Final_Tutorial.ipynb

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    "# What do People Value in a Potential Partner?\n",
    "### An Analysis on Speed Dating Success\n",
    "### Created by *Owen Maschal*\n",
    "***\n",
    "The intention of this project is discover was aspects of a person lead to a more successful date.\n",
    "\n",
    "We will be using a multitude of Python Packages in this tutorial.\n",
    "   + [Pandas](https://pandas.pydata.org/): Used to handle our data and data analysis.\n",
    "   + [Numpy](https://docs.scipy.org/doc/numpy-1.15.1/reference/): Used for linear algebra operations.\n",
    "   + [Seaborn](https://seaborn.pydata.org/): Used to make pretty graphs and charts.\n",
    "   + [MatPlotLib](https://matplotlib.org/): Extended by Seaborn. Used to create basic graphs and charts in Python.\n",
    "   + [SciKit-Learn](https://scikit-learn.org/stable/): Machine Learning and Data Analysis library"
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.neighbors import KNeighborsClassifier"
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    "# Display all charts/graphs inline\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style(\"darkgrid\") # Set the Seaborn style\n",
    "pd.set_option('display.max_columns', None) # show all cols of pandas table\n",
    "#pd.set_option('display.max_rows', None) # show all cols of pandas table"
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    "## Data Source\n",
    "This dataset was compiled by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper *Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment*. The data was gathered from a series of speed dating experiments from 2002-2004.\n",
    "\n",
    "At each event each participant would be given a 4-minute first date with each of the participants of the other gender. At the end of each date each participant was asked if they would like to see their date again and were asked to rate them on 6 traits: Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests.\n",
    "\n",
    "In addition, the dataset includes responses from multiple surveys where the participants were asked various questions on topics including demographics, dating habits, self-perception across key attributes, beliefs on what others find valuable in a mate, and lifestyle information. Three surveys were completed in total: one at the time of event registration, one the day after the event, and one 3-4 weeks after the event. For the purposes of this tutorial we will not need to use this portion data.\n",
    "\n",
    "The dataset was retrieved from [Kaggle.com](https://www.kaggle.com/annavictoria/speed-dating-experiment/kernels). The dataset included a Word document that explains the specifics of the dataset.\n",
    "\n",
    "## Data Tidying\n",
    "\n",
    "Now that we have collected the data we want to work with, we need to explore the data to understand what the dataset includes and look for relationships within the data. We start by importing the data from a `.csv` (Comma Separated Values) file into a Pandas dataframe. Thankfully, Pandas includes a function `read_scv()` that will do all of this work for us. This function automatically converts missing data to `NaN` values. We print out the head of our data to make sure that the data import worked properly."
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       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Law</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>60,521</td>\n",
       "      <td>69,487.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>13.89</td>\n",
       "      <td>22.22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>16.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iid   id  gender  idg  condtn  wave  round  position  positin1  order  \\\n",
       "0    1  1.0       0    1       1     1     10         7       NaN      4   \n",
       "1    1  1.0       0    1       1     1     10         7       NaN      3   \n",
       "2    1  1.0       0    1       1     1     10         7       NaN     10   \n",
       "3    1  1.0       0    1       1     1     10         7       NaN      5   \n",
       "4    1  1.0       0    1       1     1     10         7       NaN      7   \n",
       "\n",
       "   partner   pid  match  int_corr  samerace  age_o  race_o  pf_o_att  \\\n",
       "0        1  11.0      0      0.14         0   27.0     2.0      35.0   \n",
       "1        2  12.0      0      0.54         0   22.0     2.0      60.0   \n",
       "2        3  13.0      1      0.16         1   22.0     4.0      19.0   \n",
       "3        4  14.0      1      0.61         0   23.0     2.0      30.0   \n",
       "4        5  15.0      1      0.21         0   24.0     3.0      30.0   \n",
       "\n",
       "   pf_o_sin  pf_o_int  pf_o_fun  pf_o_amb  pf_o_sha  dec_o  attr_o  sinc_o  \\\n",
       "0      20.0      20.0      20.0       0.0       5.0      0     6.0     8.0   \n",
       "1       0.0       0.0      40.0       0.0       0.0      0     7.0     8.0   \n",
       "2      18.0      19.0      18.0      14.0      12.0      1    10.0    10.0   \n",
       "3       5.0      15.0      40.0       5.0       5.0      1     7.0     8.0   \n",
       "4      10.0      20.0      10.0      10.0      20.0      1     8.0     7.0   \n",
       "\n",
       "   intel_o  fun_o  amb_o  shar_o  like_o  prob_o  met_o   age field  field_cd  \\\n",
       "0      8.0    8.0    8.0     6.0     7.0     4.0    2.0  21.0   Law       1.0   \n",
       "1     10.0    7.0    7.0     5.0     8.0     4.0    2.0  21.0   Law       1.0   \n",
       "2     10.0   10.0   10.0    10.0    10.0    10.0    1.0  21.0   Law       1.0   \n",
       "3      9.0    8.0    9.0     8.0     7.0     7.0    2.0  21.0   Law       1.0   \n",
       "4      9.0    6.0    9.0     7.0     8.0     6.0    2.0  21.0   Law       1.0   \n",
       "\n",
       "  undergra mn_sat tuition  race  imprace  imprelig     from zipcode  \\\n",
       "0      NaN    NaN     NaN   4.0      2.0       4.0  Chicago  60,521   \n",
       "1      NaN    NaN     NaN   4.0      2.0       4.0  Chicago  60,521   \n",
       "2      NaN    NaN     NaN   4.0      2.0       4.0  Chicago  60,521   \n",
       "3      NaN    NaN     NaN   4.0      2.0       4.0  Chicago  60,521   \n",
       "4      NaN    NaN     NaN   4.0      2.0       4.0  Chicago  60,521   \n",
       "\n",
       "      income  goal  date  go_out  career  career_c  sports  tvsports  \\\n",
       "0  69,487.00   2.0   7.0     1.0  lawyer       NaN     9.0       2.0   \n",
       "1  69,487.00   2.0   7.0     1.0  lawyer       NaN     9.0       2.0   \n",
       "2  69,487.00   2.0   7.0     1.0  lawyer       NaN     9.0       2.0   \n",
       "3  69,487.00   2.0   7.0     1.0  lawyer       NaN     9.0       2.0   \n",
       "4  69,487.00   2.0   7.0     1.0  lawyer       NaN     9.0       2.0   \n",
       "\n",
       "   exercise  dining  museums  art  hiking  gaming  clubbing  reading   tv  \\\n",
       "0       8.0     9.0      1.0  1.0     5.0     1.0       5.0      6.0  9.0   \n",
       "1       8.0     9.0      1.0  1.0     5.0     1.0       5.0      6.0  9.0   \n",
       "2       8.0     9.0      1.0  1.0     5.0     1.0       5.0      6.0  9.0   \n",
       "3       8.0     9.0      1.0  1.0     5.0     1.0       5.0      6.0  9.0   \n",
       "4       8.0     9.0      1.0  1.0     5.0     1.0       5.0      6.0  9.0   \n",
       "\n",
       "   theater  movies  concerts  music  shopping  yoga  exphappy  expnum  \\\n",
       "0      1.0    10.0      10.0    9.0       8.0   1.0       3.0     2.0   \n",
       "1      1.0    10.0      10.0    9.0       8.0   1.0       3.0     2.0   \n",
       "2      1.0    10.0      10.0    9.0       8.0   1.0       3.0     2.0   \n",
       "3      1.0    10.0      10.0    9.0       8.0   1.0       3.0     2.0   \n",
       "4      1.0    10.0      10.0    9.0       8.0   1.0       3.0     2.0   \n",
       "\n",
       "   attr1_1  sinc1_1  intel1_1  fun1_1  amb1_1  shar1_1  attr4_1  sinc4_1  \\\n",
       "0     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "1     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "2     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "3     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "4     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "\n",
       "   intel4_1  fun4_1  amb4_1  shar4_1  attr2_1  sinc2_1  intel2_1  fun2_1  \\\n",
       "0       NaN     NaN     NaN      NaN     35.0     20.0      15.0    20.0   \n",
       "1       NaN     NaN     NaN      NaN     35.0     20.0      15.0    20.0   \n",
       "2       NaN     NaN     NaN      NaN     35.0     20.0      15.0    20.0   \n",
       "3       NaN     NaN     NaN      NaN     35.0     20.0      15.0    20.0   \n",
       "4       NaN     NaN     NaN      NaN     35.0     20.0      15.0    20.0   \n",
       "\n",
       "   amb2_1  shar2_1  attr3_1  sinc3_1  fun3_1  intel3_1  amb3_1  attr5_1  \\\n",
       "0     5.0      5.0      6.0      8.0     8.0       8.0     7.0      NaN   \n",
       "1     5.0      5.0      6.0      8.0     8.0       8.0     7.0      NaN   \n",
       "2     5.0      5.0      6.0      8.0     8.0       8.0     7.0      NaN   \n",
       "3     5.0      5.0      6.0      8.0     8.0       8.0     7.0      NaN   \n",
       "4     5.0      5.0      6.0      8.0     8.0       8.0     7.0      NaN   \n",
       "\n",
       "   sinc5_1  intel5_1  fun5_1  amb5_1  dec  attr  sinc  intel  fun  amb  shar  \\\n",
       "0      NaN       NaN     NaN     NaN    1   6.0   9.0    7.0  7.0  6.0   5.0   \n",
       "1      NaN       NaN     NaN     NaN    1   7.0   8.0    7.0  8.0  5.0   6.0   \n",
       "2      NaN       NaN     NaN     NaN    1   5.0   8.0    9.0  8.0  5.0   7.0   \n",
       "3      NaN       NaN     NaN     NaN    1   7.0   6.0    8.0  7.0  6.0   8.0   \n",
       "4      NaN       NaN     NaN     NaN    1   5.0   6.0    7.0  7.0  6.0   6.0   \n",
       "\n",
       "   like  prob  met  match_es  attr1_s  sinc1_s  intel1_s  fun1_s  amb1_s  \\\n",
       "0   7.0   6.0  2.0       4.0      NaN      NaN       NaN     NaN     NaN   \n",
       "1   7.0   5.0  1.0       4.0      NaN      NaN       NaN     NaN     NaN   \n",
       "2   7.0   NaN  1.0       4.0      NaN      NaN       NaN     NaN     NaN   \n",
       "3   7.0   6.0  2.0       4.0      NaN      NaN       NaN     NaN     NaN   \n",
       "4   6.0   6.0  2.0       4.0      NaN      NaN       NaN     NaN     NaN   \n",
       "\n",
       "   shar1_s  attr3_s  sinc3_s  intel3_s  fun3_s  amb3_s  satis_2  length  \\\n",
       "0      NaN      NaN      NaN       NaN     NaN     NaN      6.0     2.0   \n",
       "1      NaN      NaN      NaN       NaN     NaN     NaN      6.0     2.0   \n",
       "2      NaN      NaN      NaN       NaN     NaN     NaN      6.0     2.0   \n",
       "3      NaN      NaN      NaN       NaN     NaN     NaN      6.0     2.0   \n",
       "4      NaN      NaN      NaN       NaN     NaN     NaN      6.0     2.0   \n",
       "\n",
       "   numdat_2  attr7_2  sinc7_2  intel7_2  fun7_2  amb7_2  shar7_2  attr1_2  \\\n",
       "0       1.0      NaN      NaN       NaN     NaN     NaN      NaN    19.44   \n",
       "1       1.0      NaN      NaN       NaN     NaN     NaN      NaN    19.44   \n",
       "2       1.0      NaN      NaN       NaN     NaN     NaN      NaN    19.44   \n",
       "3       1.0      NaN      NaN       NaN     NaN     NaN      NaN    19.44   \n",
       "4       1.0      NaN      NaN       NaN     NaN     NaN      NaN    19.44   \n",
       "\n",
       "   sinc1_2  intel1_2  fun1_2  amb1_2  shar1_2  attr4_2  sinc4_2  intel4_2  \\\n",
       "0    16.67     13.89   22.22   11.11    16.67      NaN      NaN       NaN   \n",
       "1    16.67     13.89   22.22   11.11    16.67      NaN      NaN       NaN   \n",
       "2    16.67     13.89   22.22   11.11    16.67      NaN      NaN       NaN   \n",
       "3    16.67     13.89   22.22   11.11    16.67      NaN      NaN       NaN   \n",
       "4    16.67     13.89   22.22   11.11    16.67      NaN      NaN       NaN   \n",
       "\n",
       "   fun4_2  amb4_2  shar4_2  attr2_2  sinc2_2  intel2_2  fun2_2  amb2_2  \\\n",
       "0     NaN     NaN      NaN      NaN      NaN       NaN     NaN     NaN   \n",
