jeffatennis · August 1, 2015 12:43
diff --git a/Titanic_20150801 b/Titanic_20150801
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_df = pd.read_csv(r'C:\\Users\\Tennis\\Python Homework\\TitanicProject_Kaggle\\train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.) Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)\n",
    "2.) What deck were the passengers on and how does that relate to their class?\n",
    "3.) Where did the passengers come from?\n",
    "4.) Who was alone and who was with family?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td> 27.915709</td>\n",
       "      <td> 44.479818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td> 30.726645</td>\n",
       "      <td> 25.523893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Fare\n",
       "Sex                         \n",
       "female  27.915709  44.479818\n",
       "male    30.726645  25.523893"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byGender = titanic_df.groupby(['Sex'])['Age','Fare'].mean()\n",
    "byGender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17dfe0b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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cTGjXt0O8CDb7nggDcssljib+/U670ko49Tq5T43NsWPxOq+KZER1CeIfb+8k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\nfdDNIgIRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3cb7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic visualizations\n",
    "#age distribution by gender\n",
    "sns.boxplot(x=\"Sex\",y=\"Age\",data=titanic_df.sort(\"Age\"))\n",
    "sns.stripplot(x=\"Sex\",y=\"Age\",data=titanic_df.sort(\"Age\"), jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17d51198>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ecd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's seperate the genders by classes, remember we can use the 'hue' arguement here!\n",
    "sns.factorplot(x=\"Pclass\",y=\"Age\",data=titanic_df.sort(\"Pclass\"),hue=\"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# want to split above between men, women and children\n",
    "def male_female_child(passenger):\n",
    "    age,sex = passenger\n",
    "    \n",
    "    if age <16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#add new column to data frame and set value based on function defined above\n",
    "titanic_df['person'] = titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">child</th>\n",
       "      <th>1</th>\n",
       "      <td>  7.820000</td>\n",
       "      <td> 139.382633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>  4.543684</td>\n",
       "      <td>  28.323905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>  6.817586</td>\n",
       "      <td>  23.220190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td> 35.500000</td>\n",
       "      <td> 104.317995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 32.179688</td>\n",
       "      <td>  20.868624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 27.854167</td>\n",
       "      <td>  15.354351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>1</th>\n",
       "      <td> 42.382653</td>\n",
       "      <td>  65.951086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 33.588889</td>\n",
       "      <td>  19.054124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 28.995556</td>\n",
       "      <td>  11.340213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age        Fare\n",
       "person Pclass                       \n",
       "child  1        7.820000  139.382633\n",
       "       2        4.543684   28.323905\n",
       "       3        6.817586   23.220190\n",
       "female 1       35.500000  104.317995\n",
       "       2       32.179688   20.868624\n",
       "       3       27.854167   15.354351\n",
       "male   1       42.382653   65.951086\n",
       "       2       33.588889   19.054124\n",
