tensor_flow_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c527235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n"
     ]
    }
   ],
   "source": [
    "print(\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "deb84b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1200ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your Tensor flow version: 2.6.0\n"
     ]
    }
   ],
   "source": [
    "print((\"Your Tensor flow version: {0}\").format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20841666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
       "array([[1, 2],\n",
       "       [3, 4]])>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = tf.constant([[1,2],[3,4]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9b23620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(2, 2) dtype=int32, numpy=\n",
       "array([[1, 2],\n",
       "       [3, 4]])>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = tf.Variable([[1,2],[3,4]])\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5d275ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 4), dtype=int32, numpy=\n",
       "array([[1, 2, 1, 2],\n",
       "       [3, 4, 3, 4]])>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AB_Concatenated = tf.concat(values = [A,B],axis = 1)\n",
    "AB_Concatenated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e38fe488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 2), dtype=int32, numpy=\n",
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [1, 2],\n",
       "       [3, 4]])>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AB_Concatenated_1 = tf.concat(values = [A,B],axis = 0)\n",
    "AB_Concatenated_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d47997e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 4), dtype=int32, numpy=\n",
       "array([[0, 0, 0, 0],\n",
       "       [0, 0, 0, 0],\n",
       "       [0, 0, 0, 0]])>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor = tf.zeros(shape=[3,4],dtype = tf.int32)\n",
    "tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca9e369f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 4), dtype=int32, numpy=\n",
       "array([[1, 1, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 1]])>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_1 = tf.ones(shape=[3,4],dtype = tf.int32)\n",
    "tensor_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18e4c939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 4), dtype=float32, numpy=\n",
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_1 = tf.ones(shape=[3,4],dtype = tf.float32)\n",
    "tensor_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0d49729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 3), dtype=float32, numpy=\n",
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_tensor = tf.reshape(tensor = tensor_1, shape = [4,3])\n",
    "reshaped_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afd09ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 4), dtype=float32, numpy=\n",
       "array([[0.6301898 , 0.49578655, 0.61417174, 0.38227022],\n",
       "       [0.81323564, 0.66888857, 0.98950124, 0.46218598],\n",
       "       [0.13209093, 0.09898376, 0.78129244, 0.20528436]], dtype=float32)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = tf.random.uniform(shape=[3,4])\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4189f03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 3), dtype=float32, numpy=\n",
       "array([[0.6301898 , 0.49578655, 0.61417174],\n",
       "       [0.38227022, 0.81323564, 0.66888857],\n",
       "       [0.98950124, 0.46218598, 0.13209093],\n",
       "       [0.09898376, 0.78129244, 0.20528436]], dtype=float32)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_D = tf.reshape(tensor = D, shape = [4,3])\n",
    "reshaped_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "16f72338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[0.6301898  0.49578655 0.61417174 0.38227022]\n",
      " [0.81323564 0.66888857 0.98950124 0.46218598]\n",
      " [0.13209093 0.09898376 0.78129244 0.20528436]], shape=(3, 4), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]], shape=(3, 4), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "# type-cast \n",
    "D_int = tf.cast(D,tf.int32)\n",
    "print(D)\n",
    "print(D_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25e2dc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 3), dtype=float32, numpy=\n",
       "array([[0.6301898 , 0.81323564, 0.13209093],\n",
       "       [0.49578655, 0.66888857, 0.09898376],\n",
       "       [0.61417174, 0.98950124, 0.78129244],\n",
       "       [0.38227022, 0.46218598, 0.20528436]], dtype=float32)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.transpose(D)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1023052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 1), dtype=int32, numpy=\n",
       "array([[36],\n",
       "       [30]])>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix multiplication\n",
    "R = tf.constant([[5,8],[3,9]])\n",
    "S = tf.constant([[4],[2]])\n",
    "R_X_S = tf.matmul(R,S)\n",
