combined_arima_ann_naive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arima_ann(column_list):\n",
    "    column_list = column_list.tolist()\n",
    "    training_len = int(len(column_list)*0.7)\n",
    "    train,test = column_list[:training_len] , column_list[training_len:]\n",
    "    training_data = train\n",
    "    error_list = []\n",
    "    prediction_list = []\n",
    "    for t in range(len(test)):\n",
    "        model = ARIMA(training_data, order=(7,0,0))\n",
    "        model_fit = model.fit(disp=0)\n",
    "        predicted_value = model_fit.forecast()\n",
    "        predicted_value = predicted_value[0]\n",
    "        error_list.append(test[t] - predicted_value)\n",
    "        prediction_list.append(predicted_value)\n",
    "        obs = test[t]\n",
    "        training_data.append(obs)\n",
    "#         print('predicted=%f, expected=%f error=%f' % (predicted_value, obs,abs(predicted_value-obs)))\n",
    "    error = mean_squared_error(test, prediction_list)\n",
    "    print('Test MSE: %.3f' % error)\n",
    "    model = ARIMA(train, order=(7,0,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    training_error = model_fit.resid\n",
    "    \n",
    "    train_X,train_Y = [],[]\n",
    "    for i in range(0 , len(training_error) - 7):\n",
    "        train_X.append(training_error[i:i+7])\n",
    "        train_Y.append(training_error[i+7])\n",
    "    \n",
    "    new_train_X,new_train_Y = [],[]\n",
    "    for i in train_X:\n",
    "        new_train_X.append(i.reshape(-1))\n",
    "    for i in train_Y:\n",
    "        new_train_Y.append(i.reshape(-1))\n",
    "    \n",
    "    new_train_X = np.array(new_train_X)\n",
    "    new_train_Y = np.array(new_train_Y)\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Dense(100 , activation=\"tanh\" , init=\"uniform\" , input_dim=7))\n",
    "    model.add(Dense(50 , activation=\"tanh\" , init=\"uniform\"))\n",
    "    model.add(Dense(25 , activation=\"tanh\" , init=\"uniform\"))\n",
    "    model.add(Dense(1 , activation=\"linear\"))\n",
    "    model.compile(\"Adam\", loss=\"mean_squared_error\")\n",
    "    model.fit(new_train_X , new_train_Y , batch_size=20, epochs=400,verbose=0)\n",
    "    test_data = []\n",
    "    for i in error_list:\n",
    "        try:\n",
    "            test_data.append(i[0])\n",
    "        except:\n",
    "            test_data.append(i)\n",
    "    \n",
    "    test_data = np.array(test_data)\n",
    "    \n",
    "    test_X,test_Y = [],[]\n",
    "    for i in range(0 , len(test_data) - 7):\n",
    "        test_X.append(test_data[i:i+7])\n",
    "        test_Y.append(test_data[i+7])\n",
    "\n",
    "    \n",
    "    new_test_X,new_test_Y = [],[]\n",
    "    \n",
    "    for i in test_X:\n",
    "        new_test_X.append(i.reshape(-1))\n",
    "    for i in test_Y:\n",
    "        new_test_Y.append(i.reshape(-1))\n",
    "    \n",
    "    new_test_X = np.array(new_test_X)\n",
    "    new_test_Y = np.array(new_test_Y)\n",
    "\n",
    "    predict_error = model.predict(new_test_X)\n",
    "    arima_ann_predict = prediction_list[7:]+predict_error\n",
    "    print(\"error=\",mean_squared_error(test[7:] ,arima_ann_predict))\n",
    "    return arima_ann_predict\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"cleaned_Data.csv\")\n",
    "data.set_index(\"date\" , inplace = True)\n",
    "data.index = pd.DatetimeIndex(data.index)\n",
    "aggregate_data = data.pivot_table(index = data.index.date)\n",
    "aggregate_data.drop([\"rain\"] , axis=1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=84.238777, expected=82.125000 error=2.113777\n",
      "predicted=82.643313, expected=81.750000 error=0.893313\n",
      "predicted=82.338131, expected=86.375000 error=4.036869\n",
      "predicted=85.951965, expected=87.285714 error=1.333749\n",
      "predicted=86.044725, expected=85.000000 error=1.044725\n",
      "predicted=84.132735, expected=80.000000 error=4.132735\n",
      "predicted=80.714099, expected=83.857143 error=3.143044\n",
      "predicted=84.295188, expected=84.000000 error=0.295188\n",
      "predicted=84.469969, expected=84.875000 error=0.405031\n",
      "predicted=84.078146, expected=86.750000 error=2.671854\n",
      "predicted=86.634817, expected=84.125000 error=2.509817\n",
      "predicted=82.922367, expected=82.250000 error=0.672367\n",
      "predicted=82.798271, expected=81.625000 error=1.173271\n",
