{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib.pyplot import title,subplot,figure\n",
    "import os\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import tak.tak as tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "mpl.rcParams['figure.figsize'] = (8,6)\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "mpl.rcParams['font.size'] = 44.0\n",
    "\n",
    "mpl.rcParams['figure.edgecolor'] = 'black' # <- noeffect\n",
    "mpl.rcParams['axes.labelsize']=24.0\n",
    "mpl.rcParams['axes.titlesize']=24.0\n",
    "mpl.rcParams['axes.titleweight'] = 'bold'\n",
    "mpl.rcParams['legend.fontsize'] = 18\n",
    "mpl.rcParams['xtick.labelsize'] = 14\n",
    "mpl.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "# see below for the properties that we can modify\n",
    "#mpl.rcParams\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4b1e672590>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UCQV9ERGpaelUF5OmNPc5fsHUZtKprgq0qHI0py8iMkiuGyIRLhJLxgHIpjKk805dJYJV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\nQV9ERCQhFPRFREQS4v8BN0JuncYOTCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b20449fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "sns.stripplot(x=\"day\", y=\"total_bill\", data=tips)\n",
    "title('stripplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4b1e36bdd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FZCIiIiI5VpjrAoiIiHS3I46Yztq1HzNo0Fbcc8+DuS6OiGrIRESkZ1uy5A1u\nvPE63nprReO0Pn360rfv5vTp0zeHJRNJUQ2ZiIj0aLfccgMvvPAcgwdvzYgROwJw003/zHGpRJpS\nDZmIiPRYn3/+OS+99EKuiyGySaGGhoZcl6HHWLduo1amiEhAzJ79fV5//dVm03/xiwu48cbrmvUh\ne/nllzjjjB8BcOqpp7HHHntz5ZV/xNo32XzzCg477EiOO+5E1qxZzeWXX8Zrr71KKAT77vstzjzz\nXDbbbLMmn7No0X+45547efPNZcTj1Wy55VYccMCBHH/8d4hEIl2/ArrBwIHloVyXoadQk6WIiPRI\n5eXllJZGqaqKAVBaGqW4uJji4mJCoWQckYonktNCoRDr1n3CmWeeRlVVFbW1tXzyyVquvfZq+vbt\ny2233cLatR9TX19PQ0MDTzzxKKFQiPPPv6hxWXfccSvXXPOXxuUVFxfz0UcfcMstN/DKK//jyiuv\nobBQp2BJUZOliIj0SL/73Z/46U9/1vj8pz/9GfPmPcHEiZNprXWooaGBhx6ay3HHnciTT/6HU089\nrfG1P//5MoYNG86jjz7NTTfdTjQaBeCpp54gFqsE4P333+O66/4KwOjRO/PAA4/y5JP/4eKLLyUU\nCvHGG6/x4IP3d8VXljymgExERHqsjnbL6devP8ceewIFBQUcccTRhMNhAOrq6vjJT86mpKSE4cNH\nMHbsNwFIJBJ8/PEaAJ544lHq6+sBOP74E6mo6AfAuHETGTVqTOM8In6qLxUREUkzYoRpfFxSUkLf\nvn1Zv349ffr0YdCgQY2vDRq0FeACv40bNwKwcuXbja9fcskFjcEcQFVVFQBvv51KwSECCshERESa\nKS8vb/K8qKgYgNLSsrTpRY2Pk7VxyaZL9ziWcfnxeJx4PN5jOvdL5ykgExERyaKyMhe0hUIhrrvu\nZnbaaXSOSyT5QH3IRESkV0j26+pqw4e75LMNDQ1N7g4AsG7dJ3z11VfdUg7JLwrIRESkx/I3CSZz\nknVFYJZKowGTJh1IQYE7vd56602sWvW29/mvcdRRhzJ16nguvPBXWS+D5Dc1WYqISI81YoShoKCA\n+vp6HnlkHgsXPskhhxzmC6DaOwoz8/z+0ZzbbjuEk08+leuvv4a1az/mxBOPIRKJEI/HAdhyy0HM\nnn1GB76N9GSqIRMRkR5ryJDtOOOMnzFw4BYUFRVRXt6HbbcdAiRrtTInhk0XCoWazd/ae0444bv8\n5jd/YLdhu0qqAAAgAElEQVTdvsZmm5VTX1/PoEFbcdhhR3L99f+gf/8BWfyW0hPo1klZpFsniYhI\nb6JbJ2WPashEREREckwBmYiIiEiOKSATERERyTEFZCIiIiI5poBMREREJMcUkImIiIjkmAIyERER\nkRxTQCYiIiKSYwrIRERERHJMAZmIiIhIjikgExEREckxBWQiIiIiOaaATEREpBWzZ3+f/fbbgxNO\nOCrXRWlVspy//e2FuS6KdIACMhERkTYIhUK5LoL0YArIRERERHJMAZmISC8SDhcQiRZQ0T9KRf8o\nkWgB4bBOBdlwxBHTMzYZJpsSf/zjUxunnXbaKey33x5ceunFrFz5NrNnf5+JE/dlxowpzJlze7Nl\nr1z5Nr/61TkcfPBExo3bm+OOO4L77ruHRCKRsSxLlrzBD37wXSZM2JdDDz2If/1rTna/rGRdYa4L\nICIi3SMcLiBaXsjdSx5i4TvPATB+6FhmjZlObGMdiUR9jkuYEgpBJFJEqCBEfaKeeLwu10XqNH+T\nZ/LxZ599yplnzqZ///4UF0dYv/4zrr76Cvr3H8CkSQcCsHz5MmbP/j7xeJyKin4YsxPLly/j8ssv\n4+23V3DOOb9s8jkff7yGs88+ncGDt6G4uJhPP13HFVf8kYqK/kyYMKn7vrC0iy6LRER6icII3L3k\nIeatWEBlbYzK2hjzVizgnqXzKIzkunQpxcVhyvuUsvTdDTzwn3dYvb6KzcpLemRN3vPPL+KUU37I\nTTfdzm23zaGkpBSAxx6b3zjPFVf8kXg8znbbbc+cOXO57rqbOe+8/wNg3rwHeOedVU2W+eqrL3Pe\neRdw003/5NZb72pc5qOPzuumbyUd0WtryIwx2wOrMrz0urV2d2PMVOASYEfgPeC31to7urGIIiJZ\nFS0taawZ83tq1SKO2WUm8VgsB6VqKhQKESkt5hfXLGLF+xsAuOtxy+S9hvDd6aOJfRXPcQmzq1+/\n/kybNgOAAQMGsuuuu/HCC8+xZs0aADZsWM/SpYsBmDx5KqWlLriaNOlA+vTpSygE0Wi0yTKHDRvO\n/vuPB2CLLbb0LXN1d30t6YBeG5D5WOAPvuefGWO2Be4D1gAXAEcDtxlj3rTWvpqDMoqI9AqRSJhF\nr69uDMaSHn/hfQ4fP4JoUZja2sz9pvLR1ltv0+T55ptXABCPu8Bz9epUEDVo0FaNjwsLC9lnn29m\nXOa22w7JuMzq6p4VzPY0Pa/+t/3WWGtvAv5lrb3JWvsAcDwQAc6z1v4ZOB0IAd/NYTlFRDolVlXN\n+KFjm02fMGwfYlXVOShRc/WEsGnBWNKqj74gHM6/1BOJRMv93woLm9aLpKfWaGhoaHxcX9+2Pn6b\nWqYEk2rIYKQx5gNga2PMKuA4YFfvtWXe/ze9/7t3d+FERLKlLg6zxkwnFArx1KpFgAvGjhw9jdjG\nYHSaLwBGD+3P/EXvNnttxJDNqatraDY9KIqLiwH44ovPG6fV19fz0UcfdXiZgwcPbnz80UcfNj5u\naGhgzpzbqa9v4Otf3wNjRnb4MyQYFJBBFDgXGAycj2uqXOK9tjHt/4DuLZqISPYkEvXENtYxY8QU\njtllJuBqzYI0wjIer2PPMYMYs0N/lqz8rHH6jG8No6ykiKrK3DW71dfX89VXXzWptQJXA1VUVMSg\nQVvxwQfv89prr/Dpp58yYMAA5s9/kA0b1nf4M/v168+oUWNYtmwJjz02n1mzjqW8vJyFCxfw179e\nCcC1197cqe8lwdCbA7L3cAFW3FpbCWCMOQDYGxgIZLoMC+6lmYhIGyQS9SRiBKIDfyYNDQ1Ux2q4\n4Ht7s+L9Dbyz+kt233EgA/qWUhXLbR+od999h6lTx2d87aSTTmHKlGm89NILxGIxTjjhKAYP3pp1\n69ay555jefHF5oMp2ur008/iJz/5AWvWrOboo2cyePA2WPsmoVCIKVMOZvToMR1etgRHrw3IrLUN\nQPply/u4gKwQF3yVe9P7eP/XtbbMiooohYXhbBZTRKTX2mX4QHYZPrDxebS0KCflKC4u3GQ/rLKy\nCMcddyQ1NV9x4403UllZSZ8+m3HZZZdy66238uKLUFhYwMCB5U2WWVQUbpwGUFrqmj3D4VDj9HHj\nxnLXXXdx1VVX8corr7By5VsMGzaM448/nqOPPrqxbMlllpQUbXKZEjyh9KrX3sIYczhwJnC3tfZK\nb9piYDTwc+BS4Dhr7Z3GmP2BhcAV1tozW1rmunUbe+fKFBGRXmngwHKNGMiSXltDBizFddLf2RhT\nBozEBWMLgVtx/cl+Y4zZGjgJqAWuz1FZRUREpAfrtWkvrLXLgYOBxbgasYOBG4CZ1tqPgUNwnfl/\nA4SBWdbaN1tYnIiIiEiH9domy66gJksREelN1GSZPb25yVJEpNcpLg4TjkBJsbt5ZXVNnLpqelT2\ne5F8pIBMRKSXKC4OU1IW5q7FD/G0d0/LcUPHcvTO0wlVQk2NgrKgC4cLKIy4+5KCyyNXFycweeSk\n4xSQiYj0EuEI3LX4IR5esaBx2sMrFhAKwWEjD4KaHBZONikcLiBaXsjdSx5qvEn8+KFjmTVmeqCS\n+0rH9NpO/SIivU1JcaSxZsxv4arnGpswJbgKI3D3koeYt2IBlbUxKmtjzFuxgHuWzqNQP1/eU0Am\nIiKSB6KlJY01Y35PrVrU2IQp+UsBmYhIL1FdE2fc0LHNpo8fNpbqmtzelkikt1NAJiLSS9RVw9E7\nT2eamUhZUZSyoijTzESOGjOduupcl042JVZVzfgMAfWEYfsQq9IPmO+UhyyLlIdMRIJOaS/yV7JT\n/z1L5/HUqkWAC8aOHD0tZ536lYcsexSQZZECMhER6UpBS3uhgCx7lPZCRESkFbNnf5/XX3+1ybTS\n0iiDB2/NnnvuxWGHzWLQoK2azb/bbl/jL3+5LqtlSSTqScQgHotldbmSewrIRERE2qCkpJQxY3am\noaGBDRs2sGrV26xc+Rb3338vP//5+UycOBmAkSNHUVRUxPDhO+a4xJJPFJCJiIi0weDBg7n88r82\nPl+9+iMuuuh8li5dzCWXXMDgwVuz006jmT37jByWsu0SiQThcDjXxRCPRlmKiIh0wODBW/P73/+Z\n8vI+1NXVccMNrnly9uzvs99+e/DjH5/aOO9pp53CfvvtwaWXXszKlW8ze/b3mThxX2bMmMKcObc3\nWW575gVYtmwJ55zzU6ZMGc+ECfty8snf5qmnnmxxmQ8+eD/Tpk3ijDN+1AVrRTpKAZmIiAROKAQl\nJUWURouIRILbmNO37+ZMmuSaKl999X9UVVU1vhYKhZo9/uyzTznzzNnEYpUUF0dYv/4zrr76Cp58\n8rEOzfv666/yox99j+ee+w/RaJQddtiBt96yXHDBeTz++KPNlvnRRx9yxRV/ZKutBrPttkO6YI1I\nRykgExGRQCkuDlO+eYQ3v7DMf+dJPq5Zw2Z9iwiHg3nK2nFHA0BdXR1r1nzU6rzPP7+IU075ITfd\ndDu33TaHkpJSAB57bH6H5r3yyj+SSCQYM2Zn7r77Aa6//lZ++tNzALjmmquaLfO1117hZz87j+uv\nv5Vzzvllx76wdIlgbt0iItIrhUIhSsoKuXDh5fzxv9dy79KH+dWCP/CP1+6lpCyYp6xkoAQQ28To\nx379+jNt2gwABgwYyK677gbAmjVr2j3vp5+u4623VgAwYcJkCgtdTeIBB0wBXA3bihXLmyyzoqIf\nU6dOa98XlG4RzK1bRER6pUgkzPMfvMLb699tMv2pd/7LFzUbKSoKXif0L7/8svHxZpuVtzrv1ltv\n0+T55ptXABCPN7911abmXbt2beNrV131J/bbbw/2228Ppk4dD0BDQwNr137cZBn+9BwSLMFtmBcR\nkV6noaCet9a/k/G1dzd8wJiKUdTWdnOhNmHp0sWAqynbZpttW503WYuV5O9n1tZ5QwVQ0T9KeZ9I