extract_cities.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "import csv\n",
    "header = ['ids']\n",
    "file = open('predictions_som.csv', 'r+') \n",
    "with file:     \n",
    "    reader = csv.reader(file) \n",
    "    for row in reader:\n",
    "        lst.append(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = lst[1:]\n",
    "final = []\n",
    "for i in lst:\n",
    "    final.append(int(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 7832]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "lst = ['Hyderabad', 'Delhi', 'Visakhapatnam', 'Amaravati', 'Amritsar',\n",
    "'Kolkata', 'Chandigarh', 'Patna', 'Gurugram']\n",
    "dataset = pd.read_csv('preprocessed_data.csv')\n",
    "xyz=[]\n",
    "for i in range(0,len(final)):\n",
    "    d = dataset.loc[dataset['Number'] == final[i]]\n",
    "    xyz.append(d[\"City\"].values)\n",
    "l = []\n",
    "for i in range(len(final)):\n",
    "    l.append(lst[xyz[i][0]])\n",
    "newl = []\n",
    "for i in l:\n",
    "    if i in newl:\n",
    "        pass\n",
    "    else:\n",
    "        newl.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Kolkata', 'Visakhapatnam', 'Amritsar', 'Chandigarh']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2 = dataset.loc[dataset['City'] == 5]\n",
    "d3 = dataset.loc[dataset['City'] == 2]\n",
    "d4 = dataset.loc[dataset['City'] == 4]\n",
    "d5 = dataset.loc[dataset['City'] == 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kolkata:  113.97969543147208\n",
      "Visakhapatnam:  116.95402298850574\n",
      "Amritsar:  129.16981132075472\n",
      "Chandigarh:  93.2418772563177\n"
     ]
    }
   ],
   "source": [
    "print(\"Kolkata: \",d2[\"AQI\"].mean())\n",
    "print(\"Visakhapatnam: \",d3[\"AQI\"].mean())\n",
    "print(\"Amritsar: \",d4[\"AQI\"].mean())\n",
    "print(\"Chandigarh: \",d5[\"AQI\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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