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

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:
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npm install danfojs-node
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​
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or
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​
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yarn add danfojs-node
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For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the danfojs version:
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npm install danfojs
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​
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or
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​
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yarn add danfojs
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For use directly in HTML files, you can add the latest script tag from JsDelivr:
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script>
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To play with Danfo.js in a Notebook-like environment, see Dnotebooks here​

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 both browser environments and Node.js environments. Most functions except plotting which require a DOM work the same way in both environments.
Node
Browser
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const dfd = require("danfojs-node")
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<!DOCTYPE html>
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<html lang="en">
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​
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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/[email protected]/lib/bundle.min.js"></script>
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</head>
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​
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<body>
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​
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<script>
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​
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//danfo is exposed on dfd namespace
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s = new dfd.Series([1,2,3,4,5])
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​
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</script>
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​
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</body>
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​
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</html>
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Creating a DataFrame/Series

You can create a Series by passing a list of values, letting Danfo.js create a default integer index:
Node
Browser
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s = new dfd.Series([1, 3, 5, undefined, 6, 8])
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s.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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s = new dfd.Series([1, 3, 5, undefined, 6, 8])
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s.print()
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​
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</script>
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​
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</body>
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​
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</html>
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//output
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╔═══╤══════════════════════╗
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║ │ 0 ║
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╟───┼──────────────────────╢
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║ 0 │ 1 ║
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╟───┼──────────────────────╢
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║ 1 │ 3 ║
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╟───┼──────────────────────╢
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║ 2 │ 5 ║
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╟───┼──────────────────────╢
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║ 3 │ undefined ║
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╟───┼──────────────────────╢
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║ 4 │ 6 ║
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╟───┼──────────────────────╢
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║ 5 │ 8 ║
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╚═══╧══════════════════════╝
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Creating a Series from a tensor
Node
Browser
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const dfd = require("danfojs-node")
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const tf = require("@tensorflow/tfjs-node")
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​
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​
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let tensor_arr = tf.tensor([12,34,56,2])
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let s = new dfd.Series(tensor_arr)
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s.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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/[email protected]/lib/bundle.min.js"></script>
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​
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<title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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const tf = dfd.tf //get tensorflow lib from danfo
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let tensor_arr = tf.tensor([12,34,56,2])
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let s = new dfd.Series(tensor_arr)
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s.print()
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​
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</script>
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​
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</body>
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​
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</html>
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╔═══╤════╗
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║ 0 │ 12 ║
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╟───┼────╢
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║ 1 │ 34 ║
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╟───┼────╢
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║ 2 │ 56 ║
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╟───┼────╢
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║ 3 │ 2 ║
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╚═══╧════╝
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Creating a DataFrame by passing a JSON object:
Node
Browser
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const dfd = require("danfojs-node")
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​
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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df.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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df.print()
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​
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</script>
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</body>
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​
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</html>
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Creating a DataFrame from a 2D tensor
Node
Browser
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const dfd = require("danfojs-node")
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const tf = require("@tensorflow/tfjs-node")
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​
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​
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let tensor_arr = tf.tensor2d([[12, 34, 2.2, 2], [30, 30, 2.1, 7]])
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let df = new dfd.DataFrame(tensor_arr, {columns: ["A", "B", "C", "D"]})
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df.print()
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df.ctypes.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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df.print()
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​
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</script>
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</body>
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​
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</html>
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ A │ B │ C │ D ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 0 │ 12 │ 34 │ 2.20000004768... │ 2 ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 1 │ 30 │ 30 │ 2.09999990463... │ 7 ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
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​
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╔═══╤══════════════════════╗
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║ │ 0 ║
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╟───┼──────────────────────╢
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║ A │ int32 ║
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╟───┼──────────────────────╢
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║ B │ int32 ║
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╟───┼──────────────────────╢
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║ C │ float32 ║
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╟───┼──────────────────────╢
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║ D │ int32 ║
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╚═══╧══════════════════════╝
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Creating a DataFrame by passing a dictionary of objects with the same length
Node
Browser
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const dfd = require("danfojs-node")
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​
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​
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dates = new dfd.date_range({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })
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​
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console.log(dates);
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​
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obj_data = {'A': dates,
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'B': ["bval1", "bval2", "bval3", "bval4"],
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'C': [10, 20, 30, 40],
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'D': [1.2, 3.45, 60.1, 45],
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'E': ["test", "train", "test", "train"]
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}
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​
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df = new dfd.DataFrame(obj_data)
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df.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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dates = new dfd.date_range({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })
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​
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console.log(dates);
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​
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obj_data = {'A': dates,
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'B': ["bval1", "bval2", "bval3", "bval4"],
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'C': [10, 20, 30, 40],
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'D': [1.2, 3.45, 60.1, 45],
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'E': ["test", "train", "test", "train"]
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}
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​
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df = new dfd.DataFrame(obj_data)
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df.print()
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​
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</script>
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</body>
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​
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</html>
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//output in console
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ A │ B │ C │ D │ E ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 0 │ 1/1/2017, 1:0... │ bval1 │ 10 │ 1.2 │ test ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 1 │ 1/1/2018, 1:0... │ bval2 │ 20 │ 3.45 │ train ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 2 │ 1/1/2019, 1:0... │ bval3 │ 30 │ 60.1 │ test ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 3 │ 1/1/2020, 1:0... │ bval4 │ 40 │ 45 │ train ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
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The columns of the resulting DataFrame have different dtypes.
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df.ctypes.print()
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//output
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╔═══╤═════════╗
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║ A │ string ║
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╟───┼─────────╢
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║ B │ string ║
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╟───┼─────────╢
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║ C │ int32 ║
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╟───┼─────────╢
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║ D │ float32 ║
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╟───┼─────────╢
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║ E │ string ║
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╚═══╧═════════╝
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Creating a DataFrame by passing an array of arrays. Index and column labels are automatically generated for you.
Node
Browser
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const dfd = require("danfojs-node")
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​
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arr_data = [["bval1", 10, 1.2, "test"],
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["bval2", 20, 3.45, "train"],
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["bval3", 30, 60.1, "train"],
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["bval4", 35, 3.2, "test"]]
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​
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df = new dfd.DataFrame(arr_data)
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df.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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arr_data = [["bval1", 10, 1.2, "test"],
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["bval2", 20, 3.45, "train"],
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["bval3", 30, 60.1, "train"],
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["bval4", 35, 3.2, "test"]]
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​
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df = new dfd.DataFrame(arr_data)
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df.print()
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​
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</script>
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</body>
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​
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</html>
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//output in console
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​
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ 0 │ 1 │ 2 │ 3 ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 0 │ bval1 │ 10 │ 1.2 │ test ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 1 │ bval2 │ 20 │ 3.45 │ train ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 2 │ bval3 │ 30 │ 60.1 │ train ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 3 │ bval4 │ 35 │ 3.2 │ test ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
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Viewing data