      "predicted=82.693507, expected=82.625000 error=0.068507\n",
      "predicted=83.126377, expected=84.000000 error=0.873623\n",
      "predicted=84.331887, expected=84.625000 error=0.293113\n",
      "predicted=84.060778, expected=84.750000 error=0.689222\n",
      "predicted=83.978804, expected=83.500000 error=0.478804\n",
      "predicted=83.071315, expected=82.500000 error=0.571315\n",
      "predicted=82.652171, expected=85.625000 error=2.972829\n",
      "predicted=85.521370, expected=87.125000 error=1.603630\n",
      "predicted=86.291208, expected=86.750000 error=0.458792\n",
      "predicted=85.644657, expected=85.625000 error=0.019657\n",
      "predicted=85.144003, expected=82.875000 error=2.269003\n",
      "predicted=83.005111, expected=77.750000 error=5.255111\n",
      "predicted=79.621491, expected=81.000000 error=1.378509\n",
      "predicted=83.135657, expected=83.500000 error=0.364343\n",
      "predicted=84.319526, expected=82.750000 error=1.569526\n",
      "predicted=82.250065, expected=83.750000 error=1.499935\n",
      "predicted=83.359883, expected=83.375000 error=0.015117\n",
      "predicted=81.918460, expected=82.125000 error=0.206540\n",
      "predicted=81.898046, expected=83.750000 error=1.851954\n",
      "predicted=84.047773, expected=84.750000 error=0.702227\n",
      "predicted=84.172129, expected=84.875000 error=0.702871\n",
      "predicted=84.334743, expected=83.125000 error=1.209743\n",
      "predicted=83.010948, expected=79.250000 error=3.760948\n",
      "predicted=79.819946, expected=82.714286 error=2.894340\n",
      "predicted=83.645965, expected=84.250000 error=0.604035\n",
      "predicted=84.320216, expected=85.875000 error=1.554784\n",
      "predicted=84.870550, expected=85.250000 error=0.379450\n",
      "predicted=84.302732, expected=84.250000 error=0.052732\n",
      "predicted=82.887002, expected=84.875000 error=1.987998\n",
      "predicted=84.838409, expected=86.125000 error=1.286591\n",
      "predicted=85.856771, expected=86.125000 error=0.268229\n",
      "predicted=85.829959, expected=87.000000 error=1.170041\n",
      "predicted=86.382758, expected=85.428571 error=0.954187\n",
      "predicted=84.999798, expected=84.375000 error=0.624798\n",
      "predicted=84.543973, expected=85.500000 error=0.956027\n",
      "predicted=85.966598, expected=85.625000 error=0.341598\n",
      "predicted=85.613434, expected=79.250000 error=6.363434\n",
      "predicted=80.478672, expected=81.875000 error=1.396328\n",
      "predicted=83.186248, expected=84.125000 error=0.938752\n",
      "predicted=84.352815, expected=84.375000 error=0.022185\n",
      "predicted=83.760842, expected=84.750000 error=0.989158\n",
      "predicted=84.575170, expected=84.250000 error=0.325170\n",
      "predicted=82.773923, expected=84.500000 error=1.726077\n",
      "predicted=84.153294, expected=84.750000 error=0.596706\n",
      "predicted=84.723725, expected=85.375000 error=0.651275\n",
      "predicted=85.046738, expected=85.875000 error=0.828262\n",
      "predicted=85.480976, expected=85.750000 error=0.269024\n",
      "predicted=85.229779, expected=87.285714 error=2.055935\n",
      "predicted=86.662396, expected=86.000000 error=0.662396\n",
      "predicted=85.541925, expected=87.125000 error=1.583075\n",
      "predicted=86.744188, expected=86.000000 error=0.744188\n",
      "predicted=85.973605, expected=85.375000 error=0.598605\n",
      "predicted=85.389804, expected=85.375000 error=0.014804\n",
      "predicted=85.948006, expected=87.857143 error=1.909137\n",
      "predicted=87.349538, expected=86.625000 error=0.724538\n",
      "predicted=86.352903, expected=83.875000 error=2.477903\n",
      "predicted=84.046688, expected=76.625000 error=7.421688\n",
      "predicted=78.618819, expected=81.125000 error=2.506181\n",
      "predicted=83.189099, expected=82.500000 error=0.689099\n",
      "predicted=83.846784, expected=82.750000 error=1.096784\n",
      "predicted=82.116597, expected=83.500000 error=1.383403\n",
      "predicted=83.110084, expected=83.375000 error=0.264916\n",
      "predicted=81.585976, expected=83.125000 error=1.539024\n",
      "predicted=82.913256, expected=81.750000 error=1.163256\n",
      "predicted=81.998969, expected=81.500000 error=0.498969\n",
      "predicted=81.927826, expected=83.375000 error=1.447174\n",
      "predicted=83.536777, expected=83.625000 error=0.088223\n",
