Big five - Age and gender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier, XGBRegressor\n",
    "import seaborn as sns\n",
    "sns.set_style(\"dark\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "%pylab inline\n",
    "data=None\n",
    "def absolute_correlations(col, df=data):\n",
    "    #absolute_values = np.abs(df[col])\n",
    "    corrs = pd.DataFrame(df.select_dtypes(include=[np.number]).corrwith(df[col]), columns=['correlation'])\n",
    "    corrs['absol'] = np.abs(corrs['correlation'])\n",
    "    return corrs.sort_values('absol', ascending=False).drop('absol', axis=1).tail(len(corrs)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19011, 53)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data_cleaned.csv')\n",
    "data = data[[i for i in data.columns if 'Unnamed' not in i]]\n",
    "\n",
    "# Use only good data\n",
    "\n",
    "# Users could enter any number for age, so let's remove the jokers\n",
    "data = data[data['age'] < 71]\n",
    "data.replace(-1, np.nan, inplace=True)\n",
    "data = data.dropna()\n",
    "\n",
    "# Simplify genders down to two, for convenience\n",
    "data = data[(data['gender'] < 3) & (data['gender'] > 0)]\n",
    "data['gender'] = np.where(data['gender'] == 1, 'man', 'woman')\n",
    "\n",
    "# These two items were most strongly correlated.\n",
    "# Let's remove respondents who didn't answer them similarly.\n",
    "data['reliable'] = np.where(\n",
    "    np.abs(data['I have frequent mood swings.'] - data['I change my mood a lot.']) > 2, 0, 1)\n",
    "\n",
    "data = data[data.reliable == 1]\n",
    "del data['reliable']\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0007a2710>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a001022470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['age'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0006d2748>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0007bfd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Conscientiousness'] =\\\n",
    "(data['I am always prepared.'] +\n",
    "data['I get chores done right away.'] +\n",
    "data['I like order.'] +\n",
    "data['I follow a schedule.'] +\n",
    "data['I am exacting in my work.'] +\n",
    "(6 - data['I leave my belongings around.']) +\n",
    "(6 - data['I pay attention to details.']) +\n",
    "(6 - data['I make a mess of things.']) +\n",
    "(6 - data['I often forget to put things back in their proper place.']) +\n",
    "(6 - data['I shirk my duties.']))/.5\n",
    "\n",
    "data['Conscientiousness'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0008abe48>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a00079ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Agreeableness'] =\\\n",
    "(data['I take time out for others.'] +\n",
    "data['I am interested in people.'] +\n",
    "data[\"I sympathize with others' feelings.\"] +\n",
    "data['I have a soft heart.'] +\n",
    "data['I make people feel at ease.'] +\n",
    "data[\"I feel others' emotions.\"] +\n",
    "(6 - data[\"I am not interested in other people\\'s problems.\"]) +\n",
    "(6 - data[\"I insult people.\"]) +\n",
    "(6 - data[\"I feel little concern for others.\"]) +\n",
    "(6 - data['I am not really interested in others.']))/.5\n",
    "\n",
    "data['Agreeableness'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0008e35c0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a000956048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Neuroticism'] =\\\n",
    "(data['I get stressed out easily.'] +\n",
    "data['I worry about things.'] +\n",
    "data['I am easily disturbed.'] +\n",
    "data['I get upset easily.'] +\n",
    "data['I change my mood a lot.'] +\n",
    "data['I have frequent mood swings.'] +\n",
    "data['I get irritated easily.'] +\n",
    "data['I often feel blue.'] +\n",
    "(6 - data['I am relaxed most of the time.']) +\n",
    "(6 - data['I seldom feel blue.']))/.5\n",
    "\n",
    "data['Neuroticism'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0009fe9e8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a000102b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Openness'] =\\\n",
    "(data['I have a rich vocabulary.'] +\n",
    "data['I have a vivid imagination.'] +\n",
    "data['I have excellent ideas.'] +\n",
    "data['I am quick to understand things.'] +\n",
    "data['I use difficult words.'] +\n",
    "data['I spend time reflecting on things.'] +\n",
    "data['I am full of ideas.'] +\n",
    "(6 - data['I have difficulty understanding abstract ideas.']) +\n",
    "(6 - data['I do not have a good imagination.']) +\n",
    "(6 - data['I am not interested in abstract ideas.']))/.5\n",
    "\n",
    "data['Openness'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a000ad0080>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a000adb128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Extraversion'] =\\\n",
    "(data['I am the life of the party.'] +\n",
    "data['I feel comfortable around people.'] +\n",
    "data['I start conversations.'] +\n",
