can installed via package managers like npm and yarn
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![](assets/node-rec.gif)
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To use danfo.js via script tags, copy and paste the CDN below to your HTML file
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```html
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<script src="https://cdn.jsdelivr.net/npm/danfojs@0.0.15/dist/index.min.js"></script>
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```
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### Example Usage in the Browser
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```html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<script src="https://cdn.jsdelivr.net/npm/danfojs@0.0.15/dist/index.min.js"></script>
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<title>Document</title>
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</head>
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<body>
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<div id="div1"></div>
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<div id="div2"></div>
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<div id="div3"></div>
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<script>
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dfd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
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.then(df => {
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df['AAPL.Open'].plot("div1").box() //makes a box plot
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df.plot("div2").table() //display csv as table
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new_df = df.set_index({ key: "Date" }) //resets the index to Date column
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new_df.plot("div3").line({ columns: ["AAPL.Open", "AAPL.High"] }) //makes a timeseries plot
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}).catch(err => {
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console.log(err);
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})
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</script>
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</body>
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</html>
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```
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Output in Browser:
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![](assets/browser-out.gif)
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#### [See the Official Getting Started Guide](https://jsdata.gitbook.io/danfojs/getting-started)
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## Documentation

‎danfojs/src/core/frame.js‎

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@@ -79,36 +79,37 @@ export class DataFrame extends Ndframe {
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throw Error("No column found. Axis of 1 must be accompanied by an array of column(s) names")
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}
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let self = this;
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let new_col_data = {}
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let new_dtype = []
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const index = data.map((x) => {
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let col_idx = self.columns.indexOf(x)
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if (col_idx == -1) {
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throw new Error(`column "${x}" does not exist`)
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}
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return col_idx
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});
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const values = this.values
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let new_dtype = []
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let new_data = values.map(function (element) {
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let new_arr = utils.__remove_arr(element, index);
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new_dtype = utils.__remove_arr(self.dtypes, index);
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return new_arr;
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});
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this.col_data.forEach((col, idx) => {
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if (!index.includes(idx)) {
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new_col_data[self.column_names[idx]] = col
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new_dtype.push(self.dtypes[idx])
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}
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})
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if (!kwargs['inplace']) {
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let old_cols = this.columns
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let columns = utils.__remove_arr(this.columns, index);
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let df = new DataFrame(new_data, { columns: columns, index: self.index, dtypes: new_dtype })
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df.__set_col_property(df, df.col_data, columns, old_cols)
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let old_cols = self.columns
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let new_columns = Object.keys(new_col_data)
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let df = new DataFrame(new_col_data, { index: self.index, dtypes: new_dtype })
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df.__set_col_property(df, df.col_data, new_columns, old_cols)
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return df
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} else {
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let new_cols = utils.__remove_arr(this.columns, index);
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let old_cols = this.columns
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this.columns = new_cols
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this.row_data_tensor = tf.tensor(new_data);
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this.data = new_data
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this.__set_col_types(new_dtype, false)
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this.__set_col_property(this, this.col_data, new_cols, old_cols)
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let old_cols = self.columns
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let new_columns = Object.keys(new_col_data)
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this.__update_frame_in_place(null, null, new_col_data, null, new_dtype)
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this.__set_col_property(self, self.col_data, new_columns, old_cols)
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}
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} else {
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let df = new DataFrame([...this.values],
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{
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columns: [...this.column_names],
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index: this.index, dtypes: this.dtypes
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index: this.index,
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dtypes: this.dtypes
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})
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return df
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}
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}
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/**
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* Generate a new DataFrame with the specified index.
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* Set the DataFrame index (row labels) using an array of the same length.
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* @param {kwargs} {index: Array of new index values}
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*/
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// throw Error("Value Error: must specify the column to sort by")
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// }
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// sorted_index.map(idx => {
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// new_row_data.push(this.values[idx])
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// })
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// if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) {
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// this.__update_frame_in_place(new_row_data, null, null, sorted_index, null)
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// } else {
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// let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes })
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// return df
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// }
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// } else {
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// throw Error("Value Error: must specify the column to sort by")
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// }
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// }
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groupby(col) {
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let len = this.shape[0]
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let column_names = this.column_names
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let col_dict = {};
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let key_column = null;
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if (col.length == 2) {
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if (column_names.includes(col[0])) {
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// eslint-disable-next-line no-unused-vars
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var [data1, col_name1] = indexLoc(this, { "rows": [`0:${len}`], "columns": [`${col[0]}`], "type": "loc" });
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}
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let row_value = values[i]
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for (let j = 0; j < row_value.length; j++) {
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let val = row_value[j] == 0 ? 0 : !!row_value[j]
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if (!val) {
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if (isNaN(row_value[j]) && typeof row_value[j] != "string" ) {
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temp_data.push(nan_val)
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temp_data.push(row_value[j])
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}
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data.push(temp_data);
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}
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if (kwargs['inplace']) {
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this.__update_frame_in_place(data, null, null, null, null)
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} else {
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return new DataFrame(data, { columns: columns, index: this.index })
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return new DataFrame(data, { columns: columns, index: this.index })
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}
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}
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}
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// let new_row_data = []
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// let row_data = this.values;
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// let columns = this.column_names;
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// row_data.map(arr=>{
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// let temp_arr = []
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// arr.map(val=>{
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// if (isNaN(val) && typeof val != "string" ){
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// temp_arr.push(true)
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// }else{
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// temp_arr.push(false)
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// }
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// })
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// new_row_data.push(temp_arr)
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// })
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// // for (let i = 0; i < values.length; i++) {
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// // let temp_data = []
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// // let row_value = values[i]
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// // for (let j = 0; j < row_value.length; j++) {
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// // let val = row_value[j] == 0 ? true : !row_value[j]
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// // temp_data.push(val)
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// // }
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// // data.push(temp_data);
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// // }
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// return new DataFrame(new_row_data, { columns: columns, index: this.index })
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/**
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* @return Array list (int)

‎danfojs/src/plotting/plot.js‎

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//perform plotting
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// import { newPlot } from 'plotly.js' //comment out when building for Node Version
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import { newPlot } from 'plotly.js' //comment out when building for Node Version
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import { Utils } from "../core/utils"
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import { Series } from "../core/series"
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‎danfojs/tests/core/frame.js‎

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df.drop({ columns: ["C"], axis: 1, inplace: true });
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let new_data = [[1, 2], [4, 5]]
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assert.deepEqual(df.values, new_data);
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assert.deepEqual(df.dtypes.length, 2);
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})
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it("check if data is updated after row is dropped", function () {
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df.drop({ index: ["a", "b"], axis: 0, inplace: true });
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let new_data = [[20, 34, 5]]
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assert.deepEqual(df.values, new_data);
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})
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})
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let df = new DataFrame(data, { columns: column })
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let df_val = [[-999, 1, 2, 3], [3, 4, -999, 9], [5, 6, 7, 8]]
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assert.deepEqual(df.fillna({ values: -999 }).values, df_val)
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df.fillna({ values: -999, inplace:true})
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assert.deepEqual(df.values, df_val)
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});
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it("replace all NaN value", function () {
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let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]

‎package-lock.json‎

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‎package.json‎

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{
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"name": "danfojs",
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"version": "0.0.15",
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"version": "0.1.0",
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"description": "JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.",
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"main": "dist/index.js",
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"contributors": [

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