examples/example_titanic_discover_feature_relationships.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore feature-to-feature relationship in Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import datasets\n",
    "import beyond_correlation as bc\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-02 \n",
      "\n",
      "CPython 3.7.3\n",
      "IPython 7.9.0\n",
      "\n",
      "numpy 1.17.3\n",
      "matplotlib 3.1.1\n",
      "sklearn 0.21.3\n",
      "\n",
      "compiler   : GCC 7.3.0\n",
      "system     : Linux\n",
      "release    : 5.2.6-050206-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n",
      "Git hash   : 5d7759b8ad494988e1fdee8eb4dab48f01bd9802\n"
     ]
    }
   ],
   "source": [
    "# watermark is optional - it shows the versions of installed libraries\n",
    "# so it is useful to confirm your library versions when you submit bug reports to projects\n",
    "# install watermark using\n",
    "# %install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py\n",
    "%load_ext watermark\n",
    "# show a watermark for this environment\n",
    "%watermark -d -m -v -p numpy,matplotlib,sklearn -g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>SibSpParch</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>1</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>1</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>0</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>1</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>0</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  SibSpParch  \n",
       "0      0         A/5 21171   7.2500   NaN        S           1  \n",
       "1      0          PC 17599  71.2833   C85        C           1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S           0  \n",
       "3      0            113803  53.1000  C123        S           1  \n",
       "4      0            373450   8.0500   NaN        S           0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic_train.csv\")\n",
    "df['SibSpParch'] = df.SibSp + df.Parch\n",
    "\n",
    "cols = ['Pclass', 'SibSp', 'Parch', 'SibSpParch', 'Age', 'Fare', 'Sex', 'Embarked', 'Survived']\n",
    "classifier_overrides = set(['Embarked', 'Survived', 'Sex'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discover non-linear relationships\n",
