Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "setting up the browser session and importing the needed packages/libraries/modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver as wd\n",
    "\n",
    "driver=wd.Chrome(\"C:/Users/preet/Downloads/chromedriver\")\n",
    "driver.get(\"https://www.instagram.com/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait as wdw\n",
    "from selenium.webdriver.support import expected_conditions as ec\n",
    "\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# defining wait object\n",
    "\n",
    "wait=wdw(driver, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 Analyzing the habits of the food bloggers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "logging in using facebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clicking on login using facebook button\n",
    "\n",
    "login_with_facebook=driver.find_element_by_class_name(\"KPnG0\")\n",
    "login_with_facebook.click()\n",
    "\n",
    "# waiting till the inputs become visible\n",
    "\n",
    "wait.until(ec.visibility_of_element_located((By.ID, \"email\")))\n",
    "wait.until(ec.visibility_of_element_located((By.ID, \"pass\")))\n",
    "\n",
    "# filling the username and password\n",
    "\n",
    "phone_input=driver.find_element_by_id(\"email\")\n",
    "password_input=driver.find_element_by_id(\"pass\")\n",
    "\n",
    "phone_input.send_keys(\"SAMPLE USERNAME\")\n",
    "password_input.send_keys(\"SAMPLE PASSWORD\")\n",
    "\n",
    "# clicking on login button\n",
    "\n",
    "login_button=driver.find_element_by_id(\"loginbutton\")\n",
    "login_button.click()\n",
    "\n",
    "# waiting till notification message is shown\n",
    "\n",
    "wait.until(ec.visibility_of_element_located((By.CSS_SELECTOR, \"button.aOOlW.HoLwm\")))\n",
    "\n",
    "# clicking on not now button\n",
    "\n",
    "noty_button=driver.find_element_by_css_selector(\"button.aOOlW.HoLwm\")\n",
    "noty_button.click()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "searching for food and fetching the names of the first 10 food handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# searching for food\n",
    "\n",
    "search_input=driver.find_element_by_class_name(\"XTCLo\")\n",
    "search_input.send_keys(\"food\")\n",
    "\n",
    "# waiting till the results are visible\n",
    "\n",
    "wait.until(ec.visibility_of_all_elements_located((By.CSS_SELECTOR, \"._7UhW9.xLCgt.qyrsm.KV-D4.uL8Hv         \")))\n",
    "\n",
    "# fetching the first 10 food handles\n",
    "\n",
    "food_handles=driver.find_elements_by_css_selector(\"._7UhW9.xLCgt.qyrsm.KV-D4.uL8Hv         \")[:10]\n",
    "\n",
    "# clearing the search input\n",
    "\n",
    "search_input.clear()\n",
    "\n",
    "# getting the food handle names from the web elements\n",
    "\n",
    "for i in range(len(food_handles)):\n",
    "    food_handles[i]=food_handles[i].get_attribute(\"innerHTML\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the followers of each of the 10 food handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "followers=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterating on the first 10 food handles, shown when we search for food\n",
    "\n",
    "for i in range(10):\n",
    "#     waiting till the search input is visible\n",
    "\n",
    "    wait.until(ec.visibility_of_element_located((By.CLASS_NAME, \"XTCLo\")))\n",
    "\n",
    "#     searching for food\n",
    "    \n",
    "    search_input=driver.find_element_by_class_name(\"XTCLo\")\n",
    "    search_input.send_keys(\"food\")       \n",
    "    \n",
    "#     waiting till the (i+1)th food handle is visible\n",
    "    \n",
    "    wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@role='none'][position()=\"+str(i+1)+\"]\")))  \n",
    "    \n",
    "#     clicking on the (i+1)th food handle\n",
    "    \n",
    "    food_handle=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]\")\n",
    "    food_handle.click()\n",
    "    \n",
    "#     waiting till the followers link is loaded\n",
    "    \n",
    "    wait.until(ec.visibility_of_all_elements_located((By.XPATH, \"//li[@class='Y8-fY ']/a/span\")))\n",
    "    \n",
    "#     fetching the followers and appending to the followers array\n",
    "    \n",
    "    followers_count=driver.find_element_by_xpath(\"//li[@class='Y8-fY ']/a/span\").get_attribute(\"innerHTML\")   \n",
    "    followers.append(followers_count)\n",
    "    \n",
    "#     going back to visit other food handles\n",
    "    \n",
    "    driver.back()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting the elements of followers array to integers\n",
    "\n",
    "for i in range(len(followers)):\n",
    "    length=len(followers[i])\n",
    "    \n",
    "    lastChar=followers[i][length-1]\n",
    "    if lastChar=='k' or lastChar==\"m\":\n",
    "        if lastChar==\"k\":\n",
    "            mFac=1000\n",
    "        else:\n",
    "            mFac=1000000\n",
    "            \n",
    "        followers[i]=int(float(followers[i][:(length-1)])*mFac)\n",
    "    else:\n",