\n42vDhg2nX79+m1xGSYluQh5UCshERCQwQvUF7DRgOI+//Wyz13botz11tcG6Icqnn67j2WcXArDP\nPt9sFgBlUzIgi9XGOGXuOdR8WU0DEAIOO+wIZsw4vMs+W7qemixFRCQw4vEE39h6V0YNHNFk+sE7\nTqCsKEpdXXDuubl27cecd97ZVFdXU1JSwkknndKlnxcKQwMQq62isjZGbWk9JVuW0QA8PP9B4nF3\ng/Fly5Zw5pk/5uKL/48NGzZ0aZkke1RDJiIigdHQ0EDVV3Wct99s3l7/Lu9+/gG7DBpF/5IKqr6q\ny2nZVq9ezU9+4nJ3xWKVrFixnPr6ekpKSjj//IvZbrvtu/TzC8NhXEiWsvWUEay67TXeXLaMI444\nhG222QZrLbW1NUyZcjAVFRVdWibJHgVkIiISKLW1CWo/T7Bt6bZsV7YdiUSCyi9z13EsFAoRCoWI\nx6t59dX/Aa5v1lZbDWaPPfbm6KOPa9IBPzl/pmVkWnZLn9d8evOybTasgtGnjKXP4hBvvP4G1i5n\n8OCtmTLlYI455tubXKYER6ihIVjt8fls3bqNWpkiItIlItEC5q54hHkrFjSZPt1MYsaIKcRj9d1e\npoEDyxXlZYlqyEREepFwuIDCCERL3Wi7WFU1dXFIJLr/ZC7tUxeHWWOmEwqFeGrVIgAmDNuHI0dP\nI7Yxt8250nmqIcsi1ZCJSJCFwwVEywu5e8lDLHznOQDGDx3LrDHTiW2sU1CWB4IWUKuGLHtUQyYi\n0ksURuDuJQ81afKat2IBoVCIGSOmkGg9ybwEQCJRTyIG8U3cEUDyj9JeiIj0EtHSksaaMb+nVi1q\nrHERkdxQQCYiIiKSYwrIRER6iVhVNeOHjm02fcKwfYhVVeegRCKSpD5kPUQ4XEBhUZhotBiAWKyG\nutqEOumKSCON0hMJLo2yzKJcjbIMhwuIlkW4/bHlPPHi+wAcsOcQjjtwJLHKuIIyEWkUtFF6kt80\nyjJ7VEPWAxQWhbn9seXMfWZl47S5z6wkFIIjxg3XgVZEGmmUnkgwqQ9ZDxCNFjfWjPk9/sL7jU2Y\nIiIiElwKyERERERyTAFZDxCL1XDAnkOaTZ+81xBisZoclEhERETaQ33IeoC62gTHHTiSUMg1U4IL\nxo6d7Dr1i4iISLBplGUW5fJelkp7ISIi3U2jLLNHNWQ9RCJRTyJRT7y6NtdFERERkXZSHzIRERGR\nHFNAJiIiIpJjarLsIdSHTEREJH8pIOsBdOskERGR/KaArAfQrZNERETym/qQ9QC6dZKIiEh+Uw2Z\niIhIngiHCyiMQLS0BIBYVTV1cdQS0gP0+oDMGDMAWA70Ay601l5ojJkKXALsCLwH/NZae0cOi9mq\n5K2T/E2WoFsniYj0JOFwAdHyQu5e8hAL33kOgPFDxzJrzHRiG+sUlOU5NVnCn4EK73GDMWZb4D5v\n2gVADLjNGLN7jsq3SclbJx06bgfKSosoKy3i0HE7cOzkkdTVJnJdPOmBwuECItECKvpHqegfJRIt\nIBzW4USkKxVG4O4lDzFvxQIqa2NU1saYt2IB9yydR2Ek16WTzurVNWTGmInA8cD9wKFAyHseAc6z\n1s4xxiwCFgHfBX6cq7K2JpGoJ1YZ54hxwznhoFGAqzXTCEvpCrpKF8mNaGlJ4z7n99SqRRyzy0zi\nsVgOSiXZ0msDMmNMCXAt8KD3d6j30q7e/2Xe/ze9/4GtIQPdOkm6j/8qPWneigWEQiFmjJhCQucE\nEZF2681tDOcDA4Ef4WrGkvp5/zem/R/QTeUSCbTWrtKTHY1FJPtiVdWMHzq22fQJw/YhVlWdgxJJ\nNvXKGjJjzGjgbOBMa+1qY0xb3tbQtaUSEel6GqWXv+riMGvMdEKhEE+tWgS4YOzI0dOIbazLcemk\ns3plQIYbQVkPDDHGXArs7E2fBAz3Hpd7//t4/9dtaqEVFVEKC8PZLKdI4FTXxRk/bCzz7IIm0ycM\n24e6+joGDixv4Z0SBPG6GuYseZCFq7z+f8PGctSYQ4gUKmdhvjhyzDSO3WUmALX1dZQURijpp179\n+a63BmSbA8XAz9Km7wtUeo/HAItJ9Sn736YWumGDOs9IzxcOFzBr9HRCZLhK/7KOjQmlWgmqSLSA\nuSseadr/zy4ghOv/F4+plix/pPazjeRun9MFWPaEGhrUEmeMORG4Gfg18HfgLVyN2N+Ak3C1ZrtZ\na99saRkA69Zt1MqUXkHNXvmpon+UU+aeQ2Vt04vHsqIo18+8jA2f6aJS2mfgwPLQpueStuitNWTp\nQnh9xKy1HxtjDgGuAH4DvAPM2lQwJtKbJBL1JGJomH0eGly+JdPMRL422PXUeGX14mbNzyLS/VRD\nlkWqIRORICstC0NhA/9a+ghPeyNlxw0dy+Gjp0JdiKpKJZKW9lENWfaohkxEpJdoAO5f+ggP+/qQ\nPbxiAQWhEDN3nJq7golIr85DJiLSq5SWRFrMIVdaolF6+UC3Leu5VEMmIiKSB3Tbsp5NYbWISC9R\nVR1vMdN7VXU8ByWS9tDNxXs2BWQiIr1ECDhs9FSmmYmUFUUpK4oyzUzk0FFTUM/s4NNty3o2BWQi\nIr1ESUmE3z3zV4b3255rD/kd1x7yO4b3257fPfNXStSHTCSn1IdMRKQXWb1xLVc8d2OTaWVF0RyV\nRtojeXNx/50WQDcX7ylUQyYi0kskT+jpdELPD8mbi083kxqbnKebSRw5ehp16gKY95QYNouUGFZE\ngiw5Su+epfOa34dUo/TyQtBuW6bEsNkTiIDMGHMz3q2LOspa+90sFafDFJCJSNAF7YQu+U0BWfYE\npQ/ZiZ18fwOQ84BMRCTodB9SkWAKSkB2USffr5opERERyVuBaLLsKdRkKSIivYmaLLNHoyxFRERE\nciwQTZbGmP3pfKf+Z7NUHBGRHkud+kWCKRABGbAQF5C1t+oz+Z4GIJztQomI9CS6ObVIcAUlIHu/\nk+9X3y0RkU3w35w6ad6KBYRCIWaMmEJCAy9FciYQAZm1dvtcl0FEpKdr7ebUx+wyU6kw8oCanHuu\nQARkIiIi0jo1OfdsGmXZQ4TDBURKiqjoV0ZFvzIiJUWEw/p5RSRF97LMb/4m58raGJW1MeatWMA9\nS+dRGMl16aSzAlFDZox5B7jRWnuJ73m7+oVZa4d1RdnyQThcQLQswu2PLeeJF113vAP2HMJxB44k\nVhnXVZOIAKmbU4dCoYz3spRgi5aWsKHqC6486EIGlvUHYF3lZ8xZ/BDR0hI1Oee5QARkwHZA/7Tn\n0kaFRWFuf2w5c59Z2Tht7jMrCYXgiHHDFZCJCOD6GcU21jFjxBSO2WUm4GrN1NyVP07d83juXty0\nyfIHex6f41JJNgQlIJsAfJD2vD169SjLaLSYN99dz7nf/gZ7jNoSgJeWreX+Z1ZywkHFxKtrc1xC\nEQkK3csyf8Vr4/xr2fyMo2QP22lqDksm2aBbJ2VRrm6dVNGvjK9itdz71Fs8+ZJrspy0xxCOmDCC\nzaJFbFhfmYtiiYhIFlX0j3LK3HOorG0aTJcVRbl+5mVs+Kz7g2zdOil7glJD1ipjTAioAEqAmLX2\n8xwXKVCqq+u496m3eODZVJPlA8+6JsujJu6Yw5KJiIhIWwQ2IDPGlAKnAbOAMUDE91ol8CpwB3CD\ntTaRk0IGRElJYWPNmN8TL77PiQePoioWz0GpREQkm2oTtYwbOpaHfU2WAOOHjaU2oa4p+S6QAZkx\nph/wDDC6hVk2A/bz/r5jjDnAWvtVd5VPRESku9XXNXD46KmEQrBwldepf9hYDhs1lfo6dT/Kd4EM\nyICLcMHYR8BfgEXAaqAa12y5LfBNYDawF3AB8LOclDQAYrEaDthzSJNRlgCT9xpCLFaTo1KJiEg2\nxavqKSsvYt8h3+CoMYcA8OGXqykKFVFZpRqyfBfITv3GmA9wgddoa+0nrcw3GFgMfGmtHdpd5WtJ\nrjr1J/OQ3fH4ch5/wTVdTt5rCMdOVh4yEZGeJGi3TlKn/uwJakBWDdxprT2pDfPeCsyy1pZ0fcla\nl6uADLydtChMNFoMuFqzutqEgjEREekyCsiyJ6hNlutxzZNtEfPm79USiXoSiXrlHBMR6cGCVkMm\n2RPUgOxJXB+xttgbeKILy5IXVEMmIm2hE3r+0s3Fe7agBmS/BF40xlwBnGutbZa3wRhTCFwMDAB+\n0c3lCxTdy1JE2kIn9Pzmv7l4UjJT/4wRU0jo5gt5LRABmTHmAprf/uhfwA+BbxtjngZWAZW4zv7b\nAeOBvsDVwJHAFd1V3qDRvSxFpC10Qs9v0dKSxkDa76lVizhml5m6HVaeC0RAhktb0ZIK4NBWXj8L\nF8z12oAsGi1urBnze/yF9znhoFHqVyYigE7oIkEWlIDs1lwXQEREJMiqa+ItZuqvrtEdWfJdIAIy\na+13cl2GfKbEsCLSFrGqasYPHdukyRJgwrB9iFW1dWC75EpJcYRDR02hIBTiqVWLAPfbzdjpQEqK\nIlShGs58FoiALNuMMZcDp1trw7kuS3eoq01w3IEjCYXImBhWRASgLg6zxkwnlHZCP3L0NGIb63Jc\nOmmLglABY7f9OrPGTAfggy9WUxBSKrCeIJCJYTvLG515urW2oDs/V4lhRSTolPYif0X7hPnXsvnN\najinm0kcttNUYl8mur1MSgybPd0asEjXSSaF3bC+kg3rK4lX1+oAKyLSg0SKIi0OyogURXJQIsmm\nHtlkKSIizSkPmUhwqYZMRKSX8Ochq6yNUVkbY96KBdyzdB6FqmAJvNpELeOGjm02ffywsdQmlN4o\n3ykgExHpJVrLQ5bsUybBlait5/DRU5lmJlJWFKWsKMo0M5HDRk0lUavazXynJksREZE8UFPdQFl5\nEfsO+QZHjTkEgA+/XE1RqIhKJQDPe6ohExHpJZJ5yNIpD1l+SCTqqalOMKhsC8IFYcIFYQaVbUG8\nSiPqe4JeXUNmjPkW7gblu3uTngbOsdYuN8ZMBS4BdgTeA35rrb0jJwUVEckC5SHLb8XFYYpLC7hr\n8UM87TU9jxs6lqN3nk5DQ5iamu5PeyHZ02vzkBljdgAWA58DfwQMcArwPvBN4C1gDe7m5UcDXwe+\nYa19taVl5jIPmYhIW5SUFFJQ3NCYJiFeGycRDxGPKyALutLyMP96c36zWydNMxM5bORBVG1UHrJ8\n1pubLAfigq1vW2v/bK09FdgAbAt8F4gA51lr/wycDoS86SIieSlZw3LvsvmcMvccTpl7Dvcum08k\nWkBxca+4sUleKymONNaM+S1c9RwlxRomm+96bZOltfZ54HljTNgYszUwCagAngV28mZb5v1/0/u/\nOwGlTP0isinhCNy1+KEmNSwPr1hAKASHjTwIdOtbkZzpyTVkbW0+3Bn4ALgZuB+YDvT3XtuY9n9A\n1kqXReFwAdGyCPcsfJvjL3iU4y94lHsWvk20LEI43JN/YhFpD9Ww5Ld4bbzFPGTxWt23ON/15Bqy\ntrZrvw0cDOwLnAc8BrS0Zbca5FVURCks7P5q/+p4Hbc/tpy5z6xsnDb3mZWEQnDs5JGURHryzywi\nbVVX33ofo4EDy7upJNIRdfUJZu40mVDIBdHggrEZIycTKYpQNlDNzvmsR56prbVnAGdsaj5jTBEQ\nt9Y+AjxijNkT13T5uTdL8ujUx/u/rrXlbdgQ61iBO6miXxlPvPh+s+mPv/A+Jxw0inXrNmZ4l4j0\nNqXlYcYNHdusU/j4YWOpronnpFO4tF1F/yiX/ftaDjYTGvOQvbz6DS7797VcOPGsnBzrFcRnTyAC\nMmPMQtrexJiRtXZCOz/zYuCXwFnA5d7k5Pr4Ey4dxhjcSMxdven/60wZRXqKcLiAwgiN2d1jVdXU\nxVGfxYCrq4Zjdj6Eb273DbbpMxhwiUW37TOY2FcaZRl01TVxdhwwjCueu7HJ9GlmItU1arLMd4EI\nyID9c/CZdwPnAOcbY0px/cP2B1YDtwE/B37jdfg/CagFrs9BOTcpFqvhgD2HNGmyBJi81xBiMfXS\nlezSDarzV319A/UNDfz3vf81++3q65W1J+jqquHonac3a7I8asx0qr5S7Wa+C0QeMmPMdzq7DGvt\nLR343HHApbgasI3Ai8C51tqlxpjxwBXASOAdb/oDrS0vV3nIkp3673h8OY+/4JouJ+81hGMnjyRW\nGdcJUrIqEi1g7opHmJfW7DXdTGLGiCnEY9regkq/Xf4LWh455SHLnkAEZD1FLhPDFheHCRcWUlLi\nKj2rq+uoq62jtlZXTZJdFf2jnDL3HCprm/aZLCuKcv3My9jwWW76Usqm6bfLb0GsnVZAlj1BabLs\nMGPMH4F9rLX75LosuRIOFxApKeb2x5Y3du4/YM8hHHfgSOrrVUMmItITFEbg7iUPNanhnLdiAaFQ\niBkjppBQPJ3XAh+QGWP6AH1pnsYiBGyDS1mxfTcXK1AKi8Itpr04YtxwBWSSVckbVKc3e+kG1cGn\n3y6/RUtLGmvG/J5atYhjdplJPKaILJ8FNiAzxhyAG+04upXZkkHa0q4vUXBFo8Wtpr2IV9fmoFTS\nU+kG1fkrVF/AUTtPh7RO4bPGTKOmqh7QxZtIrgQyIDPGfAN4mE2X71PgFdyISBHpBolEPbGNdcwY\nMYVjdpkJuJoXjbAMvlBRA4++9QzD+23fJI/Vo289zcSh+4EqyQJNNZw9WyADMlw6ikLgEuA6XCqK\nBG7U41nADsAPgQNxNwB/LUflDASlvZDulkjUk4ihJpI8U1IcYe6bj2Xs1H+wmUQV+j2DTLXTPVtQ\nA7J9gRestf+XnGCMAcBa24C73dFZxpi3gCeNMXtaa1dmXFIvUFeb4LgDRxIKkTHthYiI5D/VTvds\nQQ3IBgIPZpjepLzW2muNMT8BfgGc3B0FC6JEop54dQ2zJuzICQeNAlzai+qqGu2k0iWUqT8/VdfE\nmbnTgWxR1p+vDd4ZgFdWL+aTyk+V6T1PqHa65wpqQFYLFKdN24jLpp/uv8DELi9RgIXDBZSUFvP+\n2pwuEuMAACAASURBVI0M2dLddnPN+kqGbFlO/VdKeyHZFcRcSNI29TUhpozYn7sWP8Tf/3cHAOOG\njuXonacrKaxIjgU1IFsJTDDGRKy1ycu2dcA3M8wbAbbqtpIFUHGkkNq6ep555SOefMmdICftMYSj\nD9iR4kghVepHJlmkXEh5LFzPnMUPN7m5+MMrFlDg/XYSfKqd7rkKcl2AFjwCbAcsMsbs6017GdjG\nGPMrY0wIwBizK3AI8FluihkM4XABdz2xggeeXUllVS2VVbU88OxK5jy5gnA4qD+x5KvWciElTxIS\nTPrt8luydnruikc4Ze45nDL3HOaueIRoeaGO9T1AUH/BP+FGVu5OKg/Z37z/FwGfGWPexaW8KCdz\nf7Neo6gozJMvNc9D9sSL71NUFM5BiUREJNv8tdOVtTEqa2PMW7GAe5bOozCS69JJZwUyILPWfgLs\nCfwBsN60Z4DTcf3LNgeG4BLDPoTykIl0m2QupHTKhRR88boaxmX47cYPG0u8Tl0bgk41nD1bUPuQ\nYa1dDZybNu1qY8wcYCyu7MustctzUb4giVXVtpyHrEpZ+iW7lAspf0UKi5m502RCaZn6Z4ycTKSw\nmBj6/URyJdTQ0JDrMjRjjPkW8KG1dlUb5v0+UGOtvaXLC7YJ69ZtzMnKDIcLiJZFuOPx5RnzkKmz\np2SbOhbnp4r+Uf76/C3M2nk6A8v6A7Cu8jPmLH6I2Xt/hw2faURGkEWiBcxd8UizTP3TzSRmjJiS\nk5GyAweWp99nWjooqAFZPXCFtfbMNsw7H9jNWju460vWulwFZOCdIIvCRKMuW0gsVkNdbUInSBFp\nVL55MXUNdfxr6SM87TV9jRs6lsNHT6UwVMjGz9VsGWThcAFl5UV8uHEN2/RxyQU+/HIN25RvReXG\n2pwc7xWQZU9gmyzbwhizHTAGqMh1WXItkaj3EsSqiVJEMkvUJ7hv2SMZ014cttPUHJZM2qqBBv77\n3kvNcgBK/gtMQObVivlrmM7wsvBvSghY1jWlEhHpOSJFkRY7hR+zy0xiupdloBWVhFrMAThzx6kk\nKnNYOOm0II2y/D2wEJeRPynUhr93gR90Z0FFRES6W2lJywF1aYnyXuS7wNSQWWvPA/CSviaAm3E5\nx1prn95ore3VSWFFRNqqOh5n/NCxzTqFTxi2D9Vx3csyHwwu35JpZmKTe5HOsws28S7JB0Ht1H8L\n8Ji19s5cl6U9ctmpX0RkU6J9CmkI1XPfskeapL04bNRUQg0FxL5U2osgC+KgDHXqz55ABmT5SqMs\nRSTIKvpHuWDBnzjYTODrg3cB4OXVb/CwfYoLJ56ltBcBV9a3kHuXPpwx7cXhow6i8ovuD6gVkGVP\nYJosMzHG7AJ8D9gbl5m/FPgKeAd4Gvi7tbb5PYN6mWQestsfW84TL7rVccCeQzjuQOUhE5GmVm9c\nyxXP3dhkWllRNEelkfYoLizGfrqKn479XrMmy+LCYiqV2DevBbaGzBhzDvBbWh94EANOCUrTZq5q\nyCIlRdyz8O1mmfoPHbcDR4wbrlQYIgJAaXmYBe/8hy3K+jc5oX9S+SkTh+5H1cZEjksoranoH6Wy\nppK5bz7epMly5k6TKSsuy0kNp2rIsieQAZkxZgLwJC4NxgPe4/eAaiAKDAOmAAfi7m25p7X29dyU\nNiVXAVlFvzJ+/tf/MPNbO7DHqC0BeGnZWu5/ZiW/n/1NNqzXWGgRgUikkEi0gLsWP9TkhH70ztOJ\nx+qJx1XDEmTRPmHuXTa/SR45gGlmIofvdBCxL7s/oFZAlj1BDcjuAw4BDrbWPtbKfEcAdwP/tNae\n0F3la0kuA7KvYrXc+9RbPPmSa7KctMcQjpgwgs2iRQrIRARwt95Z+P5/m9WQrav8jHFD9s3JrXek\n7Sr6Rzll7jlU1jatCSsrinL9zMtUQ5bngtqHbG/g360FYwDW2nuNMc8B+3dPsYKpurqOe596iwee\nTTVZPvDsSkIhOGrijjksmYgESbS0hHFD92bum4/z9//dAaSavKLFJcRj6tQvkitBSgzr1x9Y0cZ5\n3wS27MKyBF5JSWFjzZjfEy++T0lJUGNuEelutYla5r75OA+vWEBlbYzK2hgPr1jAA8sfpzahvqZB\nF6+NM27o2GbTxw8bS7xWeeTyXVADshgwqI3zVgDaEkVENqEoXNTYd8xv4arnKAoX5aBE0h6RoghH\n7TydaWYiZUVRyoqiTDMTmTVmOpEiZerPd0GtPlkG7GeMGWCt/bSlmYwxA3DNlUu6rWQBFIvVcMCe\nQ5qNspy81xBise5PFCgiIl3jnsXzmLHTZI7d5VAAKmsquXvxPI7ddWaOSyadFdQasjuAzYFnjTHT\njDFNLt2MMSXGmEOB/wD9gH/moIyBUVeb4LgDR3LouB0oKy2irLSIQ8ftwLGTR1JXq2HsIuJU17hb\nJ6WbMGws1TVqaAi6ukQdR4w5iER9avBFor6eI0YfRF1CI2TzXVBHWRYDjwPf8ibVAWuAKlzai62A\nsPfaE7jRmDnfGnOZqb+4OEy4sLCxz1h1dR11tXX8f3v3Hh9leeZ//PPMITOZCAqIWm2jAeGGAPYE\nAh45xqCkUFHwVG37a7vdbre7Paxdf9tt1562utt9tbv97Xbr1na39gCesGBpUVC0WhVs13K8Q0FB\niwdEKmEmM5nT749nEjPJJCQ65Hlm8n2/XrwemMkzueEh81xz39d9XWkFZCJSEA4HqT0hyMpta4pa\nJ62Y2kL70azeL3xu5EkR8k6elT3KlqyY1oKTdzjyp6EPqrXLsnx8GZCBOwsGfBH4M9w8sZ5eBf4V\n+IYfgjHwLiBTpX4ZasFggFDE3bUHkGhPkkmh/2sVIBoNEajJd+UcpdIpsilHNcgqQKzQOqlkHbLG\ny0iodVJF82VAZoy5GHjeWru3sFw5FWjAnR2LA3uAbdbanDHmY0CHtfaHng24wLNK/bU13Llxd+lK\n/XMnkGpXHpmUTzAYIDYixKpta3io8Cl9bsNslk9tIdGWUVDmY7p2lU11yKqbX5P6HwK+BXzGWpsG\nflf4VcpS4F3AD4dmaP4Tqw13zYx1t/7J/Vx/aaMCMimrcNRh1bY1RQ2O17ZuwHEclk5cRFZ1iH0r\nFKHPa7dkQjNZlSET8Yxfk/oHxBhzJu7sWaklTRE5Dmqjka7Zle427n2c2qi23vtZrDba57XrXH4W\n/0r2U4csqTpkFc83M2TGmBxu78pOf22M+asBnOrglskYttLpLAtm1BdV6gc3j0xJuiIi1SGXcrhq\nWguOQ69NGWp7Vfl8E5ABtwAzgOnAyMJjA1mbfg74+HEaU0XIZnNctXAijkNRUv+KBROVEyJll86m\nmdMwu1di8dxxs1Xt3edSmY4+r10qo9QG3wvmeGzfb1k4/sKuOmQH46/y2L4tTD/tXR4PTt4q3wRk\n1tqbAIwxDpAFfgB8mf6DsjZr7aEhGJ6vdaQy1J0Q4aJ3ncF1zZMB2P/SEcKhAPGjmsaW8sqmcyyb\nsqjXp/TLGxeRTesDgJ9FQjUsndzU69otmdREJFRDAu209LNYbZQfPXNPyaT+ixpmqRdphfNNQNbJ\nWps3xvwP8KC1dp/X46kE2WyO+NEUp46qJRh049dTR9USP6qSF1J+Hck8dSPCnF8/nRVT3wfAC0cO\nEHbCxJOaIfO7Wx/9LpeZeV3X7ukDv+fWR7/LzfM/6/HIZCBOH3Eqi8183nP6NAB+e2Ara+2GY5wl\nlcCXZS8qlZeFYUWGkuqQVabYyCB37/hF0S5LgBazgMsnLyJxRDmnflY3MkTOyXH39nVFhWGXTVlE\nIB8gfkR1yCqZ72bI5M0JBgOEwkFisRrA7W+ZSWd1g5TjIpvNkU2gJZIKEwlHWNrYjOM4bNz7OADz\nxp3HksmXEAlHSKDr6WdOwOGebeuKcgDvb91AwHG4YsplHo5MykEBWRVQpX4Zapohq1w/eHoly6e1\ncPU5bjPqg/FD3P70Sj4564PeDkyOKRwMY1/dy6dnf6TXkmU4GAaUMlDJtGRZRp5V6o+GufOhP5Su\n1D/nbFLK65EyUrX3ylVbF8QJwd3bf1F07ZZNuZR8BtrjWrL0s1FjYrSnk9y7Y13R9Xt/4yJqw1FV\n6q9wmiGrArFYDTufe43Pf2A6MxpPBWDzjpe5d9Merr+0RgGZlJWqvVcwBx7c82smjGlgxTQ3qf+3\nB7ayYc+vmXfWBR4PTo4lleng3h3rSv7sXd54qYcjk3IY1gGZMeadwDeB9wJJ3JZNf2Ot/aMxZhHw\nVWAisA/4urX2J54N9hi+8KGZ3LVxN9+56xkAFsyo5+8/PNPjUUk16q/a+9XnLFVemY9FIxHmjTuP\nl4++2vXY2LoxTD3VEA1HaD+qa+dnkVBNvz97KltS2YZtQGaMGQWsB2qBrwNnAn8G1BtjrgLuAV4E\nvgRcBfzIGLPTWttXT03PJJMZ7t64u6hS/32P7CHgwPL5Ez0cmYj4SSabwXEcHtu/pdcuvUxWN3MR\nL1V0L8u3aBrQCnzFWvsNa+2fA38CzgM+DESAm6y1/wJ8CrdA7Ye9Gmx/otEQD2wu0Vz8qf1Eo8M2\n5pbjJNGeZG6Jfnrzxp1Hoj3pwYhkoPLkuXu7u0svnk4QTye4v3UD9+xYRx7lE/tdZ5eMntQlozoM\n27u1tfYR4MLOPxcalY8EMrjBGrzRI3Nn4fjuIRugiE9lUrB8akuv0glXTllMok2zLH4WDoa7Zsa6\ne2jvbwqteHRT97NcPt9nl4ycNuhVvGEbkHVnjDkRuBt3xvAHQH3hqbYex5OHeGgDkk5nWXhufa9d\nlk0z1Vxcyi+bzZFoy7BkQnNX6YREe1I7LEWOs2wSorEw53XrkvH8kQPUBMIkE3qvr3TDeckSAGPM\nycBG4D3AE8Bn6Lt/pi8/gjgOrFgwkaUXj6euNkxdbZilF49n+Xy34bhIuWWzOVKJHIcPJTh8KEEq\nkVMwVgHS2XSfy81a8qoAwRy/3reZunAtwUCQYCBIXbiWX+/bDEH9/FW6YT1DVpgZewiYAtwHXGut\nTRhjOhuWjygcRxaOB/t7vVGjYoRCweMy1v5ksjm+8J+/ZslF47mueRIAT+14mS/d9gS3fPICxo4d\ncYxXEJHhIJlOsWzKpb2Wmy9vXISDo/cKn8vmsn02F7+4YRYj64b+/iPlM6wDMuCnuMHYd621n+j2\n+BZgBTAV2Aq8s9vjfTp82Jst46NG1/HCK0e59UfFw6urDQNw8GBbqdNEZJgZcVINjgOz69/L8qkt\ngLvk1UnvFf42akysz+fyeHP9FMSXz7ANyIwxLUAzEAeeMcZ8pPBUHvgx8A/A14wxZwAfws12vc2D\noR6TcshEZCBCwRDJTLYoJ8PB3X0ZCoaADo9GJgPRucvy/h7N4bXLsjoM24AMN2cMIAb8R7fH88Bc\n4H3At4CvAc8Cy621O/Ehx4GrFxouevcZ1J/qrq7uf/kIp59c59scMvVCFBl67ckUG/f9mrF1o3n7\nyNMBePnoq2x/uZW5Z6pSv9+Fg2GWTm7qtctyyaQm9bKsAuplWUZe9bIcNbqO9mSGVRtai5qLL58/\nkdpoiMOvxb0YVp/UC1HEG8FggLoRYV5oe5G3j3wbAC8ceZG3j3gb8ba0fvZ8LhIL8ND+xxhbN5r3\nnn4OAE8f+D2vxl9jTv35pBJDf/3Uy7J8hvMMWdVIp7Os2tBatGS5etOerpkzv1EvRBHv5Mnz2L7N\nvT4Mif9lUnDJ2Rdz5/a13Lblp4BqAFYTzZCVkZczZNd96ZfE24unq+tqw9xxc7PvZshGjYnx0dU3\nltwpdNvSWzl8SBGZyPEQrQuy+cXfMfmUCYytGwPAwfghdr6ymxlvezfJuHJO/SwYDHDCiBqOpuOM\njLrJ9EeSbZwQjnHUoxlOzZCVj2bIRESGidpohPPOnM7KrWuKelmumNZCKBAiGdeHIT+riTpkSzQQ\nz+Sz1EQd2v312VsGSQFZFUinsyyYUV/UXBzcPDI/7rLs7IW4tsdOIfVCFDm+UpkOHtzzKBPHNHDV\nNLfS+28PbGX97oeZP/4ij0cnxxIOh8DJs9Y+WLzkPK2FYNihHf+938vAKSCrAo4DVy10q/J3T+pf\nscCflfrVC1HEG5FQDXMaZrF653q+t+UngDtDtnRyE5FQDYkSsy/iH9l8lru23V8y/3ZZ46UejkzK\nQTlkZeTpLstUhpdejVN/WqHsxUtHOG1MnS93WYLKXoh44YSTwqzatrZXHavFZj5XTlnM0T+pbIKf\n+TH/Vjlk5TPse1lWg45UlqDjFDXazAOBgPucH6kXosjQCwfDXblj3T209zeFOlYi4hUtWVaBXD7H\nygd396rU//4541k+b4JHoxIRkXJSpf7qphmyKhCNhrtyx7pb/+R+olF96hURVyrTwdyG2b0enzdu\nNqmM2ib5nYPDsimLWGzmUxeOUReOsdjM72oOL5VNM2QiIsNEJFTDimkt0KP1zvKpLQQDISX1+1wo\nGCKTyXJ+/XRWTHV3yb5QaA6vXqSVTwFZFUins1wxbwKnjY4xo/FUADbveJmXX0v4suyFiHgjlU7x\n5Au/Y+H4C7nmnPcDcDD+Ko/t28zMt7/nGGeL1zLZDAEHuu/Fy+fdmbNMVsF0pVNAVgXyeXjfhQ20\nJdIEg+4q9OSG0ZzbeCqZrHbRiogrEo7wo2fuKblL76KG2SRQYVg/y+Zz3L39F71qOLaYBVyushcV\nTwFZFXAcyOXh3of38OBmN5dswYx6rls0yZd1yKRYMBggFA4Si9UAkEh0kElnfb3rVGVLRIZeJFTT\nVRC2u417H+fqc5ZqybnCKSCrArl8np+us0WV+u97ZE9XwVjxr2AwQKwuwo9/tauoqO+1l0wiEU/5\nMsAJBgPERoRYtW1NrwbVibaML8csrlQ61ecuvVQ65dGoRAS0y7IqRGpCXTNj3T3w1H4iNYq5/SwU\nDvLjX+1i9aY9xNvTxNvTrN60h5+s30UoHPR6eCWFIrBq2xrWtm4gnk4QTydY27qBO7evJRTxenTS\nn0g4wtLJTb126S2Z1EQkrIvnd6lMB3NK7JKdq12yVUF3axEPxWI1fZYsuf7SRlJJ/9UWitVG+102\nSSWUh+RnP9/5AEsmN3Ul9cc74ty38wGueedSj0cmxxIJ1XB5YzMXnDmdt488HXB3WZ5Sd7JaX1UB\nBWRVINmR6bO5eLJDP6Ai4spmsyyf1sLzrx8gGnLz/w7GX2PFtBayWe3IrgR54LF9W4rSBZY2Nns7\nKCkLBWRVIFoT6krgL8pDap5EKBigHeWG+FU6ne0zmPZryZJEe5Klky/hlLoxvOf0aQD89sBWDsYP\nkWhPejw66U+WHLlclsf2b+lqoTSnYTbLpiwi4PhziVzekEqnuHfHL4tyANe2bgAHlk3WLstKp4Cs\nCqTTWR595gCLZp/FDZc1AvDKawke+e0fufBdp3s8OulPOBzkinkTegXTy+ZOIOzTHLJ8xqF5wsX8\nbOsavrflJ4B7U79qWgvJuD+DSHE5wN3b1xXd0O9v3YDjwJVTFns3MBmQSDjSZy/Sa855v8qWVDgF\nZFUgHA5y+5rtxNuL843qasPMm/EOj0YlA5FIdLBhy/NMrB/Fdc2TAHhqx8ts3LKfBdP9ee2cUJ6V\nW9f0uqkHHIclE5pVLNzH+msu7uaU+S9nUWS40C5LEQ9l0lkuO6+B3c8f5oYvr+eGL69n9/OHufS8\nBjI+XbLsL6m/sy6ZiJRfZ3PxntRcvDpohqwKJBIdLDy3ntWbivOQmmbWk0housLPstkciXiKK+ac\nzfWXusvNiUSHb2uQSWVLZ9PMbZjdq9L7vHHn6YZeAcLBMCumteCU6EUaCoTQDGdlU0BWBTLpLNde\n4ib1r3/SzUNqmlnPNU1ucVHxt2w2Rzab82WJi1IS7ck+b+pK6vc3B4crplyG4zhs3Ps44F63ZY2X\n4qC2Hn6XyqR48vnfsXD8Rd16kR7i8X2bmfkO9SKtdE4+r16H5XLwYJsn/5jBYIC6EyLse6mNM08b\nCcC+l45w5mkjiB/VTIuUV2el/ju3ry26qV85ZbEq9fvciJNqyJOjPZ1iZHQEAEeSbURDEQJOgLY/\naUbdz0aNiRHviLN65/qiXbJLJzdRV1PH4UNDn9Q/duwIRfJlooCsjLwKyCK1NTyweT+njooxo/FU\nADbveJlXDidYMKOeVLveZKW81MuyMo0aEyORbmf1jl/2qmMVC9d6ckOXgTvhpDCrtq3t1fpqsZnP\nlVMWc/RPQz/LroCsfBSQlZFXAdmo0XUcTaS5a+PuoubiV8ybwAmxMIdfi3sxLBHxmRNOCrP+D4/0\nqiH3SvxVms6+2JMbugzcqDExPrr6RuLp4sC5LhzjtqW3aoaswimHrAqk01nu2ri7ZHPxa5qMhyMT\nET8JB8PMaZjF6p3ri2rILZ3cRDgYRknhIt5RQFYFwuEgu/a9xuc/ML1oyfLeTXt8W1xURIZeOptm\n9c71KgxbobRLtropIKsSX/jQTO7auJvv3PUM4C5Z/v2HZ3o8KhmImpogwVCIaNT9cUwmM2TSGd+2\nTpLKpcKwlc3B4fIpi6BH2Yv3NzZrl2wVUEBWBZKpDHeXWLIMOLB8wUQPRybHUlMTJFJbwx3rdhXl\n/123aBJOewcdHQrKpLzedVojy6e1MLZuDOCWTVi5dY3Ho5KBCAVDfGXDt7nMzGPF1PcB8PSB3/OP\nm/4fN8//LGqTUdkUkFWBaCTEzj6WLKOREO2qReZbwVCIO9btKpn/t2L+RFBAJmWUzWb5s3OvY+XW\nNUVlEz5+7nVks/q/VgkOtL3Mt37z/aLH6sIxj0Yj5aTWSVXiCx+aya59b7Tf2bXvsJYsK0A0Guqa\nGevugaf2dy1hipRLJp9hVaEPaTydIJ5OcH/rBu7ctoZMPuP18OQY1DqpuukdvwokO/pZslyoJUsR\ncUVCEeyre/n07I8Ulb1YazcQCUVIoDpkfhYOhnl/YzOBHp0Wlky+RLtkq4ACsioQrQnxQIlZlvVP\n7ef6yxppR0uWfpVMZlgwo74omAZYeG49yaR/ZyxUGLZy3Xjhx3uVvbjxwo97PCoZiEw2QygQYPY7\n3svyqS0APP/6AYJOgEzWv+8XMjAKyEQ8lEln3AR+x12mBDcYu7Z5EkmfNobvbJ20atuaomrvy6e2\nqHWSz6UyHX2WvVjWeJmHI5OByJPnrm2/6FX2osUs4Iopun6VTgFZFUins33Osqh0gr+l01mCqTQr\nFkzkhssaAXfXbEcy7dtrF4rAqm1rim4Ka1s34DgOSyY0k9Wql29FQjX9lr1IoFkWPwsHw10fgrrb\nuPdxrj5nKVqyrGwKyKpAOBzkinkTes2yLJs7QYVhfS4YDBCuCfPjX+0qniG7ZBKZTM6Xs02x2mi/\nN4VUQhGZiMhgaZdlFUinszy4eT8T60fx319s4r+/2MTE+lFs2Lzft7Ms4gqFg/z4V7tYvWkP8fY0\n8fY0qzft4SfrdxFSMC1llsqkmFtil968ceeRyijX1O+0y7K6KSCrAuFwkMXnN9C6/42yF637D3PZ\n+Q2aIfO5WKyma2asu/VP7icWq/FgRMeW7Ej1eVNIduim7mdOLsCKaS20mAXUhWPUhWO0mAUsn7oY\nJ6fbgd9l0zmWTVnEYjO/6/otNvO5vHER2bT/ZtNlcLRkWQWSqQy/fGIfE+tHcV3zJACe2vEy9z/2\nLJfMOtPj0Um1idZEuLyxmQvOnM7bR54OwAtHDnBK3clEwxHaVTrBt2pqwmRz2V679MChpiZEXDlI\nvtaRzFM3Isz59dO7KvW/cOQAYSdMPKlrV+kUkFWBaCTEXRt3E28v/oGsqw2z5OLxqtTvY4lEBwvP\nrWf1puINGU0z60n4dJclQB54bN+Wol2WSxubvR2UHFMq08Fd2+8v2mUJsNjM1y7LCpDN5oi3pRkb\nGUsw4K5+jI2MJd6W9mW+qQyOAjIRD2XSWa69xC17sf5Jd+myaWY91zRNIuHTQDrZkeLenb8suqmv\nbd0ADlw+6VIPRybHol2WlS+bzZFNoM0zVUgBWRVIdfRdXDTVoTdYP8tmcyTiKa6YczbXX+qWvUgk\nOkjEU779xButifR7U9eSpYjI4CkgqwKRmlCfZS8iNSESqtTva9msW94ipRwQOc5SmRRLJ1/CKXVj\nilonvRJ/VbssRTymbTVV4qs/eLJX2Yuv3P6k18OSKpRoT/ZZOiHRnvRgRDJQkVCE5gkX03roWT7+\n85v4+M9vovXQszRPmEMkFPF6eCLD2rCeITPGOMAngW8AtcBZ1tr9hecWAV8FJgL7gK9ba3/i1Vj7\nk0xlaJp5JuPPOJFg0I2xx59xIs2zziSZ0pKllFcmBcuntuD0aHB85ZTFJNr0/83PUpkO7u6R1H9/\n6wYCjsPljcr/qwSxWBhCOSJhN4BOpVPk0wHa2zXDXumcfD7v9Rg8YYwJAuuBC4EEMAJosNbuN8a8\nA2gFXgS+A1wFvBeYbq39XV+vefBgmyf/mKNG15HJ5rhj3S4eLDQZXzCjnusWTSIUDHD4tbgXw5IB\nCgYDhMLBrrpjiUQHmXTWtzlkoObilWrUmBgfXX0j8XRxnl9dOMZtS2/l8CHl//lZLBYmFHH42dY1\nXXmccxpmc9W0FjKpPInE0AdlY8eOcIb8m1ap4TxDVgucBMwAvg1c1O2564AIcJO1dqUx5nHgceDD\nwF8O9UCPJdWR4afrW4uS+u97ZA+OA1ctnOjhyORYgsEAsbpIydZJfk7s106vynX6iFNZbOYX5ZCt\ntRuOcZb4QijHL3f/moljGrhqmluH7LcHtvLL3Q+zYNyFHg9O3qrhHJDFgRnW2pwxpudz7ywcdxSO\nOwvHdw/FwAYrUhPqmhnr7oGn9nPDZY1K6vex7q2TOq3e5AbTV8w527cBmVSmjmyamy7+C+7evo7v\nbXEzMOY0zOami/+CDrXe8b1IOMKchlms3rm+6PotndxEJBwhoR3OFW3YBmTW2jxufctSRheOyc0U\nVwAAEL1JREFUbT2OJx/XQcmw01/rpOsvbdTOSymrfD7PPdvXKYesQqWzaR5+9oleM2QPP/sbms6+\n2OPRyVulXZaD48uEu2ShDllPC8+tJ6k6ZCJSEAnVdHVX6G7j3seJhPzZO1XeEA6GmdMwq9cu2TkN\nswkHw14PT96iYTtDdgyHCscRhePIwvFgfyeNGhUjFBr6Zt6ZbK7POmTRmhAnjNUbrV8lU5k+Wydl\nMjnGjh3Rx5kig5fNZft8zgH9f/O59nSS1TvX95rhdBy4cspiXb8Kp4CstC3ACmAqsJU3csq29HfS\n4cPerN+PGl3HV3/wJEsuGl/UXPwrtz/JLZ+8gIMH247xCuKVYDDQb+uktiPtHo9QqkntiCBzGmb3\n6mU5d9xs2jtStLf1HbCJ90aNifXbJcOL93oFgeUzbAMyY8yJwN8U/thQOH7OGPM6cD9u0v/XjDFn\nAB8C0sBtQz7QAUi0p5l81mhu/VFxvPj+OeNJqDaNr1Vi6ySpXNGaCEsnN+E47k0c3GBsyaQmojUR\ntb0S8dCwDciAUcD/5Y28sDxukdg8sBt4H/At4GvAs8Bya+3OEq/juUxHpuIaVMsb1DpJhtKtj36X\ny8w8Vkx1k8KfPvB7bn30u9w8/7Mej0yOJdmRYm7DbNb2mOGcN242yQ6911e6YVsY9njwqjAsQDQa\nwgkGiUbcGDuZypDLZEmpUr/vVWJhWKlMkViA1a3ret3QW8wClkxoJpXQ/zk/i0RCRGIBVm5bUzTD\nuWJqC6lEzpP3exWGLR8FZGXkVUBWUxMkGqth/4tt1J/m7j/Y/9IR6k8bQbK9g44O5YX4VaUWhpXK\nFAwGiI0Icef2tSXbXun/m79FYgG2vPS/TD7lbMbWuVWYDsZfZecrf2D6ae/yJKBWQFY+w3nJsmqE\nw2HS6RybfvdHHtzsfmpaMKOeqxZOJBwOKyDzMRWGlaGUzeZItGVYMqGZq89ZCrhtrxSMVYZYbZQf\nPXNPydZXFzXMUueMCqc6ZFXACcDPHnBbJ8Xb08Tb09z3yB5WPtiKoyvsa/0Vhu1cwhQpp2w2RyqR\n4/ChBIcPJUglcgrGRHxAt+sqEA4H+2ydFA4PfV00EREpv0R7krkNs3s9Pm/ceSTakx6MSMpJAZmI\nhxKJDhae27vLQtPMehKJDg9GJNUuGAwQiQUYNSbGqDExIrEAwaBuBZUgk4LlU1toMQuoC8eoC8do\nMQu4cspiMtpkWfGU1F9GXiX1R2M1/GbbS0wdN4ZTRscAeOW1BNv3HmLW1NNI6sbuW51J/T9Zv6tk\nyRItJUk5dSb1r9q2pquF0tyG2Syf2qI8sgoRDAYIRdx8MnBnzTIpPLt2SuovHwVkZeRZQBYNEY6E\nuWPdrq6lywUz6rlu0STSqTTJpEpf+JnKXshQUdmLyqeArHppl2UVcAJB7li3i/seeWOn3n2PuDv1\nVsyfCCgg8zMVhpWhEquN9tlc/OpzlmqXns9phrO6KXGgCkSjoT6T+qNRxdwiItUgFIFV29awtnUD\n8XSCeDrB2tYN3Ll9LaGI16OTt0oBmYjIMJHOpplTYpfe3HGzSWc1Q+t3/c1wdi5hSuVSQFYFkskM\nC2b03qm38Nx65Y+JSJdwMMzSyU0sNvO7duktNvNZMqmJcDDs9fBEhjWtZ1WBTDrDdYvc5uJF7Xea\nJ2mHpYh0SbQneXj/E5w9+qyi5uKbnn2COfXnezw6OZbOOmS9m4urDlk10C7LMvKyuXhNTZBgKNSV\nM5ZMZsikM6TTapskIi71sqxsfrx+2mVZPgrIysjLgExEZCD8VjZBBsdv108