    "\n",
    "_Github note_ colours for `style` don't show up in Github, you'll have to grab a local copy of the Notebook.\n",
    "\n",
    "* Fare predicts Embarked but Embarked poorly predict Fare\n",
    "* Fare predicts Pclass but Pclass poorly predicts Fare\n",
    "* SibSp and Parch both predict SibSpParch (the sum of both), SibSpParch predicts both of its components\n",
    "* Sex (a text columns) predicts Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pass in the subset of columns that we're interested in (we could analyse all columns if we wanted a bigger output)\n",
    "# note that the data is sampled (fraction==1) to get a random shuffle\n",
    "df_results, df_info = bc.discover(df[cols].sample(frac=1), classifier_overrides, include_na_information=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.heatmap(df_results.pivot(index='target', columns='feature', values='score').fillna(1),\n",
    "            annot=True, center=0, ax=ax, vmin=-0.1, vmax=1, cmap=\"viridis\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check the information on dropped rows due to missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>feature</th>\n",
       "      <th>target</th>\n",
       "      <th>n_dropped_na</th>\n",
       "      <th>pct_dropped_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parch</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SibSpParch</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Age</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>177</td>\n",
       "      <td>0.198653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fare</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>SibSpParch</td>\n",
       "      <td>Survived</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Age</td>\n",
       "      <td>Survived</td>\n",
       "      <td>177</td>\n",
       "      <td>0.198653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Fare</td>\n",
       "      <td>Survived</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Sex</td>\n",
       "      <td>Survived</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>Survived</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature    target  n_dropped_na  pct_dropped_na\n",
       "0        SibSp    Pclass             0        0.000000\n",
