python-pandas-intro.ipynb

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   },
   "source": [
    "# Intro to Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Pandas\n",
    "* Powerful data analysis toolkit for python\n",
    "* Resembles R data.frame, data.table, and dplyr\n",
    "* Treats data in a relational way with named elements like columns\n",
    "* Can perform database like functions (selects, joins, groups, etc.)\n",
    "* Easy access to powerful statistical methods\n",
    "* Easy access to rich plots"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "## Quick look at the data"
   ]
  },
  {
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   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Read in some data\n",
    "heart = pd.read_csv(\"http://faculty.washington.edu/kenrice/heartgraphs/nhaneslarge.csv\", sep=\",\", na_values=\".\")"
   ]
  },
  {
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   },
   "source": [
    "### What type of object is it?"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(heart)"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Look at the data\n",
    "#### First five rows"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>434</td>\n",
       "      <td>1</td>\n",
       "      <td>25.83</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117.333333</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>25.73</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>24.49</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Black non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>422</td>\n",
       "      <td>1</td>\n",
       "      <td>24.63</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       BPXSAR     BPXDAR  BPXDI1  BPXDI2           race_ethc  gender  \\\n",
       "0  108.000000  56.000000    56.0    58.0               Other    Male   \n",
       "1  117.333333  60.000000    56.0    64.0            Hispanic    Male   \n",
       "2  120.000000  82.666667    80.0    86.0  White non-Hispanic    Male   \n",
       "3   88.000000  52.000000    52.0     NaN  White non-Hispanic  Female   \n",
       "4  150.666667  96.000000   100.0    94.0  Black non-Hispanic    Male   \n",
       "\n",
       "   DR1TFOLA  RIAGENDR  BMXBMI  RIDAGEYR  \n",
       "0       434         1   25.83        65  \n",
       "1       241         1   25.73        74  \n",
       "2       380         1   24.49        54  \n",
       "3       152         2   20.76         9  \n",
       "4       422         1   24.63        49  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 5 rows\n",
    "heart.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "marked": true
    }
   },
   "source": [
    "#### Last three rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>144.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>31.44</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>624</td>\n",
       "      <td>1</td>\n",
       "      <td>28.23</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>134.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>753</td>\n",
       "      <td>1</td>\n",
       "      <td>22.21</td>\n",
       "      <td>50</td>\n",
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      "text/plain": [
       "     BPXSAR  BPXDAR  BPXDI1  BPXDI2           race_ethc  gender  DR1TFOLA  \\\n",
       "997   144.0    70.0    70.0     NaN  White non-Hispanic  Female       195   \n",
       "998   120.0    80.0    84.0    80.0  White non-Hispanic    Male       624   \n",
       "999   134.0    88.0    94.0    88.0  White non-Hispanic    Male       753   \n",
       "\n",
       "     RIAGENDR  BMXBMI  RIDAGEYR  \n",
       "997         2   31.44        85  \n",
       "998         1   28.23        34  \n",
       "999         1   22.21        50  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Last 3 rows\n",
    "heart.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Just the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BPXSAR', 'BPXDAR', 'BPXDI1', 'BPXDI2', 'race_ethc', 'gender',\n",
       "       'DR1TFOLA', 'RIAGENDR', 'BMXBMI', 'RIDAGEYR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "#### Object-oriented: each operation returns a data.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>21.35</td>\n",
       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>107.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>1072</td>\n",
       "      <td>2</td>\n",
       "      <td>24.20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114.666667</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>821</td>\n",
       "      <td>2</td>\n",
       "      <td>20.81</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104.666667</td>\n",
       "      <td>72.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>435</td>\n",
       "      <td>2</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>231</td>\n",
       "      <td>2</td>\n",
       "      <td>23.83</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       BPXSAR  BPXDAR  BPXDI1  BPXDI2           race_ethc  gender  DR1TFOLA  \\\n",
       "5   99.000000    62.0    66.0    62.0  White non-Hispanic    Male       319   \n",
       "6  107.000000    57.0    54.0     NaN  White non-Hispanic  Female      1072   \n",
       "7  114.666667    58.0    58.0    60.0  White non-Hispanic  Female       821   \n",
       "8  104.666667    72.0    70.0    70.0  White non-Hispanic  Female       435   \n",
       "9  101.000000    58.0    66.0    56.0  White non-Hispanic  Female       231   \n",
       "\n",
       "   RIAGENDR  BMXBMI  RIDAGEYR  \n",
       "5         1   21.35        69  \n",
       "6         2   24.20        25  \n",
       "7         2   20.81        29  \n",
       "8         2   20.06        19  \n",
       "9         2   23.83        13  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.head(10).tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
