RK_after_FeatureEngineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-18T06:07:14.933345Z",
     "start_time": "2019-02-18T06:06:55.234192Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import mahalanobis\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import dill\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "# dill.dump_session('RKAFE.db')\n",
    "dill.load_session('RKAFE.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-05T11:54:36.595023Z",
     "start_time": "2019-02-05T11:54:36.545717Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Munirka DDA flats</td>\n",
       "      <td>156</td>\n",
       "      <td>394</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.250000</td>\n",
       "      <td>1.015228</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Munirka DDA flats</td>\n",
       "      <td>152</td>\n",
       "      <td>428</td>\n",
       "      <td>437</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.340961</td>\n",
       "      <td>1.021028</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Munirka DDA flats</td>\n",
       "      <td>155</td>\n",
       "      <td>481</td>\n",
       "      <td>482</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.161826</td>\n",
       "      <td>1.002079</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Munirka DDA flats</td>\n",
       "      <td>153</td>\n",
       "      <td>415</td>\n",
       "      <td>459</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.747277</td>\n",
       "      <td>1.106024</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Munirka DDA flats</td>\n",
       "      <td>154</td>\n",
       "      <td>471</td>\n",
       "      <td>528</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.568182</td>\n",
       "      <td>1.121019</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Locality  partno1  Male  Female  Coffee.shops  Salon    buy  rent  \\\n",
       "0  Munirka DDA flats      156   394     400             4      2  12000  27.0   \n",
       "1  Munirka DDA flats      152   428     437             4      2  12000  27.0   \n",
       "2  Munirka DDA flats      155   481     482             4      2  12000  27.0   \n",
       "3  Munirka DDA flats      153   415     459             4      2  12000  27.0   \n",
       "4  Munirka DDA flats      154   471     528             4      2  12000  27.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist  \n",
       "0  25.250000  1.015228    0.28    0.28  \n",
       "1  23.340961  1.021028    0.28    0.28  \n",
       "2  21.161826  1.002079    0.28    0.28  \n",
       "3  23.747277  1.106024    0.28    0.28  \n",
       "4  25.568182  1.121019    0.28    0.28  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('rkp511.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T06:01:40.414965Z",
     "start_time": "2019-02-06T06:01:40.093724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Backward class : 34,\n",
      "No of Middle class : 21 &\n",
      "No of Upper class families : 37\n"
     ]
    }
   ],
   "source": [
    "b_class = np.sum((df['r_dist'] < -1).values.ravel())\n",
    "u_class = np.sum((df['r_dist'] > 1).values.ravel())\n",
    "m_class = df['r_dist'].count() - (b_class + u_class)\n",
    "print('No of Backward class : {},\\nNo of Middle class : {} &\\nNo of Upper class families : {}'\n",
    "      .format(b_class, m_class, u_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:33:32.628914Z",
     "start_time": "2019-02-14T13:33:31.704122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Female to male ratio')\n",
    "plt.ylabel('Young Female Likelihood')\n",
    "\n",
    "plt.scatter(df['ratio'], df['yf_LH'])\n",
    "\n",
    "#inferences about below graph\n",
    "# young female likelihood decreases as female ratio increases\n",
    "plt.savefig('yf_lh-ratio.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T06:06:16.265191Z",
     "start_time": "2019-02-06T06:06:16.243995Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = ['B_class', 'M_class', 'U_class']\n",
    "bins = [-20, -1, 1, 20]\n",
    "df['Class'] = pd.cut(df['r_dist'], bins, labels = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:54:33.490399Z",
     "start_time": "2019-02-07T10:54:33.081280Z"
    }
   },
   "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": [
    "sns.countplot(df['Class'])\n",
    "# inference about below graph\n",
    "# there are more upper class in rk puram constitution \n",
    "# there are lesser areas near centroid compared to farther areas from the centroid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-09T10:36:44.906805Z",
     "start_time": "2019-02-09T10:36:44.800982Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = ['Low_ratio', 'Fair_ratio', 'High_ratio']\n",
    "bins = [0, 0.6, 1.0, 2.0]\n",
    "df['F2M_cat'] = pd.cut(df['ratio'], bins, labels = labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis below about female ratio according to different classes of society"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:51:37.621081Z",
     "start_time": "2019-02-07T10:51:36.995337Z"
    }
   },
   "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": [
    "sns.barplot(x=df['F2M_cat'], y=df['yf_LH'], hue = df['Class'])\n",
    "# inference about below graph\n",
    "# as we can see for low female ratio there are no young female likelihood in upper class area\n",
    "# and also for high female ratio there are no young female likelihood in backward class area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:09:27.650935Z",
     "start_time": "2019-02-07T10:09:27.511026Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "features = ['Coffee.shops', 'Salon', 'buy']\n",
    "\n",
    "# Separating out the features\n",
    "x = df.loc[:, features].values\n",
    "\n",
    "# Standardizing the features\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T07:21:44.714743Z",
     "start_time": "2019-02-11T07:21:44.187804Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# fig = plt.figure()\n",
    "cm = plt.get_cmap(\"RdYlGn\")\n",
    "col = np.arange(92)\n",
    "\n",