    "R_X_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "67e2add0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
       "array([[20, 32],\n",
       "       [ 6, 18]])>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Element-wise multiply\n",
    "R_S = tf.multiply(R,S)\n",
    "R_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9fb8afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row: 3 columns: 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=int32, numpy=\n",
       "array([[1, 0],\n",
       "       [0, 1],\n",
       "       [0, 0]])>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identity Matrix\n",
    "T = tf.constant([[4,9],[5,6],[1,8]])\n",
    "rows, columns = T.shape\n",
    "print(\"row:\",rows,\"columns:\",columns)\n",
    "Identity_T = tf.eye(num_rows = rows,num_columns = columns, dtype= tf.int32)\n",
    "\n",
    "Identity_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44af1a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import The Important Libraries\n",
    "\n",
    "import datetime, warnings, scipy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"patch.force_edgecolor\"]=True\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "mpl.rc(\"patch\",edgecolor = \"dimgray\", linewidth = 1)\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"last_expr\"\n",
    "pd.options.display.max_columns = 50\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b2a7ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ca3d220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56b962d3",
   "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>No</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Tanggal</th>\n",
       "      <th>Jam</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO</th>\n",
       "      <th>CO2</th>\n",
       "      <th>VOC</th>\n",
       "      <th>PM1</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM4</th>\n",
       "      <th>PM10</th>\n",
       "      <th>TSP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>HUM</th>\n",
       "      <th>WS</th>\n",
       "      <th>WD</th>\n",
       "      <th>ISPU</th>\n",
       "      <th>Status</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:04:19</td>\n",
       "      <td>21.35</td>\n",
       "      <td>0.78</td>\n",
       "      <td>6.77</td>\n",
       "      <td>14.06</td>\n",
       "      <td>26.168</td>\n",
       "      <td>409.86</td>\n",
       "      <td>251.202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:15:01</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>14.87</td>\n",
       "      <td>17.23</td>\n",
       "      <td>53.853</td>\n",
       "      <td>394.79</td>\n",
       "      <td>501.356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:30:06</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>22.24</td>\n",
       "      <td>2.31</td>\n",
       "      <td>38.304</td>\n",
       "      <td>390.66</td>\n",
       "      <td>753.291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.6</td>\n",
       "      <td>299.2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:49:19</td>\n",
       "      <td>13.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>19.00</td>\n",
       "      <td>5.32</td>\n",
       "      <td>18.962</td>\n",
       "      <td>401.23</td>\n",
       "      <td>251.043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>88.2</td>\n",
       "      <td>0.525</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>01:04:19</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>26.74</td>\n",
       "      <td>13.61</td>\n",
       "      <td>35.270</td>\n",
       "      <td>381.67</td>\n",
       "      <td>250.471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>0.975</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  Profile     Tanggal       Jam     O3    CO    NO2    SO2      NO  \\\n",
       "0   1  BANDUNG  2019-05-19  00:04:19  21.35  0.78   6.77  14.06  26.168   \n",
       "1   2  BANDUNG  2019-05-19  00:15:01   5.93  0.82  14.87  17.23  53.853   \n",
       "2   3  BANDUNG  2019-05-19  00:30:06   6.62  0.63  22.24   2.31  38.304   \n",
       "3   4  BANDUNG  2019-05-19  00:49:19  13.29  0.44  19.00   5.32  18.962   \n",
       "4   5  BANDUNG  2019-05-19  01:04:19   2.44  0.47  26.74  13.61  35.270   \n",
       "\n",
       "      CO2      VOC  PM1  PM2.5  PM4   PM10  TSP  TEMP    HUM     WS  WD  ISPU  \\\n",
       "0  409.86  251.202  0.0    0.0  0.0   2.77  0.0  21.4  100.0  0.600   5     9   \n",
       "1  394.79  501.356  0.0    0.0  0.0  11.95  0.0  42.6  200.0  0.000   2    12   \n",
       "2  390.66  753.291  0.0    0.0  0.0  12.71  0.0  63.6  299.2  0.000   2    13   \n",
       "3  401.23  251.043  0.0    0.0  0.0   1.20  0.0  21.3   88.2  0.525   5     6   \n",
       "4  381.67  250.471  0.0    0.0  0.0   3.52  0.0  21.3   97.4  0.975   5     9   \n",
       "\n",
       "  Status  Unnamed: 22  \n",
       "0   BAIK          NaN  \n",
       "1   BAIK          NaN  \n",
       "2   BAIK          NaN  \n",
       "3   BAIK          NaN  \n",