      "predicted=83.230794, expected=84.125000 error=0.894206\n",
      "predicted=83.539695, expected=83.250000 error=0.289695\n",
      "predicted=82.817587, expected=82.875000 error=0.057413\n",
      "predicted=82.663706, expected=82.875000 error=0.211294\n",
      "predicted=83.168607, expected=81.750000 error=1.418607\n",
      "predicted=82.108650, expected=79.250000 error=2.858650\n",
      "predicted=80.284202, expected=77.750000 error=2.534202\n",
      "predicted=79.156433, expected=75.125000 error=4.031433\n",
      "predicted=76.865012, expected=78.750000 error=1.884988\n",
      "predicted=79.853656, expected=78.250000 error=1.603656\n",
      "predicted=78.576001, expected=79.125000 error=0.548999\n",
      "predicted=78.495385, expected=81.000000 error=2.504615\n",
      "error= 3.7563689999117145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out_full[ind] += zi\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = out_full[ind]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  zf = out_full[ind]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:43: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:44: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:45: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    }
   ],
   "source": [
    "pred = arima_ann(aggregate_data['temp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['temp'] = pred.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dew_pt\n",
      "Test MSE: 1.955\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 0.5253303620539147\n",
      "hum\n",
      "Test MSE: 31.495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 1.7187518853128458\n",
      "pressure\n",
      "Test MSE: 0.002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 0.002414948945652124\n",
      "temp\n",
      "Test MSE: 3.756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 1.0437422445613014\n",
      "vis\n",
      "Test MSE: 0.051\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 0.052093222514077386\n",
      "wind_spd\n",
      "Test MSE: 31.319\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(100, activation=\"tanh\", input_dim=7, kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:40: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(50, activation=\"tanh\", kernel_initializer=\"uniform\")`\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(25, activation=\"tanh\", kernel_initializer=\"uniform\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error= 11.794588696793841\n"
     ]
    }
   ],
   "source": [
    "for i in aggregate_data.columns:\n",
    "    print(i)\n",
    "    pred = arima_ann(aggregate_data[i])\n",
    "    new_data[i] = pred.flatten() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>dew_pt</th>\n",
       "      <th>hum</th>\n",
       "      <th>wind_spd</th>\n",
       "      <th>vis</th>\n",
       "      <th>pressure</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>84.585253</td>\n",
       "      <td>77.592904</td>\n",
       "      <td>78.354329</td>\n",
       "      <td>4.811010</td>\n",
       "      <td>1.619794</td>\n",
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       "      <th>1</th>\n",
       "      <td>84.663637</td>\n",
       "      <td>80.112681</td>\n",
       "      <td>83.854779</td>\n",
       "      <td>4.230057</td>\n",
       "      <td>1.616145</td>\n",
       "      <td>29.635678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86.119821</td>\n",
       "      <td>81.043827</td>\n",
       "      <td>78.562335</td>\n",
       "      <td>3.151507</td>\n",
       "      <td>1.655221</td>\n",
       "      <td>29.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85.286227</td>\n",
       "      <td>79.744264</td>\n",
       "      <td>81.270026</td>\n",
       "      <td>0.824502</td>\n",
       "      <td>1.751578</td>\n",
       "      <td>29.463505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82.523389</td>\n",
       "      <td>78.349781</td>\n",
       "      <td>89.225883</td>\n",
       "      <td>0.062748</td>\n",
       "      <td>1.666078</td>\n",
       "      <td>29.457236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>82.052247</td>\n",
       "      <td>80.304473</td>\n",
       "      <td>94.221264</td>\n",
       "      <td>1.106363</td>\n",
       "      <td>1.390444</td>\n",
