{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d3a4df91",
   "metadata": {},
   "source": [
    "# Lemurs Dataset\n",
    "The [Duke Lemur Center](https://lemur.duke.edu/) (DLC)\n",
    "is an 85 acre research facility in Durham, North Carolina\n",
    "that currently houses over 200 lemurs from many different\n",
    "species of lemurs. Researchers at the DLC care for\n",
    "lemurs and record data on their growth, diet, and\n",
    "social interactions, to learn more about these amazing\n",
    "animals on the edge of extinction in their native Madagascar.\n",
    "\n",
    "<img src=\"../images/leaping.jpg\" alt=\"\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84ab6170",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Import pandas\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# Import matplotlib and seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Apply the default theme\n",
    "sns.set_theme()\n",
    "\n",
    "# import sklearn libraries\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ad2f87",
   "metadata": {},
   "source": [
    "# Load Weight Data\n",
    "The lemurs are regularly weighed as part of their medical\n",
    "checkups throughout the year. The **Weight_File** table\n",
    "holds the information recorded during\n",
    "each lemur's weighing. Each row is a different\n",
    "weighing instance. The columns are described in the\n",
    "**Descriptions and Usage Notes** file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33535fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "wdf = pd.read_csv('../data/lemur_weight.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc700b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82609, 33)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40a559a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Taxon</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>DLC_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOB_Estimated</th>\n",
       "      <th>Weight_g</th>\n",
       "      <th>Weight_Date</th>\n",
       "      <th>MonthOfWeight</th>\n",
       "      <th>AgeAtWt_d</th>\n",
       "      <th>AgeAtWt_wk</th>\n",
       "      <th>AgeAtWt_mo</th>\n",
       "      <th>AgeAtWt_mo_NoDec</th>\n",
       "      <th>AgeAtWt_y</th>\n",
       "      <th>Change_Since_PrevWt_g</th>\n",
       "      <th>Days_Since_PrevWt</th>\n",
       "      <th>Avg_Daily_WtChange_g</th>\n",
       "      <th>DOD</th>\n",
       "      <th>DaysBeforeDeath</th>\n",
       "      <th>Birth_Type</th>\n",
       "      <th>Birth_Institution</th>\n",
       "      <th>Litter_Size</th>\n",
       "      <th>R_Min_Dam_AgeAtConcep_y</th>\n",
       "      <th>Age_Category</th>\n",
       "      <th>Current_Resident</th>\n",
       "      <th>Preg_Status</th>\n",
       "      <th>Expected_Gestation_d</th>\n",
       "      <th>ConcepDate_IfPreg</th>\n",
       "      <th>InfantDOB_IfPreg</th>\n",
       "      <th>DaysBeforeInfBirth_IfPreg</th>\n",
       "      <th>Pct_PregRemain_IfPreg</th>\n",
       "      <th>InfantLitSz_IfPreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>KANGA</td>\n",
       "      <td>25Aug1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>16Feb1972</td>\n",
       "      <td>2</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>546.71</td>\n",
       "      <td>125.82</td>\n",
       "      <td>125.0</td>\n",
       "      <td>10.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07Feb1977</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>KANGA</td>\n",
       "      <td>25Aug1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>20Jun1972</td>\n",
       "      <td>6</td>\n",
       "      <td>3952.0</td>\n",
       "      <td>564.57</td>\n",
       "      <td>129.93</td>\n",
       "      <td>129.0</td>\n",
       "      <td>10.83</td>\n",
       "      <td>104.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>07Feb1977</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>6</td>\n",
       "      <td>F</td>\n",
       "      <td>ROO</td>\n",
       "      <td>17Mar1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>947.0</td>\n",
       "      <td>16Feb1972</td>\n",
       "      <td>2</td>\n",
       "      <td>3988.0</td>\n",
       "      <td>569.71</td>\n",
       "      <td>131.11</td>\n",
       "      <td>131.0</td>\n",
       "      <td>10.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15Oct1974</td>\n",
       "      <td>972.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>6</td>\n",
       "      <td>F</td>\n",
       "      <td>ROO</td>\n",
       "      <td>17Mar1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>26Jun1972</td>\n",
       "      <td>6</td>\n",
       "      <td>4119.0</td>\n",
       "      <td>588.43</td>\n",
       "      <td>135.42</td>\n",
       "      <td>135.0</td>\n",
       "      <td>11.28</td>\n",
       "      <td>227.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>15Oct1974</td>\n",
       "      <td>841.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>9</td>\n",
       "      <td>M</td>\n",
       "      <td>POOH BEAR</td>\n",
       "      <td>30Sep1963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>899.0</td>\n",
       "      <td>16Feb1972</td>\n",
       "      <td>2</td>\n",
       "      <td>3061.0</td>\n",
       "      <td>437.29</td>\n",