       "1        Parch    Pclass             0        0.000000\n",
       "2   SibSpParch    Pclass             0        0.000000\n",
       "3          Age    Pclass           177        0.198653\n",
       "4         Fare    Pclass             0        0.000000\n",
       "..         ...       ...           ...             ...\n",
       "67  SibSpParch  Survived             0        0.000000\n",
       "68         Age  Survived           177        0.198653\n",
       "69        Fare  Survived             0        0.000000\n",
       "70         Sex  Survived             0        0.000000\n",
       "71    Embarked  Survived             2        0.002245\n",
       "\n",
       "[72 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check 2D relationships using a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df.plot(kind=\"scatter\", x=\"Fare\", y=\"SibSpParch\", alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a regression plot to show the direction of the relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.regplot(data=df, x=\"Fare\", y=\"SibSpParch\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter for Pclass (a categorical) is more sensibly represented using a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df.plot(kind=\"scatter\", x=\"Fare\", y=\"Pclass\", alpha=0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P/da1k4HhY34/vnGJiINtXDDu6cs1fAy6tsPAAb9MkTh43li4A7h56NqmAa+UmnMzDfiQiDQDfwT8TQnLc2bijWBHMAZEjN9yrlkM2QTk0pOuMmmwj5dLw3A3OFnIJMCOAAYDOCYEi6+evvUOgBkLd4BcEjID/t6Hu/0zj2glxBfOqrpKKTUTMx0m+XfAL4BOY8wLIrIM2Fe6Ys1Sqh/yGcLiYYvnt4qHjoHngnGmXG3aMTOW7fe9Z5P+GYIxYFwGvXL2OM1w+YdOXy5joKqFVCrNjp07SGUdf3RPZoj8wBHSvYfJ9B7y+/mVUmqOzSjgjTH/YoxZZ4z5s8Lz/caY26dbR0QWi8ivRWS3iOwSkU/NRYEnlRuG5DFcBNdYIAL9+8AKMVVb/bQDIiPxsTcZD/BAQgx7ZTybXQFVM7gNoLIJmi/l1/sS7E1E+OXuAb/FnhlgKOOQ8SyGRtLw6uMzr6tSSs3QTC+yxoA/AS4GYqPLjTF/PM1qDvAXxpitIlIJbBGRxwojcuZWqAzyacol5z9PngA7BjL18EMDGATBTH6UsyyIVUO6HxB/W1aILreWBis5s3JZNp3xK3n2ta8TD5Vx4LjHlQmXmlSKnDe21+5DnXrXmFJqzs20i+afgCbgJuApoBUYnm4FY0y3MWZr4fEwsBtYdOZFnYZYEF9If9ZiIGPY35vmocee4t7PfhrXeH7vyklNdg/IE8Fj4kHAGP866NHeBHv7XYZzHjknRzqTpmckz0iin33HhmZctL/7H/+Lp3pqeLi7npeHKvjbb/+c/T3pYnmynvDA8yfO8gNQSqlTzTTgO4wx9wIjxpjvAn8IXDLTnYhIO3A58Nwkr31cRDaLyOaenp6ZbnIiOwSNazmSqeBouowjbh19XiUOIdxp+mIM4GCf2l0jUC1J4laWHBGGvDKyhBEMR9JlHM7EZ1y0gwcPTnj+yoGjfLWzhR2JCvYnYzzSVc/D3fUz3p5SSs3UjKcqKPweFJG1wDGgfSYrikgcf7KyTxtjTmn6GmO+AXwDYP369Wc2V0BZLbSuJ1P2KwBuecetvPmyu6C8Hv7bQ/4NRUxsxduAkEOY2Esv4j+P2w7xxiX+DUjG9VeOVZFIhlhUPfNAbm9vnxDy7e3tvHzoELt2V4zb52nH9Cil1KzNtAX/jcIdrPcCDwEvA//9dCuJSBg/3B8wxvz4jEs5E6vfwXPZDnbkFsOGT/gXQbNDfmJPwcIgU15uFf/irZOBioXQdAk0XUranG5o5ET33HPPhOf33Xcfn/70pycs+4u/+ItZbVMppWZipqNovmmMGTDGPGWMWWaMWWiM+dp064jfLP0WsNsY8+W5KOy0+l5lXfgwl4Rfg72/8MevG8bGoJ9cvpN+n8IK++PrQzHIDBbOEq7w56GZhZUrV9Le3g74rfeOjg5uu+22YqtdRLj11ltntU2llJqJabtoRGTaqQhOE9zXAB8GdorItsKyvzbGPDK7Is6Ak4WXf0KZVRhF0/OK37XScoU/Jn4K03aMVDb7wyxbrwA359/YlBlibeg1ek2cwl1VMyrePffcw6c+9Snuu+++4rJPf/rTfOUrX9HWu1KqZE7XB195phs2xvyOGdwwOidSff7cMeMNdUHNMpAQmMlDfrRzZtJCWrb/Izakh2DXg9D/Km8u289rTr0/WdiKG2dUvJUrV/Loo49OWHbbbbdx2223zWh9pZQ6E9MGvDHm8+eqIGelYgGEYxOX1bRBTStUNECy+5RVzEmPJ4S8hGDpdf4UBbEafxjm0a0A2HhUWWnoetGfOMwO1NfaKqUCZKbfyfrd8d/BKiK1InJ/6Yo1S3YY1t5Owisjb2xouQza3uSH79v+BsIT+8394ZHgYOGOW+aHvuXPMVPZDAtWQ8MKP+DHdccYKAy30dEvSqnz10xH0awzxhTn3DXGDOCPaz9/1LTxTHYVv8pcAqtuGWtZv+HDcOcPoKqVtAmTcWHEscgRY4gahqkhj03Owz84hKJQu8xft7zeb6VXNED1YvwJhS2GvDJo26Bf1KGUOq/NtH/BEpHaQrAjInWzWHf+LXszfPD/48WvfISRwR6OZsq5camFKQR00ouz7XiatvoKLnvz+2DJGyFaBc2X+l0/b7gbenbD8Al+seP/pd/E+YP2a+e5UkopNb2ZhvSXgN+LyL/g91D8EfD3JStVKTRdzI/S1/DK3q0sqXS5Yfm4+WQsi58dayQWu5JNN37u1HVDET/sm+FV96fnrsxKKXUWZvqVfd8Tkc3A2/CvR763JJOGKaWUmjOnGwcfw/+y7Q5gJ/A1Y6aZYF0ppdR543QXWb8LrMcP91uA/1nyEimllJoTp+uiucgYcwmAiHwLeL70RVJKKTUXThfwxVtAjTHO+T7rYUdHRyD2oZRSc+F0AX+piIxO8StAWeG5AMYYU1XS0s3Sxo0bA7EPpZSaC6ebqkDv5FFKqdepmd7JqpRS6nVGA14ppQJKA14ppQJKA14ppQJKA14ppQJKA14ppQJKA14ppQJKA14ppQJKA14ppQJKA14ppQLq9fO1e3Mk6wqHhm2+sHniF3EfTtqsnKcyKaVUKVxQAd/R0cGRI0cAiLW2TnhtJTpTpFIqWMQYM99lKFq/fr3ZvHnzfBdDKaVeN0RkizFm/WSvaR+8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoF1AU1m+RMbdq0ic7OzrPezujMla0nzVx5pjo6Oti4ceOcbEspFXwa8JPo7OzkxV0vQs1Zbijh/+qRnrMuE4Nnvwml1IVFA34qNeBd753VJqwn/R6ws93O+G0ppdRMaWoopVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRAacArpVRABSrgN23axKZNm+a7GBc0/Rsodf4I1GRjczHFrzo7+jdQ6vwRqBa8UkqpMRrwSikVUBrwSikVUBrw4wzlhsi62TNa12DwxMNg5rhUSil1ZgJ1kfVMDWYG+cun/pI9A3sQEcwyQ8bOYFs2tmdPu67BkA/nyYfyGDGIEcL5MGEnPOX7XdtFjGB5FoIUl49/PNl7SimVT3EsdYwFZQuojFSWfH9KXch6Uj0cHDpITbSG5TXLsaQ0bW0NeOAzv/0MLxx/Ac8UWuDVwEUgliCuECJEOBfGNqeGfT6UJxfO4VmFr+UzIGFBEGwmvt8Tj3zYPxAA2K6N7dpkw1lc