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     "marked": true
    }
   },
   "source": [
    "## Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>119.932667</td>\n",
       "      <td>68.021000</td>\n",
       "      <td>68.333702</td>\n",
       "      <td>68.442308</td>\n",
       "      <td>409.2650</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>27.077619</td>\n",
       "      <td>40.109000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.996055</td>\n",
       "      <td>13.951905</td>\n",
       "      <td>14.371156</td>\n",
       "      <td>14.041936</td>\n",
       "      <td>242.0019</td>\n",
       "      <td>0.50025</td>\n",
       "      <td>6.552778</td>\n",
       "      <td>20.379363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>14.240000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>250.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>22.520000</td>\n",
       "      <td>21.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>117.333333</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>354.0000</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>26.550000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>492.2500</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>30.860000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>1906.0000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>57.410000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BPXSAR       BPXDAR      BPXDI1      BPXDI2   DR1TFOLA  \\\n",
       "count  1000.000000  1000.000000  905.000000  832.000000  1000.0000   \n",
       "mean    119.932667    68.021000   68.333702   68.442308   409.2650   \n",
       "std      17.996055    13.951905   14.371156   14.041936   242.0019   \n",
       "min      82.000000     0.000000    0.000000    0.000000     0.0000   \n",
       "25%     108.000000    60.000000   60.000000   60.000000   250.0000   \n",
       "50%     117.333333    69.000000   68.000000   70.000000   354.0000   \n",
       "75%     130.000000    77.000000   78.000000   78.000000   492.2500   \n",
       "max     216.000000   112.000000  110.000000  114.000000  1906.0000   \n",
       "\n",
       "         RIAGENDR      BMXBMI     RIDAGEYR  \n",
       "count  1000.00000  991.000000  1000.000000  \n",
       "mean      1.50000   27.077619    40.109000  \n",
       "std       0.50025    6.552778    20.379363  \n",
       "min       1.00000   14.240000     8.000000  \n",
       "25%       1.00000   22.520000    21.750000  \n",
       "50%       1.50000   26.550000    40.000000  \n",
       "75%       2.00000   30.860000    54.000000  \n",
       "max       2.00000   57.410000    85.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">RIDAGEYR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_ethc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black non-Hispanic</th>\n",
       "      <td>102</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>114</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>68</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White non-Hispanic</th>\n",
       "      <td>716</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RIDAGEYR       \n",
       "                       size median\n",
       "race_ethc                         \n",
       "Black non-Hispanic      102   33.0\n",
       "Hispanic                114   34.5\n",
       "Other                    68   37.0\n",
       "White non-Hispanic      716   43.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.groupby('race_ethc').agg({'RIDAGEYR': [np.size, np.median]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## Selecting data (slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>21.35</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>107.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>1072</td>\n",
       "      <td>2</td>\n",
       "      <td>24.20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114.666667</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>821</td>\n",
       "      <td>2</td>\n",
       "      <td>20.81</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104.666667</td>\n",
       "      <td>72.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>435</td>\n",
       "      <td>2</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>231</td>\n",
       "      <td>2</td>\n",
       "      <td>23.83</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BPXSAR  BPXDAR  BPXDI1  BPXDI2           race_ethc  gender  DR1TFOLA  \\\n",
       "5   99.000000    62.0    66.0    62.0  White non-Hispanic    Male       319   \n",
       "6  107.000000    57.0    54.0     NaN  White non-Hispanic  Female      1072   \n",
       "7  114.666667    58.0    58.0    60.0  White non-Hispanic  Female       821   \n",
       "8  104.666667    72.0    70.0    70.0  White non-Hispanic  Female       435   \n",
       "9  101.000000    58.0    66.0    56.0  White non-Hispanic  Female       231   \n",
       "\n",
       "   RIAGENDR  BMXBMI  RIDAGEYR  \n",
       "5         1   21.35        69  \n",
       "6         2   24.20        25  \n",
       "7         2   20.81        29  \n",
       "8         2   20.06        19  \n",
       "9         2   23.83        13  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rows 5...10\n",
    "heart[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    56.000000\n",
       "1    60.000000\n",
       "2    82.666667\n",
       "3    52.000000\n",
       "4    96.000000\n",
       "Name: BPXDAR, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Single column\n",
    "heart['BPXDAR'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82.666667</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>Black non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BPXDAR           race_ethc  gender\n",
       "0  56.000000               Other    Male\n",