       "       3       28.995556   11.340213"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byPerson = titanic_df.groupby(['person','Pclass'])['Age','Fare'].mean()\n",
    "byPerson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1828e9e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zSSrAzO6ZLzLmxYZGipFabJz3yjTGlFno0/f18U96mXrOzIb5oKFCCzuWWJqIcd4rUxpT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\nwDCXpAIY5pJUAMNckgpgmEtSAf4fdpZQuD9D1nkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1840c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplot histogram\n",
    "titanic_df['Age'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x180c3710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UCyM9BnwqIp6j6nkPHbPeG7gyIqAa1x7V0Oh49ZwlqXQPApfWPdz3A4vr7VsbO+6hGrL4\ndGaeAPwzm+fqz4Dj6+POp5rrMaLxHnOWpK3qf3LE4dgmjtUCPgt8OSLmAb1UV5MN7GMrj/+O6sbX\n/5fqPNrLh+w/Dfh6ROxSb/uL0RQ04qp03cZV6aTu5XXOm9hzllQMV6XbxDFnSSqQ4SxJBTKcJalA\nhrMkFcgTgpKK4dUamxjOkkoy47y3vT2nT53aloM93t/PubfcvNVV6YaKiBOAyMxPDNn+TeADVNOx\nv5mZNw7Z/8+Z+Ybtr7piOEsqyvSpU3ll70irBjdq2MkfmfnnABHR2tJr2slwljShRcTuwFeA1wCT\ngKXAwRFxI7AncGVmXhURjwAx6H0vopoduAfwL1Sr0rWNJwQlTXSnAj/PzEOAPwOeA9Zl5pHAsVR3\nbILf7S331O+7PzMPAy6kCva2MZwlTXQzgR8DZOYqqlXlflrv+zfgRVt4X1DfTq++S9IT7SzKcJY0\n0T0IHAgQEf8R+B/AxlG87wHgLfX7Xkc1vNE2jjlrMw1dzjSgKy9r0vh5vL9/vI/1ReDqiLiNatz4\nUn43aFtD/jvw+Av1++4EHgF+s53l/g5XpRulibQqXU9Pz8zD33N2Tunbs63HXbv6CW5betGE+A01\nNl7nvIk9Zw1rSt+etPsPnDQSV6XbxHAepY0b1gPM6OkZeif37daVf9UlNctwHqVn+59izllzb5y6\nR2/bjtn/5Bp+cMl12zR7SdLEYDhvg6l79NI3/SUjv1CStpOX0klSgQxnSSqQ4SxJBTKcJalAjZ8Q\njIg3Axdm5hER8XvAYqqpkSuBj2RmKyJOAeYB64EFmXl903VJUska7TlHxNlUC1NPrjddAsyvV3Hq\nAeZGxHTgdOAQ4Ejggoho6+pOktRtmh7WWAUcRxXEAG/KzGX14xuAOVQLjizPzHWZuaZ+z6yG65Kk\nojU6rJGZ346IGYM2DZ5e9wzQB/RSLdE3dLvUtVw8SttrvCehDF6Grxd4GlgDTBu0fRrw1AjHWQns\nM9yOzOSsi76zPTWOq3od2KI0+RuW+H2bkJl86Jwv08TiUV88/6S2HrPD2r4ewo5ivMP5noiYnZm3\nA0cBtwArgPMjYjKwG7A3Vfhuzb5b2hERM48+6eKuCYCIKG6VtiZ/wxK/bxMGfsMmFo+aKL/hRDde\n4TywLunHgKvqE34PAEvrqzUuA+6gGgOfn5n+k03ShNZ4OGfmI1RXYpCZDwOHD/OaRcCipmuRpG7h\nJBRJKpCr0mncuCa2NHqGs8aNa2JLo2c4a1y5JrY0Oo45S1KBDGdJKpDhLEkFMpwlqUCGsyQVyHCW\npAIZzpJUIMNZkgpkOEtSgQxnSSqQ4SxJBTKcJalAhrMkFchV6aQu4prYE4fhLHUR18SeOAxnqcu4\nJvbE4JizJBXIcJakAjms0UEb12+AZk7ugCd4pK5mOHfQs0+v5by3vf3G6VOntvW4j/f3c+4tN3uC\nR+pihnOHTZ86lVf29nW6DEmF6Ug4R8RPgdX1058DFwCLgY3ASuAjmdnqRG2SVIJxD+eI2A0gM48Y\ntO07wPzMXBYRVwJzgWvHuzZJKkUnes5vBF4UETfWn38O8KbMXFbvvwH4IwxnSRNYJy6lWwtcnJlH\nAqcC3xiyvx9wEFbShNaJcH6IOpAz82Hg18Beg/ZPA54e4RgrgdZwLTOz3QV3o/p3GPY3Gql122+4\nPd+1qeZvOOqmLehEOJ8ILASIiFdQhfFNETG73n8UsGwL7x2wL9AzXIuIaKLoblP/DsP+RiO1bvsN\nt+e7NtX8DUfdtAWdGHP+MvCViBgI4BOpes9XRcQk4AFgaQfqkqRijHs4Z+Z64Phhdh0+zqVIUrFc\nW0OSCmQ4S1KBDGdJKpDhLEkFMpwlqUCGsyQVyHCWpAIZzpJUIMNZkgpkOEtSgQxnSSqQ4SxJBTKc\nJalAhrMkFchwlqQCdWKxfUkF2bh+A8CMnp5GbkzySKvVeqGJA+/oDGdpgnv26bWc97a33zh96tS2\nHvfx/n7OveXmoLpvqLaR4SyJ6VOn8speb3pfEsecJalAhrMkFchwlqQCGc6SVCDDWZIKZDhLUoG8\nlE5dzQkU2lEVE84RsRNwBTALeB44OTP/pbNVqXROoNCOqphwBo4BJmXmIRHxZmBhvU3aKidQaEdU\n0pjzW4DvA2TmXcABnS1HkjqnpJ5zL7Bm0PMNEbFTZm7c1gOtXf1E+6qqPffMb9j1yTUjv3AbrH2q\nn8f72//38fH+/u0+hr+hv+H2asdvOJGVFM5rgGmDno8pmFut1kNAI2eHusmntuO9/oYVf8Pttz2/\n4URX0rDGcuCdABFxMHBfZ8uRpM4pqed8DfD2iFhePz+xk8VIUif1tFqtTtcgSRqipGENSVLNcJak\nAhnOklQgw1mSClTS1RpdLyJ2Bq4CZgIt4NTMvL+zVXWnegr/hZl5RKdr6TYRsStwNfBaYDKwIDO/\n29mqtK3sObfXu4CNmXko8Eng/A7X05Ui4myqP3KTO11Ll3of8ERmHga8A/ifHa5HY2A4t1FmXgd8\nqH46A3iqc9V0tVXAcTjDbqyWAOfWj3cC1newFo2RwxptlpkbImIxcCzwng6X05Uy89sRMaPTdXSr\nzFwLEBHTqIL6nM5WpLGw59yAzDyBatz5qojYvcPlaAKKiFcDtwJfy8xvdboebTt7zm0UEccDr8rM\nC4DngI11k8ZNROwF3AR8ODN/2Ol6NDaGc3stBRZHxO3ArsAZmfl8h2vqZq4tMDbzgT7g3IgYGHs+\nKjN/28GatI1cW0OSCuSYsyQVyHCWpAIZzpJUIMNZkgpkOEtSgQxnSSqQ1zmrMfUU7IeA+6muWZ4E\nPAacmJm/HOb1JwCzM9P7R2rCM5zVtF9m5n4DTyLis8DnqRY2GsqL7qWa4azxdgfw7oiYAyykWnnu\nUeC/MmgVuoj4E+AsYPe6nZyZd0TEWcAHqKbFr8jMUyNiFvBFqv+ff0vVM181jt9JajvHnDVu6kXg\n3wusAP4OOD4zZwH3AR+k7jlHRA/V0qtHZ+Z/Bv4W+Kv6Zgb/Hdi/bhsi4hXAmcDCzDyQqld+8Lh+\nMakBTt9WY+ox5wQeqDdNBu4CrgC+kJn7D3n9B4HDM/PEernLdwMBzAbWZ+bbIuJaqjt8XAcsycz7\nI+K/AJcD36