Titanic Survival Analysis v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up the Titanic csv file as a DataFrame\n",
    "df = pd.read_csv('train.csv')\n",
    "\n",
    "# Let's see a preview of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 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",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Questions to Answer:**\n",
    "\n",
    "- Who were the passengers on the Titanic? (Ages,Gender,Class,..etc)\n",
    "- What deck were the passengers on and how does that relate to their class?\n",
    "- Where did the passengers come from?\n",
    "- Who was alone and who was with family?\n",
    "- What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Who were the passengers?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122d5b610>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What was the difference in gender?\n",
    "\n",
    "sns.countplot('Sex',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e4575d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What about genders by the ticket classes? More males than females in 3rd class.\n",
    "sns.countplot('Pclass',data=df,hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many of those were children?\n",
    "\n",
    "# First make a function to break these out\n",
    "def male_female_child(passenger):\n",
    "    age, sex = passenger\n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex\n",
    "\n",
    "# Then create a new feature called 'person' based on the function\n",
    "df['person'] = df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123388e50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retry the same countplot on 'person'\n",
    "# many children in 3rd class, very few in first\n",
    "sns.countplot('Pclass',data=df,hue='person')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123cc3d10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add age context - plot a histogram of ages\n",
    "df['Age'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0    30\n",
       "22.0    27\n",
       "18.0    26\n",
       "19.0    25\n",
       "30.0    25\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add age counts:\n",
    "df['Age'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the mean age?\n",
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "Name: person, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many men, women and children on board?\n",
    "df['person'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x124975b10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 934.5x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Age with FacetGrid and kdeplot using multiple subplots\n",
    "fig = sns.FacetGrid(df, hue='Sex',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True) # add the kdeplot\n",
    "oldest = df['Age'].max() # what should the upper limit of the x axis be?\n",
    "fig.set(xlim=(0,oldest)) # set the xlimits from non-negative numbers to oldest\n",
    "fig.add_legend() # add a legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10ec6a310>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T7YuLX4x2GYOer93HcxXPsXjPYrpCXZxdcDY3TbqJWdmzNJRZRERERKT/6Mt2Lw26EJo3Ps8+9fpT0S5jyGjyN/H6rtd5tfJVmvxNjE4ZzdVjr+bSsktJjkmOdnkiIiIiIsONQmgvDboQOmryKPvoy49Gu4whxx/0s7RqKW/sfoOKxgo8Tg8XlVzEgrELmJY5Tb2jIiIiIiJ9Q1+se2nQhdAJ0ybYh194ONplDGk7m3byxu43WFa1jPZAO6OSR7Fg3AL1joqIiIiI9N6gDqHGGJe1dlA/11EhdBjrCHSwfN9y3qh8g4qmcO/oxSUXc/OkmxmdOjra5YmIiIiIDEX9HkKNMSXA88B7wExgA3AjMAH4OZAA+ICbrbVVxpjXgdXAWcDfgF3A94Eg0GitnWuM8QK/AWYBAeCr1trXjDE3A5cDccAoYKG19hv9eX2u/jy4RJfX5WVuwVzmFsw92Dv6/I7neWrbU5xbeC7/NuXfmJo5NdplioiIiIjIB40DPmutfdsY8yDwn8CVwBXW2hpjzDXAD4HPRNp7rLWzAIwx64ALrbV7jDEpke3/CVhr7RRjzHjgRWPM2Mi26cAMoBPYbIz5lbW2sr8uTCF0hChOKubGiTdy5egreWXXK7yy6xVeq3yNU3NO5cszvsz0rOnRLlFERERERA6ptNa+HXn/F+DbwGTgpch8L06gqlv7x7q9fxt4yBjzOPDPyLqzgF8BWGvfN8bsBA6E0FestY0AxpiNQDGgECp9I9GTyPzR87mo5KKDPaM3PHcDFxZfyH+d8l8UJhZGu0QREREREYEj75tsBjZYa08/RvvWgzta+wVjzBzgEuA9Y8wpxzlXZ7f3Qfo5Jzr68+AyeHldXi4suZD/Oet/uHzU5by++3WuePIK7llxD42djdEuT0RERERkpCsyxhwInNcDS4HMA+uMMW5jzKSj7WiMGWWtXWat/R5QAxQCbwGfjGwfCxQBm/v5Go5KIXSE87q8zB89nx+d9SPm5M7h4Y0Pc/mTl/Ps9mcZbJNWiYiIiIiMIJuB/zTGbAJSCQ+lvRq42xizhvBERGccY9+fGmPWGWPWA+8Aa4D/AxyR+0UfIzypUecx9u9Xmh1XDrOzaSd/2vAndjTt4Iy8M7jttNs0RFdERERE5JCBmh33aWvt5P4+VzSoJ1QOU5xUzG2n3cb1469nZfVK5j81nwfWPUBXqCvapYmIiIiIyDCgnlA5prqOOh7Z9Agr969kfNp47jjjDiakT4h2WSIiIiIi0dTvPaHDnXpC5ZjSvGl8ccYXuWXaLVS1VHHdM9fxy5W/pDMYlaHjIiIiIiIyDCiEynHNypnFXWfdxel5p/OHdX/gqkVXsbJ6ZbTLEhERERGRIUghVHok3h3PZyZ/hq+d8jVau1q56fmbuGvpXbT4W6JdmoiIiIiIDCEKoXJCJmVM4o4z7mBe8Twe3/w4ly68lIVbFxKyoWiXJiIiIiIiQ4BCqJwwr8vLdeOv47unfZdUbyrfe+d7XP/M9aypWRPt0kRERERE5CQYY84xxjw9EOdSCJWTVpJcwq2zb+VzUz7H3pa9fOrZT/GNN79BRWNFtEsTEREREZFBSiFUesUYw+l5p/PDs37IJWWX8OquV5n/1Hy++eY32Vi7MdrliYiIiIiMGMaYEmPM+8aYh4wxW4wxfzXGfNQY87YxZqsxZnZkWWKMWWWMeccYM+4ox4k3xjxojFkeaXdFn9bZk+eEGmMuAn4JOIE/WGt/fMT2GOBh4BSgFrjGWruj2/YiYCNwu7X2ng87l54TOrQ1dTbx3I7neKPyDTqCHczMmsnHx3ycecXziHPHRbs8EREREZHeOu5zQku+9cy9wPQ+Pu/qHT++5Csf1sAYUwKUAzOADcAKYA3wWeBy4NPAjUCbtTZgjPko8B/W2quMMecAX7fWXmqM+RGw0Vr7F2NMCrAcmGGtbe2LC3Edr4Exxgn8GpgH7AZWGGMWWWu7d3N9Fqi31o42xlwL3A1c0237z4Hn+qJgGdySYpK4Ztw1XFZ2GW/ueZM3Kt/gtrdv466ld3Fm/pmcW3gup+acSm58LsboOb8iIiIiIn2swlq7DsAYswF4xVprjTHrgBIgGfiTMWYMYAH3UY5xAXC5Mebrkc9eoAjY1BcFHjeEArOBcmvtdgBjzKPAFYR7Ng+4Arg98v4fwH3GGBO52PlABdAnqVmGhjh3HBeVXMSFxReytWErS6uWsrJ6Ja/segWAzNhMpmdNZ1rmNCakTaAspYx0b7qCqYiIiIgMecfrsexnnd3eh7p9DhHOf3cCr1lrr4z0nL5+lGMY4Cpr7eb+KLAnITQfqOz2eTcw51htIt26jUC6MaYD+CbhXtSvcwzGmM8DnwfIyc/pcfEy+BljGJs6lrGpYwlNCFHZXEl5fTnbGrexev9qXtr50sG2SZ4kypLLGJUyitLkUsqSyyhLKSM3PheH0e3LIiIiIiJ9IBnYE3l/8zHavAB8yRjzpUjH4gxr7aq+KqAnIbQ3bgd+Ya1t+bAeLmvt74HfQ/ie0H6uSaLEYRwUJxVTnFTM+ZwPQGNnI7ubd7O3dS9VLVXsbd3LSztfosnfdHC/WFcskzMmMzNrJmfmn8nUjKk4Hc5oXYaIiIiIyFD2E8LDcW8DnjlGmzuBe4G1xhgH4ZGtl/ZVAcedmMgYczrhCYUujHy+FcBa+z/d2rwQabPEGOMC9gGZwJtAYaRZCuEu4O9Za+871vk0MZEANPubqWqtYm/LXva07KG8oZzKpkpChEjzpnFRyUV8YtwnGJUyKtqlioiIiMjIovvHeqknPaErgDHGmFLC3bbXAtcf0WYRcBOwBLgaeNWG0+1HDjQwxtwOtHxYABU5INGTSKInkbGpYw+ua+tqY51vHe9Vv8fjWx7nkfcf4fTc0/nSjC8xJXNKFKsVEREREZGeOm4Ijdzj+UXC44KdwIPW2g3GmDuAd621i4AHgD8bY8qBOsJBVaRPxbnjmJM7hzm5c2jyN7F492Je2PkC1z97PfOK53Hr7FvJjMuMdpkiIiIiIvIhevSc0IGk4bhyItoD7by440WerXgWr8vLN0/9JpePulyz7IqIiIhIf9EXzV7SlKMypMW6Yrli9BX84IwfkBufy21v38b33/k+ncHO4+8sIiIiIiIDTiFUhoWc+By+ceo3uKzsMhaWL+Sm527C1+6LdlkiIiIiInKE/n5Ei8iAcRgHV465kpLkEn6/9vfc9NxN3H/B/eQl5EW7NDmCPxCizR+gK2ix1hKyELSWUMgSinx2OQwup8HtdOB2OvA4HbidBqfDaLi1iIiIyBCme0JlWCpvKOeX7/2SBE8CD174IEVJRdEuaXixFjoaCDZVU1ezl6aGOtpaGuloqcff1kSgvYlARyt+v59QsAsbDECwCxsKB8+AdRCk++KkHQ/tNoZ2PHQQQ7v10IqXJuJpsnE0EUeTjaeFODweNwkxrsMXb/g1OdZNeryH1HjPB17T4jw4HAqwIiIi0iuD+suEMebLwH8AK621n+yH499O+Kkn95z0MRRCZbja1bSLn733MxLdifz5Y38mKy4r2iUNHYFOqN8J9RUEayto3LuFLl8FpqUaT0cN8V11uAkcc/eQNfiNmyBOgsZFECfWOAkZBw7ASQgHoXAEteFXV6gL54ccs7s2RwKNjhQaHKnUmWRqbAo1Npn9oST2dCWypyuBGpuCj2T8uA/u53IYMhNjyE32kpPsJTvJS27ktSA1lsK0ODITYtTTKiIiIh9mUH9RMMa8D3zUWru7n45/O70MoRqOK8NWUVIRX5n5FX664qf8+0v/zkMXPURyTHK0yxpcQiGor4B966B6A1171xKsWkdM614M4R+onECMjaHaZlFt02g042l3JxOMSQZvCu7YRGJj4/B6vcR544iP8+L2xIA5iVvOQwEcIT+OoB8T7MQR7MAZaMPZ1YYj0Ioz0Iqzqw1nVzNx/kaSOhsp82/D5W/EGWgLH8MBxBw6ZKcrkSZPFnWuHPY7MtljM9jZmkZ5QyqvtKdQ2ZWI7XZ7vNftoDA1jqK0OArTwq+lGfGUZcZTkBqHUz2pIiIiMkgZY34LlAHPGWMeBUYBkwE3cLu19iljzM3AfCAeGAPcA3iAG4BO4GPW2jpjzOeAz0e2lQM3WGvbjjjfKODXQCbQBnzOWvv+cetUT6gMd5tqN/GLlb9gRtYMfjfvd7gd7uPvNFy11UHlcqhcit21FLt3NY5AOwBBDNtDeWyyRWwL5VHjyiYUn4U3OZvM5CTyEw05cZDoifI1HIMJ+nH5m3D5G3D6G3F1NuHyN+Ly1+Nur8Xd4cPTUYsz0HrYfiGHm1ZvDo2eXGqcmVSSTXkgi42dmaxqTaO261Ci9TgdlGTEMTorgVGZ4WV0Vnjxup0DfckiIiISHcf/Rfr25HuB6X183tXc3viV4zUyxuwAZgFfBTZaa/9ijEkBlgMzgAXAbZH3XsIB85vW2t8aY34B7LTW3muMSbfW1kaOeRdQba39VfeeUGPMK8AXrLVbjTFzgP+x1p53vBrVEyrD3oT0Cdw86Wb+sO4P/GT5T/jOad+JdkkDp70etr8O21/H7lyC8W0GIIiTTZSwIjCXjbaY3c4iPKkFlKZ5KE2CWUmQ5o1u6SfKOj10xWbQFZvxoe0cgTbc7b5IKPUdfJ/W4SO7ZQszOhsONXZCZ1w6DbFF7HPls4NcNvuzWLsjnQfWp9JmwwHVaQylmfGMz0lkQm4S43MSGZeTSH5KrIb2ioiISLRcAFxujPl65LMXODBRymvW2mag2RjTCPwrsn4dMDXyfnIkfKYACcAL3Q9ujEkAzgD+3u37Tgw9oBAqI8IZeWewu3k3j25+lLFpY1kwdkG0S+ofoRBUrYbyV6D8JezudzE2SIcjjtV2LG91fYL37Fh2eUYxOi2GKelwXjoUJsBIyUohVxydiUV0Jh59sioT7MDTth9P2z5i2vbhaasioW0fE5sWM83fLaDGQJs3m/0xJex0FrGxK5d3t2Xzu7VZNBEPQFKsiyl5yUwuSGZKfngpSotTMBURERnuetBjOQAMcJW1dvNhK8M9lp3dVoW6fQ5xKCM+BMy31q6JDOE954jjO4AGa+0J9/gqhMqIcfXYq6lsruTHy37MlIwpjE8bH+2S+kYoCDvfhg1PwqZF0FoDwC7PKJ4PXcbz/ulsNKOYlOFkTjZ8JgPy4kdO6DxR1uk9GFKbj9jmCLThaavG07YvElKryG7ZQ1HzKs4O+cONvNDmyaQqpoRtppB1vlyW7sjib8E8mognyeticn4yUwtSmFEUXrISh1i3s4iIiAwFLwBfMsZ8yVprjTEzrLWrTmD/RKDKGOMGPgns6b7RWttkjKkwxiyw1v7dhH9ln2qtXXO8AyuEyojhMA4+N/Vz/OCdH/D1N77OY5c+Rrw7PtplnZyjBM+gM4a1nhk8EljAq4EpdAaTmZUFF+TArZkQN4Jvhe0rIVccHUmldCSVHr7BhnC31xDTugdvy25iWveQ17qH0ta1XBDsDE8F4IYmdxYVrlLW1xSydEc+L4SK2GGzyUuJY0ZRamRJYVJeEjEu3WMqIiIivXIncC+w1hjjACqAS09g/+8Cy4CayGviUdp8EviNMeY2wt94HgWOG0I1MZGMOJvrNvPTFT/lkrJL+NFHfhTtck5M7TZY9RdY8wg07yPkjGFL3Ez+1DKbJ9un4XR7mZsHZ+XBpDRwncQEtdKHbAh3h4+Ylj3EtO7G27wLb8suYlr3YGwIAL/xssNVwrpAIav8BWwMFVPhLGFcUQ6zS9OZU5rGjKIU4jz6zVBERGSQ0HiyXlIIlRHpyfInWbRtEfeeey/nF50f7XI+nL8NNj4Fq/4MO9/GGgd7k2bwiP8sHqyfht94mZkF5xfAnGzwqANt0DNBf7jXtHkn3pZd4dfmXQdn7g1h2GNyWR0oYm2ojE2UEcqZxtTRRcwpTeOUklSSvOraFhERiRKF0F5SCJURKRAKcNfSu2jpauGpK54ixZsS7ZI+yFcOy38Ha/4Gnc10xeey2HsOd9WcyTZ/GiWJcH4hnJM/9GaylaOwFneHL9JbuhNv805imiqI6fAdbLLd5rIuVMp6W0pjymTSRp/K9NGFzC5NIy1+kD5X/1JSAAAgAElEQVQ7R0REZPhRCO0lhVAZsSqbK7ljyR1cUHwBPzn7J9EuJ8xa2PYqLPstbH0R63BRm30mD3eew6+rxmBxcFYuXFEG41I0udBI4PQ3EdtUgbepAk/jdjyNFcR31QIQsoYKm8NaW8a+uPG4C2dSPPl0Zo8rIjlWPaUiIiL9RN/AekkhVEa0RdsW8WT5k9x7zr2cXxzFYbld7bDmUVj6G/BtxnpTeD/jAn5Ydx6L65JI9MDFRXBJCWTERq9MGRyc/kZiI6E0VFtBQksFycE6IBxMt9k89sSOJZQ7g6zxpzF22pl4YhOiXLWIiMiwoRDaSwqhMqIFQgF+uOyHNPubefKKJ0n1pg5sAZ3NsOIBWHIftNYQShvFspSP8e2dp1LR6qIoES4vhXPzwat5aeRDuDobcDdW0Lq/Amf9drI6K8iw9QAErIO9nhLaMqeRMvo0siecjsmaCE71loqIiJwEhdBeUgiVEW93825+sOQHAzsst60Olv0uPOy2o4FQ7gxeSbqS75ePZm8rjE2B68bCqVkacisnz99ST83e7QR920hr28a40HZSTHjyI7/x0JwyAW/JbOJLZ0PeTEgrA4emVBYRETkOfTvrJYVQEeBf2/7FwvKF3HfefZxdeHb/nai1Ft75Jaz4A/hbCRacxgsJV3Ln1iKqWi3jU8Ph85RMhU/pe/tbLTuq9tNRs42E5m2Ms9uZYiqINX4AutxJOPJn4Cw4BfJnQv4pkJQX5apFREQGnSH1Lc0Y8xDwtLX2H0eszwP+11p7tTHmHODr1toPPEfUGLMDmGWt9R257WRpgJ8IcHHpxSzbt4wfLfsRs3NnE+vq4xsvOxphya/Dw279bYRKPsILCVdy+8YcqtssE1Itt0yGGQqf0o+y4g1Zo7NhdDYhewY7muCFmiB11XuIbdrGpMB2pldsY9yOxbgIAmATsjH5kVCaNxPyZkBcWpSvRERERHrLWrsXuDoa51YIFQFcDhc3TLiBu1fczf1r7+fLM7/cNwf2t8Ly38Pie6GjAVt8Jkszr+F767PY2hBifKrly1NhWobCpwwsh4GyZChLdsLoIjqDRWyoPZc/1MD6/X4SWncyzbGN2S3bOWXbarI3P3to59TScC/pgWCaOw08cdG7GBERETnIGHMj8HXAAmuBIDDXGPNVIAf4hrX2H8aYEsI9pJOP2D8d+BuQDyyhH3p+FUJFIsaljePMvDP544Y/cmnZpZSllJ38wYJd8N5D8Mbd0FoDBbPYXHQ9392Uz/LNQQriQ3x7FpyRo/Apg0OME2ZmhRcmefC1j2FVzRgerYFv+sD4W5nsqOCChApOD2ynpPx1YtZHRvUYJ2SNDwfSA+FUEx+JiMgINuVPU+4FpvfxYVevu2ndVz6sgTFmEnAbcIa11meMSQN+DuQCZwHjgUXAP459FL4PLLbW3mGMuQT4bJ9U341CqEg3C8YuYHXNau5ceicPXvgg5kQTorXw/tPw0vegbjtkT6Fq5te4a1sZz7waICUmyC1T4MIicGn+FxnEMmJhXlF4CVrY1hjPyv2T+YdvMnf6wuuK3Q1cnb6Ds73bGR3aTuzGpzCr/hw+gMsLOVPCoTRvZjiYpo3SxEciIiL96zzg7wfu37TW1kW+zz5prQ0BG40x2cc5xlzg45H9nzHG1Pd1kQqhIt0kxSRx1ZireHjjwzy9/WkuG3VZz3fe/S688B2oXAopRbTP/R737pvCAy934XIEuH4sXDkK4vSvToYYpwnP2Dw2Ba4dC21dsMYHK2tSeMQ3nZ/tC//QW5gAVxTW8dH47Uyw24lpKA+PCFj22/CBYpLC95QeGMZ7YOIjDQcQEZFh5ng9llHQ2e191P/j1ddhkSPMLZjL4j2Luefdezi78GySPEkfvkP9Dnj5dtiwEGJTsaf9J884zuXOxV1Ut3UxrxBuHA9p3oGoXqT/xbnh9NzwAlDVCu/VwKoaeKAijfsCaTjNLGZkOzl7nGFeahVjg9tw1G6B2m2wczGEwhMfkZDdbRjvjPB7TXwkIiJysl4FFhpjfm6trY0Mxz1RbwLXA3cZYy4GUvu0QhRCRT7AYRzcMPEG7lxyJ/+78n+57bTbjt6wswUW/xzeuS/ckzPtOrblX8FtSw1L9voZlQz/fSZM0PdpGeZy4+HSeLi0BLpCsLke3tsPq31Bfv4e/IxMUmIyOTP/LM4e7eIjuSFy/TvBtwVqt8L+DbDluUMHTC05fBhv7jTwxEfr8kRERIYMa+0GY8wPgTeMMUFg1Ukc5gfA34wxG4B3gF19WSPoOaEix/TIpkd4ZdcrPHLJI0zO6DZpWCgEax+Dl78PLdVQdi5tU2/il5sSeGCtH68r3PN5UXF4GKPISNbYCat84V7SVTVQ2xFePybVwUcKXMwtcDEn10msbYPacvBthdot4CuH1v3hxsYBmeMPPbs0byZkT9LERyIiEi36htdLPQqhxpiLgF8CTuAP1tofH7E9BngYOAWoBa6x1u4wxswDfgx4AD/w39baVz/sXAqhMli0B9r5zuLvkBefxyOXPILT4Qzf9/nsN2Dve5AxDnvqv/Fs82jueKeD6jbLvEL49ARIjol29SKDj7Wws/nQ0N31teGeU48T5uQ6+UiBi48UuBiX5sBhDLTXh0Opb0s4oNZuDT9zF8AZc2jiowP3mKaP1sRHIiIyEBRCe+m4IdQY4wS2APOA3cAK4Dpr7cZubW4Bplprv2CMuRa40lp7jTFmBlBtrd1rjJkMvGCtzf+w8ymEymCyvGo5v137W26d9iWu37EaVv8V4tJh5o2Up87l++/4eXtPkLIkuGWKht6KnIiOAKyvCwfSlTWwqzm8PiUGTs0N95DOyXUxMd2B02HCKbalOhxKfVuhrjzcYxpoD+8Ykxie+OjAMN78UyApXxMfiYhIX9N/LL3UkxB6OnC7tfbCyOdbAay1/9OtzQuRNkuMMS5gH5Bpux3chOcGrgVyrbXdZ2c6jEKoDCY2GOC+N7/N1o79LNq7n8xxl9M6YQG/WufkD2v9eJ1wwzi4uERDb0V6y9cOq33hHtL1deEJjwAS3DArx8WcPCdzcp1MyXDiPvAPLhSExspDPaZ15VBXAaFAeHt81uGz8eZr4iMREek1fevrpZ5MTJQPVHb7vBuYc6w21tqAMaYRSAd83dpcBaw8WgA1xnwe+DxATn5Oj4sX6U+Je9dQ9NavuLN5Nx8vyOOnk8/jnIRPcsc/O9jXGmReIdw8IdxrIyK9lxELHy0MLxAOpevrwqF0Q12A1yvDwTLWBadkO5kd6S2dllWMN7UExswL7xj0h4PowYmPNsKWF4DI76IpxYcP482dBjEJA369IiIiI9WAzI5rjJkE3A1ccLTt1trfA7+HcE/oQNQkcizuFh+FS39HevlrdMal4Z95A2f623iuYQlPrF1LsXMMPz0TJqozRaRfZcTCOfnhBaChEzbUwro6WF8b5Bd7gljC95ROz3IyI8vJjGwnM7NcZGWOg8xxhw7mj0x8VBsZyrtzMWz4Z3ibcYTb5kUeE5N/CmRNApdnwK9ZRERkJOhJCN0DFHb7XBBZd7Q2uyPDcZMJD73FGFMALARutNZu63XFIv3EBLvIXvdP8t79CybUxZ5xF7Cj9Hz+vjmVZzZ7iC3dQnbRk/yk5CvEujQrp8hAS4mBM/PCC0CzHzYc7CkN8sDaIIHIz5i58YaZ2eFQOj3LycT0WOJyp0Lu1EMHPDDxUe3W8Ov7/4LVfwlvc8ZA9uRwKM2J7Jc5Adx64K+IiEhv9eSeUBfhiYnOJxw2VwDXW2s3dGvzn8CUbhMTfdxa+wljTArwBvADa+0/e1KQ7gmVaEiqfI+ixb8itnE39TmT2TXpCt6oz+NPqxKpbXcxK7eJiYVrWNj8DFenfZSr0z4a7ZJF5Aj+IGxvgvfrw88q3VwP1ZE5ixwGRiU7mJLpZHKmgykZTiZmOIl3d7ut58DERwdCqW8L1G2HrrbIQVyQMTY8fDdnanh23pwpEJsy8BcrIiLRpHtCe6mnj2j5GHAv4Ue0PGit/aEx5g7gXWvtImOMF/gzMAOoA6611m43xtwG3Aps7Xa4C6y1+491LoVQGUie5moK3/kNaRWL6YjPYNeU+WyMncKDK5NYWx1DbkInV47zUZISfrjhP+tfZXP7Dn5a9P/I9WREuXoROZ66DtjSAOWNUN4A2xqhLjIzgQHKUhxMSHcwIc3J+HQH49Oc5CUYzIEZdW0ImveFw+iBpb4C2moPnSSlOBxGu4fTpDzNyisiMnzpD3wv9SiEDiSFUBkIjq4OctY8Tu6qR7FYqsbOY0fxOTyxOYVF78fjdlouLKvltPwmnN0eO9gSbOP/9v+dMd4ivpP32UNfVEVkyKjrOBRKyxvDzy7d13Zoe6IHxqc5GZ/mYEyqk9GpDkanOMiM6xZO2+s/GEwb93Bw8qO4NMiZFuktnQrZEyF9jO4zFREZHvQFsJcUQmVksZbU7W9RuOS3xLTspy5vOrsmXsbi+hz+uDKJ2nYnp+Q087HRtSTGBI96iHdbN/Jc49v8e9ZVnJt06gBfgIj0h7Yu2NEMO5qgoin8fmcTtAYOtUn0wOgUB6NTnYxOcVCc5KAoOfwa7zbhYbv1O44Ipzsh1BU+gMMFaaPCgTRrImRNCL+mloDDGY3LFhGRk6MQ2ksKoTJixNZup+jtX5O0dw1tSXnsmjKfzTHjeHBlImv2eclN6GT+OB+lkaG3x2Kt5S+1z1DdVcc9xV8l3ZU8QFcgIgPJ2vDQ3cpmqGyBXc2wuyX8vv6Ih42lew3FyYbiJCdFSYbipEhITQiR4d+DadwF9bugYSc07ILmKg72mrpiIHN8OJBmjg8vGWPCw3ydAzKJvYiInBiF0F5SCJVhz9nRRP6KP5G18V8E3bHsHn8Rlfmn88/3k/jX5nhcDssFZXWcnt942NDbD1MXaOL3NU8wOXYU38i9WcNyRUaYli6oag0P461qhao22Bd59bUfjJcAxLmgMBJKi5Mc5CUYCmL9lNo9ZHdVktC6E9OwKxxO27o9XtvhhrTS8GRIGWPDwTRjLKSP1mRIIiLRpS9+vaQQKsNXKEjmpmcoWP5HnP4W9pecwe5xF7GkJo2HViXia3Mdd+jth1nesp4XmpbwH1kLODvplH64ABEZirqC4Vl5jwyn+yKh1R86vL3HCTnxhvwEB2VxbUx07qXMUUVecA9p/r3Ete3F2bIXQt3+TsVnHgqkaWWRpRRSSyEmYWAvWERk5FEI7SWFUBmWEvaupfjt+4ir3U5Txmh2TZ7PZlvEQ6sTWReZ9Xb+WB+lqR8+9PbDWGt5uPZpfIEG7in6KmmupD68AhEZjqyFJj/sbw/3mNZ0RF7bwdcRfq1thyNyKsnuANNjfUyLqWKss4pi9pId2EtK5148XU2HN47PDN97eiCYppWFw2laKcSmatZeEZHe0x/SXlIIlWHF07SPgmV/IH3b63TGplI5+XJ2pk3jsfVJvLQtllhXiI+W1Z/Q0NsPUxto5Pc1TzA1diz/nXujhuWKSK8FQ+F7UWvaDwXUmo5wOPV1hGf3res4FFQTaaPIVDPKWc1ETzVjXfsppJrsUDVJgdrDjm09iZjUIkgpgZSiDy4a5isi0hP6wtdLmvFAhgVnZzO5Kx8he91CwLBn3AXsLjuP5ypSeHxpAh0Bw+kFTcwrqyPefWQfw8lLdyVzbuIsXmpaxuvN72q2XBHpNacDMmPDy7EEQ+HJkXwdUNseR21HKb6OUpZ2wNPtUNsZDrCOkJ8is58Ss49iU01hoIYyv4/CmrXk2Ffw2sNHgwQ9iZiUIhypJZBa/MGQ6tVEbCIi0nsKoTKkmaCfrPWLyHvvLzj9rfgKZ7F7/MUsa8jm4ZcT2dvsYmxaG5eO8ZGT0NUvNcyOn8zWjkr+WLOIcd4S8jyZ/XIeEZEDnA7IiA0vpB69jbXQ3OXB115AbUcBvg54vwMWt0NtB9S2WwKdLaQGaigwPgpMDQWBGgrbayiuXkO+eZlYDp8GuMudSDCpEGdaMe60SEhNLoSUwvBsvhruKyIiPaDhuDI02RBp294gf9mDeJuraMwcS+Wky1gXKOaRdeH7PjPj/Fw6ppbx6W39/p2oKdjK/TX/JMuVxp2Ft+A2+n1HRIaGjkA4lPq6Dfmt7QBfm6Wrsxlvh4/krhryTQ2FpoZ84yPf+Cg0NcSbw3tSA85YAokFONOKcaUVY1KKwgE1OdKTmpClkCoiw4H+kPWSQqgMLdaSvGsZBcsfJK52O21JuVROuoxN3kn8bV0iy3Z7iXcHOb+kntMKGnH1wX2fPbWlYyeP1b3IBcmn85nMKwbuxCIi/SwQCt+HeiCs7m+H/a2W1rZWXO01eDt9ZIbCvandQ2qyaT3sOEGHh0BCHiY13JNqDg71LQy/JuaCwxmlqxQR6TGF0F5Sd40MDdaSuGcVBcv/SML+TXTEZ7B95vVsSZvF4xuSeK0iFo/TMq+0jo8UNeB1DfyPK2O9xZweP5UXG5cwxlvERxJnDHgNIiL9weWArLjwcogBEoAErC2lpQuq22BnO6xoCwfV5tY2HG0+Yjp9pB8IqfU+Chp2U7BjFRnm8Jl9Q8ZJID4Hk9q9J7XbkN+kAnB5BvDKRUSkPyiEyqCXULWO/OUPkVS1Bn9sChXTFlCRPYeFm5N5fmkcIQtnFjZyXkkDCZ4Tf95nXzov6VSqumq4f/8TFHmyKY7Ji2o9IiIDwRhI9ISX0YdNsBsHFAFFtHTB/jbY1w7r2sLPUq1v7cTR5sPT4SPtQEht9FHQ5KNg1/tkmXocHPpR0WIIxGXhSC3CmVYSCafdh/wWgvtDZnQSEZFBQcNxZXCylqTKFeStfITEfevpiklk79iPsiXrDJ7cmsLL22LxBw0zc5uZV1pPWmwg2hUf1BJs44GaJ3E7XPyw4IukuBKjXZKIyKDX1gX72sK9qfsiIdXXGoC2WjwdPrJseAKlfOMjHx9FzhpyqMV5xFNVg7HpkRl+iw+/HzWlMBxavXqms4j0mobj9pJCqAwuNkRqxWJyVz5CvK8cf2wKVaPOYUPGmSzcmsprFbGELEzPbuHcknqy4/tnxtveqvL7+FPtvyj05PD9/M8T49DwMRGRk2UtNPoPhdQDS01bkFBbPTEdPnI4dE9qgfFRHAmpHg7/fyIUk4xJKTr8ftSDPapFmuFXRHpCfyR6SSFUBgUT6CR96yvkrPk7sQ2VdMRnUjXmPFYnzeafm1N4e5cXA5ya18TZxQ2kD6Kez2PZ3L6Dv9e/xClxE/l/uZ/EaTTZhohIfwja8MRJB3tRDyytIQLtjcR2+iLhNBJSHT6KnT7yrA8vh8/wG3LFYZMLcaQVYw4+fqbo0HDf+CxwDOCsdyIyGCmE9pJCqESVu6WGrA2LyNz4DO7OJtqS8tgz5nzecJzKs9sSWLU3BrfTMie/iblFDSTHRPeezxO1vGU9LzQt4azEGdyStQCH0RcXEZGB1hUMT5R0IJzua4ea9vBjaPwdLcR2+sil23Bf46PQEf6cxDFm+E0pwpVegqP77L7JheEZfp2ackNkmFMI7SX9lZSBZy3x1ZvIXvdP0ra/CVgaciazo3AuTzVO5vk18exrcZHoCXB+aT1nFjQS7wkd97CD0eyEyXTaLl5vfhev8fCZzCsUREVEBpjbCfkJ4eVwBkgkaBNp6CwNB9N2WNEOz3WEg2pbexueDh8Jfh95kYBaUF9DQcMe8neuIdM0HnbEEE5avdl0xudjk/JxphYQm16EN70Qk1wASfkQl6YhvyIyoimEyoBxtTeQvuVlMt5/jrj6nQTcsewbNZdV6WezcHchb77tpTPooDi5nesm1TIlq2VAn/PZX85KmE6XDfBy0zL8tot/z7pKQ3NFRAYRp4F0b3gh9cit4Rl+A6Gig89KreqEjZ1Q3wkt7X6c7T5iO30kdPlIDdSQ2+ojv81Hnq+CbOpxm8NH8XQSQ4Mrg+aYbNpjc+iKzyGUmI9JLsCTWkBsZhFJqZmkxMXgGQ7/EYqIHEEhVPpXKEhy5btkbH6elB1LcIQCtKQWs3HiJ1gUOoNXKlPYut6D2xFienYLpxc0UpDkj3bVfcoYw7mJs3AbJ683v0d7qJMvZV+Lx+GOdmkiItJDR39WKoAHyIss4UmUWrqgthPKO6G+PYS/rRFHRy0xHXXEdtWS2FVHSqiWtNY6slqWkVVTj8scPuKnzcZQadPYb9KpdWRQ786i2ZNNmzebjrgcAnG5OONTSYx1kxDjIsnrJtHrIsHrItF7YF34c6zbiVHPq4gMIronVPqeDZGwbyNp294gdfsbeNrq6PIksD//FF6J+QhPVpexZl8MQWvITehkZk4zs/KaiXcPzSG3J2J5y3pebFpCaUw+X8+9iTSXHhUgIjKSWQsdgRCdLQ0EW2uhvQ5Hey2ezjpi/bUkdNWSHKwnJVR32DNTATqtm2qbQjWpVNtU9tvwa7VNPbiu2qbSYeJI8LoOC6YHgmp8jCscXmO6LZHPh23zuoj3uHA6FGZF0D2hvaYQKn3Dhoivfp+0ba+Ttv0NPK21hBwu6jIn8F7CHB5vmcXbexLoCDhIjgkwI6eZGdkt5CYOr17PntjcsZMn618j3uHlSznXMTG2LNoliYjIYBcK4vI34O6oxd1Rh6uzHndnPc7Oehwd9bg6G/D463EH2z+wa4eJpcGVTp0jnVqTSrVNo9qmUhVKYXcgmcpAMrsDyXRy/MeJxbqdB0NqwpEB1nvEa7f1iTHhntrESADWMGMZ4hRCe0khVE6as7OFpD0rSd61nORdy/G01RFyuKhJH8+SmFP5e+ssVuxPxh904HWFmJLZwoycZspSOxjpP6RWd9XxRN3L1AWbmJ96LlelnY9L94mKiEgvOQLtuDrrD4bU8GvDwXWuzgbcnXU4Qh98znaXO4l2bxZtMVm0eDJodGXQ4Eyn3plOrUmjxqZQY5NpDjpp9wdp7wrS7g/S0XXofVtXkGDo+N8tY1yOg4H0YDg9GFQPrUvyHlqXcDDEhtf///buPUaus7zj+Pc558xtL/ZmvbaJL3UcYuKYNCQkUFLSKoRboAgjGkHSFqEqLf9QAVUrBFWlFiqkIlVtUUuropCWUkoKAVorapOmkBYQEEJwDE4CTkhix4kdO77sfWbO5ekf5+zurC94k7XnzHp/H2l03vc9Z9bv+tmZ2d+eWy0KdJixlEU/eIukECoLl6X0HfkZK/bvZOW++xk4+DCBpyRRg/0rt/Ld8Cq+MPYqdh9fCcAF9ZjLRqbYNjLJxRdMnxcXGTqb2lnM3aPfYdf0HjZW1/K7q9/Jyxqbyp6WiIic79wJk8mTwupsYG0ezfe6to5hfvKpMkllBXFjhHZjbbFcTdxYTbu+mnZ9hOnqCGPRMKO2gunEmW4nTM2E1NlH0tFPmI7z9szYdHzmW7JVQps7H7aRh9iBk8JrZ6itFOsiBorA21fV+bLyouiHZpEUQuW0LI3pP7yHwWd3MXDgxwwe3E0Y54f5HOlbz87qFdzdupK7Ri+jmUUYzqaVTS4bmeKykSnW9rd1BfoF+Mn0U9wz9h3G00l+ZfCV3DT8BtZUhsueloiILHeeEbbH50Jq+zhRa7RYHidqj+aP1nHCtHny0y0krq+aDalxffVcYG2sJq6PFO01ZJX+ec/NMp8NpvMCa5z35wXWIsROz65PmWrl25zpt9wwsNnDijtD6sw5sz9vz+zMhaAGqhHBcj/Ea/lRwRdJIVRyWUrj2F76D++h7/CefHnkCYI0P2fz+dqFPBK9jG/Gl3HX+OUc9GEMZ/1gm81D02wearJ5aHrJ3s+zbK2szbfGd/LA5MM4zutWvIq3Dl3HuurqsqcmIiJyRkHSnB9O5y1n2nmAPdXe1TRqzAup+Z7VEZL6MHF9FXFtmLjoJ9UVsIB7bmfu+aHCHXtXZwLsVEeAnWk347Rjj20+PtlKONPRxWbQX41OG1T7qiGNarGshDSqIX3Fo1GJ5trF+r5qRL2iQ417nIqzSAqhy41n1MYPUj+2j8bRvdSP76Nx9Cn6jj45GzhbQY0nw03syi7mf5tbuT/dylFWUA0z1g+2ZgPnppVN6lFv/fwsdaPpBN8e38muqT2kZFzR2ML1K67hmv5tuqWLiIgsfZ4RxhMdIfXYKUJrsUwmT/0lLCSuXZCH0/qqIqiOFEF1mKS+irgIr0ltmKQyCMGLu+6Cu9NKso49r3OhdSbUdh5WPDc+t30zyWgnL+yP9AbUqyF980JrVITUE8Y6tmucJtz2FSG4XsmDbiU0hdzF0X/eIimEnm/cCdsTVCcOUxs/SG3sINXxg9TGDlAZPUB9/FmidO6KtEdtiCdYx4+STTyUXsxu38yT/hIGqhnrBtusG2ixbrDFusE2qxrxsr+gULdMpFM8OPkou6b3MJpOULcqV/Rt4ar+rVxav4gLKyP68BARkfOaZTFhe5woHsuX7dHZZRSPE7bHiNpjhPF4vkymTvl1HCOtDpJUh0iqK0lqQyS1lR39Yqy6cm59Nd/Gw9pZ+V4yd9pJRivJaMVpvkxSmnE+1k7S/FY9cT4+f7tiLO5on/C1kgVcDKpTaEa9GtCozAXTRvWEZSWk3tFuVDu3PfVz6ye0z+Nb+py331i3LCiEmtmNwKeAELjN3f/8hPU14J+Bq4EjwLvd/ali3UeBW4EU+IC73/Pz/i2F0PksTfI33NYEYXOcqJU/wtY4QXMcnxwjmDxKZfII9ann6Wsdo+Lzb3syRY19voanszXs9TU85ht4LFvPXruQSl+NkUabkb6YVY2Ykb6YNX0xg7UzXxBAzj1356n2szwy/QSPNZ9mPMv/KtwfNLikvpFLahtZX13DmsowayoXMBj0K5yKiMiylIfWMaL2OGFcBNT2OGEySRhPFI/Joj852zdOv5cyDeuk1RWklX7SaIC0MpC3Zx7RXD+L+uevjzq3HcCDKufqYhlJls2F1LgItEmaB7lMJcIAAAsoSURBVN84o1kE15kg3D6pn9FO554z158Lxi8w5wJQjWbCanBS0K1FIdUwoBoF1KJ8mbfDubEwoFYJOrYLO7YrnhMG1CsB1TCkEhlhYFSCgCg0KmFAGBhRcNb3/OqXrUWKzrSBmYXAp4E3AvuBB8xsh7s/0rHZrcAxd7/EzG4GPgm828y2ATcDLwfWAf9jZi9z9yWRcCxtE7YnsSzNH57SaiV4ms4+yBI8S/E0y9tpClkKM8ssxdIES9tY3MKSFha3CNIWQZI/wrRFmLQIsxZR2qKWTtNIJ+hLJ2j4ySf6dxr3Bod8iKf9Ap7jpbM3q37ehjgSDTNeHcZrDVY2UlbWElbUElbVUl5aj1lRO6Q9mz3OzNhcW8/m2nrcncPJMZ5pH+KZ+BDPtA/zo6k98y66ULMqQ9Eg/UGd/qBBI6jTF9QILSS0gLcPXc9IZai070dERORc8aBCUl9FUl/1Ap6UESRNwmRiXjAN44mO8DpJkDaL298cpTr1bP6cYizIFn7P8yyskQU1sqhYhjWysI5HM+3a7DYe1sii+txzwhoeVnAL8aBjGYS4RXgQnTwWRXilc6yCW5CHYQtwrDi/NszHyde5GTC3jWOkGbRSaKVOO3VaaUY7melDM/E8uKYZcRF0Z0LuXD8mTppMNjOOpxlTWYVmFhCnGXHqxTJvn21RkAfUKDSiICAq2pUgIAzzoFoJg9n1lWIZBkYQGKHlF7IKA+Mf3nPNWZ/fcnPGEAq8Gnjc3Z8AMLM7gO1AZwjdDvxp0b4T+FvL/9ywHbjD3VvAk2b2ePH1vnu6fyywgL5K3wv9Ps6JFXu/x8b/+uNz8rWbXqFJlWlqRbvGFNVibJhx28C4DTBpfUwGAzTDPtpR/ogr/SSVBl5rUKuEDNSc/oozUHMGqxkXVpzqvMrOZP6IhZVcetVKRriELbP9dtbmSHyco/EoR+PjHE1GmUimaGZNxrJpDsXHaWUtMncyMt689gaqjRUlfgciIiK9KSseJ99B9UxPTAiSJkEylYfS4mEd7SCZzndIZG0sbRPMtmMsaxOkMVE8hjXbWBYX461i2xhLWz93j+1S9dQbb2N805tOGnd3ksxnA2k7yeYF1Jl2u+gnab73NsmcNPNimZFmkGZz43PrOvun3q4VZ0x5ft/bzJ3M86s2Zz12KuNStZBEsh54uqO/H/il023j7omZjQKrivHvnfDc9Sf+A2b2PuB9Rbd1+cjluxc0e+mmEeD5sich87zgmryZ3zhHU5EOeq30HtWk96gmvUl16T3nd00+9q6yZ/Ci2IfZ7e6Xlz2Ppawndou5+2eAzwCY2Q/cXfu4e4zq0ntUk96kuvQe1aT3qCa9SXXpPapJbzKzH5Q9h6XuzDdZgmeAjR39DcXYKbcxswhYSX6BooU8V0RERERERJaJhYTQB4AtZrbZzKrkFxraccI2O4D3Fu2bgG94ftndHcDNZlYzs83AFuD7Z2fqIiIiIiIistSc8XDc4hzP3wPuIb9Fy+3u/rCZfRz4gbvvAD4LfL648NBR8qBKsd2XyC9ilADvX8CVcT/z4r8dOYdUl96jmvQm1aX3qCa9RzXpTapL71FNepPqskgLuk+oiIiIiIiIyNmwkMNxRURERERERM4KhVARERERERHpmp4KoWZ2o5n91MweN7OPlD2f5crMbjezQ2a2u2Ns2MzuNbPHiuUFZc5xuTGzjWZ2n5k9YmYPm9kHi3HVpSRmVjez75vZrqImHyvGN5vZ/cX72L8VF3STLjKz0Mx2mtldRV81KZmZPWVmPzazh2ZubaD3r3KZ2ZCZ3WlmPzGzR83sWtWkXGZ2afEamXmMmdmHVJdymdnvF5/zu83si8Xnvz5XFqlnQqiZhcCngbcA24BbzGxbubNatv4JuPGEsY8AX3f3LcDXi750TwL8gbtvA14DvL94fagu5WkBN7j7K4ArgRvN7DXAJ4G/cvdLgGPArSXOcbn6IPBoR1816Q2vc/crO+55qPevcn0KuNvdtwKvIH/NqCYlcvefFq+RK4GrgSnga6gupTGz9cAHgGvc/XLyi7TejD5XFq1nQijwauBxd3/C3dvAHcD2kue0LLn7N8mvctxpO/C5ov054B1dndQy5+4H3P2HRXuc/JeF9agupfHcRNGtFA8HbgDuLMZVky4zsw3ArwG3FX1DNelVev8qiZmtBH6V/O4GuHvb3Y+jmvSS1wM/c/e9qC5li4CGmUVAH3AAfa4sWi+F0PXA0x39/cWY9Ia17n6gaB8E1pY5meXMzC4CrgLuR3UpVXHY50PAIeBe4GfAcXdPik30PtZ9fw18GMiK/ipUk17gwH+b2YNm9r5iTO9f5dkMHAb+sTh0/TYz60c16SU3A18s2qpLSdz9GeAvgH3k4XMUeBB9rixaL4VQWSI8v6+P7u1TAjMbAL4CfMjdxzrXqS7d5+5pcdjUBvKjObaWPKVlzczeBhxy9wfLnouc5Dp3fyX5KTfvN7Nf7Vyp96+ui4BXAn/v7lcBk5xwiKdqUp7i/MK3A18+cZ3q0l3F+bfbyf9wsw7o5+RT1uRF6KUQ+gywsaO/oRiT3vCcmV0IUCwPlTyfZcfMKuQB9Avu/tViWHXpAcVhbPcB1wJDxSE7oPexbnst8HYze4r8lI4byM97U01KVuxNwN0PkZ/j9mr0/lWm/cB+d7+/6N9JHkpVk97wFuCH7v5c0VddyvMG4El3P+zuMfBV8s8afa4sUi+F0AeALcXVpqrkhyHsKHlOMmcH8N6i/V7gP0qcy7JTnNf2WeBRd//LjlWqS0nMbLWZDRXtBvBG8nN17wNuKjZTTbrI3T/q7hvc/SLyz5BvuPtvopqUysz6zWxwpg28CdiN3r9K4+4HgafN7NJi6PXAI6gmveIW5g7FBdWlTPuA15hZX/G72MxrRZ8ri2T5Xv3eYGZvJT+fJwRud/dPlDylZcnMvghcD4wAzwF/Avw78CXgF4C9wLvc/cSLF8k5YmbXAd8CfszcuW5/RH5eqOpSAjO7gvxiBCH5H/S+5O4fN7OLyffCDQM7gd9y91Z5M12ezOx64A/d/W2qSbmK//+vFd0I+Fd3/4SZrULvX6UxsyvJL+BVBZ4AfpvivQzVpDTFH2r2ARe7+2gxptdKiYpbsL2b/E4FO4HfIT8HVJ8ri9BTIVRERERERETOb710OK6IiIiIiIic5xRCRUREREREpGsUQkVERERERKRrFEJFRERERESkaxRCRUREREREpGsUQkVEZEkzs3eYmZvZ1rLnIiIiImemECoiIkvdLcC3i6WIiIj0OIVQERFZssxsALgOuBW4uRgLzOzvzOwnZnavmf2nmd1UrLvazP7PzB40s3vM7MISpy8iIrIsKYSKiMhSth242933AEfM7GrgncBFwDbgPcC1AGZWAf4GuMndrwZuBz5RxqRFRESWs6jsCYiIiCzCLcCnivYdRT8CvuzuGXDQzO4r1l8KXA7ca2YAIXCgu9MVERERhVAREVmSzGwYuAH4RTNz8lDpwNdO9xTgYXe/tktTFBERkVPQ4bgiIrJU3QR83t03uftF7r4ReBI4Cvx6cW7oWuD6YvufAqvNbPbwXDN7eRkTFxERWc4UQkVEZKm6hZP3en4FeAmwH3gE+Bfgh8Cou7fJg+snzWwX8BDwy92broiIiACYu5c9BxERkbPKzAbcfcLMVgHfB17r7gfLnpeIiIjonFARETk/3WVmQ0AV+DMFUBERkd6hPaEiIiIiIiLSNTonVERERERERLpGIVRERERERES6RiFUREREREREukYhVERERERERLpGIVRERERERES65v8BRMYEBZhHfAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 934.5x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Children using the same method\n",
    "fig = sns.FacetGrid(df, hue='person',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True) # add the kdeplot\n",
    "oldest = df['Age'].max() # what should the upper limit of the x axis be?\n",
    "fig.set(xlim=(0,oldest)) # set the xlimits from non-negative numbers to oldest\n",
    "fig.add_legend() # add a legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x122e42a50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 906.375x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View usingTicket Class\n",
    "fig = sns.FacetGrid(df, hue='Pclass',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True) # add the kdeplot\n",
    "oldest = df['Age'].max() # what should the upper limit of the x axis be?\n",
    "fig.set(xlim=(0,oldest)) # set the xlimits from non-negative numbers to oldest\n",
    "fig.add_legend() # add a legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What deck and Class were they?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How do we see what decks people were on? There's a cabin row, but not all are filled out. \n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using a subset of the data that has Cabin information\n",
    "deck = df['Cabin'].dropna().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many passengers have cabin data?\n",
    "len(deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# strip off the first letter\n",
    "decks = []\n",
    "# Use the first letter as the deck \n",
    "for d in deck:\n",
    "    decks.append(d[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T', 'F', 'A', 'D', 'G', 'C', 'E', 'B']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alldecks = list(set(decks))\n",
    "alldecks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe of the decks with the Column Name 'Deck'\n",
    "deck_df = pd.DataFrame(decks)\n",
    "deck_df.columns = ['Deck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Deck\n",
       "0    C\n",
       "1    C\n",
       "2    E\n",
       "3    G\n",
       "4    C"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12585e3d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the number of people per deck\n",
    "sns.countplot('Deck',data=deck_df,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.dummies.com/education/history/titanic-facts-the-layout-of-the-ship/\n",
    "\n",
    "There's not an explanation for T - possibly Tank Top, which was for crew, but not passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Deck T \n",
    "deck_df = deck_df[deck_df['Deck'] != 'T']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124e0f0d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replot\n",
    "sns.countplot('Deck',data=deck_df,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now add this infomation to the main dataframe to examine Pclass/Deck "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def whatdeck(thedeck):\n",
    "    decks = ['T', 'F', 'A', 'D', 'G', 'C', 'E', 'B']\n",
    "    if 'nan' not in str(thedeck):\n",
    "        if str(thedeck[0]) in decks:\n",
    "            return str(thedeck[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then create a new feature called 'Deck' based on the function\n",
    "df['Deck'] = df['Cabin'].apply(whatdeck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  Deck  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  None  \n",
       "1      0          PC 17599  71.2833   C85        C  female     C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  None  \n",
       "3      0            113803  53.1000  C123        S  female     C  \n",
