econ_484_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from pandas.plotting import scatter_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import tree\n",
    "#import pydotplus\n",
    "import collections\n",
    "from IPython.display import Image  \n",
    "#import pydotplus\n",
    "from sklearn.tree import export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_stata(\"Econ_484_data_final.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isin([\"''\"]).sum(axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"DAYSWAIT_CHRON\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df[\"TX_DATE\"]).year\n",
    "df['month'] = pd.DatetimeIndex(df[\"TX_DATE\"]).month\n",
    "df = df.drop(\"TX_DATE\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making dummy variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- gender\n",
    "- abo{9}\n",
    "- EXH_PERIT_ACCCESS{3}\n",
    "- EXH_VASC_ACCESS{3}\n",
    "- PREV_TX{2}\n",
    "- PREV_KI_TX{2}\n",
    "- MALIG_TRR{3}\n",
    "- PERM_STATE_TRR{51}\n",
    "- txkid{3}\n",
    "- ABO_DON{9}\n",
    "- DON_TY{3}\n",
    "- GENDER_DON{2}\n",
    "- HOME_STATE_DON{51}\n",
    "- ABO_MAT{3}\n",
    "- GRF_STAT_KI{2}\n",
    "- DWFG_KI{2}\n",
    "- TX_PROCEDUR_TY_KI{[did you get one or two? See data_sheet]}\n",
    "- PRV_TX_ANY{2}\n",
    "- PX_STAT{4}\n",
    "- SHARE_TY{4}\n",
    "- AGE_GROUP{2}\n",
    "- malig{3} (previous malignancies)\n",
    "- LT_ONE_WEEK_DON {2} (7 days old or less?)\n",
    "- RECOV_OUT_US{2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_columns = ['gender', 'abo', 'EXH_PERIT_ACCESS', 'EXH_VASC_ACCESS', 'PREV_TX', 'PREV_KI_TX',\n",
    "                 'MALIG_TRR', 'txkid', 'ABO_DON', 'DON_TY', 'GENDER_DON', 'DON_TY', 'GENDER_DON',\n",
    "                 'HOME_STATE_DON', 'ABO_MAT', 'GRF_STAT_KI', 'DWFG_KI', 'PREV_TX_ANY', 'PX_STAT',\n",
    "                 'SHARE_TY', 'AGE_GROUP', 'malig', 'LT_ONE_WEEK_DON', 'RECOV_OUT_US', 'year', 'month',\n",
    "                 'PERM_STATE']\n",
    "funky_columns = ['TX_PROCEDUR_TY_KI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_non_dummy = df.drop(dummy_columns, axis=1)\n",
    "#df_non_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummified = pd.get_dummies(df[dummy_columns])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = df_non_dummy.merge(df_dummified, left_index=True, right_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_small = df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "econ_484_data = df_small.to_excel(r'C:\\Users\\jordan79\\Desktop\\export_dataframe.xlsx', index= None, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NUM_PREV_TX', 'A1', 'A2', 'B1', 'B2', 'DR1', 'DR2', 'REM_CD',\n",
       "       'END_STAT', 'INIT_AGE',\n",
       "       ...\n",
       "       'PERM_STATE_TX', 'PERM_STATE_UT', 'PERM_STATE_VA', 'PERM_STATE_VI',\n",
       "       'PERM_STATE_VT', 'PERM_STATE_WA', 'PERM_STATE_WI', 'PERM_STATE_WV',\n",
       "       'PERM_STATE_WY', 'PERM_STATE_ZZ'],\n",
       "      dtype='object', length=221)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_variable = 'DAYSWAIT_CHRON_KI'\n",
    "X_variables = list(df.columns[df.columns != y_variable])\n",
    "X = df[X_variables].to_numpy()\n",
    "y = df[y_variable].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "forest = RandomForestRegressor(n_estimators=5)\n",
    "model = forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8279539199267968"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances_(model):\n",
    "    n_features = X_test.shape[1]\n",
    "    plt.barh(np.arange(n_features),model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features),df.columns)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plot_feature_importances_(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 230)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21 =  X[21,:].copy().reshape(1, -1)\n",
    "num_21.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "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>NUM_PREV_TX</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>DR1</th>\n",
       "      <th>DR2</th>\n",
       "      <th>REM_CD</th>\n",
       "      <th>END_STAT</th>\n",
       "      <th>INIT_AGE</th>\n",
       "      <th>...</th>\n",
       "      <th>PERM_STATE_TX</th>\n",
       "      <th>PERM_STATE_UT</th>\n",
       "      <th>PERM_STATE_VA</th>\n",
       "      <th>PERM_STATE_VI</th>\n",
       "      <th>PERM_STATE_VT</th>\n",
       "      <th>PERM_STATE_WA</th>\n",
       "      <th>PERM_STATE_WI</th>\n",
       "      <th>PERM_STATE_WV</th>\n",
       "      <th>PERM_STATE_WY</th>\n",
       "      <th>PERM_STATE_ZZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4010.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NUM_PREV_TX    A1    A2   B1    B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  11.0  28.0  8.0  44.0  3.0  4.0     4.0    4010.0      43.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            1.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21  = pd.DataFrame(num_21, columns = df[X_variables].columns)\n",