    "        followers[i]=int(followers[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing the top 5 food handles with their follower count(decreasing order of count of followers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_followers=np.array(followers)\n",
    "sort_order=np_followers.argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_top_5_followers=np_followers[sort_order][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_handles=[]\n",
    "\n",
    "for i in range(5):\n",
    "    index=sort_order[i]\n",
    "    \n",
    "    top_5_handles.append(food_handles[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yourfoodlab 1600000\n",
      "foodie_incarnate 285000\n",
      "delhifoodwalks 139000\n",
      "food 115000\n",
      "sailusfood 66400\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(top_5_handles)):\n",
    "    print(top_5_handles[i], np_top_5_followers[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the number of posts these handles have done in the past 3 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_of_posts=[0 for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check to make sure whether we have to search for food again or not\n",
    "\n",
    "new_page_was_opened=True\n",
    "\n",
    "# iterating on the first 10 food handles\n",
    "\n",
    "for i in range(10):    \n",
    "#     waiting till the search input is visible\n",
    "\n",
    "    wait.until(ec.visibility_of_element_located((By.CLASS_NAME, \"XTCLo\")))\n",
    "    \n",
    "#     searching for food if a new page was opened\n",
    "    \n",
    "    if new_page_was_opened:\n",
    "        search_input=driver.find_element_by_class_name(\"XTCLo\")\n",
    "        search_input.send_keys(\"food\")       \n",
    "        \n",
    "#     waiting till the food handle to be clicked is visible\n",
    "        \n",
    "    wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@role='none'][position()=\"+str(i+1)+\"]\")))    \n",
    "    \n",
    "#     fetching the food handle and its name\n",
    "    \n",
    "    food_handle=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]\")\n",
    "    food_handle_name=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]/a/div/div[position()=2]/div/div/div/div\").get_attribute(\"innerHTML\")        \n",
    "    \n",
    "#     exploring the food handle's page only if it is in the top 5 handles\n",
    "    \n",
    "    if not food_handle_name in top_5_handles:\n",
    "        new_page_was_opened=False\n",
    "        continue\n",
    "    else:\n",
    "        new_page_was_opened=True    \n",
    "    \n",
    "    food_handle.click()    \n",
    "    \n",
    "#     finding the count of posts the handle has done in the past 3 days and setting it at the respective index(wrt the top 5\n",
    "#     handle ordering) of the count_of_posts array\n",
    "\n",
    "    count=0\n",
    "    index=top_5_handles.index(food_handle_name)\n",
    "    \n",
    "#     waiting for 3 seconds(to make sure the page has loaded properly and the content has changed)\n",
    "    \n",
    "    time.sleep(3)\n",
    "    \n",
    "    i=0\n",
    "    \n",
    "    while True:    \n",
    "        pos1=str((i//3)+1)   \n",
    "        \n",
    "        pos2=str((i%3)+1)\n",
    "        \n",
    "#         waiting till the post to be selected is visible\n",
    "        \n",
    "        wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")))\n",
    "    \n",
    "#         clicking on the post\n",
    "    \n",
    "        post=driver.find_element_by_xpath(\"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")\n",
    "        post.click()\n",
    "        \n",
    "#         waiting till the time is visible\n",
    "        \n",
    "        wait.until(ec.visibility_of_element_located((By.CSS_SELECTOR, \"time._1o9PC.Nzb55\")))                        \n",
    "        \n",
    "#         getting the post time and current time\n",
    "\n",
    "        time_element=driver.find_element_by_css_selector(\"time._1o9PC.Nzb55\")        \n",
    "        \n",
    "        post_time=time_element.get_attribute(\"datetime\")\n",
    "        \n",
    "        current_time=driver.execute_script(\"return new Date().getTime();\")\n",
    "        post_time=driver.execute_script(\"return new Date(arguments[0]).getTime();\", post_time)\n",
    "        \n",
    "#         getting the minutes ago value when the post was posted\n",
    "                \n",
    "        minute_diff=driver.execute_script(\"return (arguments[0]-arguments[1])\", current_time, post_time)\n",
    "        minute_diff/=60000                             \n",
    "        \n",
    "#         closing the post\n",
    "        \n",
    "        close_button=driver.find_element_by_css_selector(\"div.Igw0E.IwRSH.eGOV_._4EzTm.BI4qX.qJPeX.fm1AK.TxciK.yiMZG button.wpO6b  \")\n",
    "        close_button.click()\n",
    "        \n",
    "#         increasing the count if the post was posted 3 days ago or after it or we break \n",
    "        \n",
    "        if minute_diff<=(3*24*60):\n",