       "1     NaN     NaN      NaN      NaN      NaN       NaN     NaN     NaN   \n",
       "2     NaN     NaN      NaN      NaN      NaN       NaN     NaN     NaN   \n",
       "3     NaN     NaN      NaN      NaN      NaN       NaN     NaN     NaN   \n",
       "4     NaN     NaN      NaN      NaN      NaN       NaN     NaN     NaN   \n",
       "\n",
       "   shar2_2  attr3_2  sinc3_2  intel3_2  fun3_2  amb3_2  attr5_2  sinc5_2  \\\n",
       "0      NaN      6.0      7.0       8.0     7.0     6.0      NaN      NaN   \n",
       "1      NaN      6.0      7.0       8.0     7.0     6.0      NaN      NaN   \n",
       "2      NaN      6.0      7.0       8.0     7.0     6.0      NaN      NaN   \n",
       "3      NaN      6.0      7.0       8.0     7.0     6.0      NaN      NaN   \n",
       "4      NaN      6.0      7.0       8.0     7.0     6.0      NaN      NaN   \n",
       "\n",
       "   intel5_2  fun5_2  amb5_2  you_call  them_cal  date_3  numdat_3  num_in_3  \\\n",
       "0       NaN     NaN     NaN       1.0       1.0     0.0       NaN       NaN   \n",
       "1       NaN     NaN     NaN       1.0       1.0     0.0       NaN       NaN   \n",
       "2       NaN     NaN     NaN       1.0       1.0     0.0       NaN       NaN   \n",
       "3       NaN     NaN     NaN       1.0       1.0     0.0       NaN       NaN   \n",
       "4       NaN     NaN     NaN       1.0       1.0     0.0       NaN       NaN   \n",
       "\n",
       "   attr1_3  sinc1_3  intel1_3  fun1_3  amb1_3  shar1_3  attr7_3  sinc7_3  \\\n",
       "0     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "1     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "2     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "3     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "4     15.0     20.0      20.0    15.0    15.0     15.0      NaN      NaN   \n",
       "\n",
       "   intel7_3  fun7_3  amb7_3  shar7_3  attr4_3  sinc4_3  intel4_3  fun4_3  \\\n",
       "0       NaN     NaN     NaN      NaN      NaN      NaN       NaN     NaN   \n",
       "1       NaN     NaN     NaN      NaN      NaN      NaN       NaN     NaN   \n",
       "2       NaN     NaN     NaN      NaN      NaN      NaN       NaN     NaN   \n",
       "3       NaN     NaN     NaN      NaN      NaN      NaN       NaN     NaN   \n",
       "4       NaN     NaN     NaN      NaN      NaN      NaN       NaN     NaN   \n",
       "\n",
       "   amb4_3  shar4_3  attr2_3  sinc2_3  intel2_3  fun2_3  amb2_3  shar2_3  \\\n",
       "0     NaN      NaN      NaN      NaN       NaN     NaN     NaN      NaN   \n",
       "1     NaN      NaN      NaN      NaN       NaN     NaN     NaN      NaN   \n",
       "2     NaN      NaN      NaN      NaN       NaN     NaN     NaN      NaN   \n",
       "3     NaN      NaN      NaN      NaN       NaN     NaN     NaN      NaN   \n",
       "4     NaN      NaN      NaN      NaN       NaN     NaN     NaN      NaN   \n",
       "\n",
       "   attr3_3  sinc3_3  intel3_3  fun3_3  amb3_3  attr5_3  sinc5_3  intel5_3  \\\n",
       "0      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "1      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "2      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "3      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "4      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "\n",
       "   fun5_3  amb5_3  \n",
       "0     NaN     NaN  \n",
       "1     NaN     NaN  \n",
       "2     NaN     NaN  \n",
       "3     NaN     NaN  \n",
       "4     NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the dataset into a Pandas dataframe so we can begin manipulating the data\n",
    "dataframe = pd.read_csv('Speed_Dating_Data.csv',  encoding = 'ISO-8859-1') # encode with ISO-8859-1 because the data includes old chars\n",
    "#df.loc[df['wave'] == 7]\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step of the data tidying step is looking for missing data. The easiest way of doing this is using Pandas' `describe()` function. This function outputs the count of each column. This makes it easy to see columns that are missing data because they will have less data than the other columns. This function also calculates various other statistics about the each column but for our purposes these aren't important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>iid</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>idg</th>\n",
       "      <th>condtn</th>\n",
       "      <th>wave</th>\n",
       "      <th>round</th>\n",
       "      <th>position</th>\n",
       "      <th>positin1</th>\n",
       "      <th>order</th>\n",
       "      <th>partner</th>\n",
       "      <th>pid</th>\n",
       "      <th>match</th>\n",
       "      <th>int_corr</th>\n",
       "      <th>samerace</th>\n",
       "      <th>age_o</th>\n",
       "      <th>race_o</th>\n",
       "      <th>pf_o_att</th>\n",
       "      <th>pf_o_sin</th>\n",
       "      <th>pf_o_int</th>\n",
       "      <th>pf_o_fun</th>\n",
       "      <th>pf_o_amb</th>\n",
       "      <th>pf_o_sha</th>\n",
       "      <th>dec_o</th>\n",
       "      <th>attr_o</th>\n",
       "      <th>sinc_o</th>\n",
       "      <th>intel_o</th>\n",
       "      <th>fun_o</th>\n",
       "      <th>amb_o</th>\n",
       "      <th>shar_o</th>\n",
       "      <th>like_o</th>\n",
       "      <th>prob_o</th>\n",
       "      <th>met_o</th>\n",
       "      <th>age</th>\n",
       "      <th>field_cd</th>\n",
       "      <th>race</th>\n",
       "      <th>imprace</th>\n",
       "      <th>imprelig</th>\n",
       "      <th>goal</th>\n",
       "      <th>date</th>\n",
       "      <th>go_out</th>\n",
       "      <th>career_c</th>\n",
       "      <th>sports</th>\n",
       "      <th>tvsports</th>\n",
       "      <th>exercise</th>\n",
       "      <th>dining</th>\n",
       "      <th>museums</th>\n",
       "      <th>art</th>\n",
       "      <th>hiking</th>\n",
       "      <th>gaming</th>\n",
       "      <th>clubbing</th>\n",
       "      <th>reading</th>\n",
       "      <th>tv</th>\n",
       "      <th>theater</th>\n",
       "      <th>movies</th>\n",
       "      <th>concerts</th>\n",
       "      <th>music</th>\n",
       "      <th>shopping</th>\n",
       "      <th>yoga</th>\n",
       "      <th>exphappy</th>\n",
       "      <th>expnum</th>\n",
       "      <th>attr1_1</th>\n",
       "      <th>sinc1_1</th>\n",
       "      <th>intel1_1</th>\n",
       "      <th>fun1_1</th>\n",
       "      <th>amb1_1</th>\n",
       "      <th>shar1_1</th>\n",
       "      <th>attr4_1</th>\n",
       "      <th>sinc4_1</th>\n",
       "      <th>intel4_1</th>\n",
       "      <th>fun4_1</th>\n",
       "      <th>amb4_1</th>\n",
       "      <th>shar4_1</th>\n",
       "      <th>attr2_1</th>\n",
       "      <th>sinc2_1</th>\n",
       "      <th>intel2_1</th>\n",
       "      <th>fun2_1</th>\n",
       "      <th>amb2_1</th>\n",
       "      <th>shar2_1</th>\n",
       "      <th>attr3_1</th>\n",
       "      <th>sinc3_1</th>\n",
       "      <th>fun3_1</th>\n",
       "      <th>intel3_1</th>\n",
       "      <th>amb3_1</th>\n",
       "      <th>attr5_1</th>\n",
       "      <th>sinc5_1</th>\n",
       "      <th>intel5_1</th>\n",
       "      <th>fun5_1</th>\n",
       "      <th>amb5_1</th>\n",
       "      <th>dec</th>\n",
       "      <th>attr</th>\n",
       "      <th>sinc</th>\n",
       "      <th>intel</th>\n",
       "      <th>fun</th>\n",
       "      <th>amb</th>\n",
       "      <th>shar</th>\n",
       "      <th>like</th>\n",
       "      <th>prob</th>\n",
       "      <th>met</th>\n",
       "      <th>match_es</th>\n",
       "      <th>attr1_s</th>\n",
       "      <th>sinc1_s</th>\n",
       "      <th>intel1_s</th>\n",
       "      <th>fun1_s</th>\n",
       "      <th>amb1_s</th>\n",
       "      <th>shar1_s</th>\n",
       "      <th>attr3_s</th>\n",
       "      <th>sinc3_s</th>\n",
       "      <th>intel3_s</th>\n",
       "      <th>fun3_s</th>\n",
       "      <th>amb3_s</th>\n",
       "      <th>satis_2</th>\n",
       "      <th>length</th>\n",
       "      <th>numdat_2</th>\n",
       "      <th>attr7_2</th>\n",
       "      <th>sinc7_2</th>\n",
       "      <th>intel7_2</th>\n",
       "      <th>fun7_2</th>\n",
       "      <th>amb7_2</th>\n",
       "      <th>shar7_2</th>\n",
       "      <th>attr1_2</th>\n",
       "      <th>sinc1_2</th>\n",
       "      <th>intel1_2</th>\n",
       "      <th>fun1_2</th>\n",
       "      <th>amb1_2</th>\n",
       "      <th>shar1_2</th>\n",
       "      <th>attr4_2</th>\n",
       "      <th>sinc4_2</th>\n",
       "      <th>intel4_2</th>\n",
       "      <th>fun4_2</th>\n",
       "      <th>amb4_2</th>\n",
       "      <th>shar4_2</th>\n",
       "      <th>attr2_2</th>\n",
       "      <th>sinc2_2</th>\n",
       "      <th>intel2_2</th>\n",
       "      <th>fun2_2</th>\n",
       "      <th>amb2_2</th>\n",
       "      <th>shar2_2</th>\n",
       "      <th>attr3_2</th>\n",
       "      <th>sinc3_2</th>\n",
       "      <th>intel3_2</th>\n",
       "      <th>fun3_2</th>\n",
       "      <th>amb3_2</th>\n",
       "      <th>attr5_2</th>\n",
       "      <th>sinc5_2</th>\n",
       "      <th>intel5_2</th>\n",
       "      <th>fun5_2</th>\n",
       "      <th>amb5_2</th>\n",
       "      <th>you_call</th>\n",
       "      <th>them_cal</th>\n",
       "      <th>date_3</th>\n",
       "      <th>numdat_3</th>\n",
       "      <th>num_in_3</th>\n",
       "      <th>attr1_3</th>\n",
       "      <th>sinc1_3</th>\n",
       "      <th>intel1_3</th>\n",
       "      <th>fun1_3</th>\n",
       "      <th>amb1_3</th>\n",
       "      <th>shar1_3</th>\n",
       "      <th>attr7_3</th>\n",
       "      <th>sinc7_3</th>\n",
       "      <th>intel7_3</th>\n",
       "      <th>fun7_3</th>\n",
       "      <th>amb7_3</th>\n",
       "      <th>shar7_3</th>\n",
       "      <th>attr4_3</th>\n",
       "      <th>sinc4_3</th>\n",
       "      <th>intel4_3</th>\n",
       "      <th>fun4_3</th>\n",
       "      <th>amb4_3</th>\n",
       "      <th>shar4_3</th>\n",
       "      <th>attr2_3</th>\n",
       "      <th>sinc2_3</th>\n",
       "      <th>intel2_3</th>\n",
       "      <th>fun2_3</th>\n",
       "      <th>amb2_3</th>\n",
       "      <th>shar2_3</th>\n",
       "      <th>attr3_3</th>\n",
       "      <th>sinc3_3</th>\n",
       "      <th>intel3_3</th>\n",
       "      <th>fun3_3</th>\n",
       "      <th>amb3_3</th>\n",
       "      <th>attr5_3</th>\n",
       "      <th>sinc5_3</th>\n",
       "      <th>intel5_3</th>\n",
       "      <th>fun5_3</th>\n",
       "      <th>amb5_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8377.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>6532.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8368.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8220.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8274.000000</td>\n",
       "      <td>8305.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8280.000000</td>\n",
       "      <td>8271.000000</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8166.000000</td>\n",
       "      <td>8091.000000</td>\n",
       "      <td>8072.000000</td>\n",
       "      <td>8018.000000</td>\n",
       "      <td>7656.000000</td>\n",
       "      <td>7302.000000</td>\n",
       "      <td>8128.000000</td>\n",
       "      <td>8060.000000</td>\n",
       "      <td>7993.000000</td>\n",
       "      <td>8283.000000</td>\n",
       "      <td>8296.000000</td>\n",
       "      <td>8315.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8281.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8240.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8277.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8279.000000</td>\n",
       "      <td>8257.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6467.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8299.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8289.000000</td>\n",
       "      <td>8273.000000</td>\n",
       "      <td>8273.000000</td>\n",
       "      <td>8273.000000</td>\n",
       "      <td>8273.000000</td>\n",
       "      <td>8273.000000</td>\n",
       "      <td>4906.000000</td>\n",
       "      <td>4906.000000</td>\n",
       "      <td>4906.000000</td>\n",
       "      <td>4906.000000</td>\n",