    "num_21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21.iloc[0,221]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>NUM_PREV_TX</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
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       "      <th>PERM_STATE_WV</th>\n",
       "      <th>PERM_STATE_WY</th>\n",
       "      <th>PERM_STATE_ZZ</th>\n",
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      ],
      "text/plain": [
       "   NUM_PREV_TX    A1    A2   B1    B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  11.0  28.0  8.0  44.0  3.0  4.0     4.0    4010.0      43.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            1.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21_different_state = copy.deepcopy(num_21)\n",
    "num_21_different_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   NUM_PREV_TX    A1    A2   B1    B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  11.0  28.0  8.0  44.0  3.0  4.0     4.0    4010.0      43.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "\n",
    "num_21_different_state.iloc[0,220]=0\n",
    "num_21_different_state.iloc[0,180]=1\n",
    "num_21_different_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   NUM_PREV_TX    A1    A2   B1    B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  11.0  28.0  8.0  44.0  3.0  4.0     4.0    4010.0      43.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            1.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "   NUM_PREV_TX    A1    A2   B1    B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  11.0  28.0  8.0  44.0  3.0  4.0     4.0    4010.0      43.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21_different_state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "num_21 = num_21.to_numpy()\n",
    "num_21_different_state = num_21_different_state.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[175.4]\n",
      "[175.4]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(num_21))\n",
    "print(model.predict(num_21_different_state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NUM_PREV_TX', 'A1', 'A2', 'B1', 'B2', 'DR1', 'DR2', 'REM_CD',\n",
       "       'END_STAT', 'INIT_AGE',\n",
       "       ...\n",
       "       'PERM_STATE_TX', 'PERM_STATE_UT', 'PERM_STATE_VA', 'PERM_STATE_VI',\n",
       "       'PERM_STATE_VT', 'PERM_STATE_WA', 'PERM_STATE_WI', 'PERM_STATE_WV',\n",
       "       'PERM_STATE_WY', 'PERM_STATE_ZZ'],\n",
       "      dtype='object', length=230)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[X_variables].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "ave_people = pd.read_excel('ave_person_2.xlsx', sheet_name= 'Sheet2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4010</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NUM_PREV_TX  A1  A2  B1  B2  DR1  DR2  REM_CD  END_STAT  INIT_AGE  ...  \\\n",
       "0            0   2  24  27  49    4   11       4      4010        47  ...   \n",
       "1            0   2  24  35  50    7   11       4      4010        48  ...   \n",
       "2            0   2  28  35  51    7   12       4      4010        47  ...   \n",
       "\n",
       "   PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  PERM_STATE_VT  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "\n",
       "   PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  PERM_STATE_ZZ  \n",
       "0              0              0              0              0              0  \n",
       "1              0              0              0              0              0  \n",
       "2              0              0              0              0              0  \n",
       "\n",
       "[3 rows x 220 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave_people= ave_people.drop(\"DAYSWAIT_CHRON_KI\", axis=1)\n",
    "ave_people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    2,   24,   27,   49,    4,   11,    4, 4010,   47,    0,\n",
       "          1,    5,   35,    0,   37,    1, 4010,   48, 1643,    0,  101,\n",
       "          0, 2031,    3, 2006,    7,    0,    1,    1,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    1,    0,    0,    0,    1,\n",
       "          0,    0,    1,    0,    1,    0,    0,    1,    0,    0,    0,\n",
       "          1,    0,    0,    0,    0,    0,    0,    0,    0,    0,    1,\n",
       "          0,    1,    0,    0,    0,    1,    1,    0,    0,    0,    1,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    1,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    1,    0,    0,    0,    0,    1,\n",
       "          1,    0,    1,    0,    1,    0,    0,    0,    1,    0,    1,\n",
       "          0,    0,    1,    0,    0,    1,    0,    0], dtype=int64)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_0 = ave_people.iloc[0,:162].copy().to_numpy()\n",