    "fig = plt.figure(figsize=(12.,8.))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(df['Coffee.shops'], df['Salon'], df['buy'], c=x[:,2], marker='o', s=40, linewidths=5)\n",
    "# ax.scatter(x[:,0], x[:,1], x[:,2], c=x[:,2], marker='o', s=40, linewidths=5)\n",
    "ax.set_xlabel('Coffee shops')\n",
    "ax.set_ylabel('Salon')\n",
    "ax.set_zlabel('Buy')\n",
    "fig.savefig('beforeNorm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:49:00.074841Z",
     "start_time": "2019-02-15T08:48:59.900108Z"
    }
   },
   "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>Constituency</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Part No</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee Shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>Buy/per sqft</th>\n",
       "      <th>Rent/per sqrt</th>\n",
       "      <th>Young Women %</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "      <th>MYF</th>\n",
       "      <th>YM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RKPURAM- AC-44</td>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "      <td>76</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "      <td>39</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626</td>\n",
       "      <td>525</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.952381</td>\n",
       "      <td>362</td>\n",
       "      <td>235</td>\n",
       "      <td>89</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443</td>\n",
       "      <td>362</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.342541</td>\n",
       "      <td>240</td>\n",
       "      <td>175</td>\n",
       "      <td>72</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444</td>\n",
       "      <td>356</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.202247</td>\n",
       "      <td>252</td>\n",
       "      <td>135</td>\n",
       "      <td>57</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Constituency      Locality  Part No  Unnamed: 3  Male  Female  \\\n",
       "0  RKPURAM- AC-44  satyaniketan        1         NaN   660     588   \n",
       "1             NaN  satyaniketan        2         NaN   486     480   \n",
       "2             NaN  satyaniketan        3         NaN   626     525   \n",
       "3             NaN  satyaniketan        4         NaN   443     362   \n",
       "4             NaN  satyaniketan        5         NaN   444     356   \n",
       "\n",
       "   Coffee Shops  Salon  Buy/per sqft  Rent/per sqrt  Young Women %  Married_F  \\\n",
       "0            10      4        9200.0           49.0      36.564626        427   \n",
       "1            10      4        9200.0           49.0      29.583333        342   \n",
       "2            10      4           NaN            NaN      44.952381        362   \n",
       "3            10      2           NaN            NaN      48.342541        240   \n",
       "4            10      2           NaN            NaN      38.202247        252   \n",
       "\n",
       "    YF  MYF   YM  \n",
       "0  209   76  256  \n",
       "1  139   39  153  \n",
       "2  235   89  290  \n",
       "3  175   72  206  \n",
       "4  135   57  178  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.sort_values(by=['m_dist'])\n",
    "# df[df['Class'] == 'M_class'].count()\n",
    "df_rk = pd.read_excel('data about rk puram.xlsx')\n",
    "df_rk.head()\n",
    "# df_rk.sort_values(by=['Part No'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:49:06.265340Z",
     "start_time": "2019-02-15T08:49:05.651171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_rk.sort_values(by=['Married_F'])\n",
    "a = [0]*len(df1)\n",
    "for i in range(92):\n",
    "    for j in range(156):\n",
    "        if (df1['partno1'][i] == df_rk['Part No'][j]):\n",
    "            a[i] = df_rk['YM'][j]\n",
    "        \n",
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:49:15.060269Z",
     "start_time": "2019-02-15T08:49:14.981163Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "      <th>Class</th>\n",
       "      <th>F2M_cat</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "      <th>MYF</th>\n",
       "      <th>MYF_LH</th>\n",
       "      <th>YM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "      <td>76</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "      <td>39</td>\n",
       "      <td>28.057554</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>6</td>\n",
       "      <td>514</td>\n",
       "      <td>442</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.986425</td>\n",
       "      <td>0.859922</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>338</td>\n",
       "      <td>186</td>\n",
       "      <td>98</td>\n",
       "      <td>52.688172</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.621622</td>\n",
       "      <td>0.811404</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>295</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>8</td>\n",
       "      <td>446</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.488372</td>\n",
       "      <td>0.867713</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>290</td>\n",
       "      <td>198</td>\n",
       "      <td>116</td>\n",
       "      <td>58.585859</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Locality  partno1  Male  Female  Coffee.shops  Salon   buy  rent  \\\n",
       "0  satyaniketan        1   660     588            10      4  9200  49.0   \n",
       "1  satyaniketan        2   486     480            10      4  9200  49.0   \n",
       "2     Nanakpura        6   514     442             3      2     0   0.0   \n",
       "3     Nanakpura        7   456     370             3      2     0   0.0   \n",
       "4     Nanakpura        8   446     387             3      3     0   0.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist    Class     F2M_cat  Married_F   YF  \\\n",
       "0  36.564626  0.890909    7.52   -7.52  B_class  Fair_ratio        427  209   \n",
       "1  29.583333  0.987654    7.52   -7.52  B_class  Fair_ratio        342  139   \n",
       "2  42.986425  0.859922    1.22   -1.22  B_class  Fair_ratio        338  186   \n",