       "      <td>4906.000000</td>\n",
       "      <td>8378.000000</td>\n",
       "      <td>8176.000000</td>\n",
       "      <td>8101.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8028.000000</td>\n",
       "      <td>7666.000000</td>\n",
       "      <td>7311.000000</td>\n",
       "      <td>8138.000000</td>\n",
       "      <td>8069.000000</td>\n",
       "      <td>8003.000000</td>\n",
       "      <td>7205.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4096.000000</td>\n",
       "      <td>4000.00000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7433.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>7445.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.00000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>5775.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>7463.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>1496.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2959.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>283.675937</td>\n",
       "      <td>8.960248</td>\n",
       "      <td>0.500597</td>\n",
       "      <td>17.327166</td>\n",
       "      <td>1.828837</td>\n",
       "      <td>11.350919</td>\n",
       "      <td>16.872046</td>\n",
       "      <td>9.042731</td>\n",
       "      <td>9.295775</td>\n",
       "      <td>8.927668</td>\n",
       "      <td>8.963595</td>\n",
       "      <td>283.863767</td>\n",
       "      <td>0.164717</td>\n",
       "      <td>0.196010</td>\n",
       "      <td>0.395799</td>\n",
       "      <td>26.364999</td>\n",
       "      <td>2.756653</td>\n",
       "      <td>22.495347</td>\n",
       "      <td>17.396867</td>\n",
       "      <td>20.270759</td>\n",
       "      <td>17.459714</td>\n",
       "      <td>10.685375</td>\n",
       "      <td>11.845930</td>\n",
       "      <td>0.419551</td>\n",
       "      <td>6.190411</td>\n",
       "      <td>7.175256</td>\n",
       "      <td>7.369301</td>\n",
       "      <td>6.400599</td>\n",
       "      <td>6.778409</td>\n",
       "      <td>5.474870</td>\n",
       "      <td>6.134498</td>\n",
       "      <td>5.208251</td>\n",
       "      <td>1.960215</td>\n",
       "      <td>26.358928</td>\n",
       "      <td>7.662488</td>\n",
       "      <td>2.757186</td>\n",
       "      <td>3.784793</td>\n",
       "      <td>3.651645</td>\n",
       "      <td>2.122063</td>\n",
       "      <td>5.006762</td>\n",
       "      <td>2.158091</td>\n",
       "      <td>5.277791</td>\n",
       "      <td>6.425232</td>\n",
       "      <td>4.575491</td>\n",
       "      <td>6.245813</td>\n",
       "      <td>7.783829</td>\n",
       "      <td>6.985781</td>\n",
       "      <td>6.714544</td>\n",
       "      <td>5.737077</td>\n",
       "      <td>3.881191</td>\n",
       "      <td>5.745993</td>\n",
       "      <td>7.678515</td>\n",
       "      <td>5.304133</td>\n",
       "      <td>6.776118</td>\n",
       "      <td>7.919629</td>\n",
       "      <td>6.825401</td>\n",
       "      <td>7.851066</td>\n",
       "      <td>5.631281</td>\n",
       "      <td>4.339197</td>\n",
       "      <td>5.534131</td>\n",
       "      <td>5.570556</td>\n",
       "      <td>22.514632</td>\n",
       "      <td>17.396389</td>\n",
       "      <td>20.265613</td>\n",
       "      <td>17.457043</td>\n",
       "      <td>10.682539</td>\n",
       "      <td>11.845111</td>\n",
       "      <td>26.394360</td>\n",
       "      <td>11.071506</td>\n",
       "      <td>12.636308</td>\n",
       "      <td>15.566805</td>\n",
       "      <td>9.780089</td>\n",
       "      <td>11.014845</td>\n",
       "      <td>30.362192</td>\n",
       "      <td>13.273691</td>\n",
       "      <td>14.416891</td>\n",
       "      <td>18.422620</td>\n",
       "      <td>11.744499</td>\n",
       "      <td>11.854817</td>\n",
       "      <td>7.084733</td>\n",
       "      <td>8.294935</td>\n",
       "      <td>7.704460</td>\n",
       "      <td>8.403965</td>\n",
       "      <td>7.578388</td>\n",
       "      <td>6.941908</td>\n",
       "      <td>7.927232</td>\n",
       "      <td>8.284346</td>\n",
       "      <td>7.426213</td>\n",
       "      <td>7.617611</td>\n",
       "      <td>0.419909</td>\n",
       "      <td>6.189995</td>\n",
       "      <td>7.175164</td>\n",
       "      <td>7.368597</td>\n",
       "      <td>6.400598</td>\n",
       "      <td>6.777524</td>\n",
       "      <td>5.474559</td>\n",
       "      <td>6.134087</td>\n",
       "      <td>5.207523</td>\n",
       "      <td>0.948769</td>\n",
       "      <td>3.207814</td>\n",
       "      <td>20.791624</td>\n",
       "      <td>15.434255</td>\n",
       "      <td>17.243708</td>\n",
       "      <td>15.260869</td>\n",
       "      <td>11.144619</td>\n",
       "      <td>12.457925</td>\n",
       "      <td>7.21125</td>\n",
       "      <td>8.082000</td>\n",
       "      <td>8.257750</td>\n",
       "      <td>7.692500</td>\n",
       "      <td>7.589250</td>\n",
       "      <td>5.711510</td>\n",
       "      <td>1.843495</td>\n",
       "      <td>2.338087</td>\n",
       "      <td>32.819556</td>\n",
       "      <td>13.529923</td>\n",
       "      <td>15.293851</td>\n",
       "      <td>18.868448</td>\n",
       "      <td>7.286957</td>\n",
       "      <td>12.156028</td>\n",
       "      <td>26.217194</td>\n",
       "      <td>15.865084</td>\n",
       "      <td>17.813755</td>\n",
       "      <td>17.654765</td>\n",
       "      <td>9.913436</td>\n",
       "      <td>12.760263</td>\n",
       "      <td>26.806234</td>\n",
       "      <td>11.929177</td>\n",
       "      <td>12.103030</td>\n",
       "      <td>15.163810</td>\n",
       "      <td>9.342511</td>\n",
       "      <td>11.320866</td>\n",
       "      <td>29.344369</td>\n",
       "      <td>13.89823</td>\n",
       "      <td>13.958265</td>\n",
       "      <td>17.967233</td>\n",
       "      <td>11.909735</td>\n",
       "      <td>12.887976</td>\n",
       "      <td>7.125285</td>\n",
       "      <td>7.931529</td>\n",
       "      <td>8.238912</td>\n",
       "      <td>7.602171</td>\n",
       "      <td>7.486802</td>\n",
       "      <td>6.827964</td>\n",
       "      <td>7.394106</td>\n",
       "      <td>7.838702</td>\n",
       "      <td>7.279415</td>\n",
       "      <td>7.332191</td>\n",
       "      <td>0.780825</td>\n",
       "      <td>0.981631</td>\n",
       "      <td>0.376950</td>\n",
       "      <td>1.230615</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>24.384524</td>\n",
       "      <td>16.588583</td>\n",
       "      <td>19.411346</td>\n",
       "      <td>16.233415</td>\n",
       "      <td>10.898075</td>\n",
       "      <td>12.699142</td>\n",
       "      <td>31.330357</td>\n",
       "      <td>15.654266</td>\n",
       "      <td>16.679563</td>\n",
       "      <td>16.418155</td>\n",
       "      <td>7.823909</td>\n",
       "      <td>12.207837</td>\n",
       "      <td>25.610341</td>\n",
       "      <td>10.751267</td>\n",
       "      <td>11.524839</td>\n",
       "      <td>14.276783</td>\n",
       "      <td>9.207503</td>\n",
       "      <td>11.253802</td>\n",
       "      <td>24.970936</td>\n",
       "      <td>10.923285</td>\n",
       "      <td>11.952687</td>\n",
       "      <td>14.959108</td>\n",
       "      <td>9.526191</td>\n",
       "      <td>11.966270</td>\n",
       "      <td>7.240312</td>\n",
       "      <td>8.093357</td>\n",
       "      <td>8.388777</td>\n",
       "      <td>7.658782</td>\n",
       "      <td>7.391545</td>\n",
       "      <td>6.810020</td>\n",
       "      <td>7.615079</td>\n",
       "      <td>7.932540</td>\n",
       "      <td>7.155258</td>\n",
       "      <td>7.048611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>158.583367</td>\n",
       "      <td>5.491329</td>\n",
       "      <td>0.500029</td>\n",
       "      <td>10.940735</td>\n",
       "      <td>0.376673</td>\n",
       "      <td>5.995903</td>\n",
       "      <td>4.358458</td>\n",
       "      <td>5.514939</td>\n",
       "      <td>5.650199</td>\n",
       "      <td>5.477009</td>\n",
       "      <td>5.491068</td>\n",
       "      <td>158.584899</td>\n",
       "      <td>0.370947</td>\n",
       "      <td>0.303539</td>\n",
       "      <td>0.489051</td>\n",
       "      <td>3.563648</td>\n",
       "      <td>1.230689</td>\n",
       "      <td>12.569802</td>\n",
       "      <td>7.044003</td>\n",
       "      <td>6.782895</td>\n",
       "      <td>6.085526</td>\n",
       "      <td>6.126544</td>\n",
       "      <td>6.362746</td>\n",
       "      <td>0.493515</td>\n",
       "      <td>1.950305</td>\n",
       "      <td>1.740575</td>\n",
       "      <td>1.550501</td>\n",
       "      <td>1.954078</td>\n",
       "      <td>1.794080</td>\n",
       "      <td>2.156163</td>\n",
       "      <td>1.841258</td>\n",
       "      <td>2.129354</td>\n",
       "      <td>0.245925</td>\n",
       "      <td>3.566763</td>\n",
       "      <td>3.758935</td>\n",
       "      <td>1.230905</td>\n",
       "      <td>2.845708</td>\n",
       "      <td>2.805237</td>\n",
       "      <td>1.407181</td>\n",
       "      <td>1.444531</td>\n",
       "      <td>1.105246</td>\n",
       "      <td>3.309520</td>\n",
       "      <td>2.619024</td>\n",
       "      <td>2.801874</td>\n",
       "      <td>2.418858</td>\n",
       "      <td>1.754868</td>\n",
       "      <td>2.052232</td>\n",
       "      <td>2.263407</td>\n",
       "      <td>2.570207</td>\n",
       "      <td>2.620507</td>\n",
       "      <td>2.502218</td>\n",
       "      <td>2.006565</td>\n",
       "      <td>2.529135</td>\n",
       "      <td>2.235152</td>\n",
       "      <td>1.700927</td>\n",
       "      <td>2.156283</td>\n",
       "      <td>1.791827</td>\n",
       "      <td>2.608913</td>\n",
       "      <td>2.717612</td>\n",
       "      <td>1.734059</td>\n",
       "      <td>4.762569</td>\n",
       "      <td>12.587674</td>\n",
       "      <td>7.046700</td>\n",
       "      <td>6.783003</td>\n",
       "      <td>6.085239</td>\n",
       "      <td>6.124888</td>\n",
       "      <td>6.362154</td>\n",
       "      <td>16.297045</td>\n",
       "      <td>6.659233</td>\n",
       "      <td>6.717476</td>\n",
       "      <td>7.328256</td>\n",
       "      <td>6.998428</td>\n",
       "      <td>6.060150</td>\n",
       "      <td>16.249937</td>\n",
       "      <td>6.976775</td>\n",
       "      <td>6.263304</td>\n",
       "      <td>6.577929</td>\n",
       "      <td>6.886532</td>\n",
       "      <td>6.167314</td>\n",
       "      <td>1.395783</td>\n",
       "      <td>1.407460</td>\n",
       "      <td>1.564321</td>\n",
       "      <td>1.076608</td>\n",
       "      <td>1.778315</td>\n",
       "      <td>1.498653</td>\n",
       "      <td>1.627054</td>\n",
       "      <td>1.283657</td>\n",
       "      <td>1.779129</td>\n",
       "      <td>1.773094</td>\n",
       "      <td>0.493573</td>\n",
       "      <td>1.950169</td>\n",
       "      <td>1.740315</td>\n",
       "      <td>1.550453</td>\n",
       "      <td>1.953702</td>\n",
       "      <td>1.794055</td>\n",
       "      <td>2.156363</td>\n",
       "      <td>1.841285</td>\n",
       "      <td>2.129565</td>\n",
       "      <td>0.989889</td>\n",
       "      <td>2.444813</td>\n",
       "      <td>12.968524</td>\n",
       "      <td>6.915322</td>\n",
       "      <td>6.596420</td>\n",
       "      <td>5.356969</td>\n",
       "      <td>5.514028</td>\n",
       "      <td>5.921789</td>\n",
       "      <td>1.41545</td>\n",
       "      <td>1.455741</td>\n",
       "      <td>1.179317</td>\n",
       "      <td>1.626839</td>\n",
       "      <td>1.793136</td>\n",
       "      <td>1.820764</td>\n",
       "      <td>0.975662</td>\n",
       "      <td>0.631240</td>\n",
       "      <td>17.155270</td>\n",
       "      <td>7.977482</td>\n",
       "      <td>7.292868</td>\n",
       "      <td>8.535963</td>\n",
       "      <td>6.125187</td>\n",
       "      <td>8.241906</td>\n",
       "      <td>14.388694</td>\n",
       "      <td>6.658494</td>\n",
       "      <td>6.535894</td>\n",
       "      <td>6.129746</td>\n",
       "      <td>5.675550</td>\n",
       "      <td>6.651547</td>\n",
       "      <td>16.402836</td>\n",
       "      <td>6.401556</td>\n",
