lattice-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "np.random.seed(sum(map(ord, \"axis_grids\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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></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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</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",
       "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\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4\n",
       "5       25.29  4.71    Male     No  Sun  Dinner     4\n",
       "6        8.77  2.00    Male     No  Sun  Dinner     2\n",
       "7       26.88  3.12    Male     No  Sun  Dinner     4\n",
       "8       15.04  1.96    Male     No  Sun  Dinner     2\n",
       "9       14.78  3.23    Male     No  Sun  Dinner     2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y2Ji7dOmSe/jhh3OWw7l/P9d169a5jRs3Tjg/mUy6Rx991F26dMmNjY25DRs2uAsXLkz5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\nAGACgwQAMOH/ANGRrpwgb93EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c3a68710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# init the grid - matplotlib figure\n",
    "g = sns.FacetGrid(tips, col=\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c3d51710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize the FacetGrid object with the dataset and the variables that are used to structure the grid\n",
    "# they return the object when they are finsihed so that it can be used for further tweaking.\n",
    "g = sns.FacetGrid(tips, col=\"time\")\n",
    "# plot a histogram based on tips during specific time intervals \n",
    "g.map(plt.hist, \"tip\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f73c375e2b0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DSktLQ58+faBSqdCnTx+o1Wo0NDQgIyND8vnLly/HihUrwtnEqGY0C9A3m/Bl2Rf49swO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\nkYX/DxaPMRpnW9/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c378c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tips, col=\"sex\", hue=\"smoker\")\n",
    "g.map(plt.scatter, \"total_bill\", \"tip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}