       "      <td>29.472943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82.123428</td>\n",
       "      <td>79.660274</td>\n",
       "      <td>91.867147</td>\n",
       "      <td>5.890757</td>\n",
       "      <td>1.487714</td>\n",
       "      <td>29.412029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>83.145245</td>\n",
       "      <td>80.257681</td>\n",
       "      <td>77.482389</td>\n",
       "      <td>9.277407</td>\n",
       "      <td>1.512870</td>\n",
       "      <td>29.396533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85.328575</td>\n",
       "      <td>78.610376</td>\n",
       "      <td>77.463118</td>\n",
       "      <td>5.552695</td>\n",
       "      <td>1.503126</td>\n",
       "      <td>29.520275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84.341620</td>\n",
       "      <td>79.050142</td>\n",
       "      <td>77.931179</td>\n",
       "      <td>1.284431</td>\n",
       "      <td>1.686198</td>\n",
       "      <td>29.461280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>84.672614</td>\n",
       "      <td>79.443645</td>\n",
       "      <td>82.467346</td>\n",
       "      <td>2.138038</td>\n",
       "      <td>1.477048</td>\n",
       "      <td>29.448974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>82.149860</td>\n",
       "      <td>79.204595</td>\n",
       "      <td>89.988561</td>\n",
       "      <td>0.892697</td>\n",
       "      <td>1.335031</td>\n",
       "      <td>29.441476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>83.052409</td>\n",
       "      <td>79.142308</td>\n",
       "      <td>81.021563</td>\n",
       "      <td>3.119698</td>\n",
       "      <td>1.554676</td>\n",
       "      <td>29.480046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>86.478891</td>\n",
       "      <td>78.850842</td>\n",
       "      <td>68.321635</td>\n",
       "      <td>2.747496</td>\n",
       "      <td>1.604591</td>\n",
       "      <td>29.528200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>85.976975</td>\n",
       "      <td>78.466952</td>\n",
       "      <td>72.261705</td>\n",
       "      <td>4.251390</td>\n",
       "      <td>1.622813</td>\n",
       "      <td>29.583677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>84.968171</td>\n",
       "      <td>78.022481</td>\n",
       "      <td>76.716194</td>\n",
       "      <td>0.400576</td>\n",
       "      <td>1.602936</td>\n",
       "      <td>29.561176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>82.197928</td>\n",
       "      <td>79.504798</td>\n",
       "      <td>84.481257</td>\n",
       "      <td>-6.483935</td>\n",
       "      <td>1.713604</td>\n",
       "      <td>29.540063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>80.001444</td>\n",
       "      <td>76.844814</td>\n",
       "      <td>95.895017</td>\n",
       "      <td>1.838231</td>\n",
       "      <td>1.499976</td>\n",
       "      <td>29.534363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>81.214081</td>\n",
       "      <td>76.059568</td>\n",
       "      <td>85.531486</td>\n",
       "      <td>2.062945</td>\n",
       "      <td>1.436609</td>\n",
       "      <td>29.457950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>83.415446</td>\n",
       "      <td>78.063099</td>\n",
       "      <td>81.523919</td>\n",
       "      <td>5.172807</td>\n",
       "      <td>1.624044</td>\n",
       "      <td>29.528428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>83.163926</td>\n",
       "      <td>79.572632</td>\n",
       "      <td>88.274823</td>\n",
       "      <td>4.125377</td>\n",
       "      <td>1.625995</td>\n",
       "      <td>29.593082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>84.204561</td>\n",
       "      <td>80.573061</td>\n",
       "      <td>90.182532</td>\n",
       "      <td>2.590064</td>\n",
       "      <td>1.436085</td>\n",
       "      <td>29.627877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>84.053029</td>\n",
       "      <td>79.955589</td>\n",
       "      <td>84.922601</td>\n",
       "      <td>7.120328</td>\n",
       "      <td>1.501416</td>\n",
       "      <td>29.570377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>82.695904</td>\n",
       "      <td>78.773272</td>\n",
       "      <td>86.766126</td>\n",
       "      <td>4.034211</td>\n",
       "      <td>1.484971</td>\n",
       "      <td>29.588088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>82.473097</td>\n",