       "4   BAIK          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"air_quality.csv\",sep=\" \")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67300f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9231, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b1f2759",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Tanggal</th>\n",
       "      <th>Jam</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO</th>\n",
       "      <th>CO2</th>\n",
       "      <th>VOC</th>\n",
       "      <th>PM1</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM4</th>\n",
       "      <th>PM10</th>\n",
       "      <th>TSP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>HUM</th>\n",
       "      <th>WS</th>\n",
       "      <th>WD</th>\n",
       "      <th>ISPU</th>\n",
       "      <th>Status</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:04:19</td>\n",
       "      <td>21.35</td>\n",
       "      <td>0.78</td>\n",
       "      <td>6.77</td>\n",
       "      <td>14.06</td>\n",
       "      <td>26.168</td>\n",
       "      <td>409.86</td>\n",
       "      <td>251.202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:15:01</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.82</td>\n",
       "      <td>14.87</td>\n",
       "      <td>17.23</td>\n",
       "      <td>53.853</td>\n",
       "      <td>394.79</td>\n",
       "      <td>501.356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:30:06</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>22.24</td>\n",
       "      <td>2.31</td>\n",
       "      <td>38.304</td>\n",
       "      <td>390.66</td>\n",
       "      <td>753.291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.6</td>\n",
       "      <td>299.2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>00:49:19</td>\n",
       "      <td>13.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>19.00</td>\n",
       "      <td>5.32</td>\n",
       "      <td>18.962</td>\n",
       "      <td>401.23</td>\n",
       "      <td>251.043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>88.2</td>\n",
       "      <td>0.525</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>BANDUNG</td>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>01:04:19</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>26.74</td>\n",
       "      <td>13.61</td>\n",
       "      <td>35.270</td>\n",
       "      <td>381.67</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>0.975</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>BAIK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-19 01:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  Profile     Tanggal       Jam     O3    CO    NO2    SO2      NO  \\\n",
       "0   1  BANDUNG  2019-05-19  00:04:19  21.35  0.78   6.77  14.06  26.168   \n",
       "1   2  BANDUNG  2019-05-19  00:15:01   5.93  0.82  14.87  17.23  53.853   \n",
       "2   3  BANDUNG  2019-05-19  00:30:06   6.62  0.63  22.24   2.31  38.304   \n",
       "3   4  BANDUNG  2019-05-19  00:49:19  13.29  0.44  19.00   5.32  18.962   \n",
       "4   5  BANDUNG  2019-05-19  01:04:19   2.44  0.47  26.74  13.61  35.270   \n",
       "\n",
       "      CO2      VOC  PM1  PM2.5  PM4   PM10  TSP  TEMP    HUM     WS  WD  ISPU  \\\n",
       "0  409.86  251.202  0.0    0.0  0.0   2.77  0.0  21.4  100.0  0.600   5     9   \n",
       "1  394.79  501.356  0.0    0.0  0.0  11.95  0.0  42.6  200.0  0.000   2    12   \n",
       "2  390.66  753.291  0.0    0.0  0.0  12.71  0.0  63.6  299.2  0.000   2    13   \n",
       "3  401.23  251.043  0.0    0.0  0.0   1.20  0.0  21.3   88.2  0.525   5     6   \n",
       "4  381.67  250.471  0.0    0.0  0.0   3.52  0.0  21.3   97.4  0.975   5     9   \n",
       "\n",
       "  Status  Unnamed: 22            Datetime  \n",
       "0   BAIK          NaN 2019-05-19 00:00:00  \n",
       "1   BAIK          NaN 2019-05-19 00:00:00  \n",
       "2   BAIK          NaN 2019-05-19 00:00:00  \n",
       "3   BAIK          NaN 2019-05-19 00:00:00  \n",
       "4   BAIK          NaN 2019-05-19 01:00:00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Time to Datetime format:\n",
    "\n",
    "def combine_date(df, tab_name):\n",
    "    list_tab = []\n",
    "    for i in range(df.shape[0]):\n",
    "        list_tab.append(df.loc[i,\"Tanggal\"] + \"T\"+ df.loc[i,tab_name][0:2])\n",
    "    return np.array(list_tab, dtype = \"datetime64\")\n",
    "\n",
    "df[\"Datetime\"]= combine_date(df,\"Jam\")\n",
    "#df = df[[\"Datetime\",df.columns]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb9f4dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
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       "      <th>ISPU</th>\n",
       "      <th>Unnamed: 22</th>\n",
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       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-19 00:00:00</th>\n",
       "      <td>2.5</td>\n",
       "      <td>11.797500</td>\n",
       "      <td>0.667500</td>\n",
       "      <td>15.720</td>\n",
       "      <td>9.7300</td>\n",
       "      <td>34.32175</td>\n",
       "      <td>399.135000</td>\n",
       "      <td>439.22300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.157500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.225000</td>\n",
       "      <td>171.850000</td>\n",