BWfloyVJEZBjJZnNk\nE6jERYXS9ateCshEZND89ildRKTSKSATkUFRcUoRkfJTQCYig9K9OGWnta0bcByHJROayWolRURk\n0FSHTEQGRcUpRUTKTwGZiIiIiMcUkInIoHQWp+xJxSlFRN485ZCJyKBkUrB8aguO45QsTikiIoOn\nwrBlpMKwMlyo7IWIgArDlpNmyERk0FScUkSkvJRDJiIiIuIxBWQiIiIiHlNAJiIiIuIxBWQiIiIi\nHlNAJiIiIuIxBWQiIiIiHlPZCxGRYUQ15ET8SQGZiMgwEQwGiI0IsWrbmq4G8XMbZrN8aguJtoyC\nMhEPKSATERkmQhFYtW0Na1s3dD22tnUDjuOwZEIzWdX5FfGMcshERIaJWG20a2asu417H+9awhQR\nbyggExEREfGYAjIRkWEi0Z5kbsPsXo/PG3ceifakByMSkU7KIRMRGSYyKVg+tQXHcdi493HADcau\nnLKYRFvG49GJDG9OPp/3egxV4+DBNv1jioivqeyFlNPYsSMcr8dQLTRDJiIyjGSzObIJSCW0pVLE\nT5RDJiIiIuIxBWQiIiIiHlNAJiIiIuIxBWQiIiIiHlNAJiIiIuIxBWQiIiIiHlPZiz4YY84G/h2Y\nDhwBfgbcZK1VrTEREREpK82QlWCMcYB7gYuAbwJPADcCn/JyXCIiIlKdFJCVNguYAvzAWvs14APA\nUeAjno5KREREqpICstLeWTjuALDWpoE9wCRjTI1noxIREZGqpICstNGFY1u3x9pw/71G9/5yERER\nkTdPAdngOICS+kVERKSstMuytEOF44huj40EssBrfZ2krvciIiLyZmiGrLQtheNUAGNMLTAB2FrI\nJxMREREpG82QlWCtfdoY8zRwgzHmeWAmUAv8h7cjExERkWqkGbK+LQM2AX+HWxz2S9ba27wdkoiI\niFQjJ59XjrqIiIiIlzRDJiIiIuIxBWQiIiIiHlNAJkWMMf9gjMkZY27weixy/BWu9bNej0NEZLjT\nLsthxhjzHFDfz5fsKxyVXFhBjDFnAXv7et5a29eHr49Q3JFChpgxZhTwRWAJ8DbceofPAP9irb17\nAOefCzwBnGWt3X88xyqut3rNREpRQDb83AicgNt14PPA2cA/Abbw/DzgzHJ+Q2NMwFqbK+drSp9e\nw73G/eq8Jtba24dgTNK/+4ALgJXAo8AZwF8Bq4wx8621Dx/j/MWFowpTD523es1EelFANsxYa1d1\n/t4Y8yHcgOx+a+0jhcc6Z89ONMbcA1wC/AH4qLX2qW4zMZustXML53wQuB34sbX2A4XX/T7wbWAa\ncFbh+8jxlygVZBljbgb+vvDrCuAwMM8YkwP2WWsbhnaYAmCMORX3xn7EWnt1t8d/D8wCcsaY0cA3\ngWbcD1NbgE9aa7cbYx4GLiqc9qwx5mZr7c1D+XcYbgZwzfLdrkvXrGXhZw3c+24ed1btJWA58D3c\noO4XwP+x1iaG5m8jfqIcMunL3wKtwE9xg6r/6fF8qSXNfI/j1cAfAd0gvNd5TT4JPAz8W4nnZOi9\nDqSBkcaYW40xEwGstSuttZ8ufFD6DnA9cD/wFeA8oPOD1a24P6fgznjfN5SDH6aOdc02Fb6uz/dI\na23ncycB38D9APscsAL4y+M4dvExzZBJX+631v4tgDHmEmCCMebEQb7G69ZabQ4YWoHCJ/juy1fd\n231ttdZ+eojHJH2w1iaNMV8Evg58DvicMeZVYAPwPWvtQ8AtuLPNz+J+iF4OvNsYM9pa+wtjzI3A\nRGClcsiOvwFeMxjYEnIEuMla+6gx5n+BB3Bn2WQY0gyZ9GVzt98/VziOHORr/L48Q5FBOB14ETjQ\n7deDvPFp/RmPxiV9sNbeApwDfBU3H6kOd6ZkQyEdYBJwJ+7y1gHgPYVTTxjywQowoGs2GJ29k58r\nHAf7PitVQjNk0pdUt99ncT/tdf/E1z2Yj/TxGu3lHpQc00HgAz0eO4KbfwS6Jr5krd2Ou2sPY0wd\n7jLWXwBfAN6B+zP4Cdz8zX8BGr0ZqXTq55p9kjd2LgcKz/f1HglvvNdmC0dtzhimFJDJYB0pHLuX\nzpjuxUCkpJS1dn3PB40xzaW+WLxljFmEu+z1hLX27wCstXFjzA9xb+6nAWHg99ba7xpjooXH8vS+\ncev9fAgM4JpFcHNnwd2x/hx6j5QB0A+wDIq19jVjzG7cnLL/xJ2RmePtqEQq1k7g3cAcY8wZuMtX\ndcAHcYOuW3B3xk4t5IotAPYDo4HPGGO+jFvqBOCLxpj/7pbDJMfHsa7ZHUAQaAG+VQjUrgQSQMyD\n8UqFUA7Z8Jan906ggTz2IWAXcC1geKPulaba/avUdRWPWWufwy2h8DPcGoD/jBuAtQGfsNZ+BfgU\n7sz03wCPANfg5pJdC5wI/Ctuftky4Nyh/RsMPwO4ZrfgXpN7gfHAx3B3rb9G6ZlNEQCcfF7v0SIi\nIiJe0gyZiIiIiMcUkImIiIh4TAGZiIiIiMcUkImIiIh4TAGZiIiIiMcUkImIiIh4TAGZiIiIiMcU\nkImILxljPmiMyRljVHleRKqeAjIR8TtVrxaRqqeATERERMRjCshEREREPBbyegAiMnwZY2pwm9N/\nADgTeB23gfaXjnHeOcBngQuB03GXNV8AfgV81Vr7UuHrTgReBKLALGvtU3283gPAfODvrLX/+Nb/\nZiIig6MZMhHx0s+ALwNnAKuBO3EDrCeBxlInGGMWAptxg7iDwA+Bnxae/gTwtDHmdABr7evAXYXn\nPtjH650MzAWywP+8xb+PiMibohkyEfGEMeZSYCmQAGZba7d2e+4TwLf7OPWrQBj4T2vtn3c7J4Q7\nQzYX+FvgU4Wn/gu4DlhujPlra21Hj9dbhvvhdL219o9v+S8mIvImaIZMRLxyTeF4R/dgDMBa++/A\n1t6nAPBN4C+BoqVFa20GuL3wxwu6Pf4I8AdgNLCkxOstLxx/MJjBi4iUk2bIRMQr0wvHR/p4fj3w\nrp4PWmtX9fOaBwrHE3s8/n3cAO4G3GVRAIwxpwAXA38C7j32kEVEjg8FZCLilTNwk/H7WibcX+pB\nY0wAuB64FpgInAJEenyZ0+PPP8Rd6mwyxpxqrX258HjncuXPSixliogMGS1ZiohXYoVjso/nUz0f\nMMY4uBsBbsfdFXkYWImbb/Yt4O5SL1QIwO7H/RD6gW5PablSRHxBM2Qi4pUkbjmKnrNbnWIlHnsf\ncAXQAVxmrd3Q/UljzFzcWa9S/qtw/jXAPxtjTgMuAnZYazcPfvgiIuWjGTIR8cqLuEuLb+vj+XEl\nHru4cPxVz2CsYHw/3+8XwEvAO40xZwMrCt//hwMarYjIcaSATES88rvC8YKeTxSWJptLnNM5q3+4\nxDkB4GOFP/bMIcNam8NdmnSAq4CrgQzwo8EOXESk3BSQiYhXOnc7Xl+Yserus0BDiXN2FI5zjTFd\nS5qF39/GG/loJxtjwiXO/z7uRoKPAefizrS9XOLrRESGlJPP570eg4gMQ4VZsA3AHOAI7pLi68B7\ngCm4ZSq+AjxsrZ1XOOcEYBduNf/nCufHgAW4S6ALcWuOjQAeAlZaa7/X4/tuLHxPgCuttSU3AoiI\nDCXNkImIJ6y1eWAxbuD1KnB54deLwPnA04UvDXQ75yhu0LUWOAl32fHdwPeAC6y1B3HbJx0AZgE9\nZ97AnSUDOAT8vKx/KRGRN0kzZCIyrBhjPgP8M/BP1trPez0eERHQDJmIDCOFXLPP4ibz/5vHwxER\n6aKATESGBWNMLe6OyrcBP7TWvuDxkEREuqgwrIhUNWPMctxisufhbgZoBT7n6aBERHrQDJmIVLuz\ncAOyGHAHcKG19oinIxIR6UFJ/SIiIiIe0wyZiIiIiMcUkImIiIh4TAGZiIiIiMcUkImIiIh4TAGZ\niIiIiMcUkImIiIh47P8DF+VHf82BatwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b1e652a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nested categorical variable with the \"hue\" parameter\n",
    "figure()\n",
    "sns.stripplot(x=\"day\", y=\"total_bill\", hue=\"time\", data=tips);\n",
    "title('nested categorical variable with the \"hue\" parameter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snippets begins here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      "total_bill    244 non-null float64\n",
      "tip           244 non-null float64\n",
      "sex           244 non-null category\n",
      "smoker        244 non-null category\n",
      "day           244 non-null category\n",
      "time          244 non-null category\n",
      "size          244 non-null int64\n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 8.7 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#** AWESOME!!! MEMORY USAGE ALSO PRINTED OUT!!!\n",
    "print tips.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.056897</td>\n",
       "      <td>2.833448</td>\n",
       "      <td>2.459770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.009209</td>\n",
       "      <td>1.159495</td>\n",
       "      <td>0.937644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.400000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.520000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill        tip       size\n",
       "count   87.000000  87.000000  87.000000\n",
       "mean    18.056897   2.833448   2.459770\n",
       "std      8.009209   1.159495   0.937644\n",
       "min      3.070000   1.000000   1.000000\n",
       "25%     12.750000   2.000000   2.000000\n",
       "50%     16.400000   2.750000   2.000000\n",
       "75%     21.520000   3.500000   3.000000\n",
       "max     44.300000   6.500000   6.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas: different ways of boolean selection\n",
    "tips[ tips['sex'].isin(['Female'])].describe() \n",
    "tips[ tips['sex'] == 'Female' ].describe()\n",
    "tips.query('sex == \"Female\"').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More practice with query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>87</td>\n",
       "      <td>18.056897</td>\n",
       "      <td>8.009209</td>\n",
       "      <td>3.07</td>\n",
       "      <td>12.75</td>\n",
       "      <td>16.40</td>\n",
       "      <td>21.52</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>87</td>\n",
       "      <td>2.833448</td>\n",
       "      <td>1.159495</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.50</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>87</td>\n",
       "      <td>2.459770</td>\n",
