Getting Started

Installation guides for Node and Browser based environments, including a quick 10 minute walk through of danfo.js

A stable version of Danfojs (v1), has been released, and it comes with full Typescript support, new features, and many bug fixes. See release note here.

There are a couple of breaking changes, so we have prepared a short migration guide for pre-v1 users.

Installation

There are three ways to install and use Danfo.js in your application

For Nodejs applications, you can install the danfojs-node version via package managers like yarn and npm:

npm install danfojs-node

or

yarn add danfojs-node

For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the danfojs version:

npm install danfojs

or

yarn add danfojs

For use directly in HTML files, you can add the latest script tag from JsDelivr:

<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>

To play with Danfo.js in a Notebook-like environment, see Dnotebooks here or the VS-Code Nodejs notebook extension.

10 minutes to danfo.js

This is a short introduction to Danfo.js, and its flow is adapted from the official 10 minutes to Pandas

We will show you how to use danfo.js in a browser, client-side libraries, and Node.js environments. Most functions except plotting which require a DOM work the same way in all environments.

const dfd = require("danfojs-node")

//or using ES6
import * as dfd from "danfojs-node"

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>
</head>

<body>

    <script>

      //danfo is exposed on dfd namespace 
      s = new dfd.Series([1,2,3,4,5]) 

    </script>

</body>

</html>

import * as dfd from "danfojs"

//import specific methods/classes
import { readCSV, DataFrame } from "danfojs"

Creating a DataFrame/Series

You can create a Series by passing a list of values, letting Danfo.js create a default integer index:

import * as dfd from "danfojs-node"

s = new dfd.Series([1, 3, 5, undefined, 6, 8])
s.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        s = new dfd.Series([1, 3, 5, undefined, 6, 8])
        s.print()

    </script>

</body>

</html>

//output
╔═══╤══════════════════════╗
║   │ 0                    ║
╟───┼──────────────────────╢
║ 0 │ 1                    ║
╟───┼──────────────────────╢
║ 1 │ 3                    ║
╟───┼──────────────────────╢
║ 2 │ 5                    ║
╟───┼──────────────────────╢
║ 3 │ undefined            ║
╟───┼──────────────────────╢
║ 4 │ 6                    ║
╟───┼──────────────────────╢
║ 5 │ 8                    ║
╚═══╧══════════════════════╝

Creating a Series from a tensor

const dfd = require("danfojs-node")
const tf = dfd.tensorflow //Tensorflow.js is exportedfrom Danfojs


let tensor_arr = tf.tensor([12,34,56,2])
let s = new dfd.Series(tensor_arr)
s.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>

    <title>Document</title>
</head>

<body>

    <script>
        const tf = dfd.tensorflow //get tensorflow lib from danfo
        let tensor_arr = tf.tensor([12,34,56,2])
        let s = new dfd.Series(tensor_arr)
        s.print()

    </script>

</body>

</html>

╔═══╤════╗
║ 0 │ 12 ║
╟───┼────╢
║ 1 │ 34 ║
╟───┼────╢
║ 2 │ 56 ║
╟───┼────╢
║ 3 │ 2  ║
╚═══╧════╝

Creating a DataFrame by passing a JSON object:

const dfd = require("danfojs-node")


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)
df.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>
<title>Document</title>
</head>

<body>

    <script>

         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)
        df.print()

    </script>
</body>

</html>

Creating a DataFrame from a 2D tensor

const dfd = require("danfojs-node")
const tf = dfd.tensorflow //Tensorflow.js is exported from Danfojs


let tensor_arr = tf.tensor2d([[12, 34, 2.2, 2], [30, 30, 2.1, 7]])
let df = new dfd.DataFrame(tensor_arr, {columns: ["A", "B", "C", "D"]})
df.print()
df.ctypes.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

         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)
        df.print()

    </script>
</body>

</html>

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ A                 │ B                 │ C                 │ D                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 12                │ 34                │ 2.20000004768...  │ 2                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 30                │ 30                │ 2.09999990463...  │ 7                 ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

╔═══╤══════════════════════╗
║   │ 0                    ║
╟───┼──────────────────────╢
║ A │ int32                ║
╟───┼──────────────────────╢
║ B │ int32                ║
╟───┼──────────────────────╢
║ C │ float32              ║
╟───┼──────────────────────╢
║ D │ int32                ║
╚═══╧══════════════════════╝

Creating a DataFrame by passing a dictionary of objects with the same length

const dfd = require("danfojs-node")