    "            count+=1    \n",
    "        else:\n",
    "            break   \n",
    "            \n",
    "        i+=1\n",
    "        \n",
    "#     setting the count_of_posts value for particular index\n",
    "        \n",
    "    count_of_posts[index]=count\n",
    "    \n",
    "#     going back to visit other food handles\n",
    "    \n",
    "    driver.back()\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing the number of posts, each of the top 5 handles have done in the past 3 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yourfoodlab 3\n",
      "foodie_incarnate 5\n",
      "delhifoodwalks 3\n",
      "food 4\n",
      "sailusfood 0\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(count_of_posts)):\n",
    "    print(top_5_handles[i], count_of_posts[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotting a bar graph for better visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Posts done by top 5 food handles in past 3 days\")\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.xlabel(\"Food handle\")\n",
    "plt.ylabel(\"Number of posts in past 3 days\")\n",
    "\n",
    "plt.bar(top_5_handles, count_of_posts, width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 Getting list of hashtags the friend should use in his posts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the hashtags used and their count for each of the top 5 food handles from their first 10 posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "hashtags=[{} for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visiting the page of each of the top 5 food handles(STEPS EXPLAINED ABOVE) and fetching the hashtags used and their\n",
    "# frequency for each of the top 5 food handles\n",
    "\n",
    "new_page_was_opened=True\n",
    "\n",
    "for i in range(10):    \n",
    "    wait.until(ec.visibility_of_element_located((By.CLASS_NAME, \"XTCLo\")))\n",
    "    \n",
    "    if new_page_was_opened:\n",
    "        search_input=driver.find_element_by_class_name(\"XTCLo\")\n",
    "        search_input.send_keys(\"food\")       \n",
    "        \n",
    "        \n",
    "    wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@role='none'][position()=\"+str(i+1)+\"]\")))    \n",
    "    \n",
    "    food_handle=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]\")\n",
    "    food_handle_name=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]/a/div/div[position()=2]/div/div/div/div\").get_attribute(\"innerHTML\")        \n",
    "    \n",
    "    if not food_handle_name in top_5_handles:\n",
    "        new_page_was_opened=False\n",
    "        continue\n",
    "    else:\n",
    "        new_page_was_opened=True\n",
    "    \n",
    "    food_handle.click()    \n",
    "\n",
    "    count=0\n",
    "    index=top_5_handles.index(food_handle_name)\n",
    "    \n",
    "    time.sleep(3)\n",
    "    \n",
    "    for i in range(10):    \n",
    "        pos1=str((i//3)+1)   \n",
    "        \n",
    "        pos2=str((i%3)+1)\n",
    "        \n",
    "        wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")))\n",
    "    \n",
    "        post=driver.find_element_by_xpath(\"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")\n",
    "        post.click()\n",
    "        \n",
    "#         enclosing the statement in try-except as the post may not have any hashtags\n",
    "        \n",
    "        try:\n",
    "            wait.until(ec.visibility_of_elements_located((By.CSS_SELECTOR, \"a.xil3i\")))               \n",
    "        except:\n",
    "            pass        \n",
    "        \n",
    "#         fetching all the hashtags of the post\n",
    "        \n",
    "        try:\n",
    "            post_hashtags=driver.find_elements_by_css_selector(\"a.xil3i\")\n",
    "                        \n",
    "#             updating the hashtags array\n",
    "            \n",
    "            for post_hashtag in post_hashtags:                \n",
    "                hashtag_content=post_hashtag.get_attribute(\"innerHTML\")[1:]\n",
    "                \n",
    "                hashtags[index][hashtag_content]=(hashtags[index].get(hashtag_content, 0)+1)\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        wait.until(ec.visibility_of_element_located((By.CSS_SELECTOR, \"div.Igw0E.IwRSH.eGOV_._4EzTm.BI4qX.qJPeX.fm1AK.TxciK.yiMZG button.wpO6b  \")))\n",
    "        \n",
    "        close_button=driver.find_element_by_css_selector(\"div.Igw0E.IwRSH.eGOV_._4EzTm.BI4qX.qJPeX.fm1AK.TxciK.yiMZG button.wpO6b  \")\n",
    "        close_button.click()\n",
    "    \n",
    "    driver.back()\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing the hashtags used by each of the top 5 food handles and their count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yourfoodlab :\n",
      "\n",
      "recipe 4\n",
      "foodiesofinstagram 4\n",
      "kathal 1\n",
      "kathalrecipe 1\n",
      "foodporn 7\n",
      "foodies 1\n",
      "recipeoftheday 4\n",
      "foodstylist 4\n",
      "foodlover 5\n",
      "foodpics 5\n",
      "indianfood 1\n",