       "3  41.621622  0.811404    1.22   -1.22  B_class  Fair_ratio        295  150   \n",
       "4  53.488372  0.867713    2.99   -2.99  B_class  Fair_ratio        290  198   \n",
       "\n",
       "   MYF     MYF_LH   YM  \n",
       "0   76  36.363636  256  \n",
       "1   39  28.057554  153  \n",
       "2   98  52.688172  217  \n",
       "3   90  60.000000  211  \n",
       "4  116  58.585859  231  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.head()\n",
    "# df['partno1']\n",
    "# df1 = df.sort_values(by=['partno1'], ascending=True)\n",
    "# x = np.randn(92)\n",
    "# df1 = df1.reset_index()\n",
    "# df1\n",
    "# df1.sort_values(by=['partno1'], ascending=False)\n",
    "df1['YM'] = a\n",
    "# df1.drop('MYF_LH', axis=1, inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:33:52.993105Z",
     "start_time": "2019-02-07T10:33:52.623389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_rk['Married_F'][0]\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Relative distance')\n",
    "plt.ylabel('Young female likelihood')\n",
    "plt.scatter(df['r_dist'], df['yf_LH'])\n",
    "# inference about below graph\n",
    "# there are more young female near the centroid that means near munirka dda flats\n",
    "plt.savefig('yf_lh-r_dist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T09:55:48.940517Z",
     "start_time": "2019-02-06T09:55:48.885178Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "      <th>Class</th>\n",
       "      <th>F2M_cat</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>6</td>\n",
       "      <td>514</td>\n",
       "      <td>442</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.986425</td>\n",
       "      <td>0.859922</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>338</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.621622</td>\n",
       "      <td>0.811404</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>295</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>8</td>\n",
       "      <td>446</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.488372</td>\n",
       "      <td>0.867713</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>290</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Locality  partno1  Male  Female  Coffee.shops  Salon   buy  rent  \\\n",
       "0  satyaniketan        1   660     588            10      4  9200  49.0   \n",
       "1  satyaniketan        2   486     480            10      4  9200  49.0   \n",
       "2     Nanakpura        6   514     442             3      2     0   0.0   \n",
       "3     Nanakpura        7   456     370             3      2     0   0.0   \n",
       "4     Nanakpura        8   446     387             3      3     0   0.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist    Class     F2M_cat  Married_F   YF  \n",
       "0  36.564626  0.890909    7.52   -7.52  B_class  Fair_ratio        427  209  \n",
       "1  29.583333  0.987654    7.52   -7.52  B_class  Fair_ratio        342  139  \n",
       "2  42.986425  0.859922    1.22   -1.22  B_class  Fair_ratio        338  186  \n",
       "3  41.621622  0.811404    1.22   -1.22  B_class  Fair_ratio        295  150  \n",
       "4  53.488372  0.867713    2.99   -2.99  B_class  Fair_ratio        290  198  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df1['partno1'][6] == df_rk['Part No'][6]\n",
    "# df_rk['Married_F'][155]\n",
    "# (df['yf_LH']/100 )* df['Female']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-06T10:11:24.507874Z",
     "start_time": "2019-02-06T10:11:24.440645Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "      <th>Class</th>\n",
       "      <th>F2M_cat</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "      <th>MYF</th>\n",
       "      <th>MYF_LH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "      <td>76</td>\n",
       "      <td>36.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "      <td>39</td>\n",
       "      <td>28.057554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>6</td>\n",
       "      <td>514</td>\n",
       "      <td>442</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.986425</td>\n",
       "      <td>0.859922</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>338</td>\n",
       "      <td>186</td>\n",
       "      <td>98</td>\n",
       "      <td>52.688172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.621622</td>\n",
       "      <td>0.811404</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>295</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>8</td>\n",
       "      <td>446</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.488372</td>\n",
       "      <td>0.867713</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>290</td>\n",
       "      <td>198</td>\n",
       "      <td>116</td>\n",
       "      <td>58.585859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Locality  partno1  Male  Female  Coffee.shops  Salon   buy  rent  \\\n",
       "0  satyaniketan        1   660     588            10      4  9200  49.0   \n",
       "1  satyaniketan        2   486     480            10      4  9200  49.0   \n",
       "2     Nanakpura        6   514     442             3      2     0   0.0   \n",
       "3     Nanakpura        7   456     370             3      2     0   0.0   \n",
       "4     Nanakpura        8   446     387             3      3     0   0.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist    Class     F2M_cat  Married_F   YF  \\\n",
       "0  36.564626  0.890909    7.52   -7.52  B_class  Fair_ratio        427  209   \n",
       "1  29.583333  0.987654    7.52   -7.52  B_class  Fair_ratio        342  139   \n",
       "2  42.986425  0.859922    1.22   -1.22  B_class  Fair_ratio        338  186   \n",
       "3  41.621622  0.811404    1.22   -1.22  B_class  Fair_ratio        295  150   \n",
       "4  53.488372  0.867713    2.99   -2.99  B_class  Fair_ratio        290  198   \n",
       "\n",
       "   MYF     MYF_LH  \n",
       "0   76  36.363636  \n",
       "1   39  28.057554  \n",
       "2   98  52.688172  \n",
       "3   90  60.000000  \n",
