devamitranjan

/* Before async / await */

function doWhenDataReceived(value){
  returnNextElement.next(value);
}

function *createFlow(){
  const data = yield fetch("/post/1");
  console.log(data);
}

devamitranjan

console.table(
  [...document.images].map((img) => {
    const entry = performance.getEntriesByName(img.currentSrc)[0];
    const bytes = (entry?.encodedBodySize * 8);
    const pixels = (img.width * img.height);
    return { src: img.currentSrc, bytes, pixels, entropy: (bytes / pixels) };
  })
)

devamitranjan

import js from "@eslint/js";
import globals from "globals";
import prettier from "eslint-config-prettier";

/** @type {import('eslint').Linter.Config[]} */
export default [
  js.configs.recommended,
  {
    files: ["**/*.js"],
    languageOptions: {

devamitranjan

{
  "name": "my-package",
  "private": true,
  "version": "0.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "start": "vite",
    "start:mock": "VITE_MOCK_SERVER=true vite",
    "build": "tsc && vite build",

devamitranjan

const getRandomNumberOfTodos = () => {
  const todos = [];
  for (let i = 0; i < Math.floor(Math.random() * 10); i++) {
    todos.push({
      id: i,
      title: `My Todo - ${i}`,
      description: `This is the description of the todo -${i}`
    });
  }

devamitranjan

import os
import googleapiclient.discovery

# Fetching the data from Youtube API
def google_api(key,vidId):
    os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"

    api_service_name = "youtube"
    api_version = "v3"
    DEVELOPER_KEY = key

devamitranjan

import matplotlib.pyplot as plt
from sklearn.feature_extraction import text 
from wordcloud import WordCloud

def generate_word_clouds(df):
  allWords = ' '.join([twts for twts in df['Comments']])
  wordCloud = WordCloud(stopwords = text.ENGLISH_STOP_WORDS ,width=1000, height=600, random_state=21, max_font_size=110).generate(allWords)
  plt.imshow(wordCloud, interpolation="bilinear")
  plt.axis('off')
  plt.show()

devamitranjan

def print_neutral_comments():
  print('Printing neutral comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Neutral'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_neutral_comments()

devamitranjan

def print_negative_comments():
  print('Printing negative comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Negative'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_negative_comments()

devamitranjan

def print_positive_comments():
  print('Printing positive comments:\n')
  sortedDF = df.sort_values(by=['Polarity']) 
  for i in range(0, sortedDF.shape[0] ):
    if( sortedDF['Analysis'][i] == 'Positive'):
      print(str(i+1) + '> '+ sortedDF['Comments'][i])
      print()
print_positive_comments()