    "num_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Number of features of the model must match the input. Model n_features is 230 and input n_features is 220 ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-112-ec11b76f8292>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mA\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mnum_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_1\u001b[0m\u001b[1;33m)\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      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    686\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'estimators_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m         \u001b[1;31m# Check data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 688\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\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    689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    690\u001b[0m         \u001b[1;31m# Assign chunk of trees to jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    360\u001b[0m                                  \"call `fit` before exploiting the model.\")\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\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    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    386\u001b[0m                              \u001b[1;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m                              \u001b[1;34m\"input n_features is %s \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m                              % (self.n_features_, n_features))\n\u001b[0m\u001b[0;32m    389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 230 and input n_features is 220 "
     ]
    }
   ],
   "source": [
    "#num_21 =  X[21,:172].copy().reshape(1, -1)\n",
    "List = []\n",
    "for i in range(58):\n",
    "    A = np.zeros((58,), dtype=int)\n",
    "    A[i]=1\n",
    "    num_1 = np.concatenate((num_0,A), axis = None).reshape(1,-1)\n",
    "    List.append(model.predict(num_1))\n",
    "    print(model.predict(num_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 230)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_21 =  X[21,:].copy().reshape(1, -1)\n",
    "num_21.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[163.]\n",
      "[257.2]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[112.8]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[191.6]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[214.6]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[673.4]\n",
      "[163.]\n",
      "[271.8]\n",
      "[163.]\n",
      "[163.]\n",
      "[454.4]\n",
      "[163.]\n",
      "[454.2]\n",
      "[126.6]\n",
      "[213.6]\n",
      "[156.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[269.8]\n",
      "[329.4]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[163.]\n",
      "[228.6]\n",
      "[195.6]\n",
      "[163.]\n",
      "[163.]\n",
      "[1353.2]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#num_21 =  X[21,:172].copy().reshape(1, -1)\n",
    "List = []\n",
    "for i in range(58):\n",
    "    A = np.zeros((58,), dtype=int)\n",
    "    A[i]=1\n",
    "    num_10000 = np.concatenate((X[18917,:172].copy(),A), axis = None).reshape(1,-1)\n",
    "    List.append(model.predict(num_10000))\n",
    "    print(model.predict(num_10000))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_names = ['AK','AL','AR','AS','AZ','CA','CO','CT','DC','DE','FL','GA',\n",
    "               'GU','HI','IA','ID','IL','IN','KS','KY','LA','MA','MD','ME',\n",
    "               'MI','MN','MO','MP','MS','MT','NA','NC','ND','NE','NH','NJ',\n",
    "               'NM','NV','NY','OH','OK','OR','PA','PR','RI','SC','SD','TN',\n",
    "               'TX','UT','VA','VI','VT','WA','WI','WV','WY','ZZ']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Person 1\n",
    "List = np.asarray(List)\n",
    "#List.reshape(-1,1)\n",
    "\n",
    "wl_times = pd.DataFrame()\n",
    "wl_times['state']= state_names\n",
    "wl_times['68592']= List\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Person 2\n",
    "List = np.asarray(List)\n",
    "wl_times['360018']=List\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 3\n",
    "List = np.asarray(List)\n",
    "wl_times['300328']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 4\n",
    "List = np.asarray(List)\n",
    "wl_times['173781']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 5\n",
    "List = np.asarray(List)\n",
    "wl_times['311525']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 6\n",
    "List = np.asarray(List)\n",
    "wl_times['336194']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 7\n",
    "List = np.asarray(List)\n",
    "wl_times['298122']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 8\n",
    "List = np.asarray(List)\n",
    "wl_times['343924']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 9\n",
    "List = np.asarray(List)\n",
    "wl_times['22757']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Person 10\n",
    "List = np.asarray(List)\n",
    "wl_times['18917']=List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "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>68592</th>\n",
       "      <th>360018</th>\n",
       "      <th>300328</th>\n",
       "      <th>173781</th>\n",