       "      <td>0.28125</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-19 01:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.816667</td>\n",
       "      <td>0.373333</td>\n",
       "      <td>22.600</td>\n",
       "      <td>20.0900</td>\n",
       "      <td>39.82100</td>\n",
       "      <td>388.536667</td>\n",
       "      <td>250.39400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.293333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.966667</td>\n",
       "      <td>95.033333</td>\n",
       "      <td>0.67500</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-19 02:00:00</th>\n",
       "      <td>9.5</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>0.332500</td>\n",
       "      <td>24.130</td>\n",
       "      <td>26.3100</td>\n",
       "      <td>51.29325</td>\n",
       "      <td>396.242500</td>\n",
       "      <td>251.04575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.487500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.150000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-19 03:00:00</th>\n",
       "      <td>13.5</td>\n",
       "      <td>4.740000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>25.975</td>\n",
       "      <td>24.3900</td>\n",
       "      <td>43.51850</td>\n",
       "      <td>401.355000</td>\n",
       "      <td>252.24875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.07500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-19 04:00:00</th>\n",
       "      <td>17.5</td>\n",
       "      <td>19.692500</td>\n",
       "      <td>0.202500</td>\n",
       "      <td>20.395</td>\n",
       "      <td>18.6225</td>\n",
       "      <td>53.37925</td>\n",
       "      <td>399.335000</td>\n",
       "      <td>694.23975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.262500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.475000</td>\n",
       "      <td>272.500000</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No         O3        CO     NO2      SO2        NO  \\\n",
       "Datetime                                                                    \n",
       "2019-05-19 00:00:00   2.5  11.797500  0.667500  15.720   9.7300  34.32175   \n",
       "2019-05-19 01:00:00   6.0   3.816667  0.373333  22.600  20.0900  39.82100   \n",
       "2019-05-19 02:00:00   9.5   6.450000  0.332500  24.130  26.3100  51.29325   \n",
       "2019-05-19 03:00:00  13.5   4.740000  0.180000  25.975  24.3900  43.51850   \n",
       "2019-05-19 04:00:00  17.5  19.692500  0.202500  20.395  18.6225  53.37925   \n",
       "\n",
       "                            CO2        VOC  PM1  PM2.5  PM4      PM10  TSP  \\\n",
       "Datetime                                                                     \n",
       "2019-05-19 00:00:00  399.135000  439.22300  0.0    0.0  0.0  7.157500  0.0   \n",
       "2019-05-19 01:00:00  388.536667  250.39400  0.0    0.0  0.0  3.293333  0.0   \n",
       "2019-05-19 02:00:00  396.242500  251.04575  0.0    0.0  0.0  6.487500  0.0   \n",
       "2019-05-19 03:00:00  401.355000  252.24875  0.0    0.0  0.0  4.700000  0.0   \n",
       "2019-05-19 04:00:00  399.335000  694.23975  0.0    0.0  0.0  5.262500  0.0   \n",
       "\n",
       "                          TEMP         HUM       WS        WD       ISPU  \\\n",
       "Datetime                                                                   \n",
       "2019-05-19 00:00:00  37.225000  171.850000  0.28125  3.500000  10.000000   \n",
       "2019-05-19 01:00:00  20.966667   95.033333  0.67500  4.666667  12.666667   \n",
       "2019-05-19 02:00:00  20.150000   97.500000  0.09375  3.750000  16.250000   \n",
       "2019-05-19 03:00:00  19.850000  100.000000  0.07500  5.000000  15.000000   \n",
       "2019-05-19 04:00:00  53.475000  272.500000  0.18750  5.250000  13.250000   \n",
       "\n",
       "                     Unnamed: 22  \n",
       "Datetime                          \n",
       "2019-05-19 00:00:00          NaN  \n",
       "2019-05-19 01:00:00          NaN  \n",
       "2019-05-19 02:00:00          NaN  \n",
       "2019-05-19 03:00:00          NaN  \n",
       "2019-05-19 04:00:00          NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby([\"Datetime\"]).mean()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b2d49c9",
   "metadata": {},
   "outputs": [
    {
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       "\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>No</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO</th>\n",
       "      <th>CO2</th>\n",
       "      <th>VOC</th>\n",
       "      <th>PM1</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM4</th>\n",
       "      <th>PM10</th>\n",
       "      <th>TSP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>HUM</th>\n",
       "      <th>WS</th>\n",
       "      <th>WD</th>\n",
       "      <th>ISPU</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4283.312608</td>\n",
       "      <td>8.538909</td>\n",
       "      <td>0.233172</td>\n",
       "      <td>16.681910</td>\n",
       "      <td>12.013894</td>\n",
       "      <td>37.086256</td>\n",
       "      <td>394.616480</td>\n",
       "      <td>260.053778</td>\n",
       "      <td>0.309564</td>\n",
