finalproject1(multiple regression).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('full_energydata.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19735, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>date</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>...</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-11 17:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.53</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.275433</td>\n",
       "      <td>13.275433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-11 17:10:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.483333</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "      <td>18.606195</td>\n",
       "      <td>18.606195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-11 17:20:00</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "      <td>28.642668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-11 17:30:00</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.410389</td>\n",
       "      <td>45.410389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11 17:40:00</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.133333</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.084097</td>\n",
       "      <td>10.084097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
       "0  2016-01-11 17:00:00          60      30  19.89  47.596667  19.2  44.790000   \n",
       "1  2016-01-11 17:10:00          60      30  19.89  46.693333  19.2  44.722500   \n",
       "2  2016-01-11 17:20:00          50      30  19.89  46.300000  19.2  44.626667   \n",
       "3  2016-01-11 17:30:00          50      40  19.89  46.066667  19.2  44.590000   \n",
       "4  2016-01-11 17:40:00          60      40  19.89  46.333333  19.2  44.530000   \n",
       "\n",
       "      T3       RH_3         T4    ...             T9   RH_9     T_out  \\\n",
       "0  19.79  44.730000  19.000000    ...      17.033333  45.53  6.600000   \n",
       "1  19.79  44.790000  19.000000    ...      17.066667  45.56  6.483333   \n",
       "2  19.79  44.933333  18.926667    ...      17.000000  45.50  6.366667   \n",
       "3  19.79  45.000000  18.890000    ...      17.000000  45.40  6.250000   \n",
       "4  19.79  45.000000  18.890000    ...      17.000000  45.40  6.133333   \n",
       "\n",
       "   Press_mm_hg  RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
       "0        733.5    92.0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
       "1        733.6    92.0   6.666667   59.166667        5.2  18.606195  18.606195  \n",
       "2        733.7    92.0   6.333333   55.333333        5.1  28.642668  28.642668  \n",
       "3        733.8    92.0   6.000000   51.500000        5.0  45.410389  45.410389  \n",
       "4        733.9    92.0   5.666667   47.666667        4.9  10.084097  10.084097  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "      <td>19735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.694958</td>\n",
       "      <td>3.801875</td>\n",
       "      <td>21.686571</td>\n",
       "      <td>40.259739</td>\n",
       "      <td>20.341219</td>\n",
       "      <td>40.420420</td>\n",
       "      <td>22.267611</td>\n",
       "      <td>39.242500</td>\n",
       "      <td>20.855335</td>\n",
       "      <td>39.026904</td>\n",
       "      <td>...</td>\n",
       "      <td>19.485828</td>\n",
       "      <td>41.552401</td>\n",
       "      <td>7.411665</td>\n",
       "      <td>755.522602</td>\n",
       "      <td>79.750418</td>\n",
       "      <td>4.039752</td>\n",
       "      <td>38.330834</td>\n",
       "      <td>3.760707</td>\n",
       "      <td>24.988033</td>\n",
       "      <td>24.988033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.524891</td>\n",
       "      <td>7.935988</td>\n",
       "      <td>1.606066</td>\n",
       "      <td>3.979299</td>\n",
       "      <td>2.192974</td>\n",
       "      <td>4.069813</td>\n",
       "      <td>2.006111</td>\n",
       "      <td>3.254576</td>\n",
       "      <td>2.042884</td>\n",
       "      <td>4.341321</td>\n",
       "      <td>...</td>\n",
       "      <td>2.014712</td>\n",
       "      <td>4.151497</td>\n",
       "      <td>5.317409</td>\n",
       "      <td>7.399441</td>\n",
       "      <td>14.901088</td>\n",
       "      <td>2.451221</td>\n",
       "      <td>11.794719</td>\n",
       "      <td>4.194648</td>\n",
       "      <td>14.496634</td>\n",
       "      <td>14.496634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.790000</td>\n",
       "      <td>27.023333</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>20.463333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>28.766667</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>27.660000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>729.300000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.600000</td>\n",
       "      <td>0.005322</td>\n",
       "      <td>0.005322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>19.530000</td>\n",
       "      <td>35.530000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>750.933333</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12.497889</td>\n",
       "      <td>12.497889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>39.656667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>38.530000</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>38.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>40.900000</td>\n",
       "      <td>6.916667</td>\n",
       "      <td>756.100000</td>\n",
       "      <td>83.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.433333</td>\n",
       "      <td>24.897653</td>\n",
       "      <td>24.897653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>43.066667</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>43.260000</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>41.760000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>42.156667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>44.338095</td>\n",
       "      <td>10.408333</td>\n",
       "      <td>760.933333</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.566667</td>\n",
       "      <td>37.583769</td>\n",
       "      <td>37.583769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1080.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>63.360000</td>\n",
       "      <td>29.856667</td>\n",
       "      <td>56.026667</td>\n",
       "      <td>29.236000</td>\n",
       "      <td>50.163333</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>53.326667</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>772.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>49.996530</td>\n",
       "      <td>49.996530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Appliances        lights            T1          RH_1            T2  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean      97.694958      3.801875     21.686571     40.259739     20.341219   \n",
       "std      102.524891      7.935988      1.606066      3.979299      2.192974   \n",
       "min       10.000000      0.000000     16.790000     27.023333     16.100000   \n",
       "25%       50.000000      0.000000     20.760000     37.333333     18.790000   \n",