vbdzLTBafU1RzWUNMeGzzmDBARbxzyvBfoHfR8CnA38FXgNuBe4L8BZOYx9S2s3gl8\nPyLel5n/KyJ+RHUnmjPrffMa+0bSOHBYQ52QwJ4RsXf9/K/ZdAcZqNbC3gBcQBXO7wR2joiXRcQD\nwMrM/DTVspizIuLvgYMy80tUdwB50/h8Dak5hrOattm4Wb105fuBr0XEvcDvUwXxwOvvBf4JeBC4\nnWpM+jWZ+WvgS8BPIuJu4MXAV4ALgfkR8Y/AxcBfNvqNpHHgmLMkFciesyQVyHCWpAIZzpJUIMNZ\nkgpkOEtSgQxnSSqQ4SxJBfr/ZJk4OWkxHtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x180c3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Pclass',data=titanic_df,hue='person',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[\"person\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we'll define alone as having no sibling (SibSp) or parent (Parch)\n",
    "titanic_df['Alone'] = titanic_df.SibSp+titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:121: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "#if alone = 0, they traveled alone; otherwise they traveled with someone\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']>0]='With Family'\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']==0]='Alone'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <td> 537</td>\n",
       "      <td> 537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>With Family</th>\n",
       "      <td> 354</td>\n",
       "      <td> 354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SibSp  Parch\n",
       "Alone                    \n",
       "Alone          537    537\n",
       "With Family    354    354"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byStatus = titanic_df.groupby(['Alone'])['SibSp','Parch'].count()\n",
    "byStatus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x185ee198>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a/ykzV42w1gXAMcA1wBcy86SRrEfDFxHvBt4OTM/Mx+u+f2tmZm8rK5fhdOCuBeYA36p/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\nDpeSUUh49NcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18195ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone',data=titanic_df,palette='Blues',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What facotrs helped people survive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df['Survivor'] = titanic_df.Survived.map({0:'no',1:'yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a291898>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a453080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survivor',data=titanic_df,palette='Blues',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a340048>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3409e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Was passenger class a factor to surviving?\n",
    "sns.factorplot('Survivor',data=titanic_df.sort(\"Pclass\"),palette='Blues',kind='count',hue='Pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of those who died, mostly in 3rd class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a3787b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bLF0+UDWQam7/1yl7GQK2wJYD7K/JwAgR2cUYsxP2Sebkis880fuchwbwOUoN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\nXUTFmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9980f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Pclass\",y=\"Survived\",data=titanic_df.sort(\"Pclass\"),hue=\"person\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Men had lowest survival rate.  Women and children had much higher survival rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Was age a factor in survival?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Survival rate decreases as age increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ab9cef0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZrNIGZAtJkYXCxQesRmIZbchKNplHkFjG3Uhqbr4YyCp5v73m8tTJLusflhPv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\nh7T2raEhciGBxDEA+Ozl1EU/Rm3/FYR9D3Gg+g4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wuXcDw+G1dFV3ct5R/6byvtVt/NZz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\npUAhIiJF/X8EBCAkHbMPGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab9ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Age',y='Survived',data=titanic_df,hue='Pclass',palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "unclear on what above graphs are showing with survived, if it's always a 1 or 0...try making a pivot table or group by.  After developing pivot table below, the factorplot and lmplots are showing mean of survived value and a range around the mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = pivot_table(titanic_df, values='Survived', index=['Sex'],\n",