       "      <td>0.383692</td>\n",
       "      <td>0.451982</td>\n",
       "      <td>6.157770</td>\n",
       "      <td>0.562012</td>\n",
       "      <td>25.266942</td>\n",
       "      <td>77.634191</td>\n",
       "      <td>24.291226</td>\n",
       "      <td>7.129031</td>\n",
       "      <td>10.097848</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2760.274597</td>\n",
       "      <td>3.997292</td>\n",
       "      <td>0.388102</td>\n",
       "      <td>5.373487</td>\n",
       "      <td>4.411251</td>\n",
       "      <td>12.521778</td>\n",
       "      <td>4.180224</td>\n",
       "      <td>60.773321</td>\n",
       "      <td>2.286104</td>\n",
       "      <td>2.874126</td>\n",
       "      <td>3.378291</td>\n",
       "      <td>4.149511</td>\n",
       "      <td>4.184787</td>\n",
       "      <td>6.707977</td>\n",
       "      <td>29.743129</td>\n",
       "      <td>51.993683</td>\n",
       "      <td>3.523245</td>\n",
       "      <td>5.049788</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>2.040000</td>\n",
       "      <td>2.162500</td>\n",
       "      <td>3.128750</td>\n",
       "      <td>380.420000</td>\n",
       "      <td>187.518000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>26.866667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1699.500000</td>\n",
       "      <td>5.881667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>9.015000</td>\n",
       "      <td>28.443500</td>\n",
       "      <td>391.830000</td>\n",
       "      <td>247.798333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.266667</td>\n",
       "      <td>57.850000</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>8.166667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4143.500000</td>\n",
       "      <td>7.813333</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>15.916667</td>\n",
       "      <td>11.730000</td>\n",
       "      <td>39.252250</td>\n",
       "      <td>394.602000</td>\n",
       "      <td>250.926250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.571667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>77.083333</td>\n",
       "      <td>1.312500</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6714.500000</td>\n",
       "      <td>10.373333</td>\n",
       "      <td>0.296667</td>\n",
       "      <td>19.935000</td>\n",
       "      <td>14.595000</td>\n",
       "      <td>46.015500</td>\n",
       "      <td>397.415000</td>\n",
       "      <td>253.286667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>94.066667</td>\n",
       "      <td>5.075000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9228.500000</td>\n",
       "      <td>33.741667</td>\n",
       "      <td>13.950000</td>\n",
       "      <td>99.600000</td>\n",
       "      <td>46.290000</td>\n",
       "      <td>194.554000</td>\n",
       "      <td>408.820000</td>\n",
       "      <td>1240.713500</td>\n",
       "      <td>51.600000</td>\n",
       "      <td>68.700000</td>\n",
       "      <td>79.800000</td>\n",
       "      <td>91.600000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>124.600000</td>\n",
       "      <td>313.400000</td>\n",
       "      <td>199.200000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>156.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                No           O3           CO          NO2          SO2  \\\n",
       "count  1737.000000  1737.000000  1737.000000  1737.000000  1737.000000   \n",
       "mean   4283.312608     8.538909     0.233172    16.681910    12.013894   \n",
       "std    2760.274597     3.997292     0.388102     5.373487     4.411251   \n",
       "min       2.500000     0.270000     0.010000     2.040000     2.162500   \n",
       "25%    1699.500000     5.881667     0.083333    13.010000     9.015000   \n",
       "50%    4143.500000     7.813333     0.160000    15.916667    11.730000   \n",
       "75%    6714.500000    10.373333     0.296667    19.935000    14.595000   \n",
       "max    9228.500000    33.741667    13.950000    99.600000    46.290000   \n",
       "\n",
       "                NO          CO2          VOC          PM1        PM2.5  \\\n",
       "count  1737.000000  1737.000000  1737.000000  1737.000000  1737.000000   \n",
       "mean     37.086256   394.616480   260.053778     0.309564     0.383692   \n",
       "std      12.521778     4.180224    60.773321     2.286104     2.874126   \n",
       "min       3.128750   380.420000   187.518000     0.000000     0.000000   \n",
       "25%      28.443500   391.830000   247.798333     0.000000     0.000000   \n",
       "50%      39.252250   394.602000   250.926250     0.000000     0.000000   \n",
       "75%      46.015500   397.415000   253.286667     0.000000     0.000000   \n",
       "max     194.554000   408.820000  1240.713500    51.600000    68.700000   \n",
       "\n",
       "               PM4         PM10          TSP         TEMP          HUM  \\\n",
       "count  1737.000000  1737.000000  1737.000000  1737.000000  1737.000000   \n",
       "mean      0.451982     6.157770     0.562012    25.266942    77.634191   \n",
       "std       3.378291     4.149511     4.184787     6.707977    29.743129   \n",