       "      <td>0.937644</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count       mean       std   min    25%    50%    75%   max\n",
       "total_bill     87  18.056897  8.009209  3.07  12.75  16.40  21.52  44.3\n",
       "tip            87   2.833448  1.159495  1.00   2.00   2.75   3.50   6.5\n",
       "size           87   2.459770  0.937644  1.00   2.00   2.00   3.00   6.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query will do the column selection for you! (so won't return boolean DF)\n",
    "tips.query('sex == \"Male\"').describe().T\n",
    "tips.query('sex != \"Male\"').describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>26.86</td>\n",
       "      <td>3.14</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>25.28</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>16.32</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>11.35</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>15.38</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>44.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>22.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>14.31</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>17.51</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>10.59</td>\n",
       "      <td>1.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>10.63</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>9.60</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>20.90</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>18.15</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>19.81</td>\n",
       "      <td>4.19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>43.11</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>13.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>12.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>13.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>16.40</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>16.47</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>12.76</td>\n",
       "      <td>2.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>13.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>30.14</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>13.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>10.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>22.12</td>\n",
       "      <td>2.88</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "67         3.07  1.00  Female    Yes   Sat  Dinner     1\n",
       "72        26.86  3.14  Female    Yes   Sat  Dinner     2\n",
       "73        25.28  5.00  Female    Yes   Sat  Dinner     2\n",
       "92         5.75  1.00  Female    Yes   Fri  Dinner     2\n",
       "93        16.32  4.30  Female    Yes   Fri  Dinner     2\n",
       "100       11.35  2.50  Female    Yes   Fri  Dinner     2\n",
       "101       15.38  3.00  Female    Yes   Fri  Dinner     2\n",
       "102       44.30  2.50  Female    Yes   Sat  Dinner     3\n",
       "103       22.42  3.48  Female    Yes   Sat  Dinner     2\n",
       "109       14.31  4.00  Female    Yes   Sat  Dinner     2\n",
       "164       17.51  3.00  Female    Yes   Sun  Dinner     2\n",
       "168       10.59  1.61  Female    Yes   Sat  Dinner     2\n",
       "169       10.63  2.00  Female    Yes   Sat  Dinner     2\n",
       "178        9.60  4.00  Female    Yes   Sun  Dinner     2\n",
       "186       20.90  3.50  Female    Yes   Sun  Dinner     3\n",
       "188       18.15  3.50  Female    Yes   Sun  Dinner     3\n",
       "191       19.81  4.19  Female    Yes  Thur   Lunch     2\n",
       "197       43.11  5.00  Female    Yes  Thur   Lunch     4\n",
       "198       13.00  2.00  Female    Yes  Thur   Lunch     2\n",
       "201       12.74  2.01  Female    Yes  Thur   Lunch     2\n",
       "202       13.00  2.00  Female    Yes  Thur   Lunch     2\n",
       "203       16.40  2.50  Female    Yes  Thur   Lunch     2\n",
       "205       16.47  3.23  Female    Yes  Thur   Lunch     3\n",
       "209       12.76  2.23  Female    Yes   Sat  Dinner     2\n",
       "213       13.27  2.50  Female    Yes   Sat  Dinner     2\n",
       "214       28.17  6.50  Female    Yes   Sat  Dinner     3\n",
       "215       12.90  1.10  Female    Yes   Sat  Dinner     2\n",
       "219       30.14  3.09  Female    Yes   Sat  Dinner     4\n",
       "221       13.42  3.48  Female    Yes   Fri   Lunch     2\n",
       "225       16.27  2.50  Female    Yes   Fri   Lunch     2\n",
       "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
       "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all rows where columns \"a\" and \"b\" have overlapping values\n",
    "tips.query('sex in smoker')      # <- concise and readable\n",
    "tips[tips.sex.isin(tips.smoker)] # equivalent pythonic syntax...\n",
    "\n",
    "tips.query('sex not in smoker')\n",
    "tips[~tips.sex.isin(tips.smoker)] # equivalent pythonic syntax...\n",
    "\n",
    "tips.query('sex == \"Female\" and tip < 3.5')\n",
    "tips.query('sex == \"Female\" and smoker == \"Yes\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>27.20</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22.76</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>17.29</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>19.44</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>16.66</td>\n",
       "      <td>3.40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>32.68</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>15.98</td>\n",
       "      <td>2.03</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>13.03</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>18.28</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>24.71</td>\n",
       "      <td>5.85</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>21.16</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>28.97</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>22.49</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>40.17</td>\n",
       "      <td>4.73</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>27.28</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12.03</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12.46</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>11.69</td>\n",
       "      <td>2.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>14.26</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>15.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>8.52</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>22.82</td>\n",
       "      <td>2.18</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>19.08</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>16.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>34.30</td>\n",