      "indiaclicks 1\n",
      "yourfoodlab 5\n",
      "sanjyotkeer 7\n",
      "indiancuisine 4\n",
      "streetfood 4\n",
      "dahivada 2\n",
      "yummyfood 2\n",
      "DigSwirlSpread 1\n",
      "EpigamiaGheeSpread 1\n",
      "thandai 2\n",
      "thandairecipe 2\n",
      "holifestival 2\n",
      "holi 2\n",
      "holicelebration 2\n",
      "chocolate 2\n",
      "mango 2\n",
      "mangoseason 2\n",
      "summerseason 2\n",
      "recipevideo 3\n",
      "gheeroast 2\n",
      "foodphotography 3\n",
      "foodstagram 3\n",
      "streetfoodlover 2\n",
      "egg 1\n",
      "eggcurry 1\n",
      "eggs 1\n",
      "egglover 1\n",
      "goanrecipes 1\n",
      "goanfood 1\n",
      "burger 1\n",
      "burgerlove 1\n",
      "burgertime 1\n",
      "burgerlover 1\n",
      "vegburger 1\n",
      "vegrecipesofindia 1\n",
      "vegetarian 1\n",
      "rajma 1\n",
      "burgers 1\n",
      "\n",
      "foodie_incarnate :\n",
      "\n",
      "foodieincarnate 10\n",
      "reelitfeelit 2\n",
      "reelkarofeelkaro 2\n",
      "reels 2\n",
      "reelsinstagram 2\n",
      "coconut 1\n",
      "delhifoodie 6\n",
      "indore 3\n",
      "madhyapradesh 2\n",
      "streetfood 9\n",
      "desifood 3\n",
      "delhi 6\n",
      "newdelhi 5\n",
      "sodelhi 6\n",
      "foodtalkindia 5\n",
      "delhigram 5\n",
      "indianfood 6\n",
      "mangoshake 1\n",
      "mangojuice 1\n",
      "mango 1\n",
      "mangos 1\n",
      "mangoes 1\n",
      "bangalorefood 1\n",
      "bangalorefoodies 1\n",
      "indianfoodie 5\n",
      "streetfoodindia 5\n",
      "indianfoodbloggers 6\n",
      "kolkatafood 1\n",
      "indianstreetfood 5\n",
      "lassi 1\n",
      "bananashake 1\n",
      "smoothie 1\n",
      "desikhana 5\n",
      "southindianfood 1\n",
      "goodfoodindia 1\n",
      "delhifoodbloggers 3\n",
      "delhifoodblog 1\n",
      "lbbbangalore 1\n",
      "nammabengaluru 1\n",
      "delhifoodblogger 5\n",
      "foodgasm 4\n",
      "foodporn 4\n",
      "foodpornshare 4\n",
      "foodlover 4\n",
      "foodgram 4\n",
      "foodiesofinstagram 4\n",
      "foodie 4\n",
      "delhistreetfood 4\n",
      "mumbaistreetfood 4\n",
      "foodblogger 6\n",
      "indianfoodblogger 5\n",
      "delhifood 5\n",
      "mumbai 4\n",
      "jalebi 1\n",
      "dabeli 1\n",
      "gujarati 1\n",
      "gujjugram 1\n",
      "cheese 1\n",
      "gujaratifood 1\n",
      "radisson 1\n",
      "radissonblu 1\n",
      "amarsirohi 1\n",
      "delhibloggers 1\n",
      "staycation 1\n",
      "meerutblogger 1\n",
      "mawana 1\n",
      "staycations 2\n",
      "foodbloggers 1\n",
      "lifestyleblogger 1\n",
      "delhiblogger 1\n",
      "mumbaiblogger 1\n",
      "kolkatablogger 1\n",
      "bangaloreblogger 1\n",
      "noida 1\n",
      "greaternoida 1\n",
      "hotelblogger 1\n",
      "indianhotels 1\n",
      "delhihotels 1\n",
      "animallover 1\n",
      "animallove 1\n",
      "uttarakhand 1\n",
      "gur 1\n",
      "uttarpradesh 1\n",
      "holi 2\n",
      "gujiya 1\n",
      "bhaang 1\n",
      "sandwich 1\n",
      "sandwiches 1\n",
      "mayonnaise 1\n",
      "cheesesandwich 1\n",
      "indorefood 1\n",
      "\n",
      "delhifoodwalks :\n",
      "\n",
      "DFWineBikaner 5\n",
      "DFWinRajasthan 5\n",
      "BikanerFoodTour 5\n",
      "FoodTour 5\n",
      "FoodWalks 5\n",
      "TravelforFood 5\n",
      "Bikanerstreetfood 5\n",
      "walkwithanubhav 6\n",
      "dehradun 1\n",
      "nonveg 1\n",
      "chikentikka 1\n",
      "pahadichicken 1\n",
      "pahadikebab 1\n",
      "walkwithdfw 1\n",
      "streetfoodstories 1\n",
      "newvideoalert 2\n",
      "foodtour 2\n",
      "foodwalk 2\n",
      "dwarka 2\n",
      "delhi 2\n",
      "streetsofdelhi 2\n",
      "lovly👏 1\n",
      "BikanerFood 4\n",
      "Bikanericuisine 4\n",
      "happyholi 2\n",
      "festivalofcolours 2\n",
      "festivetreats 2\n",
      "orderfoodwithFOSA 1\n",
      "WhatsApp9007500902 1\n",
      "orderfoodviawhatsapp 1\n",
      "\n",
      "food :\n",
      "\n",
      "gramercytavern 1\n",
      "food 7\n",
      "oysters 1\n",
      "oystersrockefeller 1\n",
      "cheers 1\n",
      "champagne 1\n",
      "nycfood 9\n",
      "unionsquare 1\n",
      "weekend 1\n",
      "nycfoodie 8\n",
      "osteriamorini 1\n",
      "manhattan 1\n",
      "eatlocal 2\n",
      "yummy 4\n",
      "pasta 2\n",
      "eat 3\n",
      "6lbs 2\n",
      "chickenwings 1\n",
      "chicken 1\n",
      "friedchicken 1\n",
      "yum 2\n",
      "supportlocal 2\n",
      "saverestaurants 1\n",
      "auzaatar 1\n",
      "mediterraneanfood 1\n",
      "mezze 1\n",
      "zaatar 1\n",
      "foodie 1\n",
      "macandcheese 1\n",
      "cheese 3\n",
      "thesmith 1\n",
      "hotcrossbuns 1\n",
      "easter 1\n",
      "ceremonia 1\n",
      "bakeshop 1\n",
      "nyc 2\n",
      "mozzarella 1\n",
      "mozzarellasticks 1\n",
      "rsc 1\n",
      "roebling 1\n",
      "sunday 1\n",
      "spread 1\n",
      "brunch 1\n",
      "sundayinbrooklyn 1\n",
      "brooklyn 1\n",
      "passover 1\n",