2/UPEJ5gYSHG34blWYTzkx8s5sqBxAF+cfAXeMZDRHjb4rexqm7VpO/d1buLbT3bsMXmisYrWFG7oqRlm4pnPE6kTlAeLqcqUjUn2+xN97Knfw+22KxtWDvpgW53324eO/QYFha3LLuF5TXL52Tf6sJxIHGAnx/4efFs/+DQQd6+5O0l2VegAj4XypG38+TcHBE7MqN1sk6W7Se24xp3bKEAYb8lbcTgGAc35hLNRAmZiR+ZYzl+uMvYup7njQX+OKMhPhrweTtPNpzFs8fWN7bBMx6WY2EbG8d2wIBlT36ETztpnut+juMjx2mON3NV01W8mniVEyMnaIm3sLJ2JRk3w2BmkIbyBsLWqQeLZ7ufxTN+eY0x/L7r96ysXYnIxDOHI8NHeOrIU8Xnvzr0K2pjtTSUNUz3Ec+5ZC7Jv7/67ySyCQTh8sbL2dC84ay22Z/p58f7fozjOQC80v8Kd665k6gdLb7nYOIgX97y5eJ7dvbt5PNv+jxNFU1ntW91Ydnes31CV27nQCdvbH4j8Uh8zvcVmIB/+ujTHGo8hGd5fGfXd7h9xe3Ul9Wfdr2edA9ZZ5p+dwEPP4CzsSyu6xLKh4qtedd2mawHRTzBqXFw6hxM2GB7tt+dY+eL7zFi/HVPXl/wQ7/w/4Bru+Tacni5Uw8aTxx+gkNDhwDoy/Sx7cQ2Qpb/Z93dv5udvTvpS/fhGpdYKMY7lr5jQiAZY9jTt4fDw4eJ2lFa461Y5daELqNRR5JHJjw3GI4mj57zgN96YiuJbKJYhhePv8iaujVUR6sBGMmPELJCE8L5dPYO7C0GN0DKSXEgcYDVdauLy57uenrCe3Jujme6nuG9K957tlVSF5DJulxPbkzNlUAE/HBumAf3PchA1QBGDE+99hT1sXpuX3n7adctD5VPf2F03OduxJAP5XFsh7ATxnbsSS9Te5ZHNpIluzqLZVkQKrTqxcNYM78Ia8QU++C9iEda0hP3YzwODx0uPk9kEjx37DlqY7XEQjGaK5rZ3rOdxvJGUvkUVdEqnu1+lvd0vKe4zq9f+zW7+nZxInUCgKPJo3x07Ucn7RNsiJ0a5Oc63MEP8PEMhpH8COXhch47+BgHhw5iicWi+CJG8iMk80lW1Kzg2kXXYluTX1OJWKee8Z18FlgVqSLtpIv7j4fjxYOKUjN1+cLL6Rrpwhg/C1bVraIiXFGSfQUi4HvTvewZ2ON3Z+C3yn9z5Ddc1XQVLfGWKf9RA/4HKzBlxhtA/BZ5kfghbkLGf73wnvGvG8tAJTDiB7VnTd5tM+n100JZLM/CMhau5UIt9Gf76U520xxv9l8Xi6poFYlsgqybZV9iHyP5EVJOCoCBzABD2SFybg7wW/gRKwId/vZdz+WJw0+Qc3NYYuEal6yTpSfVc2qRjKE33cuxkWP0Z/pZXLmYty5+K4vii6b44EpnWdUynjj8BN3JbqKhKJcuuJTG8kZe6nuJg0MHAci7eX7S+RNW162mIlzBrr5dVIQrWN+0ftJtrqlfw+7+3cUzg+aKZtqr2ie8Z13DOr7jfac40qosVMba+rUlq6cKpraqNu5YdQeHhw5THa0+5f+zuVSyYZIicr+InBCRl0q1j1F5L188GgJknAwHhw7y0/0/5ft7vs9wbni6ck7aeisyfriLGUvi0cdGDJaxJg9pgAi4ZW6xLx9z0unZNOE++tuxHFzbxSvzGC4b5qf7f0oylyy+/frF11MWKiORTRCSEFXRsQuOOSd3yung+GsNGTdDyAqRc3OErTAxO0bIDpHKp4p98qN29+9m64mtNFU0sapuFVWRKtY2TAw3z3jkQjk8meRANoeOp4/Tleyie6CbzS9vZnPnZhzjMJAZACCVSvHwrx4mm8uy9+Be7v/e/Rw4cIDuke4pt1kWKuOOVXewWlbz4N8/yMVcfMpZzPHUca5ffD11I3X0bumlbqBu2m0qNZW6WB25gzk++u6PsnXr1pLtp5Tj4L8D3FzC7Re1VLTQXtVOyA1heRYhK1TsZx7ODbO9Z/uU64atMEuqlkz+YqF1Lq4UQ1qMFAM/5IQmBH/R+EVhvzVf7AYyhQPEaMt/qnUFXHGLF2RxIRvJ4ngOh4fHumUWxRfxkYs+wh2r7mDdgnXURmtpLG+kNlrL6vrVtFW1URurxcKiqaKJlbUri+tWhCtYVbcK27IZyY8wkh+hNlLLwoqFp4RbV7Kr+NgW/3rCsZFjxWX9mX6+tfNb7G/Zz4HmA3QOlG5Omue7nydiR+g/2o9khZ0HdtI50Fn8O27bvo3e7l6OHj/K5v2bcRY6PP7S49REa6bdbsgK8Z0vfYfs8Sxf+K9fOOX16mg1yVySvQf2QhQ279h82m0qNZW//du/xfM87r333pLto2QBb4z5DdBfqu2PF4/E+aNVf0RlqpLyTDmL4osmBFkqn5pyXRGhIdaANdlHMdp1I37L2/Zsf+iisbAdG9dyccSZ2Ooe/zgDkvHDfPSCqsFMP/Rx9AAg+H8dy9+/KRs7SJwcKpZYtFW1cemCS1lcuZiQFWJR5SIWxRdxccPFDGQG8PDoSffQVtU2Yd32qnaqI9XF7QzmBiftdllQvuCkj0ZoKB/rf3/o1Yf4fffvScVSJOIJHtjzAK7nnryZOVEeKmdgYGDsrM2DHVt2sLR6KZdWXsr+HfuxkzZDB4cgX/g8HT/4p7N3714OHjwIwMGDB0+ZOK0mWsOWPVvwYp7/U+bx+M8eL0UVVYDl3Bxf/dVX6VneQ3Z1liEzxJYtW0qyLxnftTHnGxdpB35mjJlRR+X69evN5s2bz3h/t37gVka8ESrXVfqjWwoa+xspz5ZPuhfNwj0AABFDSURBVI7B8Pyq50mVp07tMhltwYvfarddP+DFE/+C6+iYdjlpnQJJ+8FiKgst/0Jr33ZtIvkI6VjaHy0zyT7Ht+QBcCF6LMrK9EoahsaCNRPO0FPbU7zwWz9YjxGDG3KJ5CJ0N3Tj2A6u5RJyQ5TlymjpbSmuv79lPydqTxTvwo04EeqG6ljeNXF8t8FweOFhEpUJbM+msa9xQjl2te8iG8mSTqexLIt1a9fx5bd8merY3F+E7Bzo5I5/vAMv6oEHdo9N5Y5Kfv3Er/nSl77EI488QiaaIbciVyy7IIRHwjzzzWem3O5HPvKRYsADtLe3873vfa/4/IVjL/Bn/+3PMLb/RxZXCB8J88y/Tb1NpU722yO/5bP/+FlyOf//T8kL9YfqefThR89oeyKyxRgz6cWleb/IKiIfBz4O0NbWdpp3Ty+UCxFJR2jpbSERT+BaLvF0fMpwH5UNZU8Nd4+xsemF/yyx/PC1/GWT9qGL/w9fkOKY9/HhbvD74i3PwnKssTH0Zmz9YsiPP6nIQHxvnIbqiaNWemt6ixeX05E0h5sOU5uspXKkkrAbxrVc0tE0nuURckJEnLHrDa645O18sc9cEP+CLmOhmIqmGC4fJhvOkgvniKf8sbrJ8iS1w7XF4aJhJ0w2MjbctDxUTjQ082GKs9FR20HshRhOnYOVtwidCOE6frkfe+wx8vk8lmMhecGExw33PM355Phwn+z56I1P4o67HpMr/V3GKli6R7qL4Q5gwmba64RnY94D3hjzDeAb4Lfgz2Zbra2tAGz6h9l94cSGBzYw4owbejfagh7f5WKBYzt+Sz5v++NWTw7h4up+qJicwR6x/e6V8S19GbvpacL+Co/FyNhro68bWF27ekLdPOPxte1fA/xRI9t7tpNyUqyqXUVNrIa71tzF17Z/jQOJA8V13tz6Zj669qOAf6p4/0v380L3C3SNdGGJRWN5I+9d+V7eueyddCe7+UnnT8i6WQ4NHWI4N8wlCy4p3ix1c/vNLKtZBsCOnh386JUf8fSWp7E9m5uW3kQsFJvV32GmjDFItWCqDa7rYqUtosP+weTtb387jzzyCPl8nkhnhPyiPCZiCPWFiA5Of8Bpb28/pQU/3oqaFVhDFl6Vf0C0B2yshE7npGanqaKJSCQy1oJ3pGTTg8x7wM+VnJsjUZHAsR2OJo/Oavheebh8YsAD5IEIp7SsjRiMbbBca+qhlbYfvkTwA8DzW8siY3PLOLYzFuInDbEs/h49izBgZSycsDNhN5ZYtFa2cmT4CL3pXg4PH8bC4oXsC3h4DKYHiUfjtFW1kcqnqI5WY8vYkNGIHWFdwzrybp6GZAPJXJLrFl3HDW03AP7dnPsG9jGQHWA4N+yPKc8l/esW5Q28c9k7i9tat2Ad1dFqDv78IJF8hFvab5npxz9rrwy8wob3beCpp54CG3JLcvz1u/8agLvvvptHHy2c6laBs8C/0Qzgc5/43LTbveeee/jYxz5WfH7fffdNeN22bD75tk/y9e993f+b5Cw+8YlPzGHN1IXg6uarufOmO/nuQ99FskL4tfCkF/XnQimHSf4A+D2wSkSOiMiflGpfAA/vf5j+qn6GKoZ4qPMh9if2z3jdtsq2Uy+yjubgJHeaGjGEnJA/EdlUIV+4SGpCfmvewipepAV/HP1UwwlHh1QWu3KMhZ22Jx2xc2PbjbTGW3nxxIsMZgbpTffSm+7F9Vx60j0cTx2nsbyRpdVLqYvVnXJ375sWvYnbVtzGXWvu4gvXfoEPrPkAZaEywB8