Height Weight example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy and Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"heightweight.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sex</th>\n",
       "      <th>ageYear</th>\n",
       "      <th>ageMonth</th>\n",
       "      <th>heightIn</th>\n",
       "      <th>weightLb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f</td>\n",
       "      <td>11.91667</td>\n",
       "      <td>143</td>\n",
       "      <td>56.3</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f</td>\n",
       "      <td>12.91667</td>\n",
       "      <td>155</td>\n",
       "      <td>62.3</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>12.75000</td>\n",
       "      <td>153</td>\n",
       "      <td>63.3</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f</td>\n",
       "      <td>13.41667</td>\n",
       "      <td>161</td>\n",
       "      <td>59.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f</td>\n",
       "      <td>15.91667</td>\n",
       "      <td>191</td>\n",
       "      <td>62.5</td>\n",
       "      <td>112.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex   ageYear  ageMonth  heightIn  weightLb\n",
       "0   f  11.91667       143      56.3      85.0\n",
       "1   f  12.91667       155      62.3     105.0\n",
       "2   f  12.75000       153      63.3     108.0\n",
       "3   f  13.41667       161      59.0      92.0\n",
       "4   f  15.91667       191      62.5     112.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>sex</th>\n",
       "      <th>ageYear</th>\n",
       "      <th>ageMonth</th>\n",
       "      <th>heightIn</th>\n",
       "      <th>weightLb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>m</td>\n",
       "      <td>13.66667</td>\n",
       "      <td>164</td>\n",
       "      <td>66.5</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>m</td>\n",
       "      <td>15.75000</td>\n",
       "      <td>189</td>\n",
       "      <td>65.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>m</td>\n",
       "      <td>13.66667</td>\n",
       "      <td>164</td>\n",
       "      <td>61.5</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>m</td>\n",
       "      <td>13.91667</td>\n",
       "      <td>167</td>\n",
       "      <td>62.0</td>\n",
       "      <td>107.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>m</td>\n",
       "      <td>12.58333</td>\n",
       "      <td>151</td>\n",
       "      <td>59.3</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sex   ageYear  ageMonth  heightIn  weightLb\n",
       "232   m  13.66667       164      66.5     112.0\n",
       "233   m  15.75000       189      65.0     114.0\n",
       "234   m  13.66667       164      61.5     140.0\n",
       "235   m  13.91667       167      62.0     107.5\n",
       "236   m  12.58333       151      59.3      87.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex         237\n",
       "ageYear     237\n",
       "ageMonth    237\n",
       "heightIn    237\n",
       "weightLb    237\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 237 entries, 0 to 236\n",
      "Data columns (total 5 columns):\n",
      "sex         237 non-null object\n",
      "ageYear     237 non-null float64\n",
      "ageMonth    237 non-null int64\n",
      "heightIn    237 non-null float64\n",
      "weightLb    237 non-null float64\n",
      "dtypes: float64(3), int64(1), object(1)\n",
      "memory usage: 9.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['f', 'm'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "m    126\n",
       "f    111\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.36455696202525"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.heightIn.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.weightLb.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.00000\n",
       "1    12.50000\n",
       "2    13.66667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ageYear.mode()"
   ]
  },
  {
   "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>ageYear</th>\n",
       "      <th>ageMonth</th>\n",
       "      <th>heightIn</th>\n",
       "      <th>weightLb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.702532</td>\n",
       "      <td>164.430380</td>\n",
       "      <td>61.364557</td>\n",
       "      <td>101.308017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.535481</td>\n",
       "      <td>18.425767</td>\n",
       "      <td>3.945402</td>\n",
       "      <td>19.440698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.583330</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>50.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.333330</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>58.800000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.583330</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.833330</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>64.300000</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.833330</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>171.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ageYear    ageMonth    heightIn    weightLb\n",
       "count  237.000000  237.000000  237.000000  237.000000\n",
       "mean    13.702532  164.430380   61.364557  101.308017\n",
       "std      1.535481   18.425767    3.945402   19.440698\n",
       "min     11.583330  139.000000   50.500000   50.500000\n",
       "25%     12.333330  148.000000   58.800000   85.000000\n",
       "50%     13.583330  163.000000   61.500000  101.000000\n",
       "75%     14.833330  178.000000   64.300000  112.000000\n",
       "max     20.833330  250.000000   72.000000  171.500000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ageYear</th>\n",
       "      <th>ageMonth</th>\n",
       "      <th>heightIn</th>\n",
       "      <th>weightLb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ageYear</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648857</td>\n",
       "      <td>0.634636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ageMonth</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648857</td>\n",
       "      <td>0.634636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heightIn</th>\n",
       "      <td>0.648857</td>\n",
       "      <td>0.648857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.774876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weightLb</th>\n",
       "      <td>0.634636</td>\n",
       "      <td>0.634636</td>\n",
       "      <td>0.774876</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ageYear  ageMonth  heightIn  weightLb\n",
       "ageYear   1.000000  1.000000  0.648857  0.634636\n",
       "ageMonth  1.000000  1.000000  0.648857  0.634636\n",
       "heightIn  0.648857  0.648857  1.000000  0.774876\n",
       "weightLb  0.634636  0.634636  0.774876  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAFkCAYAAACw3EhvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x97baf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.hist(df.ageYear)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJElSkSFBkiQV/T/6U9uFCpho1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d47b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.weightLb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAFkCAYAAACw3EhvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cad8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.heightIn)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0xa431470>,\n",
       "  <matplotlib.lines.Line2D at 0xa44f780>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0xa4421d0>,\n",
       "  <matplotlib.lines.Line2D at 0xa442748>,\n",
       "  <matplotlib.lines.Line2D at 0xa45e860>,\n",
       "  <matplotlib.lines.Line2D at 0xa45edd8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0xa44f278>,\n",
       "  <matplotlib.lines.Line2D at 0xa46d908>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0xa442cc0>,\n",
       "  <matplotlib.lines.Line2D at 0xa46d390>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0xa431630>,\n",
       "  <matplotlib.lines.Line2D at 0xa431c18>,\n",
       "  <matplotlib.lines.Line2D at 0xa44fd30>,\n",
       "  <matplotlib.lines.Line2D at 0xa45e2e8>]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAFkCAYAAACw3EhvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAkmSVPr/STY/a/PaXsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa096198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([df.heightIn, df.weightLb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa4dab70>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4a1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.weightLb, df.heightIn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.1572576072314554, intercept=45.433100634484191, rvalue=0.7748761066276022, pvalue=1.0286858314028471e-48, stderr=0.0083683585485207646)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "lr = stats.linregress(df.weightLb, df.heightIn)\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predictHeight(weight):\n",
    "    return lr.intercept + lr.slope * weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fitline = predictHeight(df.weightLb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb93f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.weightLb, df.heightIn)\n",
    "plt.plot(df.weightLb, fitline, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}