       "min       0.000000     0.885000     0.000000    11.250000    26.866667   \n",
       "25%       0.000000     4.340000     0.000000    21.266667    57.850000   \n",
       "50%       0.000000     5.571667     0.000000    24.100000    77.083333   \n",
       "75%       0.000000     7.090000     0.000000    28.400000    94.066667   \n",
       "max      79.800000    91.600000    92.900000   124.600000   313.400000   \n",
       "\n",
       "                WS           WD         ISPU  Unnamed: 22  \n",
       "count  1737.000000  1737.000000  1737.000000          0.0  \n",
       "mean     24.291226     7.129031    10.097848          NaN  \n",
       "std      51.993683     3.523245     5.049788          NaN  \n",
       "min       0.000000     0.000000     4.250000          NaN  \n",
       "25%       0.237500     4.666667     8.166667          NaN  \n",
       "50%       1.312500     5.833333     9.500000          NaN  \n",
       "75%       5.075000    10.500000    11.000000          NaN  \n",
       "max     199.200000    15.000000   156.500000          NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5877492e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Attribute- No :\n",
      "Q1 = 1702.75\n",
      "Q3 = 6705.50\n",
      "Min IQR = -5801.38\n",
      "Max IQR = 14209.62\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- O3 :\n",
      "Q1 = 5.88\n",
      "Q3 = 10.37\n",
      "Min IQR = -0.86\n",
      "Max IQR = 17.11\n",
      "--> High Outlier is found = 33.74\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- CO :\n",
      "Q1 = 0.08\n",
      "Q3 = 0.30\n",
      "Min IQR = -0.24\n",
      "Max IQR = 0.62\n",
      "--> High Outlier is found = 13.95\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- NO2 :\n",
      "Q1 = 13.01\n",
      "Q3 = 19.93\n",
      "Min IQR = 2.62\n",
      "Max IQR = 30.32\n",
      "--> Low Outlier is found = 2.04\n",
      "--> High Outlier is found = 99.60\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- SO2 :\n",
      "Q1 = 8.99\n",
      "Q3 = 14.57\n",
      "Min IQR = 0.63\n",
      "Max IQR = 22.93\n",
      "--> High Outlier is found = 46.29\n",
      "\n",
      "Attribute- NO :\n",
      "Q1 = 28.44\n",
      "Q3 = 46.02\n",
      "Min IQR = 2.09\n",
      "Max IQR = 72.37\n",
      "--> High Outlier is found = 194.55\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- CO2 :\n",
      "Q1 = 391.84\n",
      "Q3 = 397.42\n",
      "Min IQR = 383.47\n",
      "Max IQR = 405.79\n",
      "--> Low Outlier is found = 380.42\n",
      "--> High Outlier is found = 408.82\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- VOC :\n",
      "Q1 = 247.80\n",
      "Q3 = 253.29\n",
      "Min IQR = 239.57\n",
      "Max IQR = 261.52\n",
      "--> Low Outlier is found = 187.52\n",
      "--> High Outlier is found = 1240.71\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- PM1 :\n",
      "Q1 = 0.00\n",
      "Q3 = 0.00\n",
      "Min IQR = 0.00\n",
      "Max IQR = 0.00\n",
      "--> High Outlier is found = 51.60\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- PM2.5 :\n",
      "Q1 = 0.00\n",
      "Q3 = 0.00\n",
      "Min IQR = 0.00\n",
      "Max IQR = 0.00\n",
      "--> High Outlier is found = 68.70\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- PM4 :\n",
      "Q1 = 0.00\n",
      "Q3 = 0.00\n",
      "Min IQR = 0.00\n",
      "Max IQR = 0.00\n",
      "--> High Outlier is found = 79.80\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- PM10 :\n",
      "Q1 = 4.34\n",
      "Q3 = 7.09\n",
      "Min IQR = 0.22\n",
      "Max IQR = 11.21\n",
      "--> High Outlier is found = 91.60\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- TSP :\n",
      "Q1 = 0.00\n",
      "Q3 = 0.00\n",
      "Min IQR = 0.00\n",
      "Max IQR = 0.00\n",
      "--> High Outlier is found = 92.90\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- TEMP :\n",
      "Q1 = 21.29\n",
      "Q3 = 28.41\n",
      "Min IQR = 10.62\n",
      "Max IQR = 39.08\n",
      "--> High Outlier is found = 124.60\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- HUM :\n",
      "Q1 = 57.81\n",
      "Q3 = 94.04\n",
      "Min IQR = 3.46\n",
      "Max IQR = 148.39\n",
      "--> High Outlier is found = 313.40\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- WS :\n",
      "Q1 = 0.24\n",
      "Q3 = 5.01\n",
      "Min IQR = -6.92\n",
      "Max IQR = 12.16\n",
      "--> High Outlier is found = 199.20\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- WD :\n",
      "Q1 = 4.67\n",
      "Q3 = 10.60\n",
      "Min IQR = -4.23\n",
      "Max IQR = 19.50\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- ISPU :\n",
      "Q1 = 8.17\n",
      "Q3 = 11.00\n",
      "Min IQR = 3.92\n",
      "Max IQR = 15.25\n",
      "--> High Outlier is found = 156.50\n",
      "################ Outliers have been interpolated ######\n",
      "\n",
      "Attribute- Unnamed: 22 :\n",
      "Q1 = nan\n",
      "Q3 = nan\n",
      "Min IQR = nan\n",
      "Max IQR = nan\n"
     ]
    }
   ],
   "source": [
    "def calculate_quantile(i,df2):\n",
    "    Q1 = df2[[i]].quantile(0.25)[0]\n",