// Danfojs v1.0.0 and above
dates = new dfd.dateRange({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })

console.log(dates);

obj_data = {'A': dates,
            'B': ["bval1", "bval2", "bval3", "bval4"],
            'C': [10, 20, 30, 40],
            'D': [1.2, 3.45, 60.1, 45],
            'E': ["test", "train", "test", "train"]
            }

df = new dfd.DataFrame(obj_data)
df.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        dates = new dfd.dateRange({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })

        console.log(dates);

        obj_data = {'A': dates,
                    'B': ["bval1", "bval2", "bval3", "bval4"],
                    'C': [10, 20, 30, 40],
                    'D': [1.2, 3.45, 60.1, 45],
                    'E': ["test", "train", "test", "train"]
                    }

        df = new dfd.DataFrame(obj_data)
        df.print()

    </script>
</body>

</html>

//output in console
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ A                 │ B                 │ C                 │ D                 │ E                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ 1/1/2017, 1:0...  │ bval1             │ 10                │ 1.2               │ test              ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ 1/1/2018, 1:0...  │ bval2             │ 20                │ 3.45              │ train             ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ 1/1/2019, 1:0...  │ bval3             │ 30                │ 60.1              │ test              ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ 1/1/2020, 1:0...  │ bval4             │ 40                │ 45                │ train             ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

The columns of the resulting DataFrame have different dtypes.

df.ctypes.print()

//output
╔═══╤═════════╗
║ A │ string  ║
╟───┼─────────╢
║ B │ string  ║
╟───┼─────────╢
║ C │ int32   ║
╟───┼─────────╢
║ D │ float32 ║
╟───┼─────────╢
║ E │ string  ║
╚═══╧═════════╝

Creating a DataFrame by passing an array of arrays. Index and column labels are automatically generated for you.

const dfd = require("danfojs-node")

arr_data = [["bval1", 10, 1.2, "test"],
            ["bval2", 20, 3.45, "train"],
            ["bval3", 30, 60.1, "train"],
            ["bval4", 35, 3.2, "test"]]

df = new dfd.DataFrame(arr_data)
df.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        arr_data = [["bval1", 10, 1.2, "test"],
            ["bval2", 20, 3.45, "train"],
            ["bval3", 30, 60.1, "train"],
            ["bval4", 35, 3.2, "test"]]

        df = new dfd.DataFrame(arr_data)
        df.print()

    </script>
</body>

</html>

//output in console

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ 0                 │ 1                 │ 2                 │ 3                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ bval1             │ 10                │ 1.2               │ test              ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ bval2             │ 20                │ 3.45              │ train             ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ bval3             │ 30                │ 60.1              │ train             ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ bval4             │ 35                │ 3.2               │ test              ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Viewing data

Here is how to view the top and bottom rows of the frame above:

df.head(2).print()
df.tail(2).print()

//output from head
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ 0                 │ 1                 │ 2                 │ 3                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ bval1             │ 10                │ 1.2               │ test              ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ bval2             │ 20                │ 3.45              │ train             ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝


//output from tail

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ 0                 │ 1                 │ 2                 │ 3                 ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ bval3             │ 30                │ 60.1              │ train             ║
╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ bval4             │ 35                │ 3.2               │ test              ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Display the index, columns:

const dfd = require('danfojs-node')

let dates = new dfd.dateRange({
    start: "2017-01-01",
    end: "2020-01-01",
    period: 4,
    freq: "Y",
  });

  let obj_data = {
    A: dates,
    B: ["bval1", "bval2", "bval3", "bval4"],
    C: [10, 20, 30, 40],
    D: [1.2, 3.45, 60.1, 45],
    E: ["test", "train", "test", "train"],
  };

  let df = new dfd.DataFrame(obj_data);
  df.print();
  console.log(df.index);
  console.log(df.columns);

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

      let dates = new dfd.dateRange({
        start: "2017-01-01",
        end: "2020-01-01",
        period: 4,
        freq: "Y",
      });

      let obj_data = {
        A: dates,
        B: ["bval1", "bval2", "bval3", "bval4"],
        C: [10, 20, 30, 40],
        D: [1.2, 3.45, 60.1, 45],
        E: ["test", "train", "test", "train"],
      };

      let df = new dfd.DataFrame(obj_data);
      df.print();
      console.log(df.index);
      console.log(df.columns)
    </script>
</body>

</html>

//output

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ A                 │ B                 │ C                 │ D                 │ E                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0          │ 1/1/2017, 1:00:…  │ bval1             │ 10                │ 1.2               │ test              ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1          │ 1/1/2018, 1:00:…  │ bval2             │ 20                │ 3.45              │ train             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ 1/1/2019, 1:00:…  │ bval3             │ 30                │ 60.1              │ test              ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3          │ 1/1/2020, 1:00:…  │ bval4             │ 40                │ 45                │ train             ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

[ 0, 1, 2, 3 ]
[ 'A', 'B', 'C', 'D', 'E' ]

DataFrame.tensor returns a Tensorflow tensor representation of the underlying data. Note that Tensorflow tensors have one dtype for the entire array, while danfo DataFrames have one dtype per column.

