analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "chemical-consideration",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import datetime as dt\n",
    "import time\n",
    "import emojis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "continuous-machinery",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dict(x,state = 'language or source'):\n",
    "    x = sort_dict(x)\n",
    "    tmp = list(x)\n",
    "    s = sum(list(x.values()))\n",
    "    c = 0\n",
    "    if state == 'players':\n",
    "        for i in range(len(tmp)):\n",
    "        \n",
    "            if i>5  :\n",
    "            \n",
    "                c+=x[tmp[i]]\n",
    "                del x[tmp[i]]\n",
    "    \n",
    "        x['other'] = c\n",
    "    else:\n",
    "        for i in range(len(tmp)):\n",
    "        \n",
    "            if i>7 or x[tmp[i]]<s * 0.08 :\n",
    "            \n",
    "                c+=x[tmp[i]]\n",
    "                del x[tmp[i]]\n",
    "    \n",
    "        x['other'] = c\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "vanilla-constitutional",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_dict(x):\n",
    "    sorted_dict= dict(sorted(x.items(), key = lambda kv: kv[1]))\n",
    "    sorted_dict =dict(reversed(list(sorted_dict.items())))\n",
    "    return sorted_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "loved-reality",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot(x,state='language or source'):\n",
    "    \n",
    "    x = make_dict(x,state)\n",
    "    mylabels = list(x.keys())\n",
    "    values = list(x.values())\n",
    "    return_value = {'mylabels':mylabels,'values':values}\n",
    "    return return_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "excessive-joint",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top5_emojies(x):\n",
    "    tmp = list(sort_dict(x))\n",
    "\n",
    "    for i in range(len(tmp)):\n",
    "        if i>4:\n",
    "            \n",
    "            del x[tmp[i]]\n",
    "            \n",
    "    return sort_dict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "egyptian-underground",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hashtage_set(st):\n",
    "\n",
    "    st.pop(0)\n",
    "    if 'psgbarca' in st: #remove '#barcapsg' from hashtags\n",
    "        st.remove('psgbarca')\n",
    "    if 'psgbarça' in st:\n",
    "        st.remove('psgbarça')\n",
    "    for i in st:\n",
    "        if i not in hashtag:\n",
    "            hashtag[i]=1\n",
    "        else:\n",
    "            hashtag[i]+=1\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "comprehensive-vaccine",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bar(hashtag_dict):\n",
    "    hashtag_dict = sort_dict(hashtag_dict)\n",
    "    \n",
    "    x = list(hashtag_dict.keys())[0:5]\n",
    "    y = list(hashtag_dict.values())[0:5]\n",
    "    \n",
    "    for i in range(0,5):\n",
    "        x[i] = '#' + x[i]\n",
    "       \n",
    "    \n",
    "    plt.bar(x, y,color = \"#4CAF50\")\n",
    "    plt.title('Top 5 hashtags used in people tweets')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "recorded-klein",
   "metadata": {},
   "outputs": [],
   "source": [
    "#collecting barca and psg players\n",
    "barca_players={'stegen':0,'mingueza':0,'piqu':0,'lengle':0,'busque':0,'dest':0,'alba':0,'jong':0,'pedri':0,'messi':0,'dembele':0}\n",
    "psg_players = {'navas':0,'florenzi':0,'marquinhos':0,'kimpembe':0,'kurzawa':0,'paredes':0,'gueye':0,'verratti':0,'draxler':0,'mbapp':0,'Icardi':0,'angel':0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "sapphire-impact",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading from csv file\n",
    "df = pd.read_csv('recent_tweet.csv') \n",
    "df.columns =['id', 'text','lang','screen_name','created_at','media','hashtag','favorite_count','retweets_count','source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fixed-equity",
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding frequncy of languages in tweets\n",
    "lang = {}\n",
    "for i in df['lang']:\n",
    "    if i not in lang:\n",
    "        lang[i] = 0\n",
    "for i in df['lang']:\n",
    "    lang[i]+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daily-peninsula",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lang = make_plot(lang)\n",
    "\n",
    "plt.pie(plot_lang['values'], labels = plot_lang['mylabels'])\n",
    "plt.title('Most languages people used')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "studied-strength",
   "metadata": {},
   "outputs": [],
   "source": [
    "source = {}\n",
    "for i in df['source']:\n",
    "    if i not in source:\n",
    "        source[i] = 0\n",
    "for i in df['source']:\n",