Here is how to view the top and bottom rows of the frame above:
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df.head(2).print()
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df.tail(2).print()
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//output from head
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ 0 │ 1 │ 2 │ 3 ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 0 │ bval1 │ 10 │ 1.2 │ test ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 1 │ bval2 │ 20 │ 3.45 │ train ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
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​
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​
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//output from tail
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​
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ 0 │ 1 │ 2 │ 3 ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 2 │ bval3 │ 30 │ 60.1 │ train ║
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╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 3 │ bval4 │ 35 │ 3.2 │ test ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
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Display the index, columns:
JavaScript
Browser
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const dfd = require("danfojs-node")
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​
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dates = new dfd.date_range({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })
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​
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​
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obj_data = {'A': dates,
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'B': ["bval1", "bval2", "bval3", "bval4"],
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'C': [10, 20, 30, 40],
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'D': [1.2, 3.45, 60.1, 45],
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'E': ["test", "train", "test", "train"]
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}
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​
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df = new dfd.DataFrame(obj_data)
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​
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console.log(df.index);
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console.log(df.columns);
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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dates = new dfd.date_range({ start: '2017-01-01', end: "2020-01-01", period: 4, freq: "Y" })
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​
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​
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obj_data = {'A': dates,
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'B': ["bval1", "bval2", "bval3", "bval4"],
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'C': [10, 20, 30, 40],
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'D': [1.2, 3.45, 60.1, 45],
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'E': ["test", "train", "test", "train"]
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}
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​
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df = new dfd.DataFrame(obj_data)
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​
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console.log(df.index);
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console.log(df.columns);
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​
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</script>
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</body>
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​
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</html>
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//output
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[ 0, 1, 2, 3 ]
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[ 'A', 'B', 'C', 'D', 'E' ]
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​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.
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Browser
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const dfd = require("danfojs-node")
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​
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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console.log(df.tensor);
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//or
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df.tensor.print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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console.log(df.tensor);
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//or
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df.tensor.print()
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​
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</script>
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</body>
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​
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</html>
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//output
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​
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Tensor {
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kept: false,
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isDisposedInternal: false,
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shape: [ 4, 4 ],
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dtype: 'float32',
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size: 16,
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strides: [ 4 ],
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dataId: {},
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id: 0,
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rankType: '2'
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}
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​
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Tensor
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[[0.4612, 4.2828302, -1.5089999, -1.1352 ],
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[0.5112, -0.22863 , -3.39059 , 1.1632 ],
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[0.6911, -0.82863 , -1.5059 , 2.1352 ],
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[0.4692, -1.28863 , 4.5058999 , 4.1631999]]
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Note
​DataFrame.tensor does not include the index or column labels in the output.
​describe() shows a quick statistic summary of your data:
Node
Browser
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const dfd = require("danfojs-node")
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​
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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df.describe().print()
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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df.describe().print()
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​
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</script>
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</body>
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​
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</html>
Copied!
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//output in console
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​
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╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ A │ B │ C │ D ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ count │ 4 │ 4 │ 4 │ 4 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ mean │ 0.533175 │ 0.4842349999999… │ -0.474897500000… │ 1.5816 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ std │ 0.1075428712963… │ 2.5693167249095… │ 3.4371471031498… │ 2.2005448052698… ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ min │ 0.4612 │ -1.28863 │ -3.39059 │ -1.1352 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ median │ 0.4901999999999… │ -0.528629999999… │ -1.50745 │ 1.6492 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ max │ 0.6911 │ 4.28283 │ 4.5059 │ 4.1632 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ variance │ 0.0115654691666… │ 6.6013884328999… │ 11.813980208691… │ 4.84239744 ║
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╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
Copied!
Sorting by values (Defaults to ascending):
Node
Browser
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const dfd = require("danfojs")
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​
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let data = {"A": [-20, 30, 47.3, NaN],
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"B": [34, -4, 5, 6] ,
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"C": [20, 2, 3, 30] }
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​
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​
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let df = new dfd.DataFrame(data)
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df.sort_values({by: "C", inplace: true})
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df.print()
Copied!
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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let data = {"A": [-20, 30, 47.3, NaN],
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"B": [34, -4, 5, 6] ,
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"C": [20, 2, 3, 30] }
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​
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​
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let df = new dfd.DataFrame(data)
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df.sort_values({by: "C", inplace: true})
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df.print()
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​
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</script>
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</body>
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​
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</html>
Copied!
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╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ A │ B │ C ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 1 │ 30 │ -4 │ 2 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 2 │ 47.3 │ 5 │ 3 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 0 │ -20 │ 34 │ 20 ║
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╟────────────┼───────────────────┼───────────────────┼───────────────────╢
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║ 3 │ NaN │ 6 │ 30 ║
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╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
Copied!