       "      <td>78.335335</td>\n",
       "      <td>79.976154</td>\n",
       "      <td>5.103184</td>\n",
       "      <td>1.395105</td>\n",
       "      <td>29.625034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>84.384322</td>\n",
       "      <td>80.007329</td>\n",
       "      <td>80.361183</td>\n",
       "      <td>-0.216370</td>\n",
       "      <td>1.602323</td>\n",
       "      <td>29.635344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>85.158951</td>\n",
       "      <td>79.583970</td>\n",
       "      <td>80.866765</td>\n",
       "      <td>5.098942</td>\n",
       "      <td>1.561988</td>\n",
       "      <td>29.647000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>84.389368</td>\n",
       "      <td>78.686329</td>\n",
       "      <td>77.024904</td>\n",
       "      <td>5.881248</td>\n",
       "      <td>1.577588</td>\n",
       "      <td>29.641238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>80.809192</td>\n",
       "      <td>77.524622</td>\n",
       "      <td>90.389925</td>\n",
       "      <td>4.205893</td>\n",
       "      <td>1.543658</td>\n",
       "      <td>29.586335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>81.350793</td>\n",
       "      <td>76.715849</td>\n",
       "      <td>77.809482</td>\n",
       "      <td>-3.684221</td>\n",
       "      <td>1.370873</td>\n",
       "      <td>29.504270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>86.215263</td>\n",
       "      <td>78.995847</td>\n",
       "      <td>66.624801</td>\n",
       "      <td>1.473197</td>\n",
       "      <td>1.741811</td>\n",
       "      <td>29.815431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>86.359138</td>\n",
       "      <td>78.397957</td>\n",
       "      <td>67.766928</td>\n",
       "      <td>2.566754</td>\n",
       "      <td>1.717643</td>\n",
       "      <td>29.815279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>85.362889</td>\n",
       "      <td>76.142911</td>\n",
       "      <td>65.378717</td>\n",
       "      <td>5.618920</td>\n",
       "      <td>1.776353</td>\n",
       "      <td>29.845508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>85.859570</td>\n",
       "      <td>76.592728</td>\n",
       "      <td>66.636542</td>\n",
       "      <td>4.759233</td>\n",
       "      <td>1.759009</td>\n",
       "      <td>29.838923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>87.823352</td>\n",
       "      <td>76.465670</td>\n",
       "      <td>62.248639</td>\n",
       "      <td>2.126826</td>\n",
       "      <td>1.777086</td>\n",
       "      <td>29.833732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>86.489096</td>\n",
       "      <td>76.479996</td>\n",
       "      <td>62.538171</td>\n",
       "      <td>1.952946</td>\n",
       "      <td>1.861027</td>\n",
       "      <td>29.823148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>85.393803</td>\n",
       "      <td>73.922961</td>\n",
       "      <td>64.322414</td>\n",
       "      <td>6.546751</td>\n",
       "      <td>1.719091</td>\n",
       "      <td>29.783436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>78.331449</td>\n",
       "      <td>74.247174</td>\n",
       "      <td>85.711396</td>\n",
       "      <td>13.429114</td>\n",
       "      <td>1.794647</td>\n",
       "      <td>29.737561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>80.863181</td>\n",
       "      <td>77.689643</td>\n",
       "      <td>89.731027</td>\n",
       "      <td>38.266444</td>\n",
       "      <td>1.512794</td>\n",
       "      <td>29.587414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>82.942721</td>\n",
       "      <td>76.405641</td>\n",
       "      <td>75.611701</td>\n",
       "      <td>21.624807</td>\n",
       "      <td>1.168364</td>\n",
       "      <td>29.538287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>83.973517</td>\n",
       "      <td>75.652753</td>\n",
       "      <td>74.219925</td>\n",
       "      <td>3.834131</td>\n",
       "      <td>1.697397</td>\n",
       "      <td>29.813380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>84.075286</td>\n",
       "      <td>77.431253</td>\n",
       "      <td>79.671215</td>\n",
       "      <td>-0.290348</td>\n",
       "      <td>1.622585</td>\n",