       "      <td>5.990607</td>\n",
       "      <td>7.290107</td>\n",
       "      <td>5.856329</td>\n",
       "      <td>6.296155</td>\n",
       "      <td>14.551171</td>\n",
       "      <td>6.17169</td>\n",
       "      <td>5.398621</td>\n",
       "      <td>6.100307</td>\n",
       "      <td>6.313281</td>\n",
       "      <td>5.615691</td>\n",
       "      <td>1.371390</td>\n",
       "      <td>1.503236</td>\n",
       "      <td>1.180280</td>\n",
       "      <td>1.548200</td>\n",
       "      <td>1.744634</td>\n",
       "      <td>1.411096</td>\n",
       "      <td>1.588145</td>\n",
       "      <td>1.280936</td>\n",
       "      <td>1.647478</td>\n",
       "      <td>1.521854</td>\n",
       "      <td>1.611694</td>\n",
       "      <td>1.382139</td>\n",
       "      <td>0.484683</td>\n",
       "      <td>1.294557</td>\n",
       "      <td>0.753902</td>\n",
       "      <td>13.712120</td>\n",
       "      <td>7.471537</td>\n",
       "      <td>6.124502</td>\n",
       "      <td>5.163777</td>\n",
       "      <td>5.900697</td>\n",
       "      <td>6.557041</td>\n",
       "      <td>17.551540</td>\n",
       "      <td>9.336288</td>\n",
       "      <td>7.880088</td>\n",
       "      <td>7.231325</td>\n",
       "      <td>6.100502</td>\n",
       "      <td>8.615985</td>\n",
       "      <td>17.477134</td>\n",
       "      <td>5.740351</td>\n",
       "      <td>6.004222</td>\n",
       "      <td>6.927869</td>\n",
       "      <td>6.385852</td>\n",
       "      <td>6.516178</td>\n",
       "      <td>17.007669</td>\n",
       "      <td>6.226283</td>\n",
       "      <td>7.010650</td>\n",
       "      <td>7.935509</td>\n",
       "      <td>6.403117</td>\n",
       "      <td>7.012067</td>\n",
       "      <td>1.576596</td>\n",
       "      <td>1.610309</td>\n",
       "      <td>1.459094</td>\n",
       "      <td>1.744670</td>\n",
       "      <td>1.961417</td>\n",
       "      <td>1.507341</td>\n",
       "      <td>1.504551</td>\n",
       "      <td>1.340868</td>\n",
       "      <td>1.672787</td>\n",
       "      <td>1.717988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.150000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.220000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>14.810000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>281.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.370000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.640000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.180000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.640000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>15.790000</td>\n",
       "      <td>18.420000</td>\n",
       "      <td>15.910000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>18.370000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.520000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.950000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>16.330000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.290000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>407.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>23.810000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>23.810000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.280000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>38.460000</td>\n",
       "      <td>19.23000</td>\n",
       "      <td>17.390000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.090000</td>\n",
       "      <td>16.515000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>552.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>23.810000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>22.220000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               iid           id       gender          idg       condtn  \\\n",
       "count  8378.000000  8377.000000  8378.000000  8378.000000  8378.000000   \n",
       "mean    283.675937     8.960248     0.500597    17.327166     1.828837   \n",
       "std     158.583367     5.491329     0.500029    10.940735     0.376673   \n",
       "min       1.000000     1.000000     0.000000     1.000000     1.000000   \n",
       "25%     154.000000     4.000000     0.000000     8.000000     2.000000   \n",
       "50%     281.000000     8.000000     1.000000    16.000000     2.000000   \n",
       "75%     407.000000    13.000000     1.000000    26.000000     2.000000   \n",
       "max     552.000000    22.000000     1.000000    44.000000     2.000000   \n",
       "\n",
       "              wave        round     position     positin1        order  \\\n",
       "count  8378.000000  8378.000000  8378.000000  6532.000000  8378.000000   \n",
       "mean     11.350919    16.872046     9.042731     9.295775     8.927668   \n",
       "std       5.995903     4.358458     5.514939     5.650199     5.477009   \n",
       "min       1.000000     5.000000     1.000000     1.000000     1.000000   \n",
       "25%       7.000000    14.000000     4.000000     4.000000     4.000000   \n",
       "50%      11.000000    18.000000     8.000000     9.000000     8.000000   \n",
       "75%      15.000000    20.000000    13.000000    14.000000    13.000000   \n",
       "max      21.000000    22.000000    22.000000    22.000000    22.000000   \n",
       "\n",
       "           partner          pid        match     int_corr     samerace  \\\n",
       "count  8378.000000  8368.000000  8378.000000  8220.000000  8378.000000   \n",
       "mean      8.963595   283.863767     0.164717     0.196010     0.395799   \n",
       "std       5.491068   158.584899     0.370947     0.303539     0.489051   \n",
       "min       1.000000     1.000000     0.000000    -0.830000     0.000000   \n",
       "25%       4.000000   154.000000     0.000000    -0.020000     0.000000   \n",
       "50%       8.000000   281.000000     0.000000     0.210000     0.000000   \n",
       "75%      13.000000   408.000000     0.000000     0.430000     1.000000   \n",
       "max      22.000000   552.000000     1.000000     0.910000     1.000000   \n",
       "\n",
       "             age_o       race_o     pf_o_att     pf_o_sin     pf_o_int  \\\n",
       "count  8274.000000  8305.000000  8289.000000  8289.000000  8289.000000   \n",
       "mean     26.364999     2.756653    22.495347    17.396867    20.270759   \n",
       "std       3.563648     1.230689    12.569802     7.044003     6.782895   \n",
       "min      18.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%      24.000000     2.000000    15.000000    15.000000    17.390000   \n",
       "50%      26.000000     2.000000    20.000000    18.370000    20.000000   \n",
       "75%      28.000000     4.000000    25.000000    20.000000    23.810000   \n",
       "max      55.000000     6.000000   100.000000    60.000000    50.000000   \n",
       "\n",
       "          pf_o_fun     pf_o_amb     pf_o_sha        dec_o       attr_o  \\\n",
       "count  8280.000000  8271.000000  8249.000000  8378.000000  8166.000000   \n",
       "mean     17.459714    10.685375    11.845930     0.419551     6.190411   \n",
       "std       6.085526     6.126544     6.362746     0.493515     1.950305   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      15.000000     5.000000     9.520000     0.000000     5.000000   \n",
       "50%      18.000000    10.000000    10.640000     0.000000     6.000000   \n",
       "75%      20.000000    15.000000    16.000000     1.000000     8.000000   \n",
       "max      50.000000    53.000000    30.000000     1.000000    10.500000   \n",
       "\n",
       "            sinc_o      intel_o        fun_o        amb_o       shar_o  \\\n",
       "count  8091.000000  8072.000000  8018.000000  7656.000000  7302.000000   \n",
       "mean      7.175256     7.369301     6.400599     6.778409     5.474870   \n",
       "std       1.740575     1.550501     1.954078     1.794080     2.156163   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       6.000000     6.000000     5.000000     6.000000     4.000000   \n",
       "50%       7.000000     7.000000     7.000000     7.000000     6.000000   \n",
       "75%       8.000000     8.000000     8.000000     8.000000     7.000000   \n",
       "max      10.000000    10.000000    11.000000    10.000000    10.000000   \n",
       "\n",
       "            like_o       prob_o        met_o          age     field_cd  \\\n",
       "count  8128.000000  8060.000000  7993.000000  8283.000000  8296.000000   \n",
       "mean      6.134498     5.208251     1.960215    26.358928     7.662488   \n",
       "std       1.841258     2.129354     0.245925     3.566763     3.758935   \n",
       "min       0.000000     0.000000     1.000000    18.000000     1.000000   \n",
       "25%       5.000000     4.000000     2.000000    24.000000     5.000000   \n",
       "50%       6.000000     5.000000     2.000000    26.000000     8.000000   \n",
       "75%       7.000000     7.000000     2.000000    28.000000    10.000000   \n",
       "max      10.000000    10.000000     8.000000    55.000000    18.000000   \n",
       "\n",
       "              race      imprace     imprelig         goal         date  \\\n",
       "count  8315.000000  8299.000000  8299.000000  8299.000000  8281.000000   \n",
       "mean      2.757186     3.784793     3.651645     2.122063     5.006762   \n",
       "std       1.230905     2.845708     2.805237     1.407181     1.444531   \n",
       "min       1.000000     0.000000     1.000000     1.000000     1.000000   \n",
       "25%       2.000000     1.000000     1.000000     1.000000     4.000000   \n",
       "50%       2.000000     3.000000     3.000000     2.000000     5.000000   \n",
       "75%       4.000000     6.000000     6.000000     2.000000     6.000000   \n",
       "max       6.000000    10.000000    10.000000     6.000000     7.000000   \n",
       "\n",
       "            go_out     career_c       sports     tvsports     exercise  \\\n",
       "count  8299.000000  8240.000000  8299.000000  8299.000000  8299.000000   \n",
       "mean      2.158091     5.277791     6.425232     4.575491     6.245813   \n",
       "std       1.105246     3.309520     2.619024     2.801874     2.418858   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       1.000000     2.000000     4.000000     2.000000     5.000000   \n",
       "50%       2.000000     6.000000     7.000000     4.000000     6.000000   \n",
       "75%       3.000000     7.000000     9.000000     7.000000     8.000000   \n",
       "max       7.000000    17.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "            dining      museums          art       hiking       gaming  \\\n",
       "count  8299.000000  8299.000000  8299.000000  8299.000000  8299.000000   \n",
       "mean      7.783829     6.985781     6.714544     5.737077     3.881191   \n",
       "std       1.754868     2.052232     2.263407     2.570207     2.620507   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       7.000000     6.000000     5.000000     4.000000     2.000000   \n",
       "50%       8.000000     7.000000     7.000000     6.000000     3.000000   \n",
       "75%       9.000000     9.000000     8.000000     8.000000     6.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000    14.000000   \n",
       "\n",
       "          clubbing      reading           tv      theater       movies  \\\n",
       "count  8299.000000  8299.000000  8299.000000  8299.000000  8299.000000   \n",
       "mean      5.745993     7.678515     5.304133     6.776118     7.919629   \n",
       "std       2.502218     2.006565     2.529135     2.235152     1.700927   \n",
       "min       0.000000     1.000000     1.000000     0.000000     0.000000   \n",
       "25%       4.000000     7.000000     3.000000     5.000000     7.000000   \n",
       "50%       6.000000     8.000000     6.000000     7.000000     8.000000   \n",
       "75%       8.000000     9.000000     7.000000     9.000000     9.000000   \n",
       "max      10.000000    13.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "          concerts        music     shopping         yoga     exphappy  \\\n",
       "count  8299.000000  8299.000000  8299.000000  8299.000000  8277.000000   \n",
       "mean      6.825401     7.851066     5.631281     4.339197     5.534131   \n",
       "std       2.156283     1.791827     2.608913     2.717612     1.734059   \n",
