Clustering/Hierarchical Clustering/Titanic-Mean-Shift.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic (Mean Shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import numpy as np\n",
    "from sklearn.cluster import MeanShift\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>survived</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>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"titanic.xls\")\n",
    "original_df = pd.DataFrame.copy(df)  # Copy of the df.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"body\", \"name\"], 1, inplace=True)\n",
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling non-numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>survived</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>boat</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>746</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>522</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived  sex      age  sibsp  parch  ticket      fare  cabin  \\\n",
       "0       1         1    1  29.0000      0      0     746  211.3375    100   \n",
       "1       1         1    0   0.9167      1      2     522  151.5500      6   \n",
       "2       1         0    1   2.0000      1      2     522  151.5500      6   \n",
       "3       1         0    0  30.0000      1      2     522  151.5500      6   \n",
       "4       1         0    1  25.0000      1      2     522  151.5500      6   \n",
       "\n",
       "   embarked  boat  home.dest  \n",
       "0         1     1        163  \n",
       "1         1     9         33  \n",
       "2         1     0         33  \n",
       "3         1     0         33  \n",
       "4         1     0         33  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handle_non_numerical_data(df):\n",
    "    columns = df.columns.values\n",
    "    for column in columns:\n",
    "        text_digit_vals = {}\n",
    "        def convert_to_int(val):\n",
    "            return text_digit_vals[val]\n",
    "        \n",
    "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
    "            column_contents = df[column].values.tolist()\n",
    "            unique_elements = set(column_contents)\n",
    "            x = 0\n",
    "            for unique in unique_elements:\n",
    "                if unique not in text_digit_vals:\n",
    "                    text_digit_vals[unique] = x\n",
    "                    x+=1\n",
    "            df[column] = list(map(convert_to_int, df[column]))\n",
    "    return df\n",
    "\n",
    "df = handle_non_numerical_data(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 1 - survival rate: 0.38424821002386633\n",
      "Cluster 2 - survival rate: 0.11538461538461539\n",
      "Cluster 3 - survival rate: 0.8125\n",
      "Cluster 4 - survival rate: 0.1\n"
     ]
    }
   ],
   "source": [
    "X = np.array(df.drop([\"survived\"], 1).astype(float))\n",
    "X = preprocessing.scale(X)\n",
    "y = np.array(df[\"survived\"])\n",
    "\n",
    "clf = MeanShift()\n",
    "clf.fit(X)\n",
    "\n",
    "labels = clf.labels_\n",
    "cluster_centers = clf.cluster_centers_\n",
    "\n",
    "original_df[\"cluster_group\"] = np.nan\n",
    "# Iter through the labels and populate the values of the \"cluster_group\" column.\n",
    "for i in range(len(X)):\n",
    "    original_df[\"cluster_group\"].iloc[i] = labels[i]  # iloc[i] is the row in the df (i is the number), column: cluster_group = labels[i]\n",
    "    \n",
    "n_clustters_ = len(np.unique(labels))\n",
    "\n",
    "survival_rates = {}\n",
    "for i in range(n_clustters_):\n",
    "    temp_df = original_df[(original_df[\"cluster_group\"] == float(i))]\n",
    "    survival_cluster = temp_df[(temp_df[\"survived\"] == 1)]\n",
    "    survival_rate = len(survival_cluster) / len(temp_df)\n",
    "    survival_rates[i] = survival_rate\n",
    "\n",
    "for cluster in survival_rates:\n",
    "    print(\"Cluster \" + str(cluster + 1) + \" - survival rate: \" + str(survival_rates[cluster]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of cluster 1\n",
      "\n",
      "            pclass     survived          age        sibsp        parch  \\\n",
      "count  1257.000000  1257.000000  1004.000000  1257.000000  1257.000000   \n",
      "mean      2.291169     0.384248    30.026062     0.388226     0.291965   \n",
      "std       0.833911     0.486611    14.206585     0.707414     0.638375   \n",
      "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
      "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
      "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
      "75%       3.000000     1.000000    38.625000     1.000000     0.000000   \n",
      "max       3.000000     1.000000    80.000000     4.000000     4.000000   \n",
      "\n",
      "              fare        body  cluster_group  \n",
      "count  1256.000000  118.000000         1257.0  \n",
      "mean     29.251877  160.355932            0.0  \n",
      "std      39.273562   97.339175            0.0  \n",
      "min       0.000000    1.000000            0.0  \n",
      "25%       7.895800   72.750000            0.0  \n",
      "50%      13.500000  155.500000            0.0  \n",