       "4      0            373450   8.0500   NaN        S    male  None  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_classdeck = df[['Pclass','Deck']].dropna().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Pclass</th>\n",
       "      <th>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass Deck\n",
       "1        1    C\n",
       "3        1    C\n",
       "6        1    E\n",
       "10       3    G\n",
       "11       1    C"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_classdeck.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the limited sample, first class is A-E, but not F or G. Second  are scattered between D through F and 3rd class are scattered between E-G, with only 3rd class in G.\n",
    "\n",
    "Also - T seems to be a deck for only First Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12631dc90>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replot with Deck info\n",
    "sns.countplot('Deck',data=df_classdeck,hue='Pclass',palette='spring')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Where did the passengers come from?**\n",
    "\n",
    "Embarked column has S,C and Q values. These stand for  Southhampton, Cherbourg, and Queenstown which is the order in which they boarded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "1    C\n",
       "2    S\n",
       "3    S\n",
       "4    S\n",
       "5    Q\n",
       "6    S\n",
       "7    S\n",
       "8    S\n",
       "9    C\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "She was built in Dublin and shipped to SouthHampton, sailed to Cherbourg, then Queenstown before starting the transatlantic crossing\n",
    "\n",
    "- in Southhampton, in the South of England most passengers boarded and most were 3rd class\n",
    "- In Cherbourg first and third class passengers boarded about on par, but very few 2nd class\n",
    "- In Queenstown (now Cobh, Ireland), south of , almost all passengers boarded were 3rd class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x126965910>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Showing which Pclass originated from which port, in order of stops\n",
    "sns.catplot('Embarked',data=df,kind='count',hue='Pclass',order=['S','C','Q'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Who was with family?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'person', 'Deck'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SibSp Number of Siblings/Spouses Aboard boolean\n",
    "# Parch Number of Parents/Children Aboard boolean\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if we add the booleans together, this should create a Boolean that shows if a passenger was alone (0) or not (nonzero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Alone'] = df.Parch + df.SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See 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": [
    "# How change the column name to something friendlier sounding\n",
    "df['Alone'].loc[df['Alone'] > 0] = 'With Family'\n",
    "df['Alone'].loc[df['Alone']== 0] = 'Alone'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  Deck        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  None  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female     C  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  None        Alone  \n",
       "3      0            113803  53.1000  C123        S  female     C  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male  None        Alone  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x126e050d0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize Alone vs With Family\n",
    "sns.catplot('Alone',kind='count',data=df,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What factors helped someone survive?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  Deck        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  None  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female     C  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  None        Alone  \n",
       "3      0            113803  53.1000  C123        S  female     C  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male  None        Alone  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Survived is a boolean\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342, 891)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only 342 people survived out of 891\n",
    "df[\"Survived\"].sum(), df.PassengerId.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic_df[\"Survivor\"] = titanic_df.Survived.map({0: \"no\", 1: \"yes\"})\n",
    "# adding a new feature to answer Yes or No to a Passenger's surviving\n",
    "didsurvive={0:\"no\",1:\"yes\"}\n",