       "      <td>29.836043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>83.484659</td>\n",
       "      <td>77.535984</td>\n",
       "      <td>78.390261</td>\n",
       "      <td>2.282386</td>\n",
       "      <td>1.378111</td>\n",
       "      <td>29.856176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>83.207272</td>\n",
       "      <td>75.070329</td>\n",
       "      <td>69.270355</td>\n",
       "      <td>-1.544737</td>\n",
       "      <td>1.539780</td>\n",
       "      <td>29.893208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>83.921233</td>\n",
       "      <td>73.580294</td>\n",
       "      <td>69.861418</td>\n",
       "      <td>7.683971</td>\n",
       "      <td>1.580034</td>\n",
       "      <td>29.869917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>82.157498</td>\n",
       "      <td>73.115566</td>\n",
       "      <td>70.932892</td>\n",
       "      <td>18.352802</td>\n",
       "      <td>1.529482</td>\n",
       "      <td>29.822399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>82.927109</td>\n",
       "      <td>72.173719</td>\n",
       "      <td>63.731069</td>\n",
       "      <td>0.817483</td>\n",
       "      <td>1.497150</td>\n",
       "      <td>29.848115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>84.491675</td>\n",
       "      <td>74.451354</td>\n",
       "      <td>70.548377</td>\n",
       "      <td>2.454986</td>\n",
       "      <td>1.535255</td>\n",
       "      <td>29.879922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>82.411454</td>\n",
       "      <td>75.859254</td>\n",
       "      <td>70.956099</td>\n",
       "      <td>3.340806</td>\n",
       "      <td>1.650135</td>\n",
       "      <td>29.922867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>83.011576</td>\n",
       "      <td>74.439337</td>\n",
       "      <td>63.894793</td>\n",
       "      <td>1.473126</td>\n",
       "      <td>1.629512</td>\n",
       "      <td>29.898567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>81.863173</td>\n",
       "      <td>73.812778</td>\n",
       "      <td>66.666343</td>\n",
       "      <td>1.161866</td>\n",
       "      <td>1.670366</td>\n",
       "      <td>29.882759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>83.568623</td>\n",
       "      <td>72.072174</td>\n",
       "      <td>63.959162</td>\n",
       "      <td>1.309761</td>\n",
       "      <td>1.659493</td>\n",
       "      <td>29.891756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>83.037150</td>\n",
       "      <td>72.900594</td>\n",
       "      <td>68.683644</td>\n",
       "      <td>2.786794</td>\n",
       "      <td>1.564288</td>\n",
       "      <td>29.894713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79.171181</td>\n",
       "      <td>68.957900</td>\n",
       "      <td>62.694349</td>\n",
       "      <td>2.674695</td>\n",
       "      <td>1.629615</td>\n",
       "      <td>29.968163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79.631828</td>\n",
       "      <td>66.527872</td>\n",
       "      <td>60.825570</td>\n",
       "      <td>3.120443</td>\n",
       "      <td>1.724551</td>\n",
       "      <td>29.943518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>76.645178</td>\n",
       "      <td>66.589410</td>\n",
       "      <td>69.851035</td>\n",
       "      <td>4.236668</td>\n",
       "      <td>1.595510</td>\n",
       "      <td>29.950783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>78.982658</td>\n",
       "      <td>70.947696</td>\n",
       "      <td>71.699460</td>\n",
       "      <td>5.571280</td>\n",
       "      <td>1.600143</td>\n",
       "      <td>29.920982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>79.063632</td>\n",
       "      <td>71.115017</td>\n",
       "      <td>73.509010</td>\n",
       "      <td>6.327555</td>\n",
       "      <td>1.631110</td>\n",
       "      <td>29.836368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>79.361401</td>\n",
       "      <td>72.506130</td>\n",
       "      <td>73.523219</td>\n",
       "      <td>5.307771</td>\n",
       "      <td>1.644545</td>\n",
       "      <td>29.893377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>81.070952</td>\n",
       "      <td>73.853927</td>\n",
       "      <td>73.252073</td>\n",
       "      <td>4.179710</td>\n",
       "      <td>1.577458</td>\n",