       "      <td>6.70</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>41.19</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>9.78</td>\n",
       "      <td>1.73</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>7.51</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>28.44</td>\n",
       "      <td>2.56</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>15.48</td>\n",
       "      <td>2.02</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>16.58</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>7.56</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>10.34</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>13.51</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>18.71</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>20.53</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>12.16</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>8.58</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>13.42</td>\n",
       "      <td>1.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip   sex smoker   day    time  size\n",
       "77        27.20  4.00  Male     No  Thur   Lunch     4\n",
       "78        22.76  3.00  Male     No  Thur   Lunch     2\n",
       "79        17.29  2.71  Male     No  Thur   Lunch     2\n",
       "80        19.44  3.00  Male    Yes  Thur   Lunch     2\n",
       "81        16.66  3.40  Male     No  Thur   Lunch     2\n",
       "83        32.68  5.00  Male    Yes  Thur   Lunch     2\n",
       "84        15.98  2.03  Male     No  Thur   Lunch     2\n",
       "86        13.03  2.00  Male     No  Thur   Lunch     2\n",
       "87        18.28  4.00  Male     No  Thur   Lunch     2\n",
       "88        24.71  5.85  Male     No  Thur   Lunch     2\n",
       "89        21.16  3.00  Male     No  Thur   Lunch     2\n",
       "90        28.97  3.00  Male    Yes   Fri  Dinner     2\n",
       "91        22.49  3.50  Male     No   Fri  Dinner     2\n",
       "95        40.17  4.73  Male    Yes   Fri  Dinner     4\n",
       "96        27.28  4.00  Male    Yes   Fri  Dinner     2\n",
       "97        12.03  1.50  Male    Yes   Fri  Dinner     2\n",
       "98        21.01  3.00  Male    Yes   Fri  Dinner     2\n",
       "99        12.46  1.50  Male     No   Fri  Dinner     2\n",
       "120       11.69  2.31  Male     No  Thur   Lunch     2\n",
       "122       14.26  2.50  Male     No  Thur   Lunch     2\n",
       "123       15.95  2.00  Male     No  Thur   Lunch     2\n",
       "126        8.52  1.48  Male     No  Thur   Lunch     2\n",
       "129       22.82  2.18  Male     No  Thur   Lunch     3\n",
       "130       19.08  1.50  Male     No  Thur   Lunch     2\n",
       "138       16.00  2.00  Male    Yes  Thur   Lunch     2\n",
       "141       34.30  6.70  Male     No  Thur   Lunch     6\n",
       "142       41.19  5.00  Male     No  Thur   Lunch     5\n",
       "148        9.78  1.73  Male     No  Thur   Lunch     2\n",
       "149        7.51  2.00  Male     No  Thur   Lunch     2\n",
       "192       28.44  2.56  Male    Yes  Thur   Lunch     2\n",
       "193       15.48  2.02  Male    Yes  Thur   Lunch     2\n",
       "194       16.58  4.00  Male    Yes  Thur   Lunch     2\n",
       "195        7.56  1.44  Male     No  Thur   Lunch     2\n",
       "196       10.34  2.00  Male    Yes  Thur   Lunch     2\n",
       "199       13.51  2.00  Male    Yes  Thur   Lunch     2\n",
       "200       18.71  4.00  Male    Yes  Thur   Lunch     3\n",
       "204       20.53  4.00  Male    Yes  Thur   Lunch     4\n",
       "220       12.16  2.20  Male    Yes   Fri   Lunch     2\n",
       "222        8.58  1.92  Male    Yes   Fri   Lunch     1\n",
       "224       13.42  1.58  Male    Yes   Fri   Lunch     2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list-expressions also works in query\n",
    "tips.query('sex == \"Male\" and day in [\"Sun\",\"Sat\"]')\n",
    "tips.query('sex in \"Male\" and day == [\"Sun\",\"Sat\"]')\n",
    "tips.query('sex == \"Male\" and day not in [\"Sun\",\"Sat\"]')\n",
    "tips.query('sex == \"Male\" and day != [\"Sun\",\"Sat\"]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original structure\n",
    "tips.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>Female</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Male</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>Male</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          total_bill   tip smoker  day    time  size\n",
       "  sex                                               \n",
       "0 Female       16.99  1.01     No  Sun  Dinner     2\n",
       "1 Male         10.34  1.66     No  Sun  Dinner     3\n",
       "2 Male         21.01  3.50     No  Sun  Dinner     3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make gender an outer level index\n",
    "tips_multi_index = tips.set_index('sex', append=True)\n",
    "tips_multi_index.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>Female</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Male</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>Male</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_bill   tip smoker  day    time  size\n",
       "id sex                                               \n",
       "0  Female       16.99  1.01     No  Sun  Dinner     2\n",
       "1  Male         10.34  1.66     No  Sun  Dinner     3\n",
       "2  Male         21.01  3.50     No  Sun  Dinner     3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name the original index as \"id\"\n",
    "tips_multi_index.index.rename(['id','sex'], inplace=True)\n",
    "tips_multi_index.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "we got false above since reset_index will plug the index into the 1st column (ie, order not preserved)\n"
     ]
    }
   ],
   "source": [
    "#==============================================================================\n",
    "# Note: really weird, but to make index into column, use reset_index\n",
    "# http://stackoverflow.com/questions/20110170/turn-pandas-multi-index-into-column\n",
    "#==============================================================================\n",
    "print tips.equals( tips_multi_index.reset_index('sex',col_level = 3))\n",
    "print (\"we got false above since reset_index will plug the index into the 1st \"\n",
    "       \"column (ie, order not preserved)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>9.246469</td>\n",
       "      <td>1.489102</td>\n",
       "      <td>0.955997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>8.009209</td>\n",
       "      <td>1.159495</td>\n",
       "      <td>0.937644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill       tip      size\n",