       "min       0.000000     1.000000     1.000000     0.000000     1.000000   \n",
       "25%       5.000000     7.000000     4.000000     2.000000     5.000000   \n",
       "50%       7.000000     8.000000     6.000000     4.000000     6.000000   \n",
       "75%       8.000000     9.000000     8.000000     7.000000     7.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "            expnum      attr1_1      sinc1_1     intel1_1       fun1_1  \\\n",
       "count  1800.000000  8299.000000  8299.000000  8299.000000  8289.000000   \n",
       "mean      5.570556    22.514632    17.396389    20.265613    17.457043   \n",
       "std       4.762569    12.587674     7.046700     6.783003     6.085239   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000    15.000000    15.000000    17.390000    15.000000   \n",
       "50%       4.000000    20.000000    18.180000    20.000000    18.000000   \n",
       "75%       8.000000    25.000000    20.000000    23.810000    20.000000   \n",
       "max      20.000000   100.000000    60.000000    50.000000    50.000000   \n",
       "\n",
       "            amb1_1      shar1_1      attr4_1      sinc4_1     intel4_1  \\\n",
       "count  8279.000000  8257.000000  6489.000000  6489.000000  6489.000000   \n",
       "mean     10.682539    11.845111    26.394360    11.071506    12.636308   \n",
       "std       6.124888     6.362154    16.297045     6.659233     6.717476   \n",
       "min       0.000000     0.000000     5.000000     0.000000     0.000000   \n",
       "25%       5.000000     9.520000    10.000000     6.000000     8.000000   \n",
       "50%      10.000000    10.640000    25.000000    10.000000    10.000000   \n",
       "75%      15.000000    16.000000    35.000000    15.000000    16.000000   \n",
       "max      53.000000    30.000000    95.000000    35.000000    35.000000   \n",
       "\n",
       "            fun4_1       amb4_1      shar4_1      attr2_1      sinc2_1  \\\n",
       "count  6489.000000  6489.000000  6467.000000  8299.000000  8299.000000   \n",
       "mean     15.566805     9.780089    11.014845    30.362192    13.273691   \n",
       "std       7.328256     6.998428     6.060150    16.249937     6.976775   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      10.000000     5.000000     7.000000    20.000000    10.000000   \n",
       "50%      15.000000    10.000000    10.000000    25.000000    15.000000   \n",
       "75%      20.000000    15.000000    15.000000    40.000000    18.750000   \n",
       "max      45.000000    50.000000    40.000000   100.000000    50.000000   \n",
       "\n",
       "          intel2_1       fun2_1       amb2_1      shar2_1      attr3_1  \\\n",
       "count  8299.000000  8299.000000  8289.000000  8289.000000  8273.000000   \n",
       "mean     14.416891    18.422620    11.744499    11.854817     7.084733   \n",
       "std       6.263304     6.577929     6.886532     6.167314     1.395783   \n",
       "min       0.000000     0.000000     0.000000     0.000000     2.000000   \n",
       "25%      10.000000    15.000000     6.000000    10.000000     6.000000   \n",
       "50%      15.000000    20.000000    10.000000    10.000000     7.000000   \n",
       "75%      20.000000    20.000000    15.000000    15.630000     8.000000   \n",
       "max      40.000000    50.000000    50.000000    30.000000    10.000000   \n",
       "\n",
       "           sinc3_1       fun3_1     intel3_1       amb3_1      attr5_1  \\\n",
       "count  8273.000000  8273.000000  8273.000000  8273.000000  4906.000000   \n",
       "mean      8.294935     7.704460     8.403965     7.578388     6.941908   \n",
       "std       1.407460     1.564321     1.076608     1.778315     1.498653   \n",
       "min       2.000000     2.000000     3.000000     2.000000     2.000000   \n",
       "25%       8.000000     7.000000     8.000000     7.000000     6.000000   \n",
       "50%       8.000000     8.000000     8.000000     8.000000     7.000000   \n",
       "75%       9.000000     9.000000     9.000000     9.000000     8.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "           sinc5_1     intel5_1       fun5_1       amb5_1          dec  \\\n",
       "count  4906.000000  4906.000000  4906.000000  4906.000000  8378.000000   \n",
       "mean      7.927232     8.284346     7.426213     7.617611     0.419909   \n",
       "std       1.627054     1.283657     1.779129     1.773094     0.493573   \n",
       "min       1.000000     3.000000     2.000000     1.000000     0.000000   \n",
       "25%       7.000000     8.000000     6.000000     7.000000     0.000000   \n",
       "50%       8.000000     8.000000     8.000000     8.000000     0.000000   \n",
       "75%       9.000000     9.000000     9.000000     9.000000     1.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000     1.000000   \n",
       "\n",
       "              attr         sinc        intel          fun          amb  \\\n",
       "count  8176.000000  8101.000000  8082.000000  8028.000000  7666.000000   \n",
       "mean      6.189995     7.175164     7.368597     6.400598     6.777524   \n",
       "std       1.950169     1.740315     1.550453     1.953702     1.794055   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       5.000000     6.000000     6.000000     5.000000     6.000000   \n",
       "50%       6.000000     7.000000     7.000000     7.000000     7.000000   \n",
       "75%       8.000000     8.000000     8.000000     8.000000     8.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "              shar         like         prob          met     match_es  \\\n",
       "count  7311.000000  8138.000000  8069.000000  8003.000000  7205.000000   \n",
       "mean      5.474559     6.134087     5.207523     0.948769     3.207814   \n",
       "std       2.156363     1.841285     2.129565     0.989889     2.444813   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       4.000000     5.000000     4.000000     0.000000     2.000000   \n",
       "50%       6.000000     6.000000     5.000000     0.000000     3.000000   \n",
       "75%       7.000000     7.000000     7.000000     2.000000     4.000000   \n",
       "max      10.000000    10.000000    10.000000     8.000000    18.000000   \n",
       "\n",
       "           attr1_s      sinc1_s     intel1_s       fun1_s       amb1_s  \\\n",
       "count  4096.000000  4096.000000  4096.000000  4096.000000  4096.000000   \n",
       "mean     20.791624    15.434255    17.243708    15.260869    11.144619   \n",
       "std      12.968524     6.915322     6.596420     5.356969     5.514028   \n",
       "min       3.000000     0.000000     0.000000     1.000000     0.000000   \n",
       "25%      14.810000    10.000000    10.000000    10.000000     7.000000   \n",
       "50%      17.650000    15.790000    18.420000    15.910000    10.000000   \n",
       "75%      25.000000    20.000000    20.000000    20.000000    15.000000   \n",
       "max      95.000000    50.000000    40.000000    40.000000    23.810000   \n",
       "\n",
       "           shar1_s     attr3_s      sinc3_s     intel3_s       fun3_s  \\\n",
       "count  4096.000000  4000.00000  4000.000000  4000.000000  4000.000000   \n",
       "mean     12.457925     7.21125     8.082000     8.257750     7.692500   \n",
       "std       5.921789     1.41545     1.455741     1.179317     1.626839   \n",
       "min       0.000000     3.00000     1.000000     4.000000     3.000000   \n",
       "25%       9.000000     7.00000     7.000000     8.000000     7.000000   \n",
       "50%      12.500000     7.00000     8.000000     8.000000     8.000000   \n",
       "75%      16.280000     8.00000     9.000000     9.000000     9.000000   \n",
       "max      30.000000    10.00000    10.000000    10.000000    10.000000   \n",
       "\n",
       "            amb3_s      satis_2       length     numdat_2      attr7_2  \\\n",
       "count  4000.000000  7463.000000  7463.000000  7433.000000  1984.000000   \n",
       "mean      7.589250     5.711510     1.843495     2.338087    32.819556   \n",
       "std       1.793136     1.820764     0.975662     0.631240    17.155270   \n",
       "min       2.000000     1.000000     1.000000     1.000000    10.000000   \n",
       "25%       7.000000     5.000000     1.000000     2.000000    20.000000   \n",
       "50%       8.000000     6.000000     1.000000     2.000000    30.000000   \n",
       "75%       9.000000     7.000000     3.000000     3.000000    40.000000   \n",
       "max      10.000000    10.000000     3.000000     3.000000    80.000000   \n",
       "\n",
       "           sinc7_2     intel7_2       fun7_2       amb7_2      shar7_2  \\\n",
       "count  1955.000000  1984.000000  1984.000000  1955.000000  1974.000000   \n",
       "mean     13.529923    15.293851    18.868448     7.286957    12.156028   \n",
       "std       7.977482     7.292868     8.535963     6.125187     8.241906   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      10.000000    10.000000    10.000000     0.000000     5.000000   \n",
       "50%      10.000000    15.000000    20.000000     5.000000    10.000000   \n",
       "75%      20.000000    20.000000    24.000000    10.000000    20.000000   \n",
       "max      40.000000    50.000000    50.000000    20.000000    40.000000   \n",
       "\n",
       "           attr1_2      sinc1_2     intel1_2       fun1_2       amb1_2  \\\n",
       "count  7445.000000  7463.000000  7463.000000  7463.000000  7463.000000   \n",
       "mean     26.217194    15.865084    17.813755    17.654765     9.913436   \n",
       "std      14.388694     6.658494     6.535894     6.129746     5.675550   \n",
       "min       5.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      16.670000    10.000000    15.000000    15.000000     5.000000   \n",
       "50%      20.000000    16.670000    19.050000    18.370000    10.000000   \n",
       "75%      30.000000    20.000000    20.000000    20.000000    15.000000   \n",
       "max      85.000000    50.000000    40.000000    50.000000    22.220000   \n",
       "\n",
       "           shar1_2      attr4_2      sinc4_2     intel4_2       fun4_2  \\\n",
       "count  7463.000000  5775.000000  5775.000000  5775.000000  5775.000000   \n",
       "mean     12.760263    26.806234    11.929177    12.103030    15.163810   \n",
       "std       6.651547    16.402836     6.401556     5.990607     7.290107   \n",
       "min       0.000000     6.000000     0.000000     0.000000     0.000000   \n",
       "25%      10.000000    10.000000     8.000000     8.000000     9.000000   \n",
       "50%      13.000000    25.000000    10.000000    10.000000    15.000000   \n",
       "75%      16.670000    40.000000    15.000000    15.000000    20.000000   \n",
       "max      35.000000   100.000000    35.000000    40.000000    50.000000   \n",
       "\n",
       "            amb4_2      shar4_2      attr2_2     sinc2_2     intel2_2  \\\n",
       "count  5775.000000  5775.000000  5775.000000  5775.00000  5775.000000   \n",
       "mean      9.342511    11.320866    29.344369    13.89823    13.958265   \n",
       "std       5.856329     6.296155    14.551171     6.17169     5.398621   \n",
       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
       "25%       5.000000     7.000000    19.150000    10.00000    10.000000   \n",
       "50%      10.000000    10.000000    25.000000    15.00000    15.000000   \n",
       "75%      10.000000    15.000000    38.460000    19.23000    17.390000   \n",
       "max      35.000000    40.000000    85.000000    40.00000    30.770000   \n",
       "\n",
       "            fun2_2       amb2_2      shar2_2      attr3_2      sinc3_2  \\\n",
       "count  5775.000000  5775.000000  5775.000000  7463.000000  7463.000000   \n",
       "mean     17.967233    11.909735    12.887976     7.125285     7.931529   \n",
       "std       6.100307     6.313281     5.615691     1.371390     1.503236   \n",
       "min       0.000000     0.000000     0.000000     2.000000     2.000000   \n",
       "25%      15.000000    10.000000    10.000000     7.000000     7.000000   \n",
       "50%      18.520000    10.000000    13.950000     7.000000     8.000000   \n",
       "75%      20.000000    15.090000    16.515000     8.000000     9.000000   \n",