       "4  116  58.585859  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['MYF_LH'] = df1['MYF']/df1['YF'] *100\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-11T06:57:30.074040Z",
     "start_time": "2019-02-11T06:57:29.704890Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Married young female likelihood')\n",
    "plt.ylabel('Young female likelihood')\n",
    "plt.scatter(x = df1['MYF_LH'], y=df1['yf_LH'])\n",
    "# inference about below graph\n",
    "# as married young female likelihood increases, total young female increases\n",
    "plt.savefig('tyf-myf_lh.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:45:20.852572Z",
     "start_time": "2019-02-07T10:45:20.166602Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: 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",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df1['Class'], y=df1['MYF'], hue = df1['Class'])\n",
    "# inference about below graph\n",
    "# there are more married young female near the middle class or centroid area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:48:40.163968Z",
     "start_time": "2019-02-07T10:48:39.539665Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: 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",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "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": [
    "# Backward class consists of more married young female likelihood\n",
    "sns.barplot(x=df1['Class'], y=df1['MYF_LH'], hue = df1['Class'])\n",
    "# compared above inference where there are more married young female near centroid \n",
    "# here we can seee that married young female likelihood is more compared to centroid area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:26:16.022729Z",
     "start_time": "2019-02-07T10:26:15.619467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# area vise female to male ratio\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(x=df1['r_dist'], y=df1['ratio'])\n",
    "plt.xlabel('Relative distance')\n",
    "plt.ylabel('Female to male ratio')\n",
    "# inference about below graph\n",
    "# female are more near centroid that means female are more near munirka dda flats\n",
    "plt.savefig('fem-r_dist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T09:57:40.890371Z",
     "start_time": "2019-02-07T09:57:40.764722Z"
    }
   },
   "outputs": [],
   "source": [
    "df1.head()\n",
    "df1.to_excel('rkp511.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:48:50.610618Z",
     "start_time": "2019-02-07T10:48:50.604728Z"
    }
   },
   "outputs": [],
   "source": [
    "# df1.sort_values(by='MYF', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T10:23:58.981004Z",
     "start_time": "2019-02-07T10:23:58.535748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see married young female likelihood based on the locality\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Married young female likelihood')\n",
    "plt.ylabel('Female to male ratio')\n",
    "plt.scatter(x=df1['MYF_LH'], y=df1['ratio'])\n",
    "# inference about below scatter plot\n",
    "# for low ratio, young female marriage likelihood increases and vice versa\n",
    "plt.savefig('fem-yflh.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:34:40.078120Z",
     "start_time": "2019-02-14T13:34:40.011266Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "      <th>Class</th>\n",
       "      <th>F2M_cat</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "      <th>MYF</th>\n",
       "      <th>MYF_LH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "      <td>76</td>\n",
       "      <td>36.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "      <td>39</td>\n",
       "      <td>28.057554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>6</td>\n",
       "      <td>514</td>\n",
       "      <td>442</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.986425</td>\n",
       "      <td>0.859922</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>338</td>\n",
       "      <td>186</td>\n",
       "      <td>98</td>\n",
       "      <td>52.688172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.621622</td>\n",
       "      <td>0.811404</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>295</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>8</td>\n",
       "      <td>446</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.488372</td>\n",
       "      <td>0.867713</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>290</td>\n",
       "      <td>198</td>\n",
       "      <td>116</td>\n",
       "      <td>58.585859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Locality  partno1  Male  Female  Coffee.shops  Salon   buy  rent  \\\n",
       "0  satyaniketan        1   660     588            10      4  9200  49.0   \n",
       "1  satyaniketan        2   486     480            10      4  9200  49.0   \n",
       "2     Nanakpura        6   514     442             3      2     0   0.0   \n",
       "3     Nanakpura        7   456     370             3      2     0   0.0   \n",
       "4     Nanakpura        8   446     387             3      3     0   0.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist    Class     F2M_cat  Married_F   YF  \\\n",
       "0  36.564626  0.890909    7.52   -7.52  B_class  Fair_ratio        427  209   \n",
       "1  29.583333  0.987654    7.52   -7.52  B_class  Fair_ratio        342  139   \n",
       "2  42.986425  0.859922    1.22   -1.22  B_class  Fair_ratio        338  186   \n",
       "3  41.621622  0.811404    1.22   -1.22  B_class  Fair_ratio        295  150   \n",
       "4  53.488372  0.867713    2.99   -2.99  B_class  Fair_ratio        290  198   \n",
       "\n",
       "   MYF     MYF_LH  \n",
       "0   76  36.363636  \n",
       "1   39  28.057554  \n",
       "2   98  52.688172  \n",
       "3   90  60.000000  \n",
       "4  116  58.585859  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:56:11.135076Z",
     "start_time": "2019-02-14T13:56:11.109120Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "X = df1[['Male', 'Female', 'yf_LH', 'Married_F', 'YF']]\n",
    "X = StandardScaler().fit_transform(X)\n",
    "# X = df1[['Male', 'Female']]\n",