       "      <td>29.902131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         temp     dew_pt        hum   wind_spd       vis   pressure\n",
       "0   84.585253  77.592904  78.354329   4.811010  1.619794  29.531831\n",
       "1   84.663637  80.112681  83.854779   4.230057  1.616145  29.635678\n",
       "2   86.119821  81.043827  78.562335   3.151507  1.655221  29.550405\n",
       "3   85.286227  79.744264  81.270026   0.824502  1.751578  29.463505\n",
       "4   82.523389  78.349781  89.225883   0.062748  1.666078  29.457236\n",
       "5   82.052247  80.304473  94.221264   1.106363  1.390444  29.472943\n",
       "6   82.123428  79.660274  91.867147   5.890757  1.487714  29.412029\n",
       "7   83.145245  80.257681  77.482389   9.277407  1.512870  29.396533\n",
       "8   85.328575  78.610376  77.463118   5.552695  1.503126  29.520275\n",
       "9   84.341620  79.050142  77.931179   1.284431  1.686198  29.461280\n",
       "10  84.672614  79.443645  82.467346   2.138038  1.477048  29.448974\n",
       "11  82.149860  79.204595  89.988561   0.892697  1.335031  29.441476\n",
       "12  83.052409  79.142308  81.021563   3.119698  1.554676  29.480046\n",
       "13  86.478891  78.850842  68.321635   2.747496  1.604591  29.528200\n",
       "14  85.976975  78.466952  72.261705   4.251390  1.622813  29.583677\n",
       "15  84.968171  78.022481  76.716194   0.400576  1.602936  29.561176\n",
       "16  82.197928  79.504798  84.481257  -6.483935  1.713604  29.540063\n",
       "17  80.001444  76.844814  95.895017   1.838231  1.499976  29.534363\n",
       "18  81.214081  76.059568  85.531486   2.062945  1.436609  29.457950\n",
       "19  83.415446  78.063099  81.523919   5.172807  1.624044  29.528428\n",
       "20  83.163926  79.572632  88.274823   4.125377  1.625995  29.593082\n",
       "21  84.204561  80.573061  90.182532   2.590064  1.436085  29.627877\n",
       "22  84.053029  79.955589  84.922601   7.120328  1.501416  29.570377\n",
       "23  82.695904  78.773272  86.766126   4.034211  1.484971  29.588088\n",
       "24  82.473097  78.335335  79.976154   5.103184  1.395105  29.625034\n",
       "25  84.384322  80.007329  80.361183  -0.216370  1.602323  29.635344\n",
       "26  85.158951  79.583970  80.866765   5.098942  1.561988  29.647000\n",
       "27  84.389368  78.686329  77.024904   5.881248  1.577588  29.641238\n",
       "28  80.809192  77.524622  90.389925   4.205893  1.543658  29.586335\n",
       "29  81.350793  76.715849  77.809482  -3.684221  1.370873  29.504270\n",
       "..        ...        ...        ...        ...       ...        ...\n",
       "55  86.215263  78.995847  66.624801   1.473197  1.741811  29.815431\n",
       "56  86.359138  78.397957  67.766928   2.566754  1.717643  29.815279\n",
       "57  85.362889  76.142911  65.378717   5.618920  1.776353  29.845508\n",
       "58  85.859570  76.592728  66.636542   4.759233  1.759009  29.838923\n",
       "59  87.823352  76.465670  62.248639   2.126826  1.777086  29.833732\n",
       "60  86.489096  76.479996  62.538171   1.952946  1.861027  29.823148\n",
       "61  85.393803  73.922961  64.322414   6.546751  1.719091  29.783436\n",
       "62  78.331449  74.247174  85.711396  13.429114  1.794647  29.737561\n",
       "63  80.863181  77.689643  89.731027  38.266444  1.512794  29.587414\n",
       "64  82.942721  76.405641  75.611701  21.624807  1.168364  29.538287\n",
       "65  83.973517  75.652753  74.219925   3.834131  1.697397  29.813380\n",
       "66  84.075286  77.431253  79.671215  -0.290348  1.622585  29.836043\n",
       "67  83.484659  77.535984  78.390261   2.282386  1.378111  29.856176\n",
       "68  83.207272  75.070329  69.270355  -1.544737  1.539780  29.893208\n",
       "69  83.921233  73.580294  69.861418   7.683971  1.580034  29.869917\n",
       "70  82.157498  73.115566  70.932892  18.352802  1.529482  29.822399\n",
       "71  82.927109  72.173719  63.731069   0.817483  1.497150  29.848115\n",
       "72  84.491675  74.451354  70.548377   2.454986  1.535255  29.879922\n",
       "73  82.411454  75.859254  70.956099   3.340806  1.650135  29.922867\n",
       "74  83.011576  74.439337  63.894793   1.473126  1.629512  29.898567\n",
       "75  81.863173  73.812778  66.666343   1.161866  1.670366  29.882759\n",
       "76  83.568623  72.072174  63.959162   1.309761  1.659493  29.891756\n",
       "77  83.037150  72.900594  68.683644   2.786794  1.564288  29.894713\n",