       "1  60.000000            Hispanic    Male\n",
       "2  82.666667  White non-Hispanic    Male\n",
       "3  52.000000  White non-Hispanic  Female\n",
       "4  96.000000  Black non-Hispanic    Male"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiple columns\n",
    "heart[['BPXDAR','race_ethc','gender']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>Black non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>69.333333</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>87.333333</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BPXDAR           race_ethc  gender\n",
       "20  82.000000  Black non-Hispanic  Female\n",
       "21  73.000000  White non-Hispanic    Male\n",
       "22  86.000000  White non-Hispanic  Female\n",
       "23  69.333333  White non-Hispanic    Male\n",
       "24  87.333333            Hispanic    Male\n",
       "25  52.000000  White non-Hispanic    Male\n",
       "26  74.000000               Other  Female\n",
       "27  43.000000  White non-Hispanic    Male\n",
       "28  88.000000  White non-Hispanic  Female\n",
       "29  54.000000  White non-Hispanic    Male\n",
       "30  78.000000               Other    Male"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row indices and columns\n",
    "heart.loc[20:30,['BPXDAR','race_ethc','gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BPXSAR</th>\n",
       "      <th>BPXDAR</th>\n",
       "      <th>BPXDI1</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>race_ethc</th>\n",
       "      <th>gender</th>\n",
       "      <th>DR1TFOLA</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>434</td>\n",
       "      <td>1</td>\n",
       "      <td>25.83</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>24.49</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.666667</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Black non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>422</td>\n",
       "      <td>1</td>\n",
       "      <td>24.63</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>319</td>\n",
       "      <td>1</td>\n",
       "      <td>21.35</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>22.27</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>433</td>\n",
       "      <td>1</td>\n",
       "      <td>45.98</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>109.333333</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Black non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>703</td>\n",
       "      <td>1</td>\n",
       "      <td>18.70</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>117.333333</td>\n",
       "      <td>69.333333</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>454</td>\n",
       "      <td>1</td>\n",
       "      <td>23.61</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>134.666667</td>\n",
       "      <td>87.333333</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "      <td>32.89</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>121.333333</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>White non-Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>646</td>\n",
       "      <td>1</td>\n",
       "      <td>24.88</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BPXSAR     BPXDAR  BPXDI1  BPXDI2           race_ethc gender  \\\n",
       "0   108.000000  56.000000    56.0    58.0               Other   Male   \n",
       "2   120.000000  82.666667    80.0    86.0  White non-Hispanic   Male   \n",
       "4   150.666667  96.000000   100.0    94.0  Black non-Hispanic   Male   \n",
       "5    99.000000  62.000000    66.0    62.0  White non-Hispanic   Male   \n",
       "10  136.000000  82.000000     NaN    80.0  White non-Hispanic   Male   \n",
       "11  144.000000  90.000000    90.0     NaN  White non-Hispanic   Male   \n",
       "15  109.333333  73.333333    72.0    74.0  Black non-Hispanic   Male   \n",
       "23  117.333333  69.333333    66.0    72.0  White non-Hispanic   Male   \n",
       "24  134.666667  87.333333    92.0    82.0            Hispanic   Male   \n",
       "25  121.333333  52.000000    54.0    50.0  White non-Hispanic   Male   \n",
       "\n",
       "    DR1TFOLA  RIAGENDR  BMXBMI  RIDAGEYR  \n",
       "0        434         1   25.83        65  \n",
       "2        380         1   24.49        54  \n",
       "4        422         1   24.63        49  \n",
       "5        319         1   21.35        69  \n",
       "10       498         1   22.27        31  \n",
       "11       433         1   45.98        54  \n",
       "15       703         1   18.70        16  \n",
       "23       454         1   23.61        51  \n",
       "24       508         1   32.89        38  \n",
       "25       646         1   24.88        42  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting on criteria\n",
    "# NOTE use of \".\" dot notation\n",
    "heart[(heart.DR1TFOLA >300) & (heart.gender =='Male')].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instant plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f4318ab85f8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f431882dcc0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "heart.boxplot(column=['BPXSAR', 'BPXDAR'], by='race_ethc', figsize=(20,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4317eeb908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4317d0acf8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4317cb1390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4317cd7a20>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart.hist(column=['BPXSAR', 'BPXDAR', 'DR1TFOLA', 'RIDAGEYR'], bins=20, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "gist": {
   "data": {
    "description": "",
    "public": false
   },
   "id": ""
  },
  "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": "3.6.6"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}