    "y = df1['MYF_LH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:56:11.379509Z",
     "start_time": "2019-02-14T13:56:11.374764Z"
    }
   },
   "outputs": [],
   "source": [
    "# df2.head()\n",
    "lm = linear_model.LinearRegression()\n",
    "model = lm.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T06:49:12.294152Z",
     "start_time": "2019-02-15T06:49:12.141957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40.89667011 29.74632564 52.48302978 55.60290548 56.24177288 21.06365895\n",
      " 33.29825784 27.07873944 28.80537165 27.45041337 33.80162944 48.84816164\n",
      " 53.52483554 68.56936441 29.8729513  29.15794521 30.25258607 27.9322534\n",
      " 70.79235542 65.5685237  68.10689612 78.73864398 67.76140072 38.33854035\n",
      " 32.09855407 22.34824761 24.55344127 41.62570489 38.55061897 36.9663681\n",
      " 36.96562251 40.65823858 37.5895918  50.04531696 40.30140363 26.43251453\n",
      " 37.70671393 45.72104921 38.97454162 21.87849733 26.3175445  36.17570011\n",
      " 30.01664648 55.83704962 61.40093094 44.40648197 44.39870982 37.58884086\n",
      " 40.10214619 83.5660217  31.52921102 51.69587615 52.20683372 58.24169223\n",
      " 42.06935895 32.78694868 33.9884981  34.06113958 28.93536868 28.98123931\n",
      " 27.41585943 29.21575858 66.729851   24.41943249 27.3889688  30.85250785\n",
      " 50.11054623 38.77864141 28.52564429 47.9126534  41.60674808 52.22248882\n",
      " 38.06457846 30.40075063 48.26156105 47.66766153 62.32090725 50.76023414\n",
      " 50.87347442 58.84618538 52.47850426 46.67342345 34.95795492 48.22518163\n",
      " 51.05030638 49.51733435 29.1282698  26.04576703 29.51392237 27.98432954\n",
      " 29.10882265 24.80321061]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:56:11.773175Z",
     "start_time": "2019-02-14T13:56:11.766205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8848049127266849"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-14T13:56:14.013101Z",
     "start_time": "2019-02-14T13:56:13.929409Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>MYF_LH</td>      <th>  R-squared:         </th> <td>   0.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   132.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 14 Feb 2019</td> <th>  Prob (F-statistic):</th> <td>8.11e-39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:26:13</td>     <th>  Log-Likelihood:    </th> <td> -278.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    92</td>      <th>  AIC:               </th> <td>   569.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    86</td>      <th>  BIC:               </th> <td>   584.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   41.4404</td> <td>    0.539</td> <td>   76.915</td> <td> 0.000</td> <td>   40.369</td> <td>   42.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    4.0555</td> <td>    1.173</td> <td>    3.458</td> <td> 0.001</td> <td>    1.724</td> <td>    6.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>  -33.9985</td> <td>    2.751</td> <td>  -12.357</td> <td> 0.000</td> <td>  -39.468</td> <td>  -28.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    7.8830</td> <td>    2.539</td> <td>    3.105</td> <td> 0.003</td> <td>    2.837</td> <td>   12.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   34.3859</td> <td>    1.614</td> <td>   21.311</td> <td> 0.000</td> <td>   31.178</td> <td>   37.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td>   -1.1955</td> <td>    3.437</td> <td>   -0.348</td> <td> 0.729</td> <td>   -8.029</td> <td>    5.638</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.610</td> <th>  Durbin-Watson:     </th> <td>   1.865</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.165</td> <th>  Jarque-Bera (JB):  </th> <td>   3.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.455</td> <th>  Prob(JB):          </th> <td>   0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.077</td> <th>  Cond. No.          </th> <td>    16.5</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 MYF_LH   R-squared:                       0.885\n",
       "Model:                            OLS   Adj. R-squared:                  0.878\n",
       "Method:                 Least Squares   F-statistic:                     132.1\n",
       "Date:                Thu, 14 Feb 2019   Prob (F-statistic):           8.11e-39\n",
       "Time:                        19:26:13   Log-Likelihood:                -278.55\n",
       "No. Observations:                  92   AIC:                             569.1\n",
       "Df Residuals:                      86   BIC:                             584.2\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         41.4404      0.539     76.915      0.000      40.369      42.511\n",
       "x1             4.0555      1.173      3.458      0.001       1.724       6.387\n",
       "x2           -33.9985      2.751    -12.357      0.000     -39.468     -28.529\n",
       "x3             7.8830      2.539      3.105      0.003       2.837      12.929\n",
       "x4            34.3859      1.614     21.311      0.000      31.178      37.594\n",
       "x5            -1.1955      3.437     -0.348      0.729      -8.029       5.638\n",
       "==============================================================================\n",
       "Omnibus:                        3.610   Durbin-Watson:                   1.865\n",
       "Prob(Omnibus):                  0.165   Jarque-Bera (JB):                3.201\n",
       "Skew:                           0.455   Prob(JB):                        0.202\n",
       "Kurtosis:                       3.077   Cond. No.                         16.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) ## let's add an intercept (beta_0) to our model\n",
    "\n",
    "# Note the difference in argument order\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:50:13.361435Z",
     "start_time": "2019-02-15T08:50:13.053466Z"
    }
   },
   "outputs": [],
   "source": [
    "df1['ym_LH'] = df1['YM'] / df1['Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:52:57.814215Z",
     "start_time": "2019-02-15T08:52:57.748169Z"
    }
   },