       "max      40.000000    50.000000    30.000000    10.000000    10.000000   \n",
       "\n",
       "          intel3_2       fun3_2       amb3_2      attr5_2      sinc5_2  \\\n",
       "count  7463.000000  7463.000000  7463.000000  4377.000000  4377.000000   \n",
       "mean      8.238912     7.602171     7.486802     6.827964     7.394106   \n",
       "std       1.180280     1.548200     1.744634     1.411096     1.588145   \n",
       "min       4.000000     1.000000     2.000000     2.000000     2.000000   \n",
       "25%       8.000000     7.000000     7.000000     6.000000     6.000000   \n",
       "50%       8.000000     8.000000     8.000000     7.000000     8.000000   \n",
       "75%       9.000000     9.000000     9.000000     8.000000     8.000000   \n",
       "max      10.000000    10.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "          intel5_2       fun5_2       amb5_2     you_call     them_cal  \\\n",
       "count  4377.000000  4377.000000  4377.000000  3974.000000  3974.000000   \n",
       "mean      7.838702     7.279415     7.332191     0.780825     0.981631   \n",
       "std       1.280936     1.647478     1.521854     1.611694     1.382139   \n",
       "min       2.000000     2.000000     2.000000     0.000000     0.000000   \n",
       "25%       7.000000     6.000000     6.000000     0.000000     0.000000   \n",
       "50%       8.000000     7.000000     7.000000     0.000000     1.000000   \n",
       "75%       9.000000     8.000000     8.000000     1.000000     1.000000   \n",
       "max      10.000000    10.000000    10.000000    21.000000     9.000000   \n",
       "\n",
       "            date_3     numdat_3    num_in_3      attr1_3      sinc1_3  \\\n",
       "count  3974.000000  1496.000000  668.000000  3974.000000  3974.000000   \n",
       "mean      0.376950     1.230615    0.934132    24.384524    16.588583   \n",
       "std       0.484683     1.294557    0.753902    13.712120     7.471537   \n",
       "min       0.000000     0.000000    0.000000     0.000000     0.000000   \n",
       "25%       0.000000     1.000000    1.000000    15.220000    10.000000   \n",
       "50%       0.000000     1.000000    1.000000    20.000000    16.670000   \n",
       "75%       1.000000     1.000000    1.000000    30.000000    20.000000   \n",
       "max       1.000000     9.000000    4.000000    80.000000    65.000000   \n",
       "\n",
       "          intel1_3       fun1_3       amb1_3      shar1_3      attr7_3  \\\n",
       "count  3974.000000  3974.000000  3974.000000  3974.000000  2016.000000   \n",
       "mean     19.411346    16.233415    10.898075    12.699142    31.330357   \n",
       "std       6.124502     5.163777     5.900697     6.557041    17.551540   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      16.670000    14.810000     5.000000    10.000000    20.000000   \n",
       "50%      20.000000    16.330000    10.000000    14.290000    25.000000   \n",
       "75%      20.000000    20.000000    15.000000    16.670000    40.000000   \n",
       "max      45.000000    30.000000    30.000000    55.000000    80.000000   \n",
       "\n",
       "           sinc7_3     intel7_3       fun7_3       amb7_3      shar7_3  \\\n",
       "count  2016.000000  2016.000000  2016.000000  2016.000000  2016.000000   \n",
       "mean     15.654266    16.679563    16.418155     7.823909    12.207837   \n",
       "std       9.336288     7.880088     7.231325     6.100502     8.615985   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      10.000000    10.000000    10.000000     0.000000     5.000000   \n",
       "50%      15.000000    18.000000    17.000000    10.000000    10.000000   \n",
       "75%      20.000000    20.000000    20.000000    10.000000    20.000000   \n",
       "max      60.000000    45.000000    40.000000    30.000000    55.000000   \n",
       "\n",
       "           attr4_3      sinc4_3     intel4_3       fun4_3       amb4_3  \\\n",
       "count  2959.000000  2959.000000  2959.000000  2959.000000  2959.000000   \n",
       "mean     25.610341    10.751267    11.524839    14.276783     9.207503   \n",
       "std      17.477134     5.740351     6.004222     6.927869     6.385852   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      10.000000     7.000000     7.000000     9.000000     5.000000   \n",
       "50%      20.000000    10.000000    10.000000    12.000000     9.000000   \n",
       "75%      37.000000    15.000000    15.000000    20.000000    10.000000   \n",
       "max      80.000000    40.000000    30.000000    30.000000    40.000000   \n",
       "\n",
       "           shar4_3      attr2_3      sinc2_3     intel2_3       fun2_3  \\\n",
       "count  2959.000000  2959.000000  2959.000000  2959.000000  2959.000000   \n",
       "mean     11.253802    24.970936    10.923285    11.952687    14.959108   \n",
       "std       6.516178    17.007669     6.226283     7.010650     7.935509   \n",
       "min       0.000000     5.000000     0.000000     0.000000     0.000000   \n",
       "25%       7.000000    10.000000     7.000000     7.000000     9.000000   \n",
       "50%      10.000000    20.000000    10.000000    10.000000    15.000000   \n",
       "75%      15.000000    35.000000    15.000000    15.000000    20.000000   \n",
       "max      45.000000    80.000000    50.000000    60.000000    40.000000   \n",
       "\n",
       "            amb2_3      shar2_3      attr3_3      sinc3_3     intel3_3  \\\n",
       "count  2959.000000  2016.000000  3974.000000  3974.000000  3974.000000   \n",
       "mean      9.526191    11.966270     7.240312     8.093357     8.388777   \n",
       "std       6.403117     7.012067     1.576596     1.610309     1.459094   \n",
       "min       0.000000     0.000000     2.000000     2.000000     3.000000   \n",
       "25%       6.000000     5.000000     7.000000     7.000000     8.000000   \n",
       "50%      10.000000    10.000000     7.000000     8.000000     8.000000   \n",
       "75%      10.000000    15.000000     8.000000     9.000000     9.000000   \n",
       "max      50.000000    45.000000    12.000000    12.000000    12.000000   \n",
       "\n",
       "            fun3_3       amb3_3      attr5_3      sinc5_3     intel5_3  \\\n",
       "count  3974.000000  3974.000000  2016.000000  2016.000000  2016.000000   \n",
       "mean      7.658782     7.391545     6.810020     7.615079     7.932540   \n",
       "std       1.744670     1.961417     1.507341     1.504551     1.340868   \n",
       "min       2.000000     1.000000     2.000000     2.000000     4.000000   \n",
       "25%       7.000000     6.000000     6.000000     7.000000     7.000000   \n",
       "50%       8.000000     8.000000     7.000000     8.000000     8.000000   \n",
       "75%       9.000000     9.000000     8.000000     9.000000     9.000000   \n",
       "max      12.000000    12.000000    10.000000    10.000000    10.000000   \n",
       "\n",
       "            fun5_3       amb5_3  \n",
       "count  2016.000000  2016.000000  \n",
       "mean      7.155258     7.048611  \n",
       "std       1.672787     1.717988  \n",
       "min       1.000000     1.000000  \n",
       "25%       6.000000     6.000000  \n",
       "50%       7.000000     7.000000  \n",
       "75%       8.000000     8.000000  \n",
       "max      10.000000    10.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As imported, the data has 195 columns which is way too much for us to reasonably understand so let's try to work out which columns we really need to worry about. Using the dataset key that came with our dataset, we can attempt to pinpoint the columns that will be important to our data. It seems that the following columns will have important data that we will need.\n",
    "+ Unique participant id ('iid')\n",
    "+ Gender (of participant)('gender')\n",
    "+ Race (of participant)('race')\n",
    "+ Same Race (participant and partner have same race)('samerace')\n",
    "+ 6 Trait Values as reported by partner\n",
    "    - Physical Attraction ('attr_o')\n",
    "    - Sincerity ('sinc_o')\n",
    "    - Inteligence ('intel_o')\n",
    "    - Fun ('fun_o')\n",
    "    - Ambition ('amb_o')\n",
    "    - Shared Interests ('shar_o')\n",
    "+ Interest Correlation ('int_corr')\n",
    "    \n",
    "We will store these columns in a dataframe and dispose of the columns that are no longer needed. In addition, we will impute values based on the mean of each column in order to fill in missing data. Using the means for each column should not affect our data too much because there is a large amount of data so imputing a few values should not affect it too much. It's necessary to deal with missing data at this point in the data science pipeline because attempting to do any sort of analysis on missing data will lead to errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only get the columns that we intend to use.\n",
    "df = dataframe.loc[:, ('iid','gender', 'race', 'dec_o', 'attr_o', 'sinc_o', 'intel_o', 'fun_o', 'amb_o', 'shar_o', 'int_corr', 'samerace')]\n",
    "\n",
    "# Impute missing values with the mean for these columns\n",
    "df['attr_o'].fillna(df['attr_o'].mean(), inplace=True)\n",
    "df['sinc_o'].fillna(df['sinc_o'].mean(), inplace=True)\n",
    "df['intel_o'].fillna(df['intel_o'].mean(), inplace=True)\n",
    "df['fun_o'].fillna(df['fun_o'].mean(), inplace=True)\n",
    "df['amb_o'].fillna( df['amb_o'].mean(), inplace=True)\n",
    "df['shar_o'].fillna(df['shar_o'].mean(), inplace=True)\n",
    "df['int_corr'].fillna(df['int_corr'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "Now that we've properly imported our data and tidied it up, it's time to understand what our data actually includes and what trends are apparent. Before we start exploring trends, it's important for this dataset to understand the demographic that participated in this study. This is important because having a not diverse demographic for the study could lead to biased data or data that would not represent humans in general.\n",
    "\n",
    "The first step is to count how many males and females were a part of the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Particpants   : 274\n",
      "Female Particpants : 277\n",
      "Total Particpants  : 551\n",
      "Percentage Male    : 49.7%\n",
      "Percentage Female  : 50.3%\n"
     ]
    }
   ],
   "source": [
    "# Intialize counters\n",
    "male_total = 0\n",
    "female_total = 0\n",
    "\n",
    "# Group the data by each particiant\n",
    "groups = df.groupby('iid')\n",
    "\n",
    "# Total number of participants\n",
    "total = len(groups)\n",
    "\n",
    "# Each group is a group of all of the dates that a participant went on. \n",
    "# We take the mean of the dates which gives the participant's gender.\n",
    "# We do this for each group/participant.\n",
    "for name, group in groups:\n",
    "    if group['gender'].mean() == 0:\n",
    "        male_total += 1\n",
    "    else:\n",
    "        female_total += 1\n",
    "        \n",
    "# Calculate the perctage of each gender.\n",
    "male_perc = (male_total / total) * 100\n",
    "female_perc = (female_total / total) * 100\n",
    "\n",
    "print('Male Particpants   : %i' % male_total)\n",
    "print('Female Particpants : %i' % female_total)\n",
    "print('Total Particpants  : %i' % total)\n",
    "print('Percentage Male    : {:3.1f}%'.format(male_perc))\n",
    "print('Percentage Female  : {:3.1f}%'.format(female_perc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The particpants were almost exactly evenly split by male/female. This is what one would expect for speed dating study because every participant needs another partner of the opposite gender. This split is good because it means that our data won't be biased by gender.\n",
    "****\n",
    "Now let's do the same as before except now for race instead of gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black/African-American:  26, 4.8%\n",
      "European/Caucasian:  304, 55.8%\n",
      "Latino/Hispanic:  42, 7.7%\n",
      "Asian/Pacific-Islander:  136, 25.0%\n",
      "Native American:  0, 0.0%\n",
      "Other:  37, 6.8%\n",
      "Not Reported:  6, 1.1%\n",
      "Total Reported: 545\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1584x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(22,11)) # set graph size\n",