    "    source[i]+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "focal-antarctica",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = make_plot(source,'asdf')\n",
    "\n",
    "plt.pie(plot['values'], labels = plot['mylabels'])\n",
    "plt.title('Most sources people used')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ambient-watson",
   "metadata": {},
   "outputs": [],
   "source": [
    "for text in df['text']:\n",
    "    text = text.lower()\n",
    "    for i in barca_players:\n",
    "        if i in text:\n",
    "            barca_players[i]+=1\n",
    "    for i in psg_players:\n",
    "        if i in text:\n",
    "            psg_players[i]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "composite-doctrine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = make_plot(psg_players,'players')\n",
    "plt.pie(plot['values'], labels = plot['mylabels'])\n",
    "plt.title('Most psg players people talked about')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "unknown-footwear",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = make_plot(barca_players,'players')\n",
    "plt.pie(plot['values'], labels = plot['mylabels'])\n",
    "plt.title('Most barca players people talked about')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "affected-entity",
   "metadata": {},
   "outputs": [],
   "source": [
    "hashtag = {}\n",
    "for text in df['hashtag']:\n",
    "    if pd.isna(text)==False:\n",
    "        text = text.lower()\n",
    "        text = text.split('#')\n",
    "        hashtage_set(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "rubber-court",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcq0lEQVR4nO3de5gcVZ3/8feHJIQ7ATJGctEgRFhY18iOgA+4RlCBCAZXQFiVgGjAhRUVL4DuirvyExXB2w80iBIuy0UQiYhK5LooFwcMgXBZIgSTEMgASQi3aOC7f9RpU3S6p3tmumeSk8/refqZqnOqq86prv509enuKUUEZmaWlw0GuwFmZtZ6Dnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53Ncjko6UdGsb1jtf0rtavd51gaRTJV1Up+7tkh4a6Db1R0/9sXWLw72NJD1Xur0i6cXS/IdatI3zJf2laltDWrHuVpA0SdLCwW7HYIiI/4mIHQe7HQNlMF7k/WJUn8O9jSJis8oN+DNwYKns4hZu6hvlbUXEyy1ct5mtgxzug0DScEnflvR4un1b0vBUN0nSQkmnSHoqnQ215Cy/tP0zJC2V9Kik/UvlR0l6QNIKSY9IOqZUN1LSNZKWSXpG0v9IKh8/EyXNkbRc0mWSNpK0KfArYHTpXcVoSbtJui2ta7Gk70vasLSt90h6KK3rbEk3S/pYqtshzS9P++eyOn1c4x1D+cwytaFL0rOSnpR0Zmm5PST9PrXvHkmTSnXbpe2vkDQLGNnDfn5VG9L2P1u9n+rc90hJv0v7ZrmkByXtU6rfUtJ5af8tkvTVyjs2SRtI+pKkxyQtkXSBpC1T3XhJIWlaOvYWS/psD32ouy+qlrsQeB3wi/Q4f17SDEknpvoxabvHpfnt03