    "data['I talk to a lot of different people at parties.'] +\n",
    "data[\"I don't mind being the center of attention.\"] +\n",
    "(6 - data[\"I don't like to draw attention to myself.\"]) +\n",
    "(6 - data[\"I don't talk a lot.\"]) +\n",
    "(6 - data[\"I have little to say.\"]) +\n",
    "(6 - data['I am quiet around strangers.']) +\n",
    "(6 - data['I keep in the background.']))/.5\n",
    "\n",
    "data['Extraversion'].hist(edgecolor='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Introvert\" doesn't have a clear, agreed-upon definition in psychology. I typically just define it as someone who scores below the median in the trait of extraversion. But it's probably more accurate to say an introvert is someone whose average answer to the questions above is less than 3 (neutral).\n",
    "\n",
    "How many people are introverts under that definition?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28772815738256796"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[data.Extraversion < 50])/len(data)\n",
    "# About 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZeVFEjAZmA+sDawGHZ+aEiPglsDHQC7goM6+JiM8BZwANwATgGOB5YFOgH3BZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c06ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def correlation_matrix(df, figsize=(15,7)):\n",
    "    # Generate a mask for the upper triangle\n",
    "    mask = np.zeros_like(df.corr(), dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "    # Resize and display\n",
    "    plt.figure(figsize=figsize)\n",
    "    sns.heatmap(df.corr(), annot=True, fmt='.2f', mask=mask, center=0, cmap='seismic_r')\n",
    "    \n",
    "traits = data[['Agreeableness', 'Neuroticism', 'Openness',\n",
    "               'Conscientiousness', 'Extraversion']]\n",
    "\n",
    "correlation_matrix(traits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender Differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "women = data[data.gender == 'woman']\n",
    "men = data[data.gender == 'man']\n",
    "\n",
    "traits = [\n",
    "    'Agreeableness',\n",
    "    'Neuroticism',\n",
    "    'Openness',\n",
    "    'Conscientiousness',\n",
    "    'Extraversion'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kNcyYcQ6b370Z595zbrNLWcP0A6YDNKWu6QdM58UDX26518iGvnbHjx/T1te8AW9xZOYj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034beef128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c323c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c1a0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c177550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c392278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for trait in traits:\n",
    "    sns.boxplot(y='gender', x=trait, data=data, fliersize=.2,\n",
    "                palette=['royalblue', 'magenta'], saturation=1,\n",
    "               whis=1)\n",
    "    plt.title(trait)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]), <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U04gNvBoXwog06dQcRXRAnyTSXF2IC/lQYgTxITFfcSrwV3e/w6LW/kEiWJ+S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c1af710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_v_women = data.groupby('gender').mean()[traits].T\n",
    "\n",
    "men_v_women.plot.bar(color=['blue', 'magenta'])\n",
    "\n",
    "plt.xticks(rotation=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Men and women differ the most on agreeableness (which is the ability to empathize), and neuroticism (which is negative emotion). Let's graph those two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034eebbef0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Wmhj+g1z3YB0mCYc28fn5VUW+yuqdyy/Y0CQF2C/24ijaoaqbJVRSpnAbcBg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\nQbtOawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034ede35f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(men.Agreeableness, color='blue', shade=True, bw=5)\n",
    "sns.kdeplot(women.Agreeableness, color='magenta', shade=True, bw=5)\n",
    "plt.legend(['Men', 'Women'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034ef79160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JBP+BwDwArfUyoLhKWzdghdZ6q9Y6BiwCBle2rQSO/9G5fqW1/qDyaxuoqHyy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\nUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034eedfc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(men.Neuroticism, color='blue', shade=True, bw=7)\n",
    "sns.kdeplot(women.Neuroticism, color='magenta', shade=True, bw=7)\n",
    "plt.legend(['Men', 'Women'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034f011fd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S3YHv8rQtBVoppWoqpUIkunwWFnQipdS5JDL+/lrrlckftwYWKKWs5BhCb2DJ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\nDMOIIP8CVdm89X5AbP8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c33f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(men.Extraversion, color='blue', shade=True, bw=7)\n",
    "sns.kdeplot(women.Extraversion, color='magenta', shade=True, bw=7)\n",