    "df['Survivor'] = df['Survived'].map(didsurvive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>person</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>None</td>\n",
       "      <td>With Family</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  person  Deck        Alone  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S    male  None  With Family   \n",
       "1      0          PC 17599  71.2833   C85        C  female     C  With Family   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  None        Alone   \n",
       "\n",
       "  Survivor  \n",
       "0       no  \n",
       "1      yes  \n",
       "2      yes  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12710f450>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survivors\n",
    "sns.catplot('Survivor',data=df,kind='count',palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So first class passengers had a better survival rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1274a7a10>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare Survived with Pclass\n",
    "sns.factorplot('Pclass','Survived',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally Men did worse than women or children. \n",
    "We can see that all in first class did better than 3rd class, though Second Class children did the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x127777690>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare Gender and age outcomes\n",
    "sns.factorplot('Pclass','Survived',hue='person',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally the oldest did the worst. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x127c24d50>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare outcomes based on age\n",
    "sns.lmplot('Age','Survived',data=df,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would appear that the older did poorly across the passenger classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1284fda10>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add Pclass hue\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=df,palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12902dd90>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding in x_bins to get a StdDev dimension\n",
    "generations=[10,20,40,60,80]\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=df,palette='winter', x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would appear that older women fared better than older men, but with a wide standard deviation for men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x129acd750>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add Sex hue\n",
    "sns.lmplot('Age','Survived',hue='Sex',data=df,palette='spring',x_bins=generations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did the passenger's deck have an effect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "haddeck_df = df[['Survived','Deck']].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B, D and E decks were all associated with better outcomes, A, G and T were the worst, though G has a weirdly large standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12ab360d0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Deck','Survived',data=haddeck_df,palette='spring',order=['A','B','C','D','E','F','G','T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12ecdf450>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Deck', y='Survived', \n",
    "            data=haddeck_df,\n",
    "            kind='violin',order=['A','B','C','D','E','F','G','T'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did being with family have an effect?\n",
    "\n",
    "It appears that those that were alone had worse outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12ef62290>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Alone','Survived',data=df, kind='point')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make the lmplot we need a non-categorical value for 'Alone'\n",
    "def isloner(val):\n",
    "    \"reduce large SibSp values to 1\"\n",
    "    if val > 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new feature for a boolean 'Alone'\n",
    "df['Loner'] = df.Parch + df.SibSp\n",
    "df['Loner'] = df['Loner'].apply(isloner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outcomes were better for Children and Women with families, but there appears to be a slight uptick in survival for males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12f6a9fd0>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Loner','Survived',hue='person',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}