G0QZo/QNLstJ3fS/qH0rpHS7pSUnc6Zj+ZyvcDTgE+mLZ5T2nfPZIeo0fV4ufPOiMifBuAGzAfeFea/k/gduA1QAfwe+C/Ut0kYBVwJjAceAfwPLBjnfWeDzyTbncBH+ihDUcCfwU+DgwBPgE8DijVvxfYHlDa7gvArqnua8APgGHp9vbS/eYDdwKjga2BB4BjS/1ZWNWOfwT2AIYC49Pyn0p1I4FngX9O9SekNn8s1V8CfJHixGQjYK86fa213fJjcBvwkTS9GbBHmh4DPA1MTtt4d5rvKN2v8tj8E7ACuKiZNvS0n+o8VquAT6f9/UFgObB1qr8K+CGwKcVxdCdwTKr7KDAPeEPq28+AC1PdeCDSftwUeBPQXdovp1b602hf9HSMl9rxizT9L8CfgMtKdVen6bcAS4DdKY7LqWldw9N27wL+A9gw9ekRYN/q9qb5TSmOnx3T/LbALoP9/B+UzBnsBqwvt6pg+RMwuVS3LzA/TU9KT+pNS/WXA/9eZ727AttQBOHkFDZ71ln2SGBeaX6T9ER/bZ3lfw6ckKb/E7ga2KFO3z5cmv8G8INSfxbWWn9p+U8BV6XpI4DbSnUCFrA63C8ApgNjG6xzje1WPQa3AF8BRlYt8wVSEJbKfpMC53U1Hpv/pnfhXnM/1Xms/vbCm8ruBD4CjAJWAhuX6g4HbkzT1wP/WqrbkeIFsvJiGsBOVe04L02fyupwr7svGh3jaX57YClFQP8AOKayP4AZwGfS9Dmkk5vSfR+iOMHYHfhzVd3JwE+q25vmNwWWAR8o75/18eZhmcExGnisNP9YKqtYGhHP91D/NxFxd0Q8HRGrIuJa4GKKs956nijd94U0uRmApP0l3Z7eLi+jeLGoDDt8k+Js8Lr0lvekeuulOOPfrF4DJL1RxRDPE5KeBf5faTujKcK80sYAysMrn6cI/DslzZX00R762pOjgTcCD0r6g6QDUvnrgUPS8MCytB/2ojgDHE3tx6Y3mt5PwKLU//K2Rqc2DgMWl9r4Q4ozeKh9fA2leFGoWFBVX+v46mlfNBQRf6J41zmR4p3eNcDjknakCO6bS9s5sWo740p9HV1Vd0pVX8rbfJ7iXc6xFPvnl5J2aqa9uXG4D47HKQ7aitelsoqtVIxX16vvSVCEX6+oGPO/EjgDGBURI4BrK+uKiBURcWJEvAF4H/CZ8hhwg/ZUOwd4EJgQEVtQPFkrbV4MjC21S+X5iHgiIj4eEaMpzgTPlrRDjW08T/HOpLKeIRRDYJX1PBwRh1ME4teBK9I+X0BxtjqidNs0Ik5Pbav12LTLmNT/8rYeT21cSfGuo9LGLSJil7RcreNrFfBkqWxcjfVW62lf1FLrsb4ZOBjYMCIWpfmpwFbA7NJ2TqvaziYRcUmqe7SqbvOImFxvmxHxm4h4N8WL0IPAuXXamzWH++C4BPiSpA5JIynGE6u/zvUVSRtKejtwAPDTWiuSdLCkzdKHaO8BPgzM7EObNqQY4+wGVqn4oPU9pe0coOLDTFGM/b4MvNLEep8Etql8oJdsTjEu+lw6q/pEqe6XwJskHSRpKHAc8NpSOw6RVAn7pRRP7lrt+F9gI0nvlTQM+FLqX2U9H5bUERGvULyNJ63nIuBASftKGqLig+FJksZGxGNAF6sfm72AA5vYB331GuCTkoZJOgT4O+DaiFgMXAd8S9IW6bHfXtI70v0uAT6t4sPfzSjeGV0WEatK6/53SZtI2gU4Cqj1wXTdfVGnvU9SjImX3QwcTzEMBnBTmr81Vn+r61zgWEm7q7Bpetw2pxiKWiHpC5I2Tu34e0lvLW1zfOmD2VGSpqQX4JXAczR3nGbH4T44vkoREnOAe4G7U1nFExTB9TjFMMuxEfFgnXWdACyiCKhvAh+PiJt626CIWAF8kmJ8fynFB2DlF4kJwG8pniy3AWdHxI1NrPdBirB5JL2tHg18Nq1/BcUT+7LS8k8Bh1CMAz8N7Eyxr1amRd4K3CHpudS+EyLikRrbXQ78K/Ajiv3zPK8e3tkPmJvW8x3gsIh4MSIWAFMo3k10U5w5fo7Vz5V/oRgHfgb4MsVnAO1yB8V+fwo4DTg4Ip5OdUdQvCDfT/F4XcHq4ZIfAxdSBOqjwEvAv1Wt+2aKYbbrgTMi4rrqjTexL6p9jeKkZZlWfwPnZooX80q430rxjqoyT0R0UXzI//3Ul3kUnzmQXgAOoBjaeTTtix8BlZOFyknP05LuTm37DMVz5xmK4Z/yycN6o/JtB1tLqPiq2UURUe/saL2SzsgWAh9q5sUkF5KOpPgQea8Wr3c8RUgOqzqTt8z4zN3WOmkYYET6HKAyHn/7IDfLbJ3icLe10dsovi76FMWY9kER8eLgNsls3eJhGTOzDPnM3cwsQ0MHuwEAI0eOjPHjxw92M8zM