      "mpnewyork 1\n",
      "mpgrocery 1\n",
      "brisket 1\n",
      "family 1\n",
      "happypassover 1\n",
      "nycfoodgals 1\n",
      "baconeggcheese 1\n",
      "bec 1\n",
      "hhbagels 1\n",
      "uppereastside 1\n",
      "bagel 1\n",
      "\n",
      "sailusfood :\n",
      "\n",
      "healthy 12\n",
      "tasty 10\n",
      "indianfood 21\n",
      "recipes 10\n",
      "sailusfood 10\n",
      "simplefood 3\n",
      "thali 2\n",
      "andhrafood 2\n",
      "telugufood 2\n",
      "andhracuisine 2\n",
      "healthyfood 2\n",
      "healthyliving 2\n",
      "lunch 2\n",
      "lunchideas 2\n",
      "homecooking 2\n",
      "southindianfood 5\n",
      "andhra 2\n",
      "flavorsofindia 2\n",
      "authenticfood 3\n",
      "ammacooking 2\n",
      "traditional 2\n",
      "indiankitchen 2\n",
      "walkwithindia 9\n",
      "ilovetoeat 2\n",
      "eattherainbow 4\n",
      "comfortfood 12\n",
      "soulfood 8\n",
      "goodfoodindia 5\n",
      "indiafood_lovers 2\n",
      "foodtalkindia 9\n",
      "breakfast 2\n",
      "idli 2\n",
      "podiidli 1\n",
      "veganfoodshare 4\n",
      "idlilove 2\n",
      "idlipodi 1\n",
      "breakfastlover 4\n",
      "breakfastofchamps 5\n",
      "foodielife 2\n",
      "ndtv_food 6\n",
      "southindianbreakfast 4\n",
      "healthybreakfast 3\n",
      "foodblogfeed 3\n",
      "whatiate 3\n",
      "onmytable 3\n",
      "breakfastclub 3\n",
      "healthyish 2\n",
      "yummyinmytummy 8\n",
      "northindianfood 5\n",
      "lbreakfast 1\n",
      "desikhana 1\n",
      "aloosabji 1\n",
      "breakfastoftheday 4\n",
      "ilovecooking 1\n",
      "delhifoodie 2\n",
      "dilsefoodie 2\n",
      "delhifoodies 2\n",
      "happinessishomemade 3\n",
      "thefeedfeed 5\n",
      "huffposttaste 4\n",
      "kitchenstories 3\n",
      "vegetarianfoodshare 3\n",
      "fbci_official 2\n",
      "delhidiaries 2\n",
      "truecooks 3\n",
      "onmyplate 3\n",
      "tomatokurma 1\n",
      "glutenfreeindia 3\n",
      "traditionalfood 1\n",
      "aurhenticfood 1\n",
      "glutenfreevegan 2\n",
      "dosa 1\n",
      "dosai 1\n",
      "tiffinsofindia 1\n",
      "chutney 1\n",
      "yahoofood 1\n",
      "tiffin 1\n",
      "dosalove 1\n",
      "homechef 1\n",
      "foodiesofindia 2\n",
      "bestbreakfastever 1\n",
      "punjabifood 2\n",
      "matarpaneer 1\n",
      "roti 1\n",
      "matar 1\n",
      "greenpeas 1\n",
      "ontheplate_project 2\n",
      "latergram 1\n",
      "batura 1\n",
      "cholebhature 1\n",
      "tasteofindia 1\n",
      "sodelhi 1\n",
      "indianfoodbloggers 1\n",
      "breakfastspecial 1\n",
      "whatsforbreakfast 1\n",
      "nomnom24x7 1\n",
      "appam 1\n",
      "vegetablestew 1\n",
      "keralafood 1\n",
      "thefoodfeed 2\n",
      "lovemyfood 1\n",
      "paalpoli 1\n",
      "milkpoli 1\n",
      "milkpuri 1\n",
      "dessert 1\n",
      "recipeoftheday 1\n",
      "southindiansweet 1\n",
      "foodblog 1\n",
      "instafoodie 1\n",
      "sodelicious 1\n",
      "foodstagram 1\n",
      "ontheblog 1\n",
      "foodgawker 1\n",
      "f52grams 1\n",
      "buzzfeedfood 1\n",
      "diwali 1\n",
      "krishnashtami 1\n",
      "bhogi 1\n",
      "foodblogger 1\n",
      "feedfeed 1\n",
      "indiandessert 1\n",
      "beautifulcuisines 1\n",
      "indiansweets 1\n",
      "indiancooking 1\n",
      "traditionalsweets 1\n",
      "indianfestivalrecipes 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(hashtags)):\n",
    "    print(top_5_handles[i], \":\", end=\"\\n\\n\")\n",
    "    \n",
    "    for j in hashtags[i]:\n",
    "        print(j, hashtags[i][j])\n",
    "        \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing the hashtags and their frequency collectively for the top 5 food handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recipe 4\n",
      "foodiesofinstagram 8\n",
      "kathal 1\n",
      "kathalrecipe 1\n",
      "foodporn 11\n",
      "foodies 1\n",
      "recipeoftheday 5\n",
      "foodstylist 4\n",
      "foodlover 9\n",
      "foodpics 5\n",
      "indianfood 28\n",
      "indiaclicks 1\n",
      "yourfoodlab 5\n",
      "sanjyotkeer 7\n",
      "indiancuisine 4\n",
      "streetfood 13\n",
      "dahivada 2\n",
      "yummyfood 2\n",
      "DigSwirlSpread 1\n",
      "EpigamiaGheeSpread 1\n",
      "thandai 2\n",
      "thandairecipe 2\n",
      "holifestival 2\n",
      "holi 4\n",
      "holicelebration 2\n",
      "chocolate 2\n",
      "mango 3\n",
      "mangoseason 2\n",
      "summerseason 2\n",
      "recipevideo 3\n",
      "gheeroast 2\n",
      "foodphotography 3\n",
      "foodstagram 4\n",
      "streetfoodlover 2\n",
      "egg 1\n",
      "eggcurry 1\n",
      "eggs 1\n",
      "egglover 1\n",
      "goanrecipes 1\n",
      "goanfood 1\n",
      "burger 1\n",
      "burgerlove 1\n",
      "burgertime 1\n",
      "burgerlover 1\n",
      "vegburger 1\n",
      "vegrecipesofindia 1\n",
      "vegetarian 1\n",
      "rajma 1\n",
      "burgers 1\n",
      "foodieincarnate 10\n",
      "reelitfeelit 2\n",