   "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>Locality</th>\n",
       "      <th>partno1</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Coffee.shops</th>\n",
       "      <th>Salon</th>\n",
       "      <th>buy</th>\n",
       "      <th>rent</th>\n",
       "      <th>yf_LH</th>\n",
       "      <th>ratio</th>\n",
       "      <th>m_dist</th>\n",
       "      <th>r_dist</th>\n",
       "      <th>Class</th>\n",
       "      <th>F2M_cat</th>\n",
       "      <th>Married_F</th>\n",
       "      <th>YF</th>\n",
       "      <th>MYF</th>\n",
       "      <th>MYF_LH</th>\n",
       "      <th>YM</th>\n",
       "      <th>ym_LH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>1</td>\n",
       "      <td>660</td>\n",
       "      <td>588</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.564626</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>427</td>\n",
       "      <td>209</td>\n",
       "      <td>76</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>256</td>\n",
       "      <td>0.387879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satyaniketan</td>\n",
       "      <td>2</td>\n",
       "      <td>486</td>\n",
       "      <td>480</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9200</td>\n",
       "      <td>49.0</td>\n",
       "      <td>29.583333</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>7.52</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>342</td>\n",
       "      <td>139</td>\n",
       "      <td>39</td>\n",
       "      <td>28.057554</td>\n",
       "      <td>153</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>6</td>\n",
       "      <td>514</td>\n",
       "      <td>442</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.986425</td>\n",
       "      <td>0.859922</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>338</td>\n",
       "      <td>186</td>\n",
       "      <td>98</td>\n",
       "      <td>52.688172</td>\n",
       "      <td>217</td>\n",
       "      <td>0.422179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.621622</td>\n",
       "      <td>0.811404</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>295</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>211</td>\n",
       "      <td>0.462719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nanakpura</td>\n",
       "      <td>8</td>\n",
       "      <td>446</td>\n",
       "      <td>387</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.488372</td>\n",
       "      <td>0.867713</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>B_class</td>\n",
       "      <td>Fair_ratio</td>\n",
       "      <td>290</td>\n",
       "      <td>198</td>\n",
       "      <td>116</td>\n",
       "      <td>58.585859</td>\n",
       "      <td>231</td>\n",
       "      <td>0.517937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Locality  partno1  Male  Female  Coffee.shops  Salon   buy  rent  \\\n",
       "0  satyaniketan        1   660     588            10      4  9200  49.0   \n",
       "1  satyaniketan        2   486     480            10      4  9200  49.0   \n",
       "2     Nanakpura        6   514     442             3      2     0   0.0   \n",
       "3     Nanakpura        7   456     370             3      2     0   0.0   \n",
       "4     Nanakpura        8   446     387             3      3     0   0.0   \n",
       "\n",
       "       yf_LH     ratio  m_dist  r_dist    Class     F2M_cat  Married_F   YF  \\\n",
       "0  36.564626  0.890909    7.52   -7.52  B_class  Fair_ratio        427  209   \n",
       "1  29.583333  0.987654    7.52   -7.52  B_class  Fair_ratio        342  139   \n",
       "2  42.986425  0.859922    1.22   -1.22  B_class  Fair_ratio        338  186   \n",
       "3  41.621622  0.811404    1.22   -1.22  B_class  Fair_ratio        295  150   \n",
       "4  53.488372  0.867713    2.99   -2.99  B_class  Fair_ratio        290  198   \n",
       "\n",
       "   MYF     MYF_LH   YM     ym_LH  \n",
       "0   76  36.363636  256  0.387879  \n",
       "1   39  28.057554  153  0.314815  \n",
       "2   98  52.688172  217  0.422179  \n",
       "3   90  60.000000  211  0.462719  \n",
       "4  116  58.585859  231  0.517937  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T05:54:05.425883Z",
     "start_time": "2019-02-16T05:54:05.131338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe10ef070b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(x=df1['ym_LH'], y=df1['yf_LH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T08:56:38.583002Z",
     "start_time": "2019-02-15T08:56:38.205838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe11062f278>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Female to male ratio')\n",
    "plt.ylabel('Young Male Likelihood')\n",
    "plt.scatter(x=df1['ratio'], y=df1['ym_LH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-15T09:57:12.286061Z",
     "start_time": "2019-02-15T09:57:11.917091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe11048ffd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WbUOZdVfd7o6Kfa5ewmcftkYn36eDwtF9qdBQUh0RewJvAh4HPIJi0YDtMvMZrQ9NGiwrli7k0MUL2HDrw3smLd9/waz/6DR6i7VyQ5ldR6svnbfbro3OV1a3mfC1X/lOT2WJjclhbX7YkwqNjlR/DDgBuBS4qX3hSINrfNMEN2zZcRPS62+7l+s2N/6Hp5l62ocmq++f+OBD26q2q/eY8HVGtTs9qm0XP7BLQONJ9bOAEzPzW+0MRhpE5ZHl237xQFOjOdVusa791hZ2GR3h2JW/NuMffkc5B4MJX2dU3ulRbb/yA7sENJ5U3w3c3s5ApEE0fWR5XpXBnNkktdVusU4Bn/rGbXz2m7fNOGrtKOfgMOFTL/EDu1RoNKn+IPDuiDg9M+9uZ0DSoKgcWd429fBkhClmn9RW+8NV1ujEIEc5JbWaH9ilQs2kOiK2wA47Gj8a2BoRvwAmpx+bmfu3Jzypt8xmHdZaI8unHbeY/RftMeuktvIPV6VGS0kc5ZTUan5gl+qPVL+dHZNqaajNdpJgrVuixz1u7stMlf9wfXNsK5+5Ygvbpr1Dvd0qqR2+dPUdHLpk5g/jfmDXsKuZVGfmxzoZiNTL5rIO62xuic5mBLz8h+tXk3i7VVLbfPjymzl6f/jAF2/moUl375RmUq/8Yy3w8sycKH1dU2a+oOWRST1kruuwNnJLdK7L5Hm7VVK7jG+a4OKr7uDok3cD3NBFakS98o/JGl9LQ6eZ2e31bok2uxOZt1sltYMbukizV6/840XVvpaGUbtmt/uHS1IvKg8kTOe8Dam+euUfjW49PpWZX29RPFLPake5heu7SupFK5Yu5OSj9wN+CThvQ2pEvfKPrzR4jilgtAWxSD2v1eUWru8qqVe98vcO4tprf8kZJx3U0Oof0rCrl1Tv2rEopCHmhENJvezZT96P0VHHzqSZ1Kup3mFyYkSMACcAy4HPAIcUh+U9bY1QGhD1ls1zwmH/mc0yiJKkwdfQNuURsR9FOcihwB7A5cBfAE+KiBMyc0P7QpT631yXzVNvsj8lSZXmzXwIAOcCNwL7AveX2v4Y+D7wwTbEJQ2MWsvmXbfZmzz9yP6UJFXTaFL9/4CzMvOBckOp7OOtwNHtCEwaFN/88daay+ap/9RaBvFz376N8U0T3QlKktR1jSbV84DdqrT/GvBQ68KRBsu5l27k01ds2andZfP6V7X1ewG++oOtvOy8n3DupRs7HpMkqfsaTarXAedExKMoltCbiogjgPOAS9sVnNTPymUCUxXt80ZcNq+flZdBrJZYWwoiScOr0aT6DcAvgDuAPYGfAGPAbcAZ7QlN6m/VygQAXnSck9r63ZrVy/jYa4/kd56waKfvWdqjYTW+aYJ1V91uGZSGVkOrf2TmBPC8iDgEWFH6ufWZeV07g5P6Wa3dEo977L7dC0otU4xYw79fu9UdMTX0XBFHanCkOiL+ACAzb8zML2bmxZl5XUQcFhHfbGuEUp+qLBNwt8TBYx9LrogjlTU0Ug1cGBEvyczPAUTEbsDbgDcD17QrOKnfNbpbohuJ9C93xNSwq7UiTt5yjx8wNVQaTapfAHw2IvYAbgYuAPYBXp2ZH29XcNIgmGm3xHMv3cjnvr2FbVPFJMbnP7U1t01N1OduttfOHTE1zGqVulkGpWHTaE31uoh4NvCvFBMVPwq8NTN/2c7gpEE0PWEDWPuth1cI2TZVPD7h8fs2NcJjfePcee0E3f1Q2m8fiMtlUJXvG0epNWxqJtURsbyi6RbgVcDHgc3Ao0pL7JGZ17ctQmmAVCZsy5fM32nJvSngih9vnfMfpFr1jc0m6sPAayfo7gerfv1QZxmUVH+i4gZgfelf+evPALsD76xolzSDagnb+s33VT12ZKQy1d7xPPWWrapX36j6vHbq5qS7fp/wt2LpQp5z9KNNqDW06pV/HNqxKKQhUGvd6mqOXVl92b1GRrGsb5w7r526OenOCX9Sf6uZVGfmTzsZiDSoyvWRu46O7JSwzRsp6qgbPU8jpQnWN86d107d/GDlhzqpv9Wrqb4NWJmZWyNiC+xU+rldZu7fjuCkflc5snzo4gXcsOXe7Y+POmQvrr7+7p1+rtrI1GxGsaxvnDuv3XDr5gcrP9RJ/a1e+cfbgXIh19s6EIs0UKqNLN+w5V7edsrBPPirqe2rf1xz408aGpma7SiWy7w9zCXyNBvd/GDlhzqpf9Ur//h4ta+nK20C457LUhW1RpY33nE/+y/aA5jdyJSjWHPTr6spqLu6+cHKD3VSf2p085dajgMuB0ZbEIs0UKqNLI8An76iWJd6eoLX6MiUo1iz4xJ5kqROaTapllRD5chy5aTE6QnebEamHMVqnKspSJI6xaRaaqPpI8u3/eIBPvWNLTt83wSvvVxNQZLUKfU2f5FUx2VX38HbL7yey66+o+5x5Q0Rjl25iNGKd5wJXnuV7xaUr3sr69Bn2oRH/c3+lTRb9ZbU262Bn9+1hbFIfeOPP/BjNtx6LwBf/cFWPv+fd/DpMx5b92ecaNgd7ahDd/LjYLN/Jc1FvfKPB6izNnXJSAPHSAPlS1ffsT2hLttw6718+fs/58Qn/nrdn62V4M12yTc9rJFr18o6dCc/Djb7V9Jc1UuqT+hYFFIfufqGu6q2fzf/d8akGnZO8JoZFRv2ZLwbI4pOfhxs9q+kuaq3TvV/tPrJIuIpwAXAcuBHwGmZeUOd4z8DjGTmH7U6FmmunnLo3nz1B1t3av/N2GfW52pmVGzYb1F3a0TRyY+Dzf6VNFcdm6gYEXsA64D3AY8EvgJ8ss7xJwEv6Ehw0iw868n7cVhpN8Syww9Y0NAodaV6o2L11Eoor9tc/+cGyVyvXbPaOflR3Wf/SpqrTi6pdxxwV2auBYiIs4E3RsThmbl++oERsS/wXuCfgD06GKPUkKMO2Yu87V6mpmBkBJ7wG3vN6TxzHRXzFnV3RxSbnfw47GU7vc5NliTNRSeT6sOADeUHmTkZETcBhwPrK479B4qk+kDgkJlOPDk52cIw1YjyNR/Ga79+8wSXfHcLu09791zy3S0c/7hHctgsE7rDlsznhU/dj4uvumN7GcfJR+9H7P+Iutf20MV7sGD3nRPK5TP8XLN6qd/neu1a+fyHLZkPzO56fPjym3eK+ZW/d1C7wmyJXur3Tplr/w6SYex32e/1zHRNRqamZrd4R0QsBO7JzFn9YES8DTgiM18wre07wD9k5mentf0h8OLM/N2IOAs4pFZN9TXXXLM38L+zegGSJEnS3O1z1FFH7bRqQcMj1RFxBvAm4FHA8oh4O3AXcGZm/qqBU9wHPKKibT6wvQAyIvYD3gUc22hcACtXrmR0dHQ2P6ImTU5OMjY2NpTXfv3mCV77kfU7jRKf//IjZj1S3YpYbtxyH4csns/hB7b/FvUw93srfOnqO/jAF2/eqf2Mkw7i2U/erwsRNcZ+H072+3Cy32srX5taGkqqI+KNwOuAN1Os3gHwReDDwIOl9plsAF467ZyjwMFMKwmhWMbv0cBYREBRTz0vIh6bmTV31hgdHbXju2QYr/2Ry/bh939zx01cTv6txaw4aO+uxHLkstmvOtKsfu33btcyH7pkIQ9NVinbOWCvvrie/drvao79Ppzs99lrdKT6T4BXZOa/RcQ/AGTmJRFxH/AJGkuqrwAWRcTpwFrgLcANmTm9zvqzwPRSkLOoU/4hdYsTmTqnVYlwLyxBuGLpQg5dvGCHzYOW779gaCaXStIgazSpXsqOI8plG4Ffa+QEmXl/RJxIMdJ9HnAtcApARIwD78rMCxuMR+q6Vu7Sp+palQi3Y03ruST745smyMrdOG+5l+s2D8+qLZI0qBpNqn9IkQD/belxeZLiK0rfa0hmXgM8qUr7ihrHn9XouTut27eRpUHXykS41UsQVib7x69axBMO3mvG3wff/PFWKmd4TwFX/HirSbUk9blGk+ozga9ExFOB3YG/iojDgCOAZ7YruF7VC7eRpUHXykS4lWtaV0v2v/qDrXz1B1tn/n0wUqN5ZHarMEmSek9DOypm5lVAUIxKf5liR8RvAodn5pVti64HuZOd1BnlRHi6uSbCrdwlr1qyXzbT74NjVy6q0b7vrOOQJPWWhpfUy8zbgbe1MZa+4E52UmeUE+HKu0JzfZ+1anJptVHv6er9PlixdCEvfNpi1n5rC1MUA9cveJpbYEvSIKiZVEfE2kZPMn1Dl0HXza2RpWHT6lVWWjG5tDLZrzTT7wNXjpGkwVRvpNr9Kato9eiZpPp6cZWV6YnxD392N1+/duusfh/04muSJDWnZlKdmS/qZCD9xJEmSeXE+DlHP5o/PMbVgCRp2DW6o+II8CyK1T7K2+uMUKwEclRmntie8HqXI02Syvx9IElqdKLi+ym2Kb+eYhWQcYoNYRYCn2xLZJIkSVKfaGhJPeD5wEsz8wjgZuA5wGLgcuCuNsUmSTMa3zTBuqtuZ3zTRLdDkSQNsUZHqhcBV5S+/jHwlMxcGxFnAV8AzmhDbJJUlxsxSZJ6RaMj1bcBB5a+vh54fOnr/wV+vdVBSdJM3IhJktRLGk2qPwd8prRN+VeAF0fEHwFnAxvaFZwk1VJvIyZJkjqt0aT6bcAngH0y8xvABcC5wFHAa9oUmyTV1MptzCVJalZDNdWZOQm8c9rjt+GW5ZK6yI2YJEm9pN425X/a6Eky8yOtCUeSGudGTJKkXlFvpPoCYBvFJEUoNnupZgowqZbUFbPZeGV8kzsfSpLao15SfT5wEkVifTFwcWZe1ZGoJKnFXH5PktRONScqZubrMvMg4BTgV8AnI+KWiDgvIp5W2rpcknqey+9JktptxtU/MvO/M/MtmRnAM4E7KVb+uD0iPhIRz2h3kJLUDJffkyS1W6NL6gGQmWOZ+VfAUyjWqP5D4N/aEZgktYrL70mS2q3RbcqJiL2BZ1HUWf8OMAFcCPxre0KTpNZw+T1JUrvVTaoj4kCKJPo5wDHAJmAd8LtOWpTUT1x+T5LUTvXWqb4GWAWMUSTSr8/MsU4FJkmtNpvl9yRJmo16I9WPBx4C9gNeDrw8IqoemJn7tz40SZIkqT/US6r/pGNRSJIkSX2sZlKdmR/vZCCSJElSv5rVknqSJEmSdmZSLUmSJDXJpFqSJElqkkm1JEmS1KSGdlSMiLfW+NYU8CBwK/C1zPxFqwKTJEmS+kWj25Q/FngesAX4QaltFbAEuBp4JHB+RDwjM39Q/RSSJEnSYGq0/ON+4DPAYzJzdWauBh4DfAz4SWYeBpwPfKA9YUqSJEm9q9Gk+rnAuzPzoXJDZk4C7wdOLTV9GjiqteFJkiRJva/RpPqXFNuWVzoKmCh9/UiKEW1JGgjjmyZYd9XtjG+amPlgSdJQa7Sm+v3ARyPiccD3KZLxo4BXAX8dEQcBHwEua0uUktRh5166kYuu3MLkNhidB6ces5g1q5d1OyxJUo9qaKQ6M88FXgk8naK2+uPAU4GXZuY5wMHAlcDr2hSnJHXM+KaJ7Qk1wOQ2uOjKLVy3+Z7uBiZJ6lmNjlSTmZ8FPlvje1cAV7QqKEnqputvvXd7Ql02uQ3ylns44sA9uxOUJKmnNZxUR8SzKUo+dgVGpn8vM2utYy1JfWf5kgWMzmOHxHp0HsQBJtSSpOoa3fzl7yhKO34C3F3x7alWByVJ3bRi6UJOPWbxTjXVjlJLkmppdKT6FOBVmfmP7QxGknrFmtXLOH7VIq6/9V6WL1nAiqULux2SJKmHNZpU7wF8o52BSFKvWbF0ocm0JKkhja5T/SngDREx2s5gJEmSpH7U6Ej1AcBJwCkRsRF4cPo3M/O3WhuWJEmStKPxTRM9W5bXaFKdwHvbGYik/tbLv+gkSf2v1zflaiipzsy3tzsQSf2r13/RSZL6W61NuU54/L49szJTo0vq/Wm972fmR1oTjqR+0w+/6CRJ/a0fNuVqtPyjcqR6F2Bfitrq7wEm1dKQ6odfdJKk/tYPm3I1Wv5xYGVbROwFfIwiqR461o9KhX74RSdJ6m/9sClXw9uUV8rMuyPi7cAVwPtbF1Lvs35Uelg//KKTJPW/Xt+Ua85JdcmhwIJWBNIvrB+Vdtbrv+gkSYOhlzflanSi4tqL2RMpAAAU3klEQVQqzXsDJwCfbmlEPc76Uam6Xv5FJ0lSuzU6Uj1Z8XgK2AK8FvhkKwPqddaPSipzboUkqazRiYovancg/cL6UUng3ApJ0o4arqmOiBOAtwIrgHnABuCDmfmFNsXWs6wflYabcyskSZXmNXJQRLwQ+DJwA3Am8EbgOuCzEXFq+8LrXSuWLuQ5Rz/ahFoaQvXmVkiShlOjI9V/AZyZmR+a1vapiLiWYvT6okZOEhFPAS4AlgM/Ak7LzBsqjlkEfBQ4DngIWAu8KTMfajBWSWor51ZIkio1NFINLKMYqa70NYpl9WYUEXsA64D3AY8EvkL1SY4fAu4CFgNHAE8HXtJgnJLUduW5FaOl36DOrZAkNTpSfSPFyPFPK9qPAzY3eI7jgLsycy1ARJwNvDEiDs/M9dOOOx0YycwHI+IAYHfgFw0+hyR1hHMrduRKKJKGXaNJ9fuAj0TE4cB3S21HA68AXtfgOQ6jmNwIQGZORsRNwOHA+mntDwFExDrg94GvU4xw1zQ5Wbnin9qtfM299sPFft/RYUvmc9iS+cBgX5OZ+v3Dl9/MxVfdsX0llJOP3o9X/t5BnQxRbeD7fTjZ77XNdE0aXVLvMxExD3g98BrgfooE+QWZeUmDsSwo/dx09wHzaxz/fIoykUuBdwJvqXXisbGxBkNQq3nth5P9Ppxq9fvR+8PRJ+82reWXXHvtLzsTlNrO9/twst9nr2ZSHRFLM3NT+XFmfgr4VBPPdR/wiIq2+UDV6fKZ+QCwJSLeA/wNdZLqlStXMjo62kRomq3JyUnGxsa89kPGfh9O9fr9S1ffwQe+ePNOP3PGSQfx7Cfv16kQ1YT1mye4cct9HLJ4Pocf+HDpju/34WS/11a+NrXUG6kej4g/z8zzWxTLBuCl5QcRMQoczLSSkFL714G/zcx/LzXtTjFxsabR0VE7vku89sPJfh9O1fr90CULeWhy55VQlh+wl/+P9IFGNjHy/T6c7PfZq7f6x3uAv42IKyMiWvBcVwCLIuL0iNiNYpm+GzJzQ8VxPwTeERH7RMSBFCPUn2zB80uSWsyVUPpXrU2MrtvseuvSXNRMqjPzbOBIilHiayPiraXR5TnJzPuBE4FXA1uBE4BTACJivLTBDMA7gHGKjWb+k2Kd6o/M9Xkldcb4pgnWXXU745smuh1KWwz