      "75%      28.581250  255.750000            0.0  \n",
      "max     263.000000  328.000000            0.0  \n"
     ]
    }
   ],
   "source": [
    "print(\"Description of cluster 1\\n\")\n",
    "print(original_df[(original_df[\"cluster_group\"] == 0)].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of cluster 2\n",
      "\n",
      "       pclass   survived        age      sibsp  parch       fare  body  \\\n",
      "count    26.0  26.000000  18.000000  26.000000   26.0  26.000000   1.0   \n",
      "mean      3.0   0.115385   8.861111   5.615385    2.0  47.253365  67.0   \n",
      "std       0.0   0.325813   4.898396   1.812775    0.0  18.500701   NaN   \n",
      "min       3.0   0.000000   1.000000   4.000000    2.0   7.925000  67.0   \n",
      "25%       3.0   0.000000   5.000000   4.000000    2.0  31.387500  67.0   \n",
      "50%       3.0   0.000000   9.000000   5.000000    2.0  46.900000  67.0   \n",
      "75%       3.0   0.000000  12.500000   8.000000    2.0  69.550000  67.0   \n",
      "max       3.0   1.000000  17.000000   8.000000    2.0  69.550000  67.0   \n",
      "\n",
      "       cluster_group  \n",
      "count           26.0  \n",
      "mean             1.0  \n",
      "std              0.0  \n",
      "min              1.0  \n",
      "25%              1.0  \n",
      "50%              1.0  \n",
      "75%              1.0  \n",
      "max              1.0  \n"
     ]
    }
   ],
   "source": [
    "print(\"Description of cluster 2\\n\")\n",
    "print(original_df[(original_df[\"cluster_group\"] == 1)].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of cluster 3\n",
      "\n",
      "       pclass   survived        age    sibsp      parch        fare  body  \\\n",
      "count    16.0  16.000000  16.000000  16.0000  16.000000   16.000000   0.0   \n",
      "mean      1.0   0.812500  39.437500   0.6875   1.562500  322.156513   NaN   \n",
      "std       0.0   0.403113  16.342047   0.7932   1.364734  113.545839   NaN   \n",
      "min       1.0   0.000000  13.000000   0.0000   0.000000  247.520800   NaN   \n",
      "25%       1.0   1.000000  26.250000   0.0000   0.750000  262.375000   NaN   \n",
      "50%       1.0   1.000000  36.000000   0.5000   1.000000  262.375000   NaN   \n",
      "75%       1.0   1.000000  52.000000   1.0000   2.250000  325.332300   NaN   \n",
      "max       1.0   1.000000  64.000000   2.0000   4.000000  512.329200   NaN   \n",
      "\n",
      "       cluster_group  \n",
      "count           16.0  \n",
      "mean             2.0  \n",
      "std              0.0  \n",
      "min              2.0  \n",
      "25%              2.0  \n",
      "50%              2.0  \n",
      "75%              2.0  \n",
      "max              2.0  \n"
     ]
    }
   ],
   "source": [
    "print(\"Description of cluster 3\\n\")\n",
    "print(original_df[(original_df[\"cluster_group\"] == 2)].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "      <th>cluster_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307.0</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.609121</td>\n",
       "      <td>39.143346</td>\n",
       "      <td>0.423453</td>\n",
       "      <td>0.302932</td>\n",
       "      <td>75.279805</td>\n",
       "      <td>162.828571</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.488744</td>\n",
       "      <td>14.467914</td>\n",
       "      <td>0.596699</td>\n",
       "      <td>0.607221</td>\n",
       "      <td>56.117554</td>\n",
       "      <td>82.652172</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.916700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>109.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pclass    survived         age       sibsp       parch        fare  \\\n",
       "count   307.0  307.000000  268.000000  307.000000  307.000000  307.000000   \n",
       "mean      1.0    0.609121   39.143346    0.423453    0.302932   75.279805   \n",
       "std       0.0    0.488744   14.467914    0.596699    0.607221   56.117554   \n",
       "min       1.0    0.000000    0.916700    0.000000    0.000000    0.000000   \n",
       "25%       1.0    0.000000   28.000000    0.000000    0.000000   30.500000   \n",
       "50%       1.0    1.000000   39.000000    0.000000    0.000000   57.750000   \n",
       "75%       1.0    1.000000   49.250000    1.000000    0.000000   90.000000   \n",
       "max       1.0    1.000000   80.000000    3.000000    2.000000  263.000000   \n",
       "\n",
       "             body  cluster_group  \n",
       "count   35.000000          307.0  \n",
       "mean   162.828571            0.0  \n",
       "std     82.652172            0.0  \n",
       "min     16.000000            0.0  \n",
       "25%    109.500000            0.0  \n",
       "50%    166.000000            0.0  \n",
       "75%    233.000000            0.0  \n",
       "max    307.000000            0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_0 = original_df[(original_df[\"cluster_group\"] == 0)]\n",
    "cluster_0_fc = cluster_0[(cluster_0[\"pclass\"] == 1)]\n",
    "cluster_0_fc.describe()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}