       "      <th>311525</th>\n",
       "      <th>336194</th>\n",
       "      <th>298122</th>\n",
       "      <th>343924</th>\n",
       "      <th>22757</th>\n",
       "      <th>18917</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>818.575862</td>\n",
       "      <td>76.665517</td>\n",
       "      <td>290.627586</td>\n",
       "      <td>955.441379</td>\n",
       "      <td>711.024138</td>\n",
       "      <td>1364.110345</td>\n",
       "      <td>96.865517</td>\n",
       "      <td>327.234483</td>\n",
       "      <td>210.320690</td>\n",
       "      <td>212.910345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.616748</td>\n",
       "      <td>18.502855</td>\n",
       "      <td>9.365517</td>\n",
       "      <td>30.298764</td>\n",
       "      <td>20.536901</td>\n",
       "      <td>16.622419</td>\n",
       "      <td>13.589283</td>\n",
       "      <td>38.021954</td>\n",
       "      <td>93.719821</td>\n",
       "      <td>176.586662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>534.800000</td>\n",
       "      <td>46.200000</td>\n",
       "      <td>247.600000</td>\n",
       "      <td>866.000000</td>\n",
       "      <td>606.800000</td>\n",
       "      <td>1330.400000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>310.200000</td>\n",
       "      <td>106.400000</td>\n",
       "      <td>112.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>809.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>291.200000</td>\n",
       "      <td>953.000000</td>\n",
       "      <td>711.800000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>318.800000</td>\n",
       "      <td>190.800000</td>\n",
       "      <td>163.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>809.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>291.200000</td>\n",
       "      <td>953.000000</td>\n",
       "      <td>711.800000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>318.800000</td>\n",
       "      <td>190.800000</td>\n",
       "      <td>163.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>809.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>291.200000</td>\n",
       "      <td>953.000000</td>\n",
       "      <td>711.800000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>318.800000</td>\n",
       "      <td>190.800000</td>\n",
       "      <td>163.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1112.000000</td>\n",
       "      <td>184.200000</td>\n",
       "      <td>336.600000</td>\n",
       "      <td>1159.600000</td>\n",
       "      <td>794.800000</td>\n",
       "      <td>1482.400000</td>\n",
       "      <td>174.800000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>688.800000</td>\n",
       "      <td>1353.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             68592      360018      300328       173781      311525  \\\n",
       "count    58.000000   58.000000   58.000000    58.000000   58.000000   \n",
       "mean    818.575862   76.665517  290.627586   955.441379  711.024138   \n",
       "std      85.616748   18.502855    9.365517    30.298764   20.536901   \n",
       "min     534.800000   46.200000  247.600000   866.000000  606.800000   \n",
       "25%     809.000000   71.000000  291.200000   953.000000  711.800000   \n",
       "50%     809.000000   71.000000  291.200000   953.000000  711.800000   \n",
       "75%     809.000000   71.000000  291.200000   953.000000  711.800000   \n",
       "max    1112.000000  184.200000  336.600000  1159.600000  794.800000   \n",
       "\n",
       "            336194      298122      343924       22757        18917  \n",
       "count    58.000000   58.000000   58.000000   58.000000    58.000000  \n",
       "mean   1364.110345   96.865517  327.234483  210.320690   212.910345  \n",
       "std      16.622419   13.589283   38.021954   93.719821   176.586662  \n",
       "min    1330.400000   93.200000  310.200000  106.400000   112.800000  \n",
       "25%    1363.000000   93.200000  318.800000  190.800000   163.000000  \n",
       "50%    1363.000000   93.200000  318.800000  190.800000   163.000000  \n",
       "75%    1363.000000   93.200000  318.800000  190.800000   163.000000  \n",
       "max    1482.400000  174.800000  521.000000  688.800000  1353.200000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wl_times.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>68592</th>\n",
       "      <th>360018</th>\n",
       "      <th>300328</th>\n",
       "      <th>173781</th>\n",
       "      <th>311525</th>\n",
       "      <th>336194</th>\n",
       "      <th>298122</th>\n",
       "      <th>343924</th>\n",
       "      <th>22757</th>\n",
       "      <th>18917</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>748.0</td>\n",
       "      <td>145.6</td>\n",
       "      <td>407.2</td>\n",
       "      <td>1070.4</td>\n",
       "      <td>684.2</td>\n",
       "      <td>1329.8</td>\n",
       "      <td>75.4</td>\n",
       "      <td>397.4</td>\n",
       "      <td>307.6</td>\n",