    "plt.legend(['Men', 'Women'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034c3726d8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fq5T6AbCQ4A1jltZ6RxPf69vA/yml4sBO4Mqj230h0uOzaYVTdNX4ftA6OWGQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034f80fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(men.Openness, color='blue', shade=True, bw=7)\n",
    "sns.kdeplot(women.Openness, color='magenta', shade=True, bw=7)\n",
    "plt.legend(['Men', 'Women'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034c2eeeb8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b1zXIzQ1KNYYL3fJtinq4lLdJ/YuQRUIwqb/LzTtMfDvotBHiaEjIi4z24os27dt7dO0a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c392828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(men.Conscientiousness, color='blue', shade=True, bw=7)\n",
    "sns.kdeplot(women.Conscientiousness, color='magenta', shade=True, bw=7)\n",
    "plt.legend(['Men', 'Women'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Personality over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = np.arange(data.age.min()+1, data.age.max()+1, 10)\n",
    "data['age_group'] = pd.Series(pd.cut(data['age'], bins=bins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034f018f98>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dYf0dv0nw3AiByRHMEt+AKmGaPPnZWu6as5zWcOo5+33rvFx2xDAOsefsCyGy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c1d79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_c = data[data['gender'] == 'man'].groupby('age_group').mean()\n",
    "women_c = data[data['gender'] == 'woman'].groupby('age_group').mean()\n",
    "\n",
    "men_c['Conscientiousness'].plot(linewidth=5, grid=True, label='men')\n",
    "women_c['Conscientiousness'].plot(linewidth=5, color='magenta', grid=True, label='women')\n",
    "\n",
    "plt.title('Conscientiousness')\n",
    "\n",
    "pylab.legend(loc=9, bbox_to_anchor=(0.5, -.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034c343d68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZzLCLMw6eaww8VxsYiRKloSNMcpea5E43waYLwWHXljXm93sd0+0TBDxa67j1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\nQghRh/4fv1xEFcpWsDUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034ef9c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_n = data[data['gender'] == 'man'].groupby('age_group').mean()\n",
    "women_n = data[data['gender'] == 'woman'].groupby('age_group').mean()\n",
    "\n",
    "men_n['Neuroticism'].plot(linewidth=5, grid=True, label='men')\n",
    "women_n['Neuroticism'].plot(linewidth=5, color='magenta', grid=True, label='women')\n",
    "\n",
    "plt.title('Neuroticism')\n",
    "pylab.legend(loc=9, bbox_to_anchor=(0.5, -.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadly, I think there's survivorship bias when measuring neuroticism. People with poor mental health are not going to live as long. Nonetheless, our mental health seems to improve as we get older (as many formerly anxious teens will tell you)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034f860d30>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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znwm8ANwODAHOAJqBC3Abude3BDRdGqHxR/0UYvtmis7zVIhNPkwtFqXelJL8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\nyDWoWsFHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c2e5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_e = data[data['gender'] == 'man'].groupby('age_group').mean()\n",
    "women_e = data[data['gender'] == 'woman'].groupby('age_group').mean()\n",
    "\n",
    "men_e['Extraversion'].plot(linewidth=5, grid=True, label='men')\n",
    "women_e['Extraversion'].plot(linewidth=5, color='magenta', grid=True, label='women')\n",
    "\n",
    "plt.title('Extraversion')\n",
    "\n",
    "pylab.legend(loc=9, bbox_to_anchor=(0.5, -.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034f919c50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pjOjdniIlTOE/1vV4Uzm9o/vQCyOYygm5JA5MevP2CxIk9tNUjki50q/uWONC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c30b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_o = data[data['gender'] == 'man'].groupby('age_group').mean()\n",
    "women_o = data[data['gender'] == 'woman'].groupby('age_group').mean()\n",
    "\n",
    "men_o['Openness'].plot(linewidth=5, grid=True, label='men')\n",
    "women_o['Openness'].plot(linewidth=5, color='magenta', grid=True, label='women')\n",
    "\n",
    "plt.title('Openness')\n",
    "\n",
    "pylab.legend(loc=9, bbox_to_anchor=(0.5, -.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think there's some selection bias here. It would make a lot more sense if openness (which measures curiosity) declined over time. But the fact that it increases leads me to believe that intellectually curious old folks are more likely to take a personality test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2034f9d67f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4zexqy3yZ5B6jKiJcMqmGBZNrOf+UaiLe/r1aJ790KPxFfCCRclj/Xhurt+5n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034f8b6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_a = data[data['gender'] == 'man'].groupby('age_group').mean()\n",