1il33XXXUxHRUaturQj38ePH09XVNdjNMDNbp0iq+wtpD8uYmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWVorfiFan8c+PP3D3YTWuYXB1012E0ws0z4zN3MLEMOdzOzDDnczcwy1HS4p6uO/1HSNWl+O0l3SJon6TJJG6by4Wl+Xqof36a2m5lZHb05cz8BeKA0/3XgrIjYgeKK5Uen8qOBpan8rLScmZkNoKbCXdJY4L3Aj9K8gL2BK9IiM4CD0vSUNE+q3yctb2ZmA6TZM/dvA58HXknz2wDLImJVml8IjEnTY4AFAKl+eVr+VSRNk9Qlqau7u7tvrTczs5oahrukA4AlEXFXKzccEdMjojMiOjs6al4lyszM+qiZHzHtCbxP0mRgI2AL4DvACElD09n5WGBRWn4RMA5YKGkosCXwdMtbbmZmdTU8c4+IkyNibESMBw4DboiIDwE3AgenxaYCV6fpmWmeVH9DRERLW21mZj3qz/fcvwB8RtI8ijH181L5ecA2qfwzwEn9a6KZmfVWr/63TETcBNyUph8BdquxzEvAIS1om5mZ9ZF/oWpmliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWWomQtkbyTpTkn3SJor6Sup/HxJj0qanW4TU7kkfVfSPElzJO3a5j6YmVmVZq7EtBLYOyKekzQMuFXSr1Ld5yLiiqrl9wcmpNvuwDnpr5mZDZBmLpAdEfFcmh2Wbj1d8HoKcEG63+3ACEnb9r+pZmbWrKbG3CUNkTQbWALMiog7UtVpaejlLEnDU9kYYEHp7gtTWfU6p0nqktTV3d3d9x6Ymdkamgr3iHg5IiYCY4HdJP09cDKwE/BWYGvgC73ZcERMj4jOiOjs6OjoXavNzKxHvfq2TEQsA24E9ouIxWnoZSXwE2C3tNgiYFzpbmNTmZmZDZBmvi3TIWlEmt4YeDfwYGUcXZKAg4D70l1mAkekb83sASyPiMVtaLuZmdXRzLdltgVmSBpC8WJweURcI+kGSR2AgNnAsWn5a4HJwDzgBeColrfazMx61DDcI2IO8JYa5XvXWT6A4/rfNDMz6yv/QtXMLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8tQM5fZ20jSnZLukTRX0ldS+XaS7pA0T9JlkjZM5cPT/LxUP77NfTAzsyrNnLmvBPaOiDcDE4H90rVRvw6cFRE7AEuBo9PyRwNLU/lZaTkzMxtADcM9Cs+l2WHpFsDewBWpfAbFRbIBpqR5Uv0+6SLaZmY2QJoac5c0RNJsYAkwC/gTsCwiVqVFFgJj0vQYYAFAql8ObFNjndMkdUnq6u7u7lcnzMzs1ZoK94h4OSImAmOB3YCd+rvhiJgeEZ0R0dnR0dHf1ZmZWUmvvi0TEcuAG4G3ASMkDU1VY4FFaXoRMA4g1W8JPN2KxpqZWXOa+bZMh6QRaXpj4N3AAxQhf3BabCpwdZqemeZJ9TdERLSwzWZm1sDQxouwLTBD0hCKF4PLI+IaSfcDl0r6KvBH4Ly0/HnAhZLmAc8Ah7Wh3WZm1oOG4R4Rc4C31Ch/hGL8vbr8JeCQlrTOzMz6xL9QNTPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMtTMZfbGSbpR0v2S5ko6IZWfKmmRpNnpNrl0n5MlzZP0kKR929kBMzNbUzOX2VsFnBgRd0vaHLhL0qxUd1ZEnFFeWNLOFJfW2wUYDfxW0hsj4uVWNtzMzOpreOYeEYsj4u40vYLi4thjerjLFODSiFgZEY8C86hxOT4zM2ufXo25SxpPcT3VO1LR8ZLmSPqxpK1S2RhgQeluC6nxYiBpmqQuSV3d3d29b7mZmdXVdLhL2gy4EvhURDwLnANsD0wEFgPf6s2GI2J6RHRGRGdHR0dv7mpmZg00Fe6ShlEE+8UR8TOAiHgyIl6OiFeAc1k99LIIGFe6+9hUZmZmA6SZb8sIOA94ICLOLJVvW1rs/cB9aXomcJik4ZK2AyYAd7auyWZm1kgz35bZE/gIcK+k2ansFOBwSROBAOYDxwBExFxJlwP3U3zT5jh/U8bMbGA1DPeIuBVQjapre7jPacBp/WiXmZn1g3+hamaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlq5mIdthY78OfvH+wmtMwvDrpqsJtglo1mLrM3TtKNku6XNFfSCal8a0mzJD2c/m6VyiXpu5LmSZojadd2d8LMzF6tmWGZVcCJEbEzsAdwnKSdgZOA6yNiAnB9mgfYn+K6qROAacA5LW+1mZn1qGG4R8TiiLg7Ta8AHgDGAFOAGWmxGcBBaXoKcEEUbgdGVF1M28zM2qxXH6hKGg+8BbgDGBURi1PVE8CoND0GWFC628JUVr2uaZK6JHV1d3f3tt1mZtaDpsNd0mbAlcCnIuLZcl1EBBC92XBETI+Izojo7Ojo6M1dzcysgabCXdIwimC/OCJ+loqfrAy3pL9LUvkiYFzp7mNTmZmZDZCGX4WUJOA84IGIOLNUNROYCpye/l5dKj9e0qXA7sDy0vCNWcv4a6Bm9TXzPfc9gY8A90qancpOoQj1yyUdDTwGHJrqrgUmA/OAF4CjWtlgMzNrrGG4R8StgOpU71Nj+QCO62e7zMysH/zvB8zMMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDvliH2Toql1/o+te57eEzdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMNw13SjyUtkXRfqexUSYskzU63yaW6kyXNk/SQpH3b1XAzM6uvmTP384H9apSfFRET0+1aAEk7A4cBu6T7nC1pSKsaa2ZmzWkY7hFxC/BMk+ubAlwaESsj4lGK66ju1o/2mZlZH/RnzP14SXPSsM1WqWwMsKC0zMJUtgZJ0yR1Serq7u7uRzPMzKxaX8P9HGB7YCKwGPhWb1cQEdMjojMiOjs6OvrYDDMzq6VP4R4RT0bEyxHxCnAuq4deFgHjSouOTWVmZjaA+hTukrYtzb4fqHyTZiZwmKThkrYDJgB39q+JZmbWWw3/n7ukS4BJwEhJC4EvA5MkTQQCmA8cAxARcyVdDtwPrAKOi4iX29JyMzOrq2G4R8ThNYrP62H504DT+tMoMzPrH/9C1cwsQ77Mnpmtc3K5xCC07zKDPnM3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMNw13SjyUtkXRfqWxrSbMkPZz+bpXKJem7kuZJmiNp13Y23szMamvmzP18YL+qspOA6yNiAnB9mgfYn+K6qROAacA5rWmmmZn1RsNwj4hbgGeqiqcAM9L0DOCgUvkFUbgdGFF1MW0zMxsAfR1zHxURi9P0E8CoND0GWFBabmEqW4OkaZK6JHV1d3f3sRlmZlZLvz9QjYgAog/3mx4RnRHR2dHR0d9mmJlZSV/D/cnKcEv6uySVLwLGlZYbm8rMzGwA9TXcZwJT0/RU4OpS+RHpWzN7AMtLwzdmZjZAhjZaQNIlwCRgpKSFwJeB04HLJR0NPAYcmha/FpgMzANeAI5qQ5vNzKyBhuEeEYfXqdqnxrIBHNffRpmZWf/4F6pmZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGGl6soyeS5gMrgJeBVRHRKWlr4DJgPDAfODQilvavmWZm1hutOHN/Z0RMjIjONH8ScH1ETACuT/NmZjaA2jEsMwWYkaZnAAe1YRtmZtaD/oZ7ANdJukvStFQ2KiIWp+kngFG17ihpmqQuSV3d3d39bIaZmZX1a8wd2CsiFkl6DTBL0oPlyogISVHrjhExHZgO0NnZWXMZMzPrm36duUfEovR3CXAVsBvwpKRtAdLfJf1tpJmZ9U6fw13SppI2r0wD7wHuA2YCU9NiU4Gr+9tIMzPrnf4My4wCrpJUWc9/R8SvJf0BuFzS0cBjwKH9b6aZmfVGn8M9Ih4B3lyj/Glgn/40yszM+se/UDUzy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLUtnCXtJ+khyTNk3RSu7ZjZmZraku4SxoC/H9gf2Bn4HBJO7djW2ZmtqZ2nbnvBsyLiEci4i/ApcCUNm3LzMyq9OcC2T0ZAywozS8Edi8vIGkaMC3NPifpoTa1pVVGAk+1cwNC7Vx9f7S977B+9999XyutC8f96+tVtCvcG4qI6cD0wdp+b0nqiojOwW7HYFif+w7rd//d93W37+0allkEjCvNj01lZmY2ANoV7n8AJkjaTtKGwGHAzDZty8zMqrRlWCYiVkk6HvgNMAT4cUTMbce2BtA6M4TUButz32H97r/7vo5SRAx2G8zMrMX8C1Uzsww53M3MMuRwTyR9TdI7JR0k6eQ+ruMmSWvVV6da0a8e1j1f0shWrrPV2tz/t0uaK2m2pI3rLDPox0Q790ET237f2vDvR2rtA0nnSzo4TTd8LGus82/3ryqfJOma1vag9xzuq+0O3A68A7hlkNvSSmtlvyQN1G8s2tn/DwFfi4iJEfFii9fdSoN2DETEzIg4fSC3WUejfdCrx3IAj9++i4j1+gZ8E5gDrABmp79zgP8AbgI603IjgflpeghwBnBfWvbfUvnflh/sWxP9+k4qvw/YLd3nHalsNvBHYHOKE4CzgQeBWcC1wMFp+fnAN4B7gTuBHVL5gcAdaR2/BUal8lOBC4HfAZesy/0HPgY8AzwKXJzu/4W0L+4BTi8dE2tsay05Bs4CuoAHgLcCPwMeBr5aWseH02M7G/ghxbE/BDg/9ede4NNp2U8C96dtXJrKjgS+v5Y+D87vw2P57bTPTkz3/0Ga/1/ggLTcJOCaQc+AwW7A2nBLB/b3gGHA70rlN1E73D8BXAEMTfNbVy+/Ntwa9OvcNP1PwH1p+hfAnml6M4qvyh5MEWgbAK8FlvLqcP9imj6ickADW7H6m1gfA76Vpk8F7gI2zqT/55em9wd+D2xS45hYY1tryTHw9TR9AvA4sC0wnOLfhWwD/F3aJ8PScmenx/kfgVmldY1Ifx8HhleVHckghnuDfVB+/Jp9LM+uuv+v07ExIe23jVhLwt3DMoVdKV6hd6I4i2nkXcAPI2IVQEQ808a29UdP/boEICJuAbaQNILijPpMSZ+keHKuAvYCfhoRr0TEE8CNtdaT/r4tTY8FfiPpXuBzwC6l5WfGwA1hDET/K94F/CQiXkjrLR8TtbY1UHraB5UfFt4LzI2IxRGxEniE4hfm+1AE+R8kzU7zb0j1b5D0PUn7Ac+m9cwBLpb0YWBV+7rUa315ftd7LC+rWvbydGw8TLFfdmpBe1ti7R83aiNJEylefcdS/IOgTYpizaYIqlWs/lxio4FvYd800S+A6h84REScLumXwGTgd5L2bWJzUWP6e8CZETFT0iSKM/aK55vtR18NcP+bsca2WrTeuprcByvT31dK05X5oYCAGRGxxoewkt4M7AscCxwKfBR4L8W7kwOBL0p6Uyv71FtN7oPeqj5+B/yxbdZ6feYeEbMjYiLFeNnOwA3AvrH6Q5X5FGcuULw9r5gFHFP5UEXS1gPW6CY00S+ADwJI2gtYHhHLJW0fEfdGxNcp/oXEThRnsx+QtIGkURRvOcs+WPp7W5rektX/S2hqyzvYwAD3v2IWcJSkTdJ6y8fEGttqYXdranIfNHI9cLCk10DRJ0mvT9+Q2iAirgS+BOwqaQNgXETcSDFevSXF0Nag6cc+6OmxrHZIOja2p3hXs9b8d9v1+swdQFIHsDQiXpG0U0TcX6o+A7g8/XviX5bKfwS8EZgj6a/AucD3B6zRTWjQL4CXJP2RYhzyo6nsU5LeSXHmNhf4FfBXirfj91P8G+e7gXI4bSVpDsWZ3+Gp7FTgp5KWUjyhtmt1/xoZwP4DEBG/TmeKXZL+QjFOf0oP22q7JvZBjyLifklfAq5L4f1X4DjgReAnqQzgZIoPWS+StCXFGf93I2KZNLj/zrcv+6DBY1ntzxQfOG8BHBsRLw12nyv87wfWQ5JuAj4bEV1NLr9ZRDwnaRuKA3nPNP68Tlrf+2/rh/X+zN2ack36EHBD4L/Ww2Bb3/tv6yCfuZuZZWi9/kDVzCxXDnczsww53M3MMuRwNzPLkMPdzCxD/wdF9I5RxQRSTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar(hashtag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "awful-verification",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emoji frequency\n",
      "\n",
      "⚽ 159\n",
      "🔴 144\n",
      "💪 137\n",
      "❤️ 123\n",
      "🔵 105\n"
     ]
    }
   ],
   "source": [
    "#finding top 5 emojis used in people tweets\n",
    "em = {}\n",
    "for text in df['text']:\n",
    "    tmp = list(emojis.get(text))\n",
    "    \n",
    "    for i in tmp:\n",
    "            \n",
    "            if i in em:\n",
    "                em[i]+=1\n",
    "            else:\n",
    "                em[i]=1\n",
    "                \n",
    "em = top5_emojies(em)\n",
    "print('emoji','frequency')\n",
    "print()\n",
    "for emoji_ in em:\n",
    "    print(emoji_,em[emoji_])"
   ]
  }
 ],
 "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": 5
}