    "    Q3 = df2[[i]].quantile(0.75)[0]\n",
    "    IQR = Q3 - Q1\n",
    "    min = df2[[i]].min()[0]\n",
    "    max = df2[[i]].max()[0]\n",
    "    min_IQR = Q1-1.5*IQR\n",
    "    max_IQR = Q3+1.5*IQR\n",
    "    \n",
    "    return Q1,Q3,min,max,min_IQR,max_IQR\n",
    "\n",
    "# delete first and last row to void missing values extrapolation\n",
    "\n",
    "df2.drop(index=[df2.index[0],df2.index[df2.shape[0]-1]],inplace = True)\n",
    "\n",
    "\n",
    "# find and interpolate the outliers\n",
    "\n",
    "for i in df2.columns:\n",
    "    print(\"\\nAttribute-\",i,\":\")\n",
    "    Q1, Q3, min,max,min_IQR,max_IQR = calculate_quantile(i,df2)\n",
    "    print(\"Q1 = %.2f\" %Q1)\n",
    "    print(\"Q3 = %.2f\" %Q3)\n",
    "    print(\"Min IQR = %.2f\" %min_IQR)\n",
    "    print(\"Max IQR = %.2f\" %max_IQR)\n",
    "    if min<min_IQR:\n",
    "        print(\"--> Low Outlier is found = %.2f\" %min)\n",
    "    if max>max_IQR:\n",
    "        print(\"--> High Outlier is found = %.2f\" %max)\n",
    "    \n",
    "    def convert_nan(x,max_IQR=max_IQR,min_IQR= min_IQR):\n",
    "        if((x>max_IQR) | (x < min_IQR)):\n",
    "            x = np.nan\n",
    "        else:\n",
    "            x = x\n",
    "        return x\n",
    "    \n",
    "    def convert_nan_HUM(x,max_IQR = 100.0,min_IQR = min_IQR):\n",
    "        if((x>max_IQR) | (x < min_IQR)):\n",
    "            x = np.nan\n",
    "        else:\n",
    "            x=x\n",
    "        return x\n",
    "    if i == \"HUM\":\n",
    "        df2[i]=df2[i].map(convert_nan_HUM)\n",
    "        df2[i]=df2[i].interpolate(method=\"linear\")\n",
    "    if i != \"HUM\":\n",
    "        df2[i]=df2[i].map(convert_nan)\n",
    "        df2[i]=df2[i].interpolate(method=\"linear\")\n",
    "        \n",
    "    if(len(df2[df2[i].isnull()][i])==0):\n",
    "        print(\"################ Outliers have been interpolated ######\")\n",
    "           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc055a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1731, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log transformation to deal with skewed data\n",
    "\n",
    "dataset = np.log1p(df2[[\"TEMP\"]].values)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "700af53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'TEMP'}>,\n",
       "        <AxesSubplot:title={'center':'log_TEMP'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create New DataFrame to Compare the original Vs Log Transform data\n",
    "dist_df = pd.DataFrame({\"TEMP\": df2[\"TEMP\"].values,\"log_TEMP\":dataset[:,0]})\n",
    "\n",
    "# Histogram plot original vs log transform data\n",
    "plt.figure(figsize=(12,5))\n",
    "dist_df.hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c188aeec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1298 433\n"
     ]
    }
   ],
   "source": [
    "# split the dataset into train and test sets\n",
    "\n",
    "train_size = int(len(dataset)*0.75)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train),len(test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f746a8c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of trainX:  (1296, 1)\n",
      "Shape of TrianY:  (1296,)\n",
      "Shape of TestX:  (431, 1)\n",
      "Shape of TestY:  (431,)\n"
     ]
    }
   ],
   "source": [
    "# Convert an array of values into a dataset matrix\n",
    "\n",
    "def create_dataset(dataset, look_back = 1):\n",
    "    dataX, dataY = [],[]\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back),0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "# Reshape the train and test data\n",
    "\n",
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train,look_back)\n",
    "testX, testY = create_dataset(test,look_back)\n",
    "\n",
    "print(\"Shape of trainX: \",trainX.shape)\n",
    "print(\"Shape of TrianY: \",trainY.shape)\n",
    "print(\"Shape of TestX: \", testX.shape)\n",
    "print(\"Shape of TestY: \", testY.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b38b1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of trainX: (1296, 1, 1)\n",
      "shape of TestX: (431, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape the input array in the form - [samples, time steps, features]\n",
    "\n",
    "trainX = np.reshape(trainX,(trainX.shape[0],1,trainX.shape[1]))\n",
    "testX = np.reshape(testX,(testX.shape[0],1,testX.shape[1])) \n",
    "\n",
    "print(\"Shape of trainX:\", trainX.shape)\n",
    "print(\"shape of TestX:\", testX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9df5f477",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "41/41 - 1s - loss: 10.4624\n",
      "Epoch 2/500\n",
      "41/41 - 0s - loss: 9.6389\n",
      "Epoch 3/500\n",
      "41/41 - 0s - loss: 8.8315\n",
      "Epoch 4/500\n",
      "41/41 - 0s - loss: 7.9630\n",
      "Epoch 5/500\n",
      "41/41 - 0s - loss: 6.9730\n",
      "Epoch 6/500\n",
      "41/41 - 0s - loss: 5.8444\n",
      "Epoch 7/500\n",
      "41/41 - 0s - loss: 4.6427\n",
      "Epoch 8/500\n",
      "41/41 - 0s - loss: 3.5048\n",
      "Epoch 9/500\n",
      "41/41 - 0s - loss: 2.5446\n",
      "Epoch 10/500\n",