    "                     columns=['Pclass'], aggfunc=[np.mean], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</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",
       "      <td> 0.968085</td>\n",
       "      <td> 0.921053</td>\n",
       "      <td> 0.500000</td>\n",
       "      <td> 0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td> 0.368852</td>\n",
       "      <td> 0.157407</td>\n",
       "      <td> 0.135447</td>\n",
       "      <td> 0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td> 0.629630</td>\n",
       "      <td> 0.472826</td>\n",
       "      <td> 0.242363</td>\n",
       "      <td> 0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean                              \n",
       "Pclass         1         2         3       All\n",
       "Sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ae699b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIWJ9lPUCpYWiOBZ8HUWJ2MhnsAxYrgZ2jCFWRrVSbAtApe8wvYCol1FVts64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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a90efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's cleanup lmplot to reduce # of ages displayed\n",
    "generations = [10,20,40,60,80]\n",
    "\n",
    "sns.lmplot(x='Age',y='Survived',data=titanic_df.sort('Pclass'),hue='Pclass',palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ae69390>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cPvMkYC2Ac+52Mzs++aSZvQj4E3z516Pm8DmNKOKj/nJgRTA8Oo7vET0Vjgx2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\nUvf/A5t6ps1Dpi4iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae4c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as age increases, differences between genders and their survival rates magnifies greatly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional questions:\n",
    "1. How does the deck level impact survival rate? Does it match expectation?\n",
    "2. Does having a family member impact survival rate?  Better off alone or with someone else?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "person         891 non-null object\n",
      "Alone          891 non-null object\n",
      "Survivor       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(8)\n",
      "memory usage: 111.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#plot deck level and survival rate?\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Permanently update the NaN to 'Unknown' so I can pull \"U\" as the Cabin, then set Level accordingly\n",
    "titanic_df.fillna('Unknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df['Level']=titanic_df['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "U    687\n",
       "C     59\n",
       "B     47\n",
       "D     33\n",
       "E     32\n",
       "A     15\n",
       "F     13\n",
       "G      4\n",
       "T      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#below tries to map each level to a number\n",
    "titanic_df['Level']=titanic_df['Level'].map({'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7,'U':8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#basic update statement\n",
    "#first statements add new column, default it to 0, then updates the new column based on values in another colum\n",
    "titanic_df['DeckLevel']=0\n",
    "titanic_df.DeckLevel[titanic_df.Level==1] = 100\n",
    "\n",