For df, our DataFrame of all floating-point values, DataFrame.tensoris fast and doesn’t require copying data.

const dfd = require("danfojs-node")


j son_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 }]

let  df = new dfd.DataFrame(json_data)

console.log(df.tensor);
//or
df.tensor.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        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)

        console.log(df.tensor);
        //or
        df.tensor.print()

    </script>
</body>

</html>

//output

Tensor {
  kept: false,
  isDisposedInternal: false,
  shape: [ 4, 4 ],
  dtype: 'float32',
  size: 16,
  strides: [ 4 ],
  dataId: {},
  id: 0,
  rankType: '2'
}

Tensor
    [[0.4612, 4.2828302, -1.5089999, -1.1352  ],
     [0.5112, -0.22863 , -3.39059  , 1.1632   ],
     [0.6911, -0.82863 , -1.5059   , 2.1352   ],
     [0.4692, -1.28863 , 4.5058999 , 4.1631999]]

Note

DataFrame.tensor does not include the index or column labels in the output.

describe() shows a quick statistic summary of your data:

const dfd = require("danfojs-node")


let 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 }]

let df = new dfd.DataFrame(json_data)

df.describe().print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        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)

        df.describe().print()

    </script>
</body>

</html>

//output in console

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ A                 │ B                 │ C                 │ D                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ count      │ 4                 │ 4                 │ 4                 │ 4                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ mean       │ 0.533175          │ 0.4842349999999…  │ -0.474897500000…  │ 1.5816            ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ std        │ 0.1075428712963…  │ 2.5693167249095…  │ 3.4371471031498…  │ 2.2005448052698…  ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ min        │ 0.4612            │ -1.28863          │ -3.39059          │ -1.1352           ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ median     │ 0.4901999999999…  │ -0.528629999999…  │ -1.50745          │ 1.6492            ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ max        │ 0.6911            │ 4.28283           │ 4.5059            │ 4.1632            ║
╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
║ variance   │ 0.0115654691666…  │ 6.6013884328999…  │ 11.813980208691…  │ 4.84239744        ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Sorting by values (Defaults to ascending):

const dfd = require("danfojs")

let data = {"A": [-20, 30, 47.3, NaN],
             "B": [34, -4, 5, 6] ,
             "C": [20, 2, 3, 30] }


let df = new dfd.DataFrame(data)
df.sortValues("C", {inplace: true})
df.print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>

        let data = {"A": [-20, 30, 47.3, NaN],
             "B": [34, -4, 5, 6] ,
             "C": [20, 2, 3, 30] }


        let df = new dfd.DataFrame(data)
        df.sortValues("C", {inplace: true})
        df.print()

    </script>
</body>

</html>

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ A                 │ B                 │ C                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1          │ 30                │ -4                │ 2                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ 47.3              │ 5                 │ 3                 ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0          │ -20               │ 34                │ 20                ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 3          │ NaN               │ 6                 │ 30                ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

Selection

Getting

Selecting a single column, which yields a Series, equivalent to df.A:

const dfd = require("danfojs-node")


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)

df['A'].print()

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <!--danfojs CDN -->
<script src="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.min.js"></script>    <title>Document</title>
</head>

<body>

    <script>


        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)

        df['A'].print()

    </script>
</body>

</html>

//output
╔═══╤══════════════════════╗
║   │ A                    ║
╟───┼──────────────────────╢
║ 0 │ 0.4612               ║
╟───┼──────────────────────╢
║ 1 │ 0.5112               ║
╟───┼──────────────────────╢
║ 2 │ 0.6911               ║
╟───┼──────────────────────╢
║ 3 │ 0.4692               ║
╚═══╧══════════════════════╝

Selection by label

For getting a cross-section using a label:

const dfd = require("danfojs")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
            "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data, {index: ["a", "b", "c", "d"]})
df.print()

let sub_df = df.loc({rows: ["a", "c"]})
sub_df.print()

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ Name              │ Count             │ Price             ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ a │ Apples            │ 21                │ 200               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ b │ Mango             │ 5                 │ 300               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ c │ Banana            │ 30                │ 40                ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ d │ Pear              │ 10                │ 250               ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝


 Shape: (2,3) 

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ Name              │ Count             │ Price             ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ a │ Apples            │ 21                │ 200               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ c │ Banana            │ 30                │ 40                ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝

Selecting on a multi-axis by label:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
            "Count": [21, 5, 30, 10],
             "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)
df.print()

let sub_df = df.loc({ rows: [0,1], columns: ["Name", "Price"] })
sub_df.print()