    "races = {\n",
    "    1: 'Black/African-American',\n",
    "    2: 'European/Caucasian',\n",
    "    3: 'Latino/Hispanic',\n",
    "    4: 'Asian/Pacific-Islander',\n",
    "    5: 'Native American',\n",
    "    6: 'Other',\n",
    "    7: 'Not Reported',\n",
    "}\n",
    "# Initialize counters\n",
    "race_counts = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6:0, 7: 0}\n",
    "\n",
    "total = 0\n",
    "for name, group in groups:\n",
    "    val = group['race'].mean()\n",
    "    if (not math.isnan(val)):\n",
    "        val = int(val)\n",
    "        total += 1\n",
    "        race_counts[val] += 1\n",
    "    else:\n",
    "        race_counts[7] += 1\n",
    "        \n",
    "for key, value in race_counts.items():\n",
    "    print('{:s}:  {:d}, {:3.1f}%'.format(races[key], value, (value/total) * 100))\n",
    "    \n",
    "print('Total Reported: %i' % total)\n",
    "ax = sns.barplot(x=list(races.values()), y=list(race_counts.values()))\n",
    "ax.set(xlabel='Race', ylabel='Number of Participants', title=\"Racial Representation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common race of the participants was European/Caucausian by a significant margin (55% of the participants). The 2nd most common was Asian/Pacific-Islander at 25%. Black/African-American, Latino/Hispanic, and Other made up the remaining 24%. Interestingly, 6 (1%) of the participants did not have their race recorded.\n",
    "\n",
    "This shows that race may bias our results slightly. These factors will be important to consider if we later decide to make any assumptions from our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking for Trends\n",
    "Now that we've analyized the demographic of our dataset, let's start trying to answer the question that we initially proposed, \"what values do people look for in a partner?\" Let's start by identifing which factors could potentially influence attraction. We want to look for the factors of each participant's partner and see how they rated their partner based on these factors.  I expect the following to be important factors.\n",
    "\n",
    "- samerace (Do the participants have same race/what race)\n",
    "- int_corr (Correlation of the )\n",
    "- attr_o (for the 6 attributes)\n",
    "    \n",
    "We will judge a participants attractiveness by the number of partners that wanted to see them again. Later we normalize this value to better understand the participants' success:\n",
    "+ dec_o (Decision by partner)\n",
    "****\n",
    "Now let's try to see if there is a relationship between the 6 character traits and match success. In order to see if there is a relationship, we will calculate a linear regression line for each of the 6 traits. To make things easier, we will be using sklearn's `LinearRegression()` function in order to construct our regression lines. If you are not already familiar with linear regression I recommend you learn more [here](http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm) before proceeding.\n",
    "\n",
    "We will look at each participant's average success rate to determine the 'match success'. For example, if a participant has 10 dates and 7 of their partners decide they want to match with them, then that participant's match success is 7/10 or 0.7. This normalizes our data and this helps because each event had a different number of dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots a scatter plot with a linear regression line on top. In addition, the R^2 value of each linear regression are displayed.\n",
    "def corr_plot(x, y, groups, xlabel, ylabel, title=\"\", color='blue'):\n",
    "    match = groups[x].values.reshape(-1,1) # convert list of matches from 1D -> 2D array\n",
    "    \n",
    "    # plot our raw data\n",
    "    ax = sns.scatterplot(x, y, data=groups, color=color)\n",
    "    \n",
    "    # Calculate a linear regression model using sci-kit learn\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(match, groups[y])\n",
    "    \n",
    "    # Predict our data using the linear reg. model\n",
    "    pred = regr.predict(match)\n",
    "    \n",
    "    # Plot the predicted regression data (line)\n",
    "    sns.lineplot(groups[x], pred, color=\"red\")\n",
    "    \n",
    "    # Calculate R^2 value so we can judge the correlation strength\n",
    "    r2 = r2_score(groups[y], pred)\n",
    "    r = math.sqrt(r2)\n",
    "    \n",
    "    # Set the axis labels and title\n",
    "    title = title + ' (R: %0.3f)' % (r)\n",
    "    ax.set(xlabel=xlabel, ylabel=ylabel, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set various display settings for our charts\n",
    "figure(figsize=(12,12))\n",
    "plt.subplots_adjust(hspace=.6, wspace=.4)\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# Each group in 'groups' is one participant.\n",
    "# We calculate the mean of each participant in order to get an average over all of their dates for that day.\n",
    "means = groups.mean()\n",
    "\n",
    "# Various lists we'll use in a second...\n",
    "names = ['attr_o', 'sinc_o', 'intel_o', 'fun_o', 'amb_o', 'shar_o'] # Name of columns\n",
    "expanded_names = ['Attraction','Sincere', 'Inteligent', 'Fun', 'Ambitious', 'Shared Interest'] # Full names of traits/cols\n",
    "\n",
    "# For each of our 6 traits, we want to plot the trait data vs their partner's decision to match them (match success)\n",
    "for i, name in enumerate(names):\n",
    "    plt.subplot(3, 2, i + 1) # break into 6 separate plots\n",
    "    corr_plot('dec_o', names[i], means, 'Match Success', expanded_names[i], \"All Participants:\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reveals some telling information about what traits influenced whether a participant would want to match or not. Using an $R$ value to measure the accuracy (correlation) of our linear regression models, it's clear that Attraction, Fun, and Shared Interest are much stronger predictors of a match than Ambitious, Inteligent, and Sincere. If you're unfamiliar with $R$ values, you can read more [here](https://www.dummies.com/education/math/statistics/how-to-interpret-a-correlation-coefficient-r/). Overall it appears that physical attraction is the strongest predictor of match success.\n",
    "****\n",
    "Let's see if there is a difference between male/females when it comes to these factors. We split our previous `means` data into male and female data, then plot in the same way we did for the previous charts. The male graph has been colored blue and the female graph is colored pink for clarity. Each of the 6 attributes are shown across each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set various display settings for our charts\n",
    "figure(figsize=(12,12))\n",
    "sns.set_context(\"paper\")\n",
    "plt.subplots_adjust(hspace=.8)\n",
    "\n",
    "means_male = means[means['gender'] == 0] #only males\n",
    "means_female = means[means['gender'] == 1] #only females\n",
    "\n",
    "j = 1 # Counter for the plot number\n",
    "\n",
    "# For each of the 6 traits, plot the male and the female plots of trait vs. match success\n",
    "for i, name in enumerate(names):\n",
    "    \n",
    "    plt.subplot(6, 2, j)\n",
    "    corr_plot('dec_o', names[i], means_male, 'Match Success', expanded_names[i], \"MALES:\")\n",
    "    j += 1\n",
    "    \n",
    "    plt.subplot(6, 2, j)\n",
    "    corr_plot('dec_o', names[i], means_female, 'Match Success', expanded_names[i], \"FEMALES:\", 'pink')\n",
    "    j += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before we judge the correlation of the factors based on the linear regression's $R^2$ value.\n",
    "\n",
    "These new charts reveal interesting data about how males and females judge overall attractiveness differently. Females appear to value physical attraction slightly less than males. As expected, both genders seem to take little value in sincerity and intelligence. It appears that females value Fun slightly more than males. The most significant difference the two gender comes from their value of ambition. Males seem to value ambition in the opposite gender significantly more than females. Meanwhile, it appears that while both male and females believe shared interests are important, females believe this is more important than males.\n",
    "****\n",
    "Another way of visualizing these correlations is through the use of correlation heatmaps. These allow us to see a variety of correlations without having to manually calculate each possibility.\n",
    "\n",
    "The first step is to create the subset of our dataset that we want to look for correlations on. For this we use the same columns we were using in the graphs. Pandas provides the `corr()` function which quickly calculates all of the correlations for a given dataset. Simply because we say interesting results before, let's continue to divide the data by gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>dec_o</th>\n",
       "      <th>attr_o</th>\n",
       "      <th>sinc_o</th>\n",
       "      <th>intel_o</th>\n",
       "      <th>fun_o</th>\n",
       "      <th>amb_o</th>\n",
       "      <th>shar_o</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dec_o</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.803605</td>\n",
       "      <td>0.203929</td>\n",
       "      <td>0.337258</td>\n",
       "      <td>0.625968</td>\n",
       "      <td>0.447977</td>\n",
       "      <td>0.553075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attr_o</th>\n",
       "      <td>0.803605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266396</td>\n",
       "      <td>0.368816</td>\n",
       "      <td>0.658821</td>\n",
       "      <td>0.493388</td>\n",
       "      <td>0.600349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinc_o</th>\n",
       "      <td>0.203929</td>\n",
       "      <td>0.266396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.646020</td>\n",
       "      <td>0.384740</td>\n",
       "      <td>0.350248</td>\n",
       "      <td>0.361157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intel_o</th>\n",
       "      <td>0.337258</td>\n",
       "      <td>0.368816</td>\n",
       "      <td>0.646020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394567</td>\n",
       "      <td>0.635688</td>\n",
       "      <td>0.490269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fun_o</th>\n",
       "      <td>0.625968</td>\n",
       "      <td>0.658821</td>\n",
       "      <td>0.384740</td>\n",
       "      <td>0.394567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563761</td>\n",
       "      <td>0.703630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amb_o</th>\n",
       "      <td>0.447977</td>\n",
       "      <td>0.493388</td>\n",
       "      <td>0.350248</td>\n",
       "      <td>0.635688</td>\n",
       "      <td>0.563761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.594051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shar_o</th>\n",
       "      <td>0.553075</td>\n",
       "      <td>0.600349</td>\n",
       "      <td>0.361157</td>\n",
       "      <td>0.490269</td>\n",
       "      <td>0.703630</td>\n",
       "      <td>0.594051</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dec_o    attr_o    sinc_o   intel_o     fun_o     amb_o    shar_o\n",
       "dec_o    1.000000  0.803605  0.203929  0.337258  0.625968  0.447977  0.553075\n",
       "attr_o   0.803605  1.000000  0.266396  0.368816  0.658821  0.493388  0.600349\n",
       "sinc_o   0.203929  0.266396  1.000000  0.646020  0.384740  0.350248  0.361157\n",
       "intel_o  0.337258  0.368816  0.646020  1.000000  0.394567  0.635688  0.490269\n",
       "fun_o    0.625968  0.658821  0.384740  0.394567  1.000000  0.563761  0.703630\n",
       "amb_o    0.447977  0.493388  0.350248  0.635688  0.563761  1.000000  0.594051\n",
       "shar_o   0.553075  0.600349  0.361157  0.490269  0.703630  0.594051  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Females:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec_o</th>\n",
       "      <th>attr_o</th>\n",
       "      <th>sinc_o</th>\n",
       "      <th>intel_o</th>\n",
       "      <th>fun_o</th>\n",
       "      <th>amb_o</th>\n",
       "      <th>shar_o</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dec_o</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773089</td>\n",