662vGmtXL+Nhrj+TPn/sYPvbaI92uvU+4iZHUWnUnKmbmTcCzIuIkihVAnhcRfwM8UHHc5Y08WWZeAzypSvuKaV/fT7GqyCsaOaek7mvkFnI/G/TX1worli50Kb0+4yZGUms1tPlLZn4ReDewAvgCcNm0f19qW3SSet6g30Ie9Nen4WXpjtRaMy6pFxFPAv4eeCzwfuCvSitzSFLdW8iD8Md50F+fhpubGEmtU29JvX0oRqdfBlwNHJWZ450KTFJ/GPRbyIP++iRLd6TWqFf+cT3wh8BrMvO3TaglVTPot5AH/fVJklqjXvnHfwCvz8w7OhWMpP406LeQB/31SZKaVzOpzszndzIQSf1t0G8hD/rrkyQ1p6HVPyRJkiTVZlItSZIkNcmkWpIkSWqSSbUkSZLUJJNqSZIkqUkm1ZIkSVKTTKolSZKkJplUS5IkSU0yqZYkSZKaZFItSZIkNcmkWpIkSWqSSbUkSZLUJJNqSZIkqUkm1ZIkSVKTTKolSZKkJplUS5IkSU0yqZYkSZKaZFItSZIkNcmkWpIkSWqSSbUkSZLUJJNqSZIkqUkm1ZIkSVKTTKolSZKkJplUS5IkSU3apdsBSBoM45smuP7We1m+ZAErli7sdjiSJHWUSbWkpp176UYuunILk9tgdB6cesxi1qxe1u2wJEnqGMs/JDVlfNPE9oQaYHIbXHTlFq7bfE93A5MkqYNMqiU15fpb792eUJdNboO8xaRakjQ8TKolNWX5kgWMVvwmGZ0HccCe3QlIkqQuMKmW1JQVSxdy6jGLtyfW5ZrqIw40qZYkDQ8nKkpq2prVyzh+1SJX/5AkDS1HqiVJkqQmOVItqWkuqSdJGnaOVEtqikvqSZJkUi2pSS6pJ0mS5R+SmrR8yQJGgKlpbSO0bkk9tz+XJPUDk2pJTZua4fFcWastSeoXln9Iasr1t95btb3Z8o9Br9Ue3zTBuqtuZ3zTRLdDkSS1gCPVkppS3lFxel11K3ZUrFer3e8byzgCL0mDx5FqSU1p146Kg7r9+aCPwEvSsHKkWlLT2rGjYjlZrxzR7fdR6kEegZekYWZSLaklVixd2PLVOQZx+/N2lctIkrrL8g9JPW3F0oU85+hHD0RCDe0rl5EkdZcj1ZLUYYM4Ai9Jw86kWpK6oB3lMpKk7rH8Q5IkSWqSSbUkSZLUJJNqSZIkqUkm1ZIkSVKTTKolSZKkJnV09Y+IeApwAbAc+BFwWmbeUHHM3sD5wO8Ck8DngTMz88FOxipJkiQ1qmMj1RGxB7AOeB/wSOArwCerHHoOsAfwGGAl8ETgzzoTpSRJkjR7nSz/OA64KzPXlkadzwaOjIjDK44bBd6Zmfdk5v8Aa4Hf6mCcktRW45smWHfV7Yxvmuh2KJKkFulk+cdhwIbyg8ycjIibgMOB9dPaX1LxcycCYx2JUJLa7NxLN3LRlVuY3PbwFuVrVi/rdliSpCZ1MqleANxf0XYfML/WD0TE31Ik3X9c78STk5NNB6fZKV9zr/1wsd+bs37zBJd8dwu7jsKuo0XbJd/dwvGPeySHHbBnd4Orw34fTvb7cLLfa5vpmnQyqb4PeERF23zgnsoDI2IXigmNxwJPL5WB1DQ25kB2t3jth5P9PnfnnLzbTm0P3Hkj197ZhWBmyX4fTvb7cLLfZ6+TSfUG4KXlBxExChzMtJKQUvsewCUUkxl/KzN/PtOJV65cyejoaGujVV2Tk5OMjY157YeM/d6c9ZsneO1H1jO57eG20Xlw/suP6PmRavt9+Njvw8l+r618bWrpZFJ9BbAoIk6nmHz4FuCGzNxQcdw5wN7AcZl5XyMnHh0dteO7xGs/nOz3uTly2T78/m8u3qGm+uTfWsyKg/budmgNsd+Hk/0+nOz32etYUp2Z90fEiRRlHecB1wKnAETEOPAu4N+AVwIPAT+PiPKPfyczn9GpWCWpXdasXsbxqxZx/a33snzJAlYsXdjtkCRJLdDRzV8y8xrgSVXaV0x76MciSQNtxdKFJtOSNGDcplySJElqkkm1JEmS1CSTakmSJKlJJtWSJElSk0yqJUmSpCZ1dPUPSWqn8U0TLlUnSeoKk2pJA+HcSzfusKnKqccsZs3qZd0OS5I0JCz/kNT3xjdNbE+oASa3wUVXbuG6zfd0NzBJ0tAwqZbU966/9d7tCXXZ5DbIW0yqJUmdYVItqe8tX7KA0YrfZqPzIA7YszsBSZKGjkm1pL63YulCTj1m8fbEulxTfcSBJtWSpM5woqKkgbBm9TKOX7XI1T8kSV1hUi1pYKxYutBkWpLUFZZ/SJIkSU0yqZYkSZKaZFItSZIkNcmkWpIkSWqSSbUkSZLUJJNqSZIkqUkm1ZIkSVKTTKolSZKkJplUS5IkSU0yqZYkSZKaZFItSZIkNcmkWpIkSWqSSbUkSZLUJJNqSZIkqUm7dDuAVpicnOx2CEOnfM299sPFfh9O9vtwst+Hk/1e20zXZGRqaqpDobTeNddccyCwqdtxSJIkaWgsPeqoozZXNvb7SPUtwFLg7m4HIkmSpIG3F0X+uZO+HqmWJEmSeoETFSVJkqQmmVRLkiRJTTKpliRJkprU7xMV1UURsS/wc+C+ac1/nZnv7VJIaqOIeApwAbAc+BFwWmbe0N2o1G4RcSbwLuDBac3LM/O2LoWkNoqI5wGvy8xjSo8PBT4BPAH4KfAnmfm9LoaoNqjS788FPgf837TDTsjMq7oRX78wqVYzVgE/zMyjuh2I2isi9gDWAWcCXwDeAnwS+O0uhqXOWAW8MTPP63Ygap+IGAVeT/EB6r+nfeufgc8DTwf+CPiXiHhMZrqI8QCo0++rgA9l5hu7ElifsvxDzVhFMWKpwXcccFdmrs3MB4GzgSMj4vAux6X2830+HN4DnFT6LwCl9/chwDmZ+VBm/hMwATyjOyGqDXbq9xLf93NgUq1mrAIOj4gbIuKWiDgnInbrdlBqi8OADeUHpVGqmwCT6gFWukMRwJsj4ucR8aOIOLHbcaktPpCZT6V4X5cdBvw0M381re0GfN8Pkmr9DsXf9xdGxJaIyIh4cRdi6zuWf6iuUl3Vv1T51qeAu4D/AN4H7ENRFvD20j8NlgXA/RVt9wHzuxCLOufXge8A5wFXUIxQ/nNEPCkz13c1MrVUZm6p0uz7fsBV6/fSh+kbKf7OXwwcBVwWEZsy8z86HGJfManWTNYBC6u0P5SZ0ycw3BUR7wHegUn1ILoPeERF23zgni7Eog7JzE3A06Y1XRYRVwDPBEyqB5/v+yGUmQ8Ax05r+k5EXEhRJmJSXYflH6orMycz857Kf8CDEfGuiDhw2uG7Aw90KVS11waKVT+A7ZNbDmZaSYgGT0Q8LiLeXNHs+3x4bAAOLr3fy5bj+36gRcTSiHh3RbPv+wY4Uq05ycypiHgicHZEvBzYD3gr8I/djUxtcgWwKCJOB9ZSrP5xQ2b6x3Ww3Q28IyI2AJcBJwO/CZzW1ajUEZl5XURsBP6idCfyBRSlft/sYlhqv18AfxoRmyj+ph8DnAo8tatR9QFHqtWMFwN7A7cB3wP+FTi/qxGpLTLzfuBE4NXAVuAE4JSuBqW2y8ybgOcD76ZY9eEvgNU16m81mP6AYvWfO4E1wEml8gANqNLd6NUUf+PvBj4KvDQzr+1qYH1gZGpqqtsxSJIkSX3NkWpJkiSpSSbVkiRJUpNMqiVJkqQmmVRLkiRJTTKpliRJkppkUi1JkiQ1yc1fJKmO0uYXB1U0TwDXAmdm5tUNnuebwPcz88wGjh0BXgZ8JjMfKG26c05m7tt45HMTEUcCY8BjMnNj6fWfk5l116CPiF2BP8nMf2h3jJLUixyplqSZvRVYXPq3P/D/gAeByyNizzY831OBj/DwwMc/A0e04Xka8STgEw0c9wLgr9sciyT1LEeqJWlmE5l5+7THW0qjx5uBpwOXtvj5RqY/KO1oeX+Ln6Mhmfk/DR46MvMhkjS4TKolaW7+r/TfyXJDRJwJvBZYxMPlId+t9sMR8QbglcAy4B7gy6XH+wJXlA6biIgXl74+JzP3jYgrgf/OzDOmneuDwBGZ+YyIWAi8H3guMAV8A1iTmbfViONRwD8CzwC2AH9X8f2Npec+PyJWAOdTjF7fD3yRYuvqJwH/VDp+imJb6/8E/gp4IbAE+AVwEXBGZk5GxFnAY4GfAS8pXc9/Ad6QmZOlc70SeANwAPCT0s/+Z+l7z6TYPj1K5zgnM/+p2muUpE6w/EOSZikiFgEfBW4Hriy1vZwiwXwV8HjgcuAbEfGYKj//fOAs4AzgUOB04CTg5RSj3yeXDv0NitKP6dYCzyvVXRMR84DnAReWvv8RikTzd4CnUSTWX42IWoMo/wI8Gvj/KJL6P6vz0tcCSZEMP5uiDOYtwHeA11MkzotLj98EvAg4rfQa3wS8Gvj9aed7FrAPcDTwl9O/X/ow8X7gPaXnuwL4ckQsKiX3FwMXAEdSlJ28PyJOrRO7JLWVI9WSNLP3R8R7Sl/PA0YpkunjM/PuUvtbgbdk5pdLj98VEcdSJIqVkxNvA07PzMtKj2+OiG9RjDZPRsQvSu0/z8z7I2L6z34eOJciEf0OcAzwa8C6iDgYOBU4IDNvBYiIFwF3Ar8LXDb9RBFxOEXifWRmjpfa/gz4XI3rsIyi1OXmzPxZRKwGHszMByPiLmCqXCYTEeOl1/it0s9ujIg3UdSGX1xqux94VWY+CGRpZPqo0vdfBfxjZn6idL63lH7mkcCbgQsz84JS208j4jdK1/miGrFLUluZVEvSzN4NfBbYnSLZ+wPgrGmJ6J7AUuCjEfGP035udx4uE9kuM78VEU+MiHcChwErKEaXPz1TIJm5NSK+BpxCkVSfCnwpM++OiKeWD6tIxOeXzr9DUk0xyvt/5ddRUm81kz8D/h54ZUR8FfiXzLykRpyXRsRxEfFeYDnFaPMyig8kZTeXEuqyu4FdS18fwbRSlMycKj0/pZHqlaUR/7JdgIfqxC5JbWX5hyTN7M7MvDEzxzPz1RSj1F+KiANL3y8niqcBq6b9Oxx4ReXJSpMcr6QYYf43isR4NpMdLwSeW1rG7mSKsgx4OLF8fEUcyynVPFdTLiUpebDWcaWR4YMoaqX3AT4fER+tcc6zgH+luDYXA88EflRxWLXnGpn2vakaoewCnMeOr/FIitctSV1hUi1Js/cq4FfAhwEy8y6K+uolpeT7xsy8EXgNRW1zpVcD78vMV2Xmx4EfU9QdlxPKWslk2Rcpkto1FCO7l5fa15ceL5gWwxbgfRSJdaUfU4ymr5rW9oRqTxgRe0XE+RQlHudl5oml1/fCGjG/GnhjZr4xMz8DbKRIyBtdJeT6ylgiYjwiTi69zkMqrvWxFP0iSV1h+YckzVJm3lmq8f1YRJyUmV8E3gv8ZURsAf6bYt3m11DULFfaChwXEUdQJJlvoCh3GCt9/57Sf4+KiGuqPP99EXEJ8A7gonIJRWZmRFwKfDoiXg38D3A28JvAhirnyYi4HPh4aaLl7qXXUe01312qEd8/It5Wav790mstx7xn6TX9rPQaT4yIbwN7UUzMfGTpORrxdxTX91rguxSb4ewHfJsiQf9eRLyVosb8caXj39nguSWp5RyplqS5+QTwX8C5ETGfYvLgORRJ6XUUNc/Pzcz/qvKzayhGdr8P/DtFovluHh6ZHaOof/4a8Kc1nv9CYE8eLv0oO6103ksoEt69gRMy839rnOf5pXi/QTFB8UM1X3FRSz6fopb7e8ADFB8eAP4D+GHp34kUK5o8pvRaLqFIhD9OMRFxRpl5EfA2ig8FYxQb4jwzM/8nM6+hWDLwD4Fx4AMUq4RU/UAgSZ0wMjU1011GSZIkSfU4Ui1JkiQ1yaRakiRJapJJtSRJktQkk2pJkiSpSSbVkiRJUpNMqiVJkqQmmVRLkiRJTTKpliRJkppkUi1JkiQ16f8HwoQ8JkztU2sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.xlabel('Relative distance')\n",
    "plt.ylabel('Young Male Likelihood')\n",
    "plt.scatter(x=df1['r_dist'], y=df1['ym_LH'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}