    "women_a = data[data['gender'] == 'woman'].groupby('age_group').mean()\n",
    "\n",
    "men_a['Agreeableness'].plot(linewidth=5, grid=True, label='men')\n",
    "women_a['Agreeableness'].plot(linewidth=5, color='magenta', grid=True, label='women')\n",
    "\n",
    "plt.title('Agreeableness')\n",
    "plt.legend(['Men', 'Women'])\n",
    "pylab.legend(loc=9, bbox_to_anchor=(0.5, -.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Personality by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2034f068f98>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xEBFPBn4PeGZnqeZGlefBqsx8VXtlVgJfBh6dmd/sNtaMvAD4UEScAjyAptl+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034c1d9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries = data.country.value_counts()\n",
    "countries[countries > 99].plot.bar(figsize=(20,4), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_countries = countries[countries > 99].drop('(nu').index\n",
    "men = data[data.gender == 'man']\n",
    "women = data[data.gender == 'woman']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agreeableness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2034f082a90>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ANcAvMnN2tWyAMjzgDsAKwBXAzpl5w+LsQ5IkSZIkSZIkSb3RpELqRGC3iFit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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034f93d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('poster')\n",
    "\n",
    "# This sorts the countries by average male agreeableness\n",
    "men_a =\\\n",
    "men[men.country.isin(top_countries)].groupby('country').mean()['Agreeableness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Agreeableness', data=men[men.country.isin(top_countries)]\\\n",
    "               .sort_values('Agreeableness', ascending=False),\n",
    "               kind='bar', order=men_a,\n",
    "               palette=['royalblue'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Agreeableness')\n",
    "\n",
    "plt.title('Agreeableness in Men')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2034f4e4b00>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkhaDHVJdcPrpJ/LnP9/A3LlP8ec/38Dpp5/Y7ZJGrTlz5jBz5gymTt2JmTNnMGfOnG6X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\nCchoMOQ5TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034fad0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('poster')\n",
    "\n",
    "# This sorts the countries by average female agreeableness\n",
    "women_a =\\\n",
    "women[women.country.isin(top_countries)].groupby('country').mean()['Agreeableness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Agreeableness', data=women[women.country.isin(top_countries)]\\\n",
    "               .sort_values('Agreeableness', ascending=False),\n",
    "               kind='bar', order=women_a,\n",
    "               palette=['magenta'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Agreeableness')\n",
    "\n",
    "plt.title('Agreeableness in Women')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neuroticism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2034fc04f28>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HMX/+Dcye/QJ22eWTrL766p0OS5J6WicrpNanlLr8IyLOBd4K3A/MBQ4CXlC1u6luuZuB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\n0MsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034f77cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_n =\\\n",
    "men[men.country.isin(top_countries)].groupby('country').mean()['Neuroticism']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Neuroticism', data=men[men.country.isin(top_countries)]\\\n",
    "               .sort_values('Neuroticism', ascending=False),\n",
    "               kind='bar', order=men_n,\n",
    "               palette=['royalblue'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Neuroticism')\n",
    "\n",
    "plt.title('Neuroticism in Men')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20351a76208>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5MpMZFqtJGkc/jUtm4k7SeEy01MdkcP3cp9Rsjp2oVjHJKUmSpjsTUm02gxnMfmg2i9dZ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203519f0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "women_n =\\\n",
    "women[women.country.isin(top_countries)].groupby('country').mean()['Neuroticism']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Neuroticism', data=women[women.country.isin(top_countries)]\\\n",
    "               .sort_values('Neuroticism', ascending=False),\n",
    "               kind='bar', order=women_n,\n",
    "               palette=['magenta'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Neuroticism')\n",
    "\n",
    "plt.title('Neuroticism in Women')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20351f75470>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJEmSJEma8qZNvMhijqR02/d74GpgWeAs4ErgCeBRYItWBShJkiRJkiRJkqT+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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2034f9c2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_e =\\\n",