      "reelkarofeelkaro 2\n",
      "reels 2\n",
      "reelsinstagram 2\n",
      "coconut 1\n",
      "delhifoodie 8\n",
      "indore 3\n",
      "madhyapradesh 2\n",
      "desifood 3\n",
      "delhi 8\n",
      "newdelhi 5\n",
      "sodelhi 7\n",
      "foodtalkindia 14\n",
      "delhigram 5\n",
      "mangoshake 1\n",
      "mangojuice 1\n",
      "mangos 1\n",
      "mangoes 1\n",
      "bangalorefood 1\n",
      "bangalorefoodies 1\n",
      "indianfoodie 5\n",
      "streetfoodindia 5\n",
      "indianfoodbloggers 7\n",
      "kolkatafood 1\n",
      "indianstreetfood 5\n",
      "lassi 1\n",
      "bananashake 1\n",
      "smoothie 1\n",
      "desikhana 6\n",
      "southindianfood 6\n",
      "goodfoodindia 6\n",
      "delhifoodbloggers 3\n",
      "delhifoodblog 1\n",
      "lbbbangalore 1\n",
      "nammabengaluru 1\n",
      "delhifoodblogger 5\n",
      "foodgasm 4\n",
      "foodpornshare 4\n",
      "foodgram 4\n",
      "foodie 5\n",
      "delhistreetfood 4\n",
      "mumbaistreetfood 4\n",
      "foodblogger 7\n",
      "indianfoodblogger 5\n",
      "delhifood 5\n",
      "mumbai 4\n",
      "jalebi 1\n",
      "dabeli 1\n",
      "gujarati 1\n",
      "gujjugram 1\n",
      "cheese 4\n",
      "gujaratifood 1\n",
      "radisson 1\n",
      "radissonblu 1\n",
      "amarsirohi 1\n",
      "delhibloggers 1\n",
      "staycation 1\n",
      "meerutblogger 1\n",
      "mawana 1\n",
      "staycations 2\n",
      "foodbloggers 1\n",
      "lifestyleblogger 1\n",
      "delhiblogger 1\n",
      "mumbaiblogger 1\n",
      "kolkatablogger 1\n",
      "bangaloreblogger 1\n",
      "noida 1\n",
      "greaternoida 1\n",
      "hotelblogger 1\n",
      "indianhotels 1\n",
      "delhihotels 1\n",
      "animallover 1\n",
      "animallove 1\n",
      "uttarakhand 1\n",
      "gur 1\n",
      "uttarpradesh 1\n",
      "gujiya 1\n",
      "bhaang 1\n",
      "sandwich 1\n",
      "sandwiches 1\n",
      "mayonnaise 1\n",
      "cheesesandwich 1\n",
      "indorefood 1\n",
      "DFWineBikaner 5\n",
      "DFWinRajasthan 5\n",
      "BikanerFoodTour 5\n",
      "FoodTour 5\n",
      "FoodWalks 5\n",
      "TravelforFood 5\n",
      "Bikanerstreetfood 5\n",
      "walkwithanubhav 6\n",
      "dehradun 1\n",
      "nonveg 1\n",
      "chikentikka 1\n",
      "pahadichicken 1\n",
      "pahadikebab 1\n",
      "walkwithdfw 1\n",
      "streetfoodstories 1\n",
      "newvideoalert 2\n",
      "foodtour 2\n",
      "foodwalk 2\n",
      "dwarka 2\n",
      "streetsofdelhi 2\n",
      "lovly👏 1\n",
      "BikanerFood 4\n",
      "Bikanericuisine 4\n",
      "happyholi 2\n",
      "festivalofcolours 2\n",
      "festivetreats 2\n",
      "orderfoodwithFOSA 1\n",
      "WhatsApp9007500902 1\n",
      "orderfoodviawhatsapp 1\n",
      "gramercytavern 1\n",
      "food 7\n",
      "oysters 1\n",
      "oystersrockefeller 1\n",
      "cheers 1\n",
      "champagne 1\n",
      "nycfood 9\n",
      "unionsquare 1\n",
      "weekend 1\n",
      "nycfoodie 8\n",
      "osteriamorini 1\n",
      "manhattan 1\n",
      "eatlocal 2\n",
      "yummy 4\n",
      "pasta 2\n",
      "eat 3\n",
      "6lbs 2\n",
      "chickenwings 1\n",
      "chicken 1\n",
      "friedchicken 1\n",
      "yum 2\n",
      "supportlocal 2\n",
      "saverestaurants 1\n",
      "auzaatar 1\n",
      "mediterraneanfood 1\n",
      "mezze 1\n",
      "zaatar 1\n",
      "macandcheese 1\n",
      "thesmith 1\n",
      "hotcrossbuns 1\n",
      "easter 1\n",
      "ceremonia 1\n",
      "bakeshop 1\n",
      "nyc 2\n",
      "mozzarella 1\n",
      "mozzarellasticks 1\n",
      "rsc 1\n",
      "roebling 1\n",
      "sunday 1\n",
      "spread 1\n",
      "brunch 1\n",
      "sundayinbrooklyn 1\n",
      "brooklyn 1\n",
      "passover 1\n",
      "mpnewyork 1\n",
      "mpgrocery 1\n",
      "brisket 1\n",
      "family 1\n",
      "happypassover 1\n",
      "nycfoodgals 1\n",
      "baconeggcheese 1\n",
      "bec 1\n",
      "hhbagels 1\n",
      "uppereastside 1\n",
      "bagel 1\n",
      "healthy 12\n",
      "tasty 10\n",
      "recipes 10\n",
      "sailusfood 10\n",
      "simplefood 3\n",
      "thali 2\n",
      "andhrafood 2\n",
      "telugufood 2\n",
      "andhracuisine 2\n",
      "healthyfood 2\n",
      "healthyliving 2\n",
      "lunch 2\n",
      "lunchideas 2\n",
      "homecooking 2\n",
      "andhra 2\n",
      "flavorsofindia 2\n",
      "authenticfood 3\n",
      "ammacooking 2\n",
      "traditional 2\n",
      "indiankitchen 2\n",
      "walkwithindia 9\n",
      "ilovetoeat 2\n",
      "eattherainbow 4\n",
      "comfortfood 12\n",
      "soulfood 8\n",
      "indiafood_lovers 2\n",
      "breakfast 2\n",
      "idli 2\n",
      "podiidli 1\n",
      "veganfoodshare 4\n",
      "idlilove 2\n",
      "idlipodi 1\n",
      "breakfastlover 4\n",
      "breakfastofchamps 5\n",
      "foodielife 2\n",
      "ndtv_food 6\n",
      "southindianbreakfast 4\n",
      "healthybreakfast 3\n",
      "foodblogfeed 3\n",
      "whatiate 3\n",
      "onmytable 3\n",
      "breakfastclub 3\n",
      "healthyish 2\n",
      "yummyinmytummy 8\n",