       "      <td>0.269977</td>\n",
       "      <td>0.288326</td>\n",
       "      <td>0.689445</td>\n",
       "      <td>0.269381</td>\n",
       "      <td>0.680164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attr_o</th>\n",
       "      <td>0.773089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.304579</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>0.715588</td>\n",
       "      <td>0.313214</td>\n",
       "      <td>0.670674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinc_o</th>\n",
       "      <td>0.269977</td>\n",
       "      <td>0.304579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.682964</td>\n",
       "      <td>0.374728</td>\n",
       "      <td>0.407601</td>\n",
       "      <td>0.403616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intel_o</th>\n",
       "      <td>0.288326</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>0.682964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.371883</td>\n",
       "      <td>0.648587</td>\n",
       "      <td>0.438380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fun_o</th>\n",
       "      <td>0.689445</td>\n",
       "      <td>0.715588</td>\n",
       "      <td>0.374728</td>\n",
       "      <td>0.371883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.455820</td>\n",
       "      <td>0.785026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amb_o</th>\n",
       "      <td>0.269381</td>\n",
       "      <td>0.313214</td>\n",
       "      <td>0.407601</td>\n",
       "      <td>0.648587</td>\n",
       "      <td>0.455820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shar_o</th>\n",
       "      <td>0.680164</td>\n",
       "      <td>0.670674</td>\n",
       "      <td>0.403616</td>\n",
       "      <td>0.438380</td>\n",
       "      <td>0.785026</td>\n",
       "      <td>0.412552</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dec_o    attr_o    sinc_o   intel_o     fun_o     amb_o    shar_o\n",
       "dec_o    1.000000  0.773089  0.269977  0.288326  0.689445  0.269381  0.680164\n",
       "attr_o   0.773089  1.000000  0.304579  0.272708  0.715588  0.313214  0.670674\n",
       "sinc_o   0.269977  0.304579  1.000000  0.682964  0.374728  0.407601  0.403616\n",
       "intel_o  0.288326  0.272708  0.682964  1.000000  0.371883  0.648587  0.438380\n",
       "fun_o    0.689445  0.715588  0.374728  0.371883  1.000000  0.455820  0.785026\n",
       "amb_o    0.269381  0.313214  0.407601  0.648587  0.455820  1.000000  0.412552\n",
       "shar_o   0.680164  0.670674  0.403616  0.438380  0.785026  0.412552  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "means_traits_male = means_male[['dec_o', 'attr_o', 'sinc_o', 'intel_o', 'fun_o', 'amb_o', 'shar_o']]\n",
    "means_traits_male = means_traits_male.corr()\n",
    "print('Males:')\n",
    "display(means_traits_male)\n",
    "\n",
    "means_traits_female = means_female[['dec_o', 'attr_o', 'sinc_o', 'intel_o', 'fun_o', 'amb_o', 'shar_o']]\n",
    "means_traits_female = means_traits_female.corr()\n",
    "print('Females:')\n",
    "display(means_traits_female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this dataset contains lots of incredibly useful information, it's not exactly easy to interpret at a glance. To solve this issue we will create heatmaps using Seaborn to visually display the correlation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn settings\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# Heatmap of factors for Men\n",
    "sns.heatmap(data=means_traits_male, cmap='Blues', fmt='0.2f', square=True, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn settings\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# Heatmap of factors for Women\n",
    "sns.heatmap(data=means_traits_female, cmap=\"PuRd\", fmt='0.2f', square=True, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of data exploration these heatmaps can be incredibly helpful at finding what factors are the most influential of other factors. Because we already created linear regression plots before, these heatmaps are simply reiterating the same things we knew before, however it's important to know that there are mulitple ways of finding the most important factors in a dataset.\n",
    "****\n",
    "So far we have only looked at the 6 characters traits and related them to match success but there were a few other factors we guessed may affect match success. Those two factors were the two partners having the same race and the correlation between their interests (all participants were surveyed about their interests). Let's create another heatmap for just these factors to see if they influence match success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec_o</th>\n",
       "      <th>samerace</th>\n",
       "      <th>int_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dec_o</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023626</td>\n",
       "      <td>0.018824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samerace</th>\n",
       "      <td>0.023626</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_corr</th>\n",
       "      <td>0.018824</td>\n",
       "      <td>0.025031</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dec_o  samerace  int_corr\n",
       "dec_o     1.000000  0.023626  0.018824\n",
       "samerace  0.023626  1.000000  0.025031\n",
       "int_corr  0.018824  0.025031  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create correlation dataframe\n",
    "other_facts = df[['dec_o', 'samerace', 'int_corr']]\n",
    "other_facts = other_facts.corr()\n",
    "other_facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn settings\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# Heatmap of samerace, int_corr, and dec_O\n",
    "sns.heatmap(data=other_facts, fmt='0.2f', square=True, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap clearly shows that having the same race and having similar interests (according to their survey results) have little impact on match success, so we can rule these out as potential factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modeling\n",
    "Now that we've discovered the most important factors, let's see if we can use this knowledge to create a model to predict someone's match success based on their character traits. In order to create this prediction model we will use something called multiple linear regression. Later on, we will use a more advanced machine learning algorithm to create the same model. We will compare our model to the ML model to gauge the accuracy of our model.\n",
    "\n",
    "Multiple Linear Regression follows the same principle as Single Linear Regression (as done earlier) with the difference being that there are mulitple inputs. For our model we will have 6 inputs, one for each of the 6 attributes we discussed earlier. The mathematics behind it extend the depth of this tutorial, but if you're interested you can learn more [here](http://www.stat.yale.edu/Courses/1997-98/101/linmult.htm). We will complete a model for both men and women.\n",
    "\n",
    "When testing out our models, we are going to use a procedure known as [10-Fold Crossvalidation](https://www.openml.org/a/estimation-procedures/1). This procedure involves training and scoring 10 models using 10 different training/testing sets. Then the mean accuracy of all of the tests is recorded as the final score. This tests aims to remove the variablity in scores due to different train/test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef: [[ 0.09068553 -0.01368214  0.00542581  0.04139253 -0.02289431  0.03992637]]\n",
      "R^2: 0.2740 (+/- 0.0482), R: 0.5235\n"
     ]
    }
   ],
   "source": [
    "# Split our data into our x, y inputs\n",
    "x = df[['attr_o', 'sinc_o', 'intel_o', 'fun_o', 'amb_o', 'shar_o']].values\n",
    "y = df[['dec_o']].values\n",
    "\n",
    "# Split the data into 10 folds to test on.\n",
    "kf = KFold(n_splits=10, shuffle=True)\n",
    "\n",
    "q = 0 # iterator\n",
    "scores = np.zeros([10]) # Array for storing scores\n",
    "\n",
    "# For each of the 10 folds do the following...\n",
    "# Train: the training data\n",
    "# Test: the test data\n",
    "for train, test in kf.split(X):\n",
    "    # Create our model and fit using our training data\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(x[train], y[train])\n",
    "\n",
    "    # Score using R^2\n",
    "    scores[q] = regr.score(x[test], y[test])\n",
    "    q += 1\n",
    "    \n",
    "print(\"Coef: %s\" % regr.coef_)\n",
    "\n",
    "gd_score = (scores.mean(), scores.std() * 2, math.sqrt(scores.mean()))\n",
    "print(\"R^2: %0.4f (+/- %0.4f), R: %0.4f\" % gd_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our mulitple linear regression model has an $R$ value of about 0.52 which means that our model fits the data but the correlation is not very strong. This value is fairly good considering we are trying to predict human behavior, which is inherently eratic.\n",
    "\n",
    "It's also interesting to notice the coefficients our linear model. Notice that the 1st, 4th, and 6th coefficients are much larger than the other coefficients. This is because these represent the same 3 factors that we noticed had a strong correlation before (Physical Attraction, Fun, and Shared Interests).\n",
    "\n",
    "The best way to judge whether our model is good or bad is to compare it to another ML algorithm's performance and see how they score against eachother. Let's compare our linear model to the k-Nearest Neighbors algorithm. We also use a 10-fold Cross validation test for this algorithm so the comparison is fair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.7096 (+/- 0.0265), R: 0.8424\n"
     ]
    }
   ],
   "source": [
    "y = y.ravel() # Convert from column vector to 1D vector\n",
    "\n",
    "# Split the data into 10 folds to test on.\n",
    "kf = KFold(n_splits=10, shuffle=True)\n",
    "\n",
    "q = 0 # iterator\n",
    "scores = np.zeros([10]) # Array for storing scores\n",
    "\n",
    "# For each of the 10 folds do the following...\n",
    "# Train: the training data\n",
    "# Test: the test data\n",
    "for train, test in kf.split(X):\n",
    "    # Create kNN model and fit using our training data\n",
    "    neigh = KNeighborsClassifier(n_neighbors=6, weights='distance')\n",
    "    neigh.fit(x[train],y[train])\n",
    "    \n",
    "    # Score using R^2\n",
    "    scores[q] = neigh.score(x[test],y[test])\n",
    "    q += 1\n",
    "\n",
    "gd_score = (scores.mean(), scores.std() * 2, math.sqrt(scores.mean()))\n",
    "print(\"R^2: %0.4f (+/- %0.4f), R: %0.4f\" % gd_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's quite clear from the results that the k-NN model had a stronger correlation to the data than the multiple linear regression model. This is what I expected because the data doesn't have a particularly linear trend so the k-NN model will do a better job of classification.\n",
    "\n",
    "#### Why is this important?\n",
    "These models are incredibly important to data science as they illustrate the fundamental ideas of machine learning. The models allow us to predict someone's match success based only on people's review of them.\n",
    "\n",
    "\n",
    "### Summary\n",
    "The purpose of this analysis was to introduce many of the key elements of data science. Our goal was to discover what factors of a person would lead to a match. We did this by analyzing the relationship between certain perceived factors of a person and whether or not a partner would match with them.\n",
    "\n",
    "We found that overall physical attractiveness, fun, and shared interests were the most important personality traits for a person, meaning that high values in these categories would often lead to a match. Meanwhile sincerity, inteligence, and ambition had little affect on whether or not a partner would match. Then we split the data by gender and looked for differences in how much each gender valued the factors. The significant results of this testing was that men value ambition more than women while women value shared interests more than men.\n",
    "\n",
    "Then we created heatmaps to graphically display the correlation between the factors and match success."
   ]
  }
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