       "sex                                   \n",
       "Male      9.246469  1.489102  0.955997\n",
       "Female    8.009209  1.159495  0.937644"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#==============================================================================\n",
    "# Here's where multi-index shines IMO...summary stats based on groups!\n",
    "#....(but wtf, methods like \"describe()\" and \"quantile\" doesn't have \"level\" arg\n",
    "#==============================================================================\n",
    "# using \n",
    "tips_multi_index.std(level='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some sorting by level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         total_bill   tip smoker  day    time  size\n",
      "id sex                                             \n",
      "1  Male       10.34  1.66     No  Sun  Dinner     3\n",
      "2  Male       21.01  3.50     No  Sun  Dinner     3\n",
      "3  Male       23.68  3.31     No  Sun  Dinner     2\n",
      "5  Male       25.29  4.71     No  Sun  Dinner     4\n",
      "6  Male        8.77  2.00     No  Sun  Dinner     2\n",
      "            total_bill   tip smoker   day    time  size\n",
      "id  sex                                                \n",
      "226 Female       10.09  2.00    Yes   Fri   Lunch     2\n",
      "229 Female       22.12  2.88    Yes   Sat  Dinner     2\n",
      "238 Female       35.83  4.67     No   Sat  Dinner     3\n",
      "240 Female       27.18  2.00    Yes   Sat  Dinner     2\n",
      "243 Female       18.78  3.00     No  Thur  Dinner     2\n"
     ]
    }
   ],
   "source": [
    "# name index as \"subject\" \n",
    "tips_multi_index.index.name = 'subject'\n",
    "\n",
    "#%% sort/group by gender\n",
    "print tips_multi_index.sortlevel('sex').head()\n",
    "print tips_multi_index.sortlevel('sex').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips_multi_index_gender_sorted = tips_multi_index.sortlevel('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_bill   tip smoker  day    time  size\n",
       "sex    id                                            \n",
       "Female 0        16.99  1.01     No  Sun  Dinner     2\n",
       "Male   1        10.34  1.66     No  Sun  Dinner     3\n",
       "       2        21.01  3.50     No  Sun  Dinner     3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder so gender is the outer level (hate how you can't do this in set_index()\n",
    "tips_multi_index.reorder_levels([1,0]).head(10)\n",
    "tips_multi_index = tips_multi_index.reorder_levels(['sex','id'])\n",
    "tips_multi_index.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%% where vs boolean...\n",
    "idx1 = tips['sex'].where(tips['sex'] == 'Female') # <- returns a NAN\n",
    "idx2 = tips['sex'] == 'Female'                    # <- returns True/False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A bit on \"groupby\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">total_bill</th>\n",
       "      <th colspan=\"5\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"5\" halign=\"left\">size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>157</td>\n",
       "      <td>20.744076</td>\n",
       "      <td>9.246469</td>\n",
       "      <td>157</td>\n",
       "      <td>85.497185</td>\n",
       "      <td>157</td>\n",
       "      <td>3.089618</td>\n",
       "      <td>1.489102</td>\n",
       "      <td>157</td>\n",
       "      <td>2.217424</td>\n",
       "      <td>157</td>\n",
       "      <td>2.630573</td>\n",
       "      <td>0.955997</td>\n",
       "      <td>157</td>\n",
       "      <td>0.913931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>87</td>\n",
       "      <td>18.056897</td>\n",
       "      <td>8.009209</td>\n",
       "      <td>87</td>\n",
       "      <td>64.147429</td>\n",
       "      <td>87</td>\n",
       "      <td>2.833448</td>\n",
       "      <td>1.159495</td>\n",
       "      <td>87</td>\n",
       "      <td>1.344428</td>\n",
       "      <td>87</td>\n",
       "      <td>2.459770</td>\n",
       "      <td>0.937644</td>\n",
       "      <td>87</td>\n",
       "      <td>0.879177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill                                        tip            \\\n",
       "            count       mean       std  len        var count      mean   \n",
       "sex                                                                      \n",
       "Male          157  20.744076  9.246469  157  85.497185   157  3.089618   \n",
       "Female         87  18.056897  8.009209   87  64.147429    87  2.833448   \n",
       "\n",
       "                                 size                                     \n",
       "             std  len       var count      mean       std  len       var  \n",
       "sex                                                                       \n",
       "Male    1.489102  157  2.217424   157  2.630573  0.955997  157  0.913931  \n",
       "Female  1.159495   87  1.344428    87  2.459770  0.937644   87  0.879177  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('sex').agg(['count','mean','std',len,np.var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">total_bill</th>\n",
       "      <th colspan=\"5\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"5\" halign=\"left\">size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244</td>\n",
       "      <td>19.785943</td>\n",
       "      <td>8.902412</td>\n",
       "      <td>244</td>\n",
       "      <td>79.252939</td>\n",
       "      <td>244</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>244</td>\n",
       "      <td>1.914455</td>\n",
       "      <td>244</td>\n",
       "      <td>2.569672</td>\n",
       "      <td>0.9511</td>\n",
       "      <td>244</td>\n",
       "      <td>0.904591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_bill                                        tip                      \\\n",
       "       count       mean       std  len        var count      mean       std   \n",
       "0        244  19.785943  8.902412  244  79.252939   244  2.998279  1.383638   \n",
       "\n",
       "                  size                                   \n",
       "   len       var count      mean     std  len       var  \n",
       "0  244  1.914455   244  2.569672  0.9511  244  0.904591  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(lambda idx: 0).agg(['count','mean','std',len,np.var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'total_bill', u'tip', u'sex', u'smoker', u'day', u'time', u'size'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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