       "50%       60.000000      0.000000     21.600000     39.656667     20.000000   \n",
       "75%      100.000000      0.000000     22.600000     43.066667     21.500000   \n",
       "max     1080.000000     70.000000     26.260000     63.360000     29.856667   \n",
       "\n",
       "               RH_2            T3          RH_3            T4          RH_4  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean      40.420420     22.267611     39.242500     20.855335     39.026904   \n",
       "std        4.069813      2.006111      3.254576      2.042884      4.341321   \n",
       "min       20.463333     17.200000     28.766667     15.100000     27.660000   \n",
       "25%       37.900000     20.790000     36.900000     19.530000     35.530000   \n",
       "50%       40.500000     22.100000     38.530000     20.666667     38.400000   \n",
       "75%       43.260000     23.290000     41.760000     22.100000     42.156667   \n",
       "max       56.026667     29.236000     50.163333     26.200000     51.090000   \n",
       "\n",
       "           ...                 T9          RH_9         T_out   Press_mm_hg  \\\n",
       "count      ...       19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean       ...          19.485828     41.552401      7.411665    755.522602   \n",
       "std        ...           2.014712      4.151497      5.317409      7.399441   \n",
       "min        ...          14.890000     29.166667     -5.000000    729.300000   \n",
       "25%        ...          18.000000     38.500000      3.666667    750.933333   \n",
       "50%        ...          19.390000     40.900000      6.916667    756.100000   \n",
       "75%        ...          20.600000     44.338095     10.408333    760.933333   \n",
       "max        ...          24.500000     53.326667     26.100000    772.300000   \n",
       "\n",
       "             RH_out     Windspeed    Visibility     Tdewpoint           rv1  \\\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
       "mean      79.750418      4.039752     38.330834      3.760707     24.988033   \n",
       "std       14.901088      2.451221     11.794719      4.194648     14.496634   \n",
       "min       24.000000      0.000000      1.000000     -6.600000      0.005322   \n",
       "25%       70.333333      2.000000     29.000000      0.900000     12.497889   \n",
       "50%       83.666667      3.666667     40.000000      3.433333     24.897653   \n",
       "75%       91.666667      5.500000     40.000000      6.566667     37.583769   \n",
       "max      100.000000     14.000000     66.000000     15.500000     49.996530   \n",
       "\n",
       "                rv2  \n",
       "count  19735.000000  \n",
       "mean      24.988033  \n",
       "std       14.496634  \n",
       "min        0.005322  \n",
       "25%       12.497889  \n",
       "50%       24.897653  \n",
       "75%       37.583769  \n",
       "max       49.996530  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot broadcast operands together.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1496\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1497\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1498\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(op, op_str, a, b, use_numexpr, **eval_kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b, **eval_kwargs)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'complex'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-6d5cc025b1da>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[0;32m   1552\u001b[0m             return _combine_series_frame(self, other, na_op,\n\u001b[0;32m   1553\u001b[0m                                          \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1554\u001b[1;33m                                          level=level, try_cast=True)\n\u001b[0m\u001b[0;32m   1555\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1556\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36m_combine_series_frame\u001b[1;34m(self, other, func, fill_value, axis, level, try_cast)\u001b[0m\n\u001b[0;32m   1437\u001b[0m         \u001b[1;31m# default axis is columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1438\u001b[0m         return self._combine_match_columns(other, func, level=level,\n\u001b[1;32m-> 1439\u001b[1;33m                                            try_cast=try_cast)\n\u001b[0m\u001b[0;32m   1440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_combine_match_columns\u001b[1;34m(self, other, func, level, try_cast)\u001b[0m\n\u001b[0;32m   4771\u001b[0m         new_data = left._data.eval(func=func, other=right,\n\u001b[0;32m   4772\u001b[0m                                    \u001b[0maxes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4773\u001b[1;33m                                    try_cast=try_cast)\n\u001b[0m\u001b[0;32m   4774\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4775\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m   3685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3686\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3687\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'eval'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3689\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[0;32m   3579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3580\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mgr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3581\u001b[1;33m             \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3582\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3583\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, func, other, errors, try_cast, mgr)\u001b[0m\n\u001b[0;32m   1413\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1414\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1415\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1417\u001b[0m         \u001b[1;31m# if we have an invalid shape/broadcast error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(other)\u001b[0m\n\u001b[0;32m   1381\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1382\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1383\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m             \u001b[1;31m# mask if needed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1509\u001b[0m                     \u001b[1;31m# Without specifically raising here we get mismatched\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1510\u001b[0m                     \u001b[1;31m# errors in Py3 (TypeError) vs Py2 (ValueError)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1511\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot broadcast operands together.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1512\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1513\u001b[0m                 \u001b[0myrav\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0myrav\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot broadcast operands together."
     ]
    }
   ],
   "source": [
    "    dataset = (dataset - dataset.mean())/dataset.std()\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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