    "#second statement can updated column value based on entry within the same column (think find/replace)\n",
    "#dframe.quality[dframe.quality==5] = [1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Level</th>\n",
       "      <th>DeckLevel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>100</th>\n",
       "      <td>  39.623887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>0  </th>\n",
       "      <td> 113.505764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0  </th>\n",
       "      <td> 100.151341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>0  </th>\n",
       "      <td>  57.244576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0  </th>\n",
       "      <td>  46.026694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>0  </th>\n",
       "      <td>  18.696792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0  </th>\n",
       "      <td>  13.581250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>0  </th>\n",
       "      <td>  19.157325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Fare\n",
       "Level DeckLevel            \n",
       "1     100         39.623887\n",
       "2     0          113.505764\n",
       "3     0          100.151341\n",
       "4     0           57.244576\n",
       "5     0           46.026694\n",
       "6     0           18.696792\n",
       "7     0           13.581250\n",
       "8     0           19.157325"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byLevelandDeckLevel=titanic_df.groupby(['Level','DeckLevel'])['Fare','Age'].mean()\n",
    "byLevelandDeckLevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x20316ac8>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dl8bhL6ss10jUsbmbiIxMCuydfX4XfiPEV+Wun151gUZCNRmR7vf33OcN8Lus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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203165f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Level\",y=\"Survived\",data=titanic_df.sort(\"Level\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>  7</td>\n",
       "      <td>  594.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td> 35</td>\n",
       "      <td> 5334.7709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td> 35</td>\n",
       "      <td> 5908.9291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td> 25</td>\n",
       "      <td> 1889.0710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td> 24</td>\n",
       "      <td> 1472.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>  8</td>\n",
       "      <td>  243.0583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>  2</td>\n",
       "      <td>   54.3250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>  0</td>\n",
       "      <td>   35.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived       Fare\n",
       "Level                     \n",
       "A             7   594.3583\n",
       "B            35  5334.7709\n",
       "C            35  5908.9291\n",
       "D            25  1889.0710\n",
       "E            24  1472.8542\n",
       "F             8   243.0583\n",
       "G             2    54.3250\n",
       "T             0    35.5000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#with so many unknown level entries, the below excluded level of U\n",
    "#NOTE: NOT in is achieved by using [~idx]\n",
    "idx = titanic_df['Level'].isin(['A','B','C','D','E','F','G','T'])\n",
    "byLevel_2 = titanic_df[idx].groupby(['Level'])['Survived','Fare'].sum()\n",
    "byLevel_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x209c3160>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f74ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Level\",data=titanic_df[idx].sort(\"Level\"),hue='Survivor',kind='count',palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, split nearly 50/50 on level A with much higher survival rates on levels B, C, D, E and F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
 }
No results found