      "northindianfood 5\n",
      "lbreakfast 1\n",
      "aloosabji 1\n",
      "breakfastoftheday 4\n",
      "ilovecooking 1\n",
      "dilsefoodie 2\n",
      "delhifoodies 2\n",
      "happinessishomemade 3\n",
      "thefeedfeed 5\n",
      "huffposttaste 4\n",
      "kitchenstories 3\n",
      "vegetarianfoodshare 3\n",
      "fbci_official 2\n",
      "delhidiaries 2\n",
      "truecooks 3\n",
      "onmyplate 3\n",
      "tomatokurma 1\n",
      "glutenfreeindia 3\n",
      "traditionalfood 1\n",
      "aurhenticfood 1\n",
      "glutenfreevegan 2\n",
      "dosa 1\n",
      "dosai 1\n",
      "tiffinsofindia 1\n",
      "chutney 1\n",
      "yahoofood 1\n",
      "tiffin 1\n",
      "dosalove 1\n",
      "homechef 1\n",
      "foodiesofindia 2\n",
      "bestbreakfastever 1\n",
      "punjabifood 2\n",
      "matarpaneer 1\n",
      "roti 1\n",
      "matar 1\n",
      "greenpeas 1\n",
      "ontheplate_project 2\n",
      "latergram 1\n",
      "batura 1\n",
      "cholebhature 1\n",
      "tasteofindia 1\n",
      "breakfastspecial 1\n",
      "whatsforbreakfast 1\n",
      "nomnom24x7 1\n",
      "appam 1\n",
      "vegetablestew 1\n",
      "keralafood 1\n",
      "thefoodfeed 2\n",
      "lovemyfood 1\n",
      "paalpoli 1\n",
      "milkpoli 1\n",
      "milkpuri 1\n",
      "dessert 1\n",
      "southindiansweet 1\n",
      "foodblog 1\n",
      "instafoodie 1\n",
      "sodelicious 1\n",
      "ontheblog 1\n",
      "foodgawker 1\n",
      "f52grams 1\n",
      "buzzfeedfood 1\n",
      "diwali 1\n",
      "krishnashtami 1\n",
      "bhogi 1\n",
      "feedfeed 1\n",
      "indiandessert 1\n",
      "beautifulcuisines 1\n",
      "indiansweets 1\n",
      "indiancooking 1\n",
      "traditionalsweets 1\n",
      "indianfestivalrecipes 1\n"
     ]
    }
   ],
   "source": [
    "hashtag_vs_count={}\n",
    "\n",
    "for i in range(len(hashtags)):\n",
    "    \n",
    "    count+=len(hashtags[i])\n",
    "    \n",
    "    for j in hashtags[i]:\n",
    "        hashtag_vs_count[j]=(hashtag_vs_count.get(j, 0)+hashtags[i][j])\n",
    "\n",
    "for key, value in hashtag_vs_count.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "creating a csv file with 2 cols(word and frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the hashtag and its count in a 2d list\n",
    "\n",
    "hashtag_vs_count_list=[]\n",
    "\n",
    "for key, value in hashtag_vs_count.items():\n",
    "    hashtag_vs_count_list.append([key, value])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>recipe</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foodiesofinstagram</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kathal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kathalrecipe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foodporn</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>beautifulcuisines</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>indiansweets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>indiancooking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>traditionalsweets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>indianfestivalrecipes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      word  frequency\n",
       "0                   recipe          4\n",
       "1       foodiesofinstagram          8\n",
       "2                   kathal          1\n",
       "3             kathalrecipe          1\n",
       "4                 foodporn         11\n",
       "..                     ...        ...\n",
       "327      beautifulcuisines          1\n",
       "328           indiansweets          1\n",
       "329          indiancooking          1\n",
       "330      traditionalsweets          1\n",
       "331  indianfestivalrecipes          1\n",
       "\n",
       "[332 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a dataframe with 2 cols, word and frequency\n",
    "\n",
    "df=pd.DataFrame(hashtag_vs_count_list, columns=[\"word\", \"frequency\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a csv file from the dataframe\n",
    "\n",
    "df.to_csv(\"WordVsFreq.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing the top 5 hashtags along with their frequencies, which were the most popular among these bloggers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_order=(df[\"frequency\"].values.argsort()[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indianfood 28\n",
      "foodtalkindia 14\n",
      "streetfood 13\n",
      "comfortfood 12\n",
      "healthy 12\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    index=sort_order[i]\n",
    "    \n",
    "    print(df[\"word\"].iloc[index], df[\"frequency\"].iloc[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotting a pie chart of the top 5 hastags and their count percentage(among the top 5 only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Top 5 hashtags used\")\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "frequency_top_5=df[\"frequency\"].values[sort_order][:5]\n",
    "names_top_5=df[\"word\"].values[sort_order][:5]\n",
    "\n",
