Siddhant formatkaka

Frontend Development

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	// modules are defined as an array
	// [ module function, map of requires ]
	//
	// map of requires is short require name -> numeric require
	//
	// anything defined in a previous bundle is accessed via the
	// orig method which is the require for previous bundles
	parcelRequire = (function (modules, cache, entry, globalName) {
	// Save the require from previous bundle to this closure if any
	var previousRequire = typeof parcelRequire === 'function' && parcelRequire;

	const nlf = require('nlf');
	const fs = require('fs');
	// const stream = fs.createWriteStream('my_file.txt');
	// to only include production dependencies

	const filename = process.env.PUBLIC_DIR + 'licenses.json';
	fs.writeFile(filename, '', () => {
	console.log('done');
	});

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

	<head>
	<link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-social/5.1.1/bootstrap-social.min.css" />
	<link rel="stylesheet" type="text/css" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" />
	<meta charset="utf-8">
	</head>

	<body>

	// if both values in the < comparison are strings,
	// the comparison is made lexicographically (aka alphabetically like
	// a dictionary). But if one or both is not a string,
	// then both values are coerced to be numbers, and a typical numeric
	// comparison occurs.

	var a = 41;
	var b = "42";
	var c = "43";
	a < b; // true

	////////////// 1 ////////////

	//If using `parseInt` always specify the radix.

	//Example - If we want to convert some string to number (assuming we aren't sure about what the string formaat can be.)

	//If we want to convert it to decimal (radix - 10), we generally use

	var mystring = '10';
	var myinvalidstring = '0xff';

	from sklearn.datasets import load_iris
	from sklearn.ensemble import RandomForestClassifier
	import pandas as pd
	import numpy as np

	iris = load_iris()
	df = pd.DataFrame(iris.data, columns=iris.feature_names)
	df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
	df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
	df.head()