      "41/41 - 0s - loss: 1.7999\n",
      "Epoch 11/500\n",
      "41/41 - 0s - loss: 1.2512\n",
      "Epoch 12/500\n",
      "41/41 - 0s - loss: 0.8585\n",
      "Epoch 13/500\n",
      "41/41 - 0s - loss: 0.5832\n",
      "Epoch 14/500\n",
      "41/41 - 0s - loss: 0.3926\n",
      "Epoch 15/500\n",
      "41/41 - 0s - loss: 0.2625\n",
      "Epoch 16/500\n",
      "41/41 - 0s - loss: 0.1751\n",
      "Epoch 17/500\n",
      "41/41 - 0s - loss: 0.1170\n",
      "Epoch 18/500\n",
      "41/41 - 0s - loss: 0.0790\n",
      "Epoch 19/500\n",
      "41/41 - 0s - loss: 0.0547\n",
      "Epoch 20/500\n",
      "41/41 - 0s - loss: 0.0394\n",
      "Epoch 21/500\n",
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    },
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     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x27d4940a5e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM Neural network\n",
    "model = Sequential()\n",
    "model.add(LSTM(4,input_shape=(1,look_back)))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(loss=\"mean_squared_error\",optimizer=\"adam\")\n",
    "\n",
    "model.fit(trainX, trainY, epochs=500,batch_size=32,verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eac29449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 2.37 RMSE\n",
      "Test Score: 3.15 RMSE\n"
     ]
    }
   ],
   "source": [
    "# Make Predictions\n",
    "\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n",
    "\n",
    "\n",
    "# Invert predictions\n",
    "\n",
    "trainPredict = np.expm1(trainPredict)\n",
    "trainY = np.expm1(trainY)\n",
    "\n",
    "testPredict = np.expm1(testPredict)\n",
    "testY = np.expm1(testY)\n",
    "\n",
    "# calculate root mean squared error\n",
    "\n",
    "trainScore = math.sqrt(mean_squared_error(trainY,trainPredict[:,0]))\n",
    "print(\"Train Score: %.2f RMSE\" %(trainScore))\n",
    "\n",
    "testScore = math.sqrt(mean_squared_error(testY,testPredict[:,0]))\n",
    "print(\"Test Score: %.2f RMSE\" %(testScore))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea90b785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[ Model performance is GOOD enough ]\n",
      "\n",
      "RMSE of test prediction < Standard deviation of test datatset\n",
      "3.15 < 4.41\n"
     ]
    }
   ],
   "source": [
    "test_series = pd.Series(testY)\n",
    "    \n",
    "#State of model performance\n",
    "\n",
    "if testScore < test_series.std():\n",
    "    print(\"\\n[ Model performance is GOOD enough ]\")\n",
    "    print(\"\\nRMSE of test prediction < Standard deviation of test datatset\")\n",
    "    print(\"%.2f\" %(testScore), \"<\",\"%.2f\"%(test_series.std()))\n",
    "else:\n",
    "    print(\"\\n[ Model Performance is NOT GOOD enough ]\")\n",
    "    print(\"\\nRMSE of test prediction > Standard deviation of test datatset\")\n",
    "    print(\"%.2f\" %(testScore), \">\",\"%.2f\"%(test_series.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "11446a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1360.0, 1735.0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shift train predictions for plotting\n",
    "\n",
    "trainPredictPlot= np.empty_like(dataset)\n",
    "trainPredictPlot[:,:]= np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back,:]=trainPredict\n",
    "\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:,:]= np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1,:]=testPredict\n",
    "\n",
    "\n",
    "# plot original dataset and Predictions\n",
    "time_axis = np.linspace(0,dataset.shape[0]-1,15)\n",
    "time_axis = np.array([int(i) for i in time_axis])\n",
    "time_axisLab = np.array(df2.index,dtype = \"datetime64[D]\")\n",
    "#time_axisLab = np.array(df2.index.astype('datetime64[D]'))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,2.1,2])\n",
    "ax.plot(np.expm1(dataset),label = \"Original Dataset\")\n",
    "ax.plot(trainPredictPlot,color=\"orange\",label=\"Train Predictions\")\n",
    "ax.plot(testPredictPlot,color=\"red\",label=\"Test Predictions\")\n",
    "ax.set_xticks(time_axis)\n",
    "ax.set_xticklabels(time_axisLab[time_axis],rotation=45)\n",
    "ax.set_xlabel(\"\\nDate\",fontsize = 27,fontweight = \"bold\")\n",
    "ax.set_ylabel(\"ISPU\",fontsize=27,fontweight=\"bold\")\n",
    "ax.legend(loc=\"best\",prop={\"size\":20})\n",
    "ax.tick_params(size=10,labelsize=15)\n",
    "ax.set_xlim([-1,1735])\n",
    "\n",
    "ax1 = fig.add_axes([2.3,1.3,1,0.7])\n",
    "ax1.plot(np.expm1(dataset),label = \"Original Dataset\")\n",
    "ax1.plot(testPredictPlot,color=\"red\",label=\"Test Predictions\")\n",
    "ax1.set_xticks(time_axis)\n",
    "ax1.set_xticklabels(time_axisLab[time_axis],rotation=45)\n",
    "ax1.set_xlabel(\"Date\",fontsize = 27,fontweight = \"bold\")\n",
    "ax1.set_ylabel(\"ISPU\",fontsize=27,fontweight=\"bold\")\n",
    "ax1.tick_params(size=10,labelsize=15)\n",
    "ax1.set_xlim([1360,1735])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "42824564",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################## THANKS ###############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc3a3fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}