    "men[men.country.isin(top_countries)].groupby('country').mean()['Extraversion']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Extraversion', data=men[men.country.isin(top_countries)]\\\n",
    "               .sort_values('Extraversion', ascending=False),\n",
    "               kind='bar', order=men_e,\n",
    "               palette=['royalblue'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Extraversion')\n",
    "\n",
    "plt.title('Extraversion in Men')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x203526a8cf8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nap4kSZIkSZIkSZL6RCuB1CHAU4FfA/tTWjK9OiI+BtwAPB44YoLn2BF4MnAn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\nkiT1lAkpSZIkSZIkSZIk9dT/AQ7Bl5N4omfRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20351efb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "women_e =\\\n",
    "women[women.country.isin(top_countries)].groupby('country').mean()['Extraversion']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Extraversion', data=women[women.country.isin(top_countries)]\\\n",
    "               .sort_values('Extraversion', ascending=False),\n",
    "               kind='bar', order=women_e,\n",
    "               palette=['magenta'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Extraversion')\n",
    "\n",
    "plt.title('Extraversion in Women')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Conscientiousness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20352a39278>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NySiAzHwQOItS3TSmiFiTUln1ocx8vGn1UsC8YZY/WK2TJEmSJEmSJElSj6ib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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20352942a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_c =\\\n",
    "men[men.country.isin(top_countries)].groupby('country').mean()['Conscientiousness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Conscientiousness', data=men[men.country.isin(top_countries)]\\\n",
    "               .sort_values('Conscientiousness', ascending=False),\n",
    "               kind='bar', order=men_c,\n",
    "               palette=['royalblue'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Conscientiousness')\n",
    "\n",
    "plt.title('Conscientiousness in Men')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20352be7588>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W3UQJqzbMzD90tkRJkiRJkiRJkiT1s2XqPiAz76Yapq9p2Z3AqZ0qSpIkSZIk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\nEREREREREZGG+g+7/lGpko5RsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20352a7bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "women_c =\\\n",
    "women[women.country.isin(top_countries)].groupby('country').mean()['Conscientiousness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Conscientiousness', data=women[women.country.isin(top_countries)]\\\n",
    "               .sort_values('Conscientiousness', ascending=False),\n",
    "               kind='bar', order=women_c,\n",
    "               palette=['magenta'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Conscientiousness')\n",
    "\n",
    "plt.title('Conscientiousness in Women')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Openness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20353535828>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cUpIkSZIkSZIkSX2k7UAqIqZT5nraDJgDLAVuAv4rM89a0UIy8+VNt5cC+1UX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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20352c29d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_o =\\\n",
    "men[men.country.isin(top_countries)].groupby('country').mean()['Openness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Openness', data=men[men.country.isin(top_countries)]\\\n",
    "               .sort_values('Openness', ascending=False),\n",
    "               kind='bar', order=men_o,\n",
    "               palette=['royalblue'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Openness')\n",
    "\n",
    "plt.title('Openness in Men')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20353be1c18>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSZIkSZLU5fqrTI6INwAXUARZhwLvBN4NHA5MAc6PiNfXXaQkSZIkSZIkSZK6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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2035355d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "women_o =\\\n",
    "women[women.country.isin(top_countries)].groupby('country').mean()['Openness']\\\n",
    ".sort_values(ascending=False).index\n",
    "\n",
    "sns.factorplot('country', 'Openness', data=women[women.country.isin(top_countries)]\\\n",
    "               .sort_values('Openness', ascending=False),\n",
    "               kind='bar', order=women_o,\n",
    "               palette=['magenta'], size=4, aspect=6)\\\n",
    ".set(xlabel='Country', ylabel='Conscientiousness')\n",
    "\n",
    "plt.title('Openness in Women')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save the personality and trait features so we can analyze them separately.\n",
    "personality_items = data.drop('age', axis=1).select_dtypes(include=[np.number]).columns\n",