Selection

Getting

Selecting a single column, which yields a Series, equivalent to df.A:
Node
Browser
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const dfd = require("danfojs-node")
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​
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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df['A'].print()
Copied!
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<!DOCTYPE html>
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<html lang="en">
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​
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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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<!--danfojs CDN -->
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<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.min.js"></script> <title>Document</title>
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</head>
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​
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<body>
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​
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<script>
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​
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​
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json_data = [{ A: 0.4612, B: 4.28283, C: -1.509, D: -1.1352 },
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{ A: 0.5112, B: -0.22863, C: -3.39059, D: 1.1632 },
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{ A: 0.6911, B: -0.82863, C: -1.5059, D: 2.1352 },
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{ A: 0.4692, B: -1.28863, C: 4.5059, D: 4.1632 }]
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​
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df = new dfd.DataFrame(json_data)
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​
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df['A'].print()
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​
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</script>
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</body>
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​
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</html>
Copied!
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//output
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╔═══╤══════════════════════╗
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║ │ A ║
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╟───┼──────────────────────╢
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║ 0 │ 0.4612 ║
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╟───┼──────────────────────╢
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║ 1 │ 0.5112 ║
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╟───┼──────────────────────╢
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║ 2 │ 0.6911 ║
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╟───┼──────────────────────╢
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║ 3 │ 0.4692 ║
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╚═══╧══════════════════════╝
Copied!

Selection by label

For getting a cross-section using a label:
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const dfd = require("danfojs")
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​
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let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
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"Count": [21, 5, 30, 10] ,
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"Price": [200, 300, 40, 250] }
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​
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let df = new dfd.DataFrame(data, {index: ["a", "b", "c", "d"]})
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df.print()
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​
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let sub_df = df.loc({rows: ["a", "c"]})
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sub_df.print()
Copied!
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ Name │ Count │ Price ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ a │ Apples │ 21 │ 200 ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ b │ Mango │ 5 │ 300 ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ c │ Banana │ 30 │ 40 ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ d │ Pear │ 10 │ 250 ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝
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​
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​
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Shape: (2,3)
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​
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ Name │ Count │ Price ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ a │ Apples │ 21 │ 200 ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢
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║ c │ Banana │ 30 │ 40 ║
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╚═══╧═══════════════════╧═══════════════════╧═══════════════════╝
Copied!
Selecting on a multi-axis by label:
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const dfd = require("danfojs-node")
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​
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let data = { "Name": ["Apples", "Mango", "Banana", "Pear"] ,
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"Count": [21, 5, 30, 10],
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"Price": [200, 300, 40, 250] }
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​
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let df = new dfd.DataFrame(data)
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df.print()
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​
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let sub_df = df.loc({ rows: [0,1], columns: ["Name", "Price"] })
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sub_df.print()
Copied!
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╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
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║ │ Name │ Count │ Price ║
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╟───┼───────────────────┼───────────────────┼───────────────────╢