    "plt.pie(frequency_top_5, labels=names_top_5, autopct=\"%.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3 Calculating average Followers: Likes ratio for these top 5 food handles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the count of likes on the first 10 posts of these bloggers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_of_likes=[0 for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visiting the page of each of the top 5 food handles(STEPS EXPLAINED ABOVE) and fetching the likes on their first 10 posts\n",
    "\n",
    "new_page_was_opened=True\n",
    "\n",
    "for i in range(10):    \n",
    "    wait.until(ec.visibility_of_element_located((By.CLASS_NAME, \"XTCLo\")))\n",
    "    \n",
    "    if new_page_was_opened:\n",
    "        search_input=driver.find_element_by_class_name(\"XTCLo\")\n",
    "        search_input.send_keys(\"food\")       \n",
    "        \n",
    "        \n",
    "    wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@role='none'][position()=\"+str(i+1)+\"]\")))    \n",
    "    \n",
    "    food_handle=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]\")\n",
    "    food_handle_name=driver.find_element_by_xpath(\"//div[@role='none'][position()=\"+str(i+1)+\"]/a/div/div[position()=2]/div/div/div/div\").get_attribute(\"innerHTML\")        \n",
    "    \n",
    "    if not food_handle_name in top_5_handles:\n",
    "        new_page_was_opened=False\n",
    "        continue\n",
    "    else:\n",
    "        new_page_was_opened=True\n",
    "    \n",
    "    food_handle.click()    \n",
    "\n",
    "    count=0\n",
    "    index=top_5_handles.index(food_handle_name)    \n",
    "    \n",
    "    time.sleep(3)    \n",
    "    \n",
    "    for i in range(10):    \n",
    "        pos1=str((i//3)+1)   \n",
    "        \n",
    "        pos2=str((i%3)+1)\n",
    "        \n",
    "        wait.until(ec.visibility_of_element_located((By.XPATH, \"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")))\n",
    "    \n",
    "        post=driver.find_element_by_xpath(\"//div[@class='Nnq7C weEfm'][position()=\"+pos1+\"]/div[position()=\"+pos2+\"]\")\n",
    "        post.click()\n",
    "        \n",
    "#         fetching the likes/views(if the likes are not mentioned) on each post, after waiting till the element is visible\n",
    "        \n",
    "        try:\n",
    "            wait.until(ec.visibility_of_element_located((By.XPATH, \"//a[@class='zV_Nj']/span\")))\n",
    "            post_likes=driver.find_element_by_xpath(\"//a[@class='zV_Nj']/span\").get_attribute(\"innerHTML\")\n",
    "        except:\n",
    "#             waiting till the views are visible\n",
    "\n",
    "            wait.until(ec.visibility_of_element_located((By.XPATH, \"//span[@class='vcOH2']/span\")))\n",
    "            post_likes=driver.find_element_by_xpath(\"//span[@class='vcOH2']/span\").get_attribute(\"innerHTML\")\n",
    "             \n",
    "#         removing the commas and short-forms and converting likes/views to integer value\n",
    "        \n",
    "        post_likes=post_likes.replace(\",\", \"\")\n",
    "        post_likes=post_likes.replace(\"k\", \"000\")\n",
    "        post_likes=post_likes.replace(\"m\", \"000000\")\n",
    "        \n",
    "        post_likes=int(post_likes)\n",
    "        \n",
    "#         updating the count_of_likes array\n",
    "        \n",
    "        count_of_likes[index]+=post_likes\n",
    "        \n",
    "        close_button=driver.find_element_by_css_selector(\"div.Igw0E.IwRSH.eGOV_._4EzTm.BI4qX.qJPeX.fm1AK.TxciK.yiMZG button.wpO6b  \")\n",
    "        close_button.click()\n",
    "    \n",
    "    driver.back()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the average count of likes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_count_of_likes=np.array(count_of_likes)\n",
    "\n",
    "np_average_likes=(np_count_of_likes//10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getting the followers: likes ratio and printing it for the top 5 food handles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "food 149.74\n",
      "delhifoodwalks 35.78\n",
      "sailusfood 21.73\n",
      "yourfoodlab 12.36\n",
      "foodie_incarnate 2.61\n"
     ]
    }
   ],
   "source": [
    "np_average_followers_like_ratio=np.around((np_top_5_followers/np_average_likes), 2)\n",
    "\n",
    "sort_order=np_average_followers_like_ratio.argsort()[::-1]\n",
    "\n",
    "for i in range(len(np_average_followers_like_ratio)):\n",
    "    index=sort_order[i]\n",
    "    \n",
    "    print(top_5_handles[index], np_average_followers_like_ratio[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotting a bar graph for better visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Average followers to likes ratio for top 5 handles\")\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.xlabel(\"Food handle\")\n",
    "plt.ylabel(\"Average Followers:Likes ratio\")\n",
    "\n",
    "plt.bar(top_5_handles, np_average_followers_like_ratio, width=0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}