    "traits = 'Openness Conscientiousness Extraversion Agreeableness Neuroticism'.split()\n",
    "\n",
    "data_p = data[personality_items]\n",
    "data_t = data[traits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.7791281078725075\n",
      "2 0.6298456411745381\n",
      "3 0.483352726568548\n",
      "4 0.42818680208922333\n",
      "5 0.6413421925521577\n",
      "6 0.3442806551938966\n",
      "7 0.2751294208843026\n",
      "8 0.27369070537176565\n",
      "9 0.19671265795687903\n",
      "10 0.1873529934265861\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import FactorAnalysis\n",
    "\n",
    "fa = FactorAnalysis(n_components=10).fit(data_p)\n",
    "fa1 = fa.transform(data_p)\n",
    "\n",
    "# Stick it in a dataframe\n",
    "factors = pd.DataFrame(fa.components_, columns=data_p.columns).T\n",
    "\n",
    "for i in factors.columns:\n",
    "    factors['absol'] = abs(factors[i])\n",
    "    factors_sorted = factors.sort_values('absol', ascending=False).head(4) # Top 4\n",
    "    print(i+1, factors_sorted['absol'].min())                              # Min value\n",
    "    del factors['absol']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are definitely 5 factors here, just as we'd expect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Unsupervised Learning\n",
    "\n",
    "\"How many kinds of people are there?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 0.10520389402959085\n",
      "3 0.07627676856106134\n",
      "4 0.06596613389175696\n",
      "5 0.061222054759024105\n",
      "6 0.05811191739692118\n",
      "7 0.053104844571966375\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-4455db1cfec5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mcl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Vince\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\k_means_.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0mtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy_x\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m                 \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malgorithm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m                 return_n_iter=True)\n\u001b[0m\u001b[1;32m    897\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Vince\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\k_means_.py\u001b[0m in \u001b[0;36mk_means\u001b[0;34m(X, n_clusters, init, precompute_distances, n_init, max_iter, verbose, tol, random_state, copy_x, n_jobs, algorithm, return_n_iter)\u001b[0m\n\u001b[1;32m    344\u001b[0m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_clusters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m                 \u001b[0mprecompute_distances\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprecompute_distances\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m                 x_squared_norms=x_squared_norms, random_state=random_state)\n\u001b[0m\u001b[1;32m    347\u001b[0m             \u001b[1;31m# determine if these results are the best so far\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbest_inertia\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0minertia\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mbest_inertia\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Vince\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\k_means_.py\u001b[0m in \u001b[0;36m_kmeans_single_elkan\u001b[0;34m(X, n_clusters, max_iter, init, verbose, x_squared_norms, random_state, tol, precompute_distances)\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Initialization complete'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m     centers, labels, n_iter = k_means_elkan(X, n_clusters, centers, tol=tol,\n\u001b[0;32m--> 400\u001b[0;31m                                             max_iter=max_iter, verbose=verbose)\n\u001b[0m\u001b[1;32m    401\u001b[0m     \u001b[0minertia\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mcenters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minertia\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32msklearn\\cluster\\_k_means_elkan.pyx\u001b[0m in \u001b[0;36msklearn.cluster._k_means_elkan.k_means_elkan\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Vince\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36meuclidean_distances\u001b[0;34m(X, Y, Y_norm_squared, squared, X_norm_squared)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[1;31m# Pairwise distances\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,\n\u001b[0m\u001b[1;32m    164\u001b[0m                         X_norm_squared=None):\n\u001b[1;32m    165\u001b[0m     \"\"\"\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "for i in range(2,8):\n",
    "    cl = KMeans(n_clusters=i, random_state=42).fit(data_p)\n",
    "    print(i, silhouette_score(data_p, cl.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cl = KMeans(n_clusters=2, random_state=42).fit(data_p)\n",
    "data_t['Type'] = cl.labels_ + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diff = data_t.groupby('Type').mean().T\n",