       "      <td>334.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   68592   360018  300328  173781  311525  336194  298122  343924  22757   \\\n",
       "0   748.0   145.6   407.2  1070.4   684.2  1329.8    75.4   397.4   307.6   \n",
       "\n",
       "   18917   \n",
       "0   334.2  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_people = [68592, 360018, 300328, 173781, 311525, 336194, 298122, 343924, 22757, 18917]\n",
    "times = [748.,145.6,407.2,1070.4,684.2,1329.8,75.4,397.4,307.6, 334.2]\n",
    "times = np.array(times).reshape(1,-1)\n",
    "rand_people_wl_time  = pd.DataFrame(times, columns = rand_people)\n",
    "rand_people_wl_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 230)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_10000 = np.concatenate((X[10000,:179].copy(),np.zeros(51)), axis = None).reshape(1,-1)\n",
    "num_10000.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>NUM_PREV_TX</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>DR1</th>\n",
       "      <th>DR2</th>\n",
       "      <th>REM_CD</th>\n",
       "      <th>END_STAT</th>\n",
       "      <th>INIT_AGE</th>\n",
       "      <th>...</th>\n",
       "      <th>PERM_STATE_TX</th>\n",
       "      <th>PERM_STATE_UT</th>\n",
       "      <th>PERM_STATE_VA</th>\n",
       "      <th>PERM_STATE_VI</th>\n",
       "      <th>PERM_STATE_VT</th>\n",
       "      <th>PERM_STATE_WA</th>\n",
       "      <th>PERM_STATE_WI</th>\n",
       "      <th>PERM_STATE_WV</th>\n",
       "      <th>PERM_STATE_WY</th>\n",
       "      <th>PERM_STATE_ZZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4010.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NUM_PREV_TX   A1    A2    B1    B2  DR1   DR2  REM_CD  END_STAT  INIT_AGE  \\\n",
       "0          0.0  3.0  30.0  18.0  35.0  7.0  11.0     4.0    4010.0      56.0   \n",
       "\n",
       "   ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0  ...            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "\n",
       "[1 rows x 230 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_10000  = pd.DataFrame(num_10000, columns = df[X_variables].columns)\n",
    "num_10000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_3 =  X[300328,:].copy().reshape(1, -1)\n",
    "num_3  = pd.DataFrame(num_3, columns = df[X_variables].columns)\n",
    "\n",
    "num_4 =  X[173781,:].copy().reshape(1, -1)\n",
    "num_4  = pd.DataFrame(num_4, columns = df[X_variables].columns)\n",
    "\n",
    "num_8 =  X[343924,:].copy().reshape(1, -1)\n",
    "num_8  = pd.DataFrame(num_8, columns = df[X_variables].columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [num_3, num_4, num_8]\n",
    "results = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NUM_PREV_TX</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>DR1</th>\n",
       "      <th>DR2</th>\n",
       "      <th>REM_CD</th>\n",
       "      <th>END_STAT</th>\n",
       "      <th>INIT_AGE</th>\n",
       "      <th>...</th>\n",
       "      <th>PERM_STATE_TX</th>\n",
       "      <th>PERM_STATE_UT</th>\n",
       "      <th>PERM_STATE_VA</th>\n",
       "      <th>PERM_STATE_VI</th>\n",
       "      <th>PERM_STATE_VT</th>\n",
       "      <th>PERM_STATE_WA</th>\n",
       "      <th>PERM_STATE_WI</th>\n",
       "      <th>PERM_STATE_WV</th>\n",
       "      <th>PERM_STATE_WY</th>\n",
       "      <th>PERM_STATE_ZZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4010.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4010.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4010.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NUM_PREV_TX    A1    A2    B1    B2   DR1   DR2  REM_CD  END_STAT  \\\n",
       "0          0.0  31.0  68.0  39.0  53.0   4.0  13.0     4.0    4010.0   \n",
       "0          0.0   2.0  28.0   7.0  44.0  11.0  15.0     4.0    4010.0   \n",
       "0          0.0  31.0  32.0  45.0  64.0   7.0  11.0    15.0    4010.0   \n",
       "\n",
       "   INIT_AGE  ...  PERM_STATE_TX  PERM_STATE_UT  PERM_STATE_VA  PERM_STATE_VI  \\\n",
       "0      65.0  ...            0.0            0.0            0.0            0.0   \n",
       "0      55.0  ...            0.0            0.0            0.0            0.0   \n",
       "0      53.0  ...            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_VT  PERM_STATE_WA  PERM_STATE_WI  PERM_STATE_WV  PERM_STATE_WY  \\\n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "0            0.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "   PERM_STATE_ZZ  \n",
       "0            0.0  \n",
       "0            0.0  \n",
       "0            0.0  \n",
       "\n",
       "[3 rows x 230 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_array'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-68-46f8b94cc00f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnum_3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\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   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_array'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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