       "78  79.171181  68.957900  62.694349   2.674695  1.629615  29.968163\n",
       "79  79.631828  66.527872  60.825570   3.120443  1.724551  29.943518\n",
       "80  76.645178  66.589410  69.851035   4.236668  1.595510  29.950783\n",
       "81  78.982658  70.947696  71.699460   5.571280  1.600143  29.920982\n",
       "82  79.063632  71.115017  73.509010   6.327555  1.631110  29.836368\n",
       "83  79.361401  72.506130  73.523219   5.307771  1.644545  29.893377\n",
       "84  81.070952  73.853927  73.252073   4.179710  1.577458  29.902131\n",
       "\n",
       "[85 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = new_data[['temp', 'dew_pt', 'hum', 'wind_spd', 'vis', 'pressure']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('gaussian_model.pkl','rb') as f:\n",
    "    rain_model = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain =rain_model.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_data['rain'] = rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>dew_pt</th>\n",
       "      <th>hum</th>\n",
       "      <th>wind_spd</th>\n",
       "      <th>vis</th>\n",
       "      <th>pressure</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>82.052247</td>\n",
       "      <td>80.304473</td>\n",
       "      <td>94.221264</td>\n",
       "      <td>1.106363</td>\n",
       "      <td>1.390444</td>\n",
       "      <td>29.472943</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82.123428</td>\n",
       "      <td>79.660274</td>\n",
       "      <td>91.867147</td>\n",
       "      <td>5.890757</td>\n",
       "      <td>1.487714</td>\n",
       "      <td>29.412029</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>82.149860</td>\n",
       "      <td>79.204595</td>\n",
       "      <td>89.988561</td>\n",
       "      <td>0.892697</td>\n",
       "      <td>1.335031</td>\n",
       "      <td>29.441476</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>82.197928</td>\n",
       "      <td>79.504798</td>\n",
       "      <td>84.481257</td>\n",
       "      <td>-6.483935</td>\n",
       "      <td>1.713604</td>\n",
       "      <td>29.540063</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>80.001444</td>\n",
       "      <td>76.844814</td>\n",
       "      <td>95.895017</td>\n",
       "      <td>1.838231</td>\n",
       "      <td>1.499976</td>\n",
       "      <td>29.534363</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>81.214081</td>\n",
       "      <td>76.059568</td>\n",
       "      <td>85.531486</td>\n",
       "      <td>2.062945</td>\n",
       "      <td>1.436609</td>\n",
       "      <td>29.457950</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>81.373991</td>\n",
       "      <td>78.400730</td>\n",
       "      <td>86.163591</td>\n",
       "      <td>8.830555</td>\n",
       "      <td>1.439004</td>\n",
       "      <td>29.404392</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>80.863181</td>\n",
       "      <td>77.689643</td>\n",
       "      <td>89.731027</td>\n",
       "      <td>38.266444</td>\n",
       "      <td>1.512794</td>\n",
       "      <td>29.587414</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>82.942721</td>\n",
       "      <td>76.405641</td>\n",
       "      <td>75.611701</td>\n",
       "      <td>21.624807</td>\n",
       "      <td>1.168364</td>\n",
       "      <td>29.538287</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temp     dew_pt        hum   wind_spd       vis   pressure  rain\n",
       "5   82.052247  80.304473  94.221264   1.106363  1.390444  29.472943     1\n",
       "6   82.123428  79.660274  91.867147   5.890757  1.487714  29.412029     1\n",
       "11  82.149860  79.204595  89.988561   0.892697  1.335031  29.441476     1\n",
       "16  82.197928  79.504798  84.481257  -6.483935  1.713604  29.540063     1\n",
       "17  80.001444  76.844814  95.895017   1.838231  1.499976  29.534363     1\n",
       "18  81.214081  76.059568  85.531486   2.062945  1.436609  29.457950     1\n",
       "43  81.373991  78.400730  86.163591   8.830555  1.439004  29.404392     1\n",
       "63  80.863181  77.689643  89.731027  38.266444  1.512794  29.587414     1\n",
       "64  82.942721  76.405641  75.611701  21.624807  1.168364  29.538287     1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['rain'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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