    "\n",
    "diff['difference'] = abs(diff[1]-diff[2])/diff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Type</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extraversion</th>\n",
       "      <td>73.500524</td>\n",
       "      <td>46.939436</td>\n",
       "      <td>0.361373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neuroticism</th>\n",
       "      <td>53.429110</td>\n",
       "      <td>70.543283</td>\n",
       "      <td>0.320316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agreeableness</th>\n",
       "      <td>82.704921</td>\n",
       "      <td>71.043864</td>\n",
       "      <td>0.140996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conscientiousness</th>\n",
       "      <td>66.344293</td>\n",
       "      <td>59.605116</td>\n",
       "      <td>0.101579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Openness</th>\n",
       "      <td>80.638743</td>\n",
       "      <td>75.730472</td>\n",
       "      <td>0.060867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Type                       1          2  difference\n",
       "cluster             1.000000   2.000000    1.000000\n",
       "Extraversion       73.500524  46.939436    0.361373\n",
       "Neuroticism        53.429110  70.543283    0.320316\n",
       "Agreeableness      82.704921  71.043864    0.140996\n",
       "Conscientiousness  66.344293  59.605116    0.101579\n",
       "Openness           80.638743  75.730472    0.060867"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.sort_values('difference', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a0039d2160>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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2d8DRuOugL+5H8F7c+Xwkj2x9iDs3j4pII9w5LoerwCrgzs1OAN/jcjFu3Hv0/pyLcPah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\n8b8804z7ZyVtOo/Isfzei47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6Jj9OX85T/aexKnU79Wq6H88VG9J4+KJG3NDyCKqWK0Vq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\nICIiEkFKAERERCLo/wF14nE+fBX3VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a001297a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('poster')\n",
    "\n",
    "sns.heatmap(diff.sort_values('difference', ascending=False).drop('difference', axis=1),\n",
    "           annot=True, cmap='Blues', cbar=False)\n",
    "plt.title('Two Types of People: How Do They Differ?\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The biggest difference is in extraversion, followed by neuroticism. This would suggest that the main way people tend to differ is in their overall happiness levels: extraversion predicts happiness, while neuroticism predicts depression.\n",
    "\n",
    "If you go a step further and also take agreeableness into account, then you could say that there are people-persons, and those who a bit more solitary.\n",
    "\n",
    "But I think there's something even more interesting to point out:\n",
    "\n",
    "Notice that as you compare Type 1 people to Type 2 people, Type 1's tend to score on the more favorable end for each of the big five personality traits. So if you want to paint with a *really* broad brush, you could say there are people with \"good\" personalities, and people with not-so-good personalities. (I'm hesitant to call them \"bad\".)\n",
    "\n",
    "There's hypothesis that there is a [general factor of personality](https://www.psychologytoday.com/blog/unique-everybody-else/201211/personalitys-big-one-reality-or-artifact); a \"big one\" rather than a \"big five.\" This data would support that idea. A healthy, well-functioning brain would perform healthy behaviors in a variety of contexts. These are the Type 1's we see above one.\n",
    "\n",
    "As you consider each of the big five, you'll almost certainly agree that:\n",
    "\n",
    "- It's good to be agreeable.\n",
    "- It's good to be conscientious.\n",
    "- It's not good to be neurotic.\n",
    "\n",
    "And you'll probably agree that:\n",
    "\n",
    "- It's good to be extraverted.\n",
    "- It's good to be open to experience.\n",
    "\n",
    "But those last two are more controversial. Extraverts certainly aren't better people just because they're extraverted. But they do tend to be happier, and society tends to reward extraverted behavior. So I'm not making a value judgment, but I do think it's fair to say that it's good to be an extravert.\n",
    "\n",
    "Openness to experience has a similar dilemma. Openness predicts liberal political opinions, and I'd have a much harder time convincing everyone that openness is always good (i.e., conservatives would disagree with me). So, by that token, it's interesting that openness is the *least* influential factor in distinguishing Type 1 and Type 2 people. But I should also point out that openness predicts intelligence, so I could still make a strong argument that it's \"better\" to be high in openness.\n",
    "\n",
    "In any case, the consistency of the clusters across each of the big five is fascinating."
   ]
  }
 ],
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