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ Name              │ Count             │ Price             ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ Apples            │ 21                │ 200               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ Mango             │ 5                 │ 300               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ Banana            │ 30                │ 40                ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ Pear              │ 10                │ 250               ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝


 Shape: (2,2) 

╔═══╤═══════════════════╤═══════════════════╗
║   │ Name              │ Price             ║
╟───┼───────────────────┼───────────────────╢
║ 0 │ Apples            │ 200               ║
╟───┼───────────────────┼───────────────────╢
║ 1 │ Mango             │ 300               ║
╚═══╧═══════════════════╧═══════════════════╝

Showing label slicing:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
            "Count": [21, 5, 30, 10],
             "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)
df.print()

let sub_df = df.loc({ rows: ["0:2"], columns: ["Name", "Price"] })
sub_df.print()

//before slicing
╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ Name              │ Count             │ Price             ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 0 │ Apples            │ 21                │ 200               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ Mango             │ 5                 │ 300               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 2 │ Banana            │ 30                │ 40                ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ Pear              │ 10                │ 250               ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝

//after slicing
 

╔════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Price             ║
╟────────────┼───────────────────┼───────────────────╢
║ 0          │ Apples            │ 200               ║
╟────────────┼───────────────────┼───────────────────╢
║ 1          │ Mango             │ 300               ║
╚════════════╧═══════════════════╧═══════════════════╝

Selection by position

Select via the position of the passed integers:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
           "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({rows: [1,3]})
sub_df.print()

╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
║   │ Name              │ Count             │ Price             ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 1 │ Mango             │ 5                 │ 300               ║
╟───┼───────────────────┼───────────────────┼───────────────────╢
║ 3 │ Pear              │ 10                │ 250               ║
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝

By integer slices:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
           "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({rows: ["1:3"]})
sub_df.print()

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 1          │ Mango             │ 5                 │ 300               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ Banana            │ 30                │ 40                ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

By lists of integer position locations:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
           "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({rows: [1,3], columns: [0,2]})
sub_df.print()

╔═══╤═══════════════════╤═══════════════════╗
║   │ Name              │ Price             ║
╟───┼───────────────────┼───────────────────╢
║ 1 │ Mango             │ 300               ║
╟───┼───────────────────┼───────────────────╢
║ 3 │ Pear              │ 250               ║
╚═══╧═══════════════════╧═══════════════════╝

For slicing rows explicitly:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
           "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({rows: ["2:3"], columns: [":"]})
sub_df.print()

╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ Banana            │ 30                │ 40                ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

For slicing columns explicitly:

const dfd = require("danfojs-node")

let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
           "Count": [21, 5, 30, 10] ,
           "Price": [200, 300, 40, 250] }

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({rows: [":"], columns: ["1:2"]})
sub_df.print()

╔════════════╤═══════════════════╗
║            │ Count             ║
╟────────────┼───────────────────╢
║ 0          │ 21                ║
╟────────────┼───────────────────╢
║ 1          │ 5                 ║
╟────────────┼───────────────────╢
║ 2          │ 30                ║
╟────────────┼───────────────────╢
║ 3          │ 10                ║
╚════════════╧═══════════════════╝

Selection with Boolean Mask

You can select subsections from a DataFrame by a booelan condition mask. E.g. In the following code, we select and return only rows where the column Count is greater than 10.

let data = {
    "Name": ["Apples", "Mango", "Banana", "Pear"],
    "Count": [21, 5, 30, 10],
    "Price": [200, 300, 40, 250]
}

let df = new dfd.DataFrame(data)

let sub_df = df.iloc({ rows: df["Count"].gt(10) })
sub_df.print()

//output
╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
║            │ Name              │ Count             │ Price             ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 0          │ Apples            │ 21                │ 200               ║
╟────────────┼───────────────────┼───────────────────┼───────────────────╢
║ 2          │ Banana            │ 30                │ 40                ║
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝

A Boolean mask for filtering also works for multiple conditions using and & or functions. E.g, In the following code, we select and return only rows where the column Count is greater than 10 and column Name is equal to Apples.

let sub_df = df.iloc({
    rows: df["Count"].gt(10).and(df["Name"].eq("Apples")),
    columns: [0]
})
sub_df.print()

//output
╔════════════╤═══════════════════╗
║            │ Name              ║
╟────────────┼───────────────────╢
║ 0          │ Apples            ║
╚════════════╧═══════════════════╝

Boolean Querying/Filtering

The best way to query data is to use a boolean mask just as we demonstrated above with iloc and loc. For example, in the following code, we use a condition parameter to query the DataFrame:

let data = {
    "A": ["Ng", "Yu", "Mo", "Ng"],