ZM5D1hx5aWCSyCbpGujg2cowjQ0d49MCjEy5gL6laQv1QPZXpymnvPThb3cluli1b5t9Ehj+NQ8fl/uD+hoYGbrnlFghDzR/U4NV7eJUe+aV5cu256TbLcPUwZW8uI3NRhoVrF9LR0THh9bSTRq4QUlelyF6cxY253HnnnaWppAqsqB3lkzd+kgWdC4jtjlFNNVdccUVJ9lXKUTR3GmOajTFhY0yrMeZbpdpXX7qP/Yn9JGNJhsqHOJo8yks9Mx9+v7xm+YSWLQBT5JMgWK4fupMOkRwvBMb1jwAhd+LJkhHjT0Z28gFitFFfGEnj4V/89Mo9+iv7JwxNBP/sw/EcyuwybMv2b1ZysyTzSSoiFTSVNxXvkquL1XHtomtPKWZrZas/xDJWO2F5TaymuG7GzQD+mVLaSdOb6WXL8S28Ovjq9J9BCTRWNAJw3XXXAfCWt7xlwvQLd999N0uvWEpNcw1L2vyhk0uWLGFn784pt7k/sZ/nup/j2rdeS6gyxOXvu5z+zMRO+x/u+SGPH36cxpWN5JvzLP/wcko5SEEF1yv9r/CHf/6HuItc7v186YZJBqKLxhKLfQP7yIf9eV6OJo/O6gaUpVVL/YujRiaMV5/QD1+4eBr2wkSy/oXKSe9MHUeyQqQrglXnt94d25+YzGD8GSPHXdQs7rPQB1+cOni0NQ/0Vffx01d/ygfXfHDCrc29mV7EEkISIiQhPDzCVhhLLC5uuJib2m8i7aRnfTt0R00HF9VfxEh+hD39e+jP9BdnnHQ8h0Q2QW+md1bbnAur61bTl+7zZ31cuZarm6+eMGVAQ0MDX/zcF7n36XsJh8PU1voHrgVlC6baZHGcf319PR+660OAf6ZQF6srvmdHzw4AmhqbaGpsQhAS2QQ1MR0Lr2bupd6X+M2R3yANwl2fuYvuitLdLBeIgM97+QlTxtpiUx6efuTMeI3xRlrKWziWOobjOf7dnuNa0mL8VntZpmysi0U8f/qC0XOgSUbhxHbFiJ6I4nn+gSDkhsD1L8LmIjl/hA3itwLH7290Yydt0wk55L08h4cOs6Z+TXH5VU1X8cThJygPlxOyQjjGYUnlEhZXLuYtrW/BEuuM5rpoq2rjrYvfyq6+XeS9PCP5EV4bfg1jDFE7SjwSpzZSe/oNzTFLLK5rvY5rF1075SRNTRVNvG3x23i2+1lyXo6FZQsnXDM42cLyhaddVhOtYTA7WHweC8UoD838/zOlwG+9j3ds5BiJbKIk8xoFIuArI5Usr1nOztROPMvjkoZLaKuc+ZDLtfVr2dCygWe6nmEgM0A6k8Yb9pB4Yfy7sQk74Qm3/Yvxb3iyjOX3pY+eqXsQdsOE82EixyafAkEQIrkITsjx1y2cKQiC8QxeaGxu+fG/R7t5Tr7iftnCy/iPa/8j3971bTw8VtasZFHlIt6/8v2zOtBNZk39GtbUr6Er2cUP9vyAmB0j42SIhCJc2XglHbUdp99IiZxuBr47Vt/BVc1XMZwbZmn10mn/Aa2oWUFPqoddfbuwxWZ90/pTbu66feXtfHPnN0lkE5SHynnX8ncRCc1sWmqlRp38b9IWe1Yzn85GIAK+LFTGta3X8rDzMEYMLZUtXNl05YzXb44386eX/SnXtV5HMpfkX7/+r+w9shfzRn+SsMm+VUkQwk7YHxIZ8rt2bMcmkouc8kUfkxld3/IsnJBTnDkylA+RN3nckOuPovH8OeLFESryFaypW0NrZesp27uh/Qaubrmal/texjEOa+rWzGnXQUu8hQ+u/iDPVD1D10gXS6uXcnXT1SWZw3quWGLN+As5RIRrFl3DG1ve6N/MNsnBY23DWu7dcC9Hkkeoj9VP+ndQ6nSubLqS7pFuMk4GQbiy6cqSDSkORMADXFx/MW3H23Atl/evfP+s128oa+Ati98CwC8yv8DKWnh4035ln+3ZxHIxyE0+x/NM2J6NlbPGbohCiOaj/jDNAoNBtgltTW28te2tU24rHolzVfNVZ1SOmWiON89oCubXs9N9dVp9Wf2MvmdAqak0lDXw4TUf5tjIMaqiVSWdcjowAQ/4XSbuub3xZC6+L1WQaUfkCIKVswi7pf3qPqXUuRG2wyyuWlzy/ehteEopFVAa8EopFVAa8EopFVCB6oM/+dZyde7p30Cp80egAn7jxo3zXYQLnv4NlDp/aBeNUkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFlAa8UkoFVKAmG5tTg2A9eZbHv0H/11lvZ3Rbi85+M0qpC4cG/CTmasrbI+YIAK2L5uDLmRfpVLxKqdnRgJ+ETnmrlAoC7YNXSqmA0oBXSqmA0oBXSqmA0oBXSqmA0oBXSqmAEmPMfJehSER6gENnuZkGoHcOivN6cKHU9UKpJ1w4ddV6zp0lxpgFk71wXgX8XBCRzcaY9fNdjnPhQqnrhVJPuHDqqvU8N7SLRimlAkoDXimlAiqIAf+N+S7AOXSh1PVCqSdcOHXVep4DgeuDV0op5QtiC14ppRQa8EopFViBCngRuVlEXhGRThH57HyX52yIyP0ickJEXhq3rE5EHhORfYXfteNe+6tCvV8RkZvmp9SzJyKLReTXIrJbRHaJyKcKy4NY15iIPC8i2wt1/XxheeDqCiAitoi8KCI/KzwPaj0PishOEdkmIpsLy86PuhpjAvED2MCrwDIgAmwHLprvcp1Ffd4MvAF4adyy/w58tvD4s8AXC48vKtQ3CiwtfA72fNdhhvVsBt5QeFwJ7C3UJ4h1FSBeeBwGngM2BLGuhfL/OfB94GeF50Gt50Gg4aRl50Vdg9SCvwroNMbsN8bkgB8C757nMp0xY8xvgP6TFr8b+G7h8XeB94xb/kNjTNYYcwDoxP88znvGmG5jzNbC42FgN/53VwWxrsYYkyw8DRd+DAGsq4i0An8IfHPc4sDVcxrnRV2DFPCLgNfGPT9C8L7krtEY0w1+MAILC8sDUXcRaQcux2/ZBrKuhW6LbcAJ4DFjTFDr+g/AfwG8ccuCWE/wD9K/FJEtIvLxwrLzoq5B+kYnmWTZhTIG9HVfdxGJA/8GfNoYMyQyWZX8t06y7HVTV2OMC1wmIjXAgyKydpq3vy7rKiLvBE4YY7aIyPUzWWWSZed9Pce5xhjTJSILgcdEZM807z2ndQ1SC/4IsHjc81aga57KUirHRaQZoPD7RGH567ruIhLGD/cHjDE/LiwOZF1HGWMGgSeBmwleXa8BbhWRg/hdpW8TkX8mePUEwBjTVfh9AngQv8vlvKhrkAL+BWCFiCwVkQjwAeCheS7TXHsIuLvw+G7g38ct/4CIREVkKbACeH4eyjdr4jfVvwXsNsZ8edxLQazrgkLLHREpA24E9hCwuhpj/soY02qMacf/d/iEMeZDBKyeACJSISKVo4+BPwBe4nyp63xfgZ7jq9nvwB+F8SrwN/NdnrOsyw+AbiCPf9T/E6AeeBzYV/hdN+79f1Oo9yvALfNd/lnU81r8U9QdwLbCzzsCWtd1wIuFur4E3FdYHri6jiv/9YyNoglcPfFH7W0v/OwazZ3zpa46VYFSSgVUkLpolFJKjaMBr5RSAaUBr5RSAaUBr5RSAaUBr5RSARWkO1mVmjURcYGd4xa9xxhzcJ6Ko9Sc0mGS6oImIkljTPwM1rONP+2AUuct7aJR6iQi0i4ivxWRrYWfNxWWX1+Yu/77FFr9IvKhwhzv20Tk6yJiz2vhlRpHu2jUha6sMLsjwAFjzG3484a83RiTEZEV+HcVry+85ypgrTHmgIisAe7An2wqLyJfBe4CvneO66DUpDTg1YUubYy57KRlYeB/ichlgAusHPfa88afxxvgBuAK4IXC7JdljE0qpdS804BX6lT/GTgOXIrfjZkZ99rIuMcCfNcY81fnsGxKzZj2wSt1qmqg2xjjAR/G/zrIyTwOvK8wD/jo93AuOUdlVOq0NOCVOtVXgbtF5Fn87pmRyd5kjHkZuAf/23x2AI/hf8esUucFHSaplFIBpS14pZQKKA14pZQKKA14pZQKKA14pZQKKA14pZQKKA14pZQKKA14pZQKqP8fmndM/+uSx/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x=\"Fare\", y=\"Pclass\", orient=\"h\", order=[3, 2, 1]);\n",
    "sns.stripplot(data=df, x=\"Fare\", y=\"Pclass\", orient=\"h\", order=[3, 2, 1], jitter=True, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Fare vs Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(data=df, x=\"Fare\", y=\"Embarked\", orient=\"h\");\n",
    "sns.stripplot(data=df, x=\"Fare\", y=\"Embarked\", orient=\"h\", jitter=True, alpha=0.5);"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}