       "      <td>100.64</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13Feb1974</td>\n",
       "      <td>728.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Taxon Hybrid  DLC_ID Sex       Name        DOB DOB_Estimated  Weight_g  \\\n",
       "0   OGG      N       5   M      KANGA  25Aug1961           NaN    1086.0   \n",
       "1   OGG      N       5   M      KANGA  25Aug1961           NaN    1190.0   \n",
       "2   OGG      N       6   F        ROO  17Mar1961           NaN     947.0   \n",
       "3   OGG      N       6   F        ROO  17Mar1961           NaN    1174.0   \n",
       "4   OGG      N       9   M  POOH BEAR  30Sep1963           NaN     899.0   \n",
       "\n",
       "  Weight_Date  MonthOfWeight  AgeAtWt_d  AgeAtWt_wk  AgeAtWt_mo  \\\n",
       "0   16Feb1972              2     3827.0      546.71      125.82   \n",
       "1   20Jun1972              6     3952.0      564.57      129.93   \n",
       "2   16Feb1972              2     3988.0      569.71      131.11   \n",
       "3   26Jun1972              6     4119.0      588.43      135.42   \n",
       "4   16Feb1972              2     3061.0      437.29      100.64   \n",
       "\n",
       "   AgeAtWt_mo_NoDec  AgeAtWt_y  Change_Since_PrevWt_g  Days_Since_PrevWt  \\\n",
       "0             125.0      10.48                    NaN                NaN   \n",
       "1             129.0      10.83                  104.0              125.0   \n",
       "2             131.0      10.93                    NaN                NaN   \n",
       "3             135.0      11.28                  227.0              131.0   \n",
       "4             100.0       8.39                    NaN                NaN   \n",
       "\n",
       "   Avg_Daily_WtChange_g        DOD  DaysBeforeDeath Birth_Type  \\\n",
       "0                   NaN  07Feb1977           1818.0         CB   \n",
       "1                  0.83  07Feb1977           1693.0         CB   \n",
       "2                   NaN  15Oct1974            972.0         CB   \n",
       "3                  1.73  15Oct1974            841.0         CB   \n",
       "4                   NaN  13Feb1974            728.0         CB   \n",
       "\n",
       "   Birth_Institution  Litter_Size  R_Min_Dam_AgeAtConcep_y Age_Category  \\\n",
       "0  Duke Lemur Center          1.0                     0.59        adult   \n",
       "1  Duke Lemur Center          1.0                     0.59        adult   \n",
       "2  Duke Lemur Center          1.0                     0.59        adult   \n",
       "3  Duke Lemur Center          1.0                     0.59        adult   \n",
       "4  Duke Lemur Center          1.0                     0.59        adult   \n",
       "\n",
       "  Current_Resident Preg_Status  Expected_Gestation_d ConcepDate_IfPreg  \\\n",
       "0                N          NP                   NaN               NaN   \n",
       "1                N          NP                   NaN               NaN   \n",
       "2                N          NP                   NaN               NaN   \n",
       "3                N          NP                   NaN               NaN   \n",
       "4                N          NP                   NaN               NaN   \n",
       "\n",
       "  InfantDOB_IfPreg  DaysBeforeInfBirth_IfPreg  Pct_PregRemain_IfPreg  \\\n",
       "0              NaN                        NaN                    NaN   \n",
       "1              NaN                        NaN                    NaN   \n",
       "2              NaN                        NaN                    NaN   \n",
       "3              NaN                        NaN                    NaN   \n",
       "4              NaN                        NaN                    NaN   \n",
       "\n",
       "   InfantLitSz_IfPreg  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b64aef0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "51efb478",
   "metadata": {},
   "source": [
    "### Examining the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4808d48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf['Taxon'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8cdbe83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M     42037\n",
       "F     40557\n",
       "ND       15\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b658d45d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult          58295\n",
       "IJ             17030\n",
       "young_adult     7284\n",
       "Name: Age_Category, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf['Age_Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee1f5c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2270"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf['DLC_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "789adb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['KANGA', 'ROO', 'POOH BEAR', ..., 'SITA', 'DANU-A', 'KANTIL'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf['Name'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2275ce8",
   "metadata": {},
   "source": [
    "### Who was the first lemur to be pregnant at the DLC? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6c0229e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform string column to datetime\n",
    "wdf['conception_date'] = pd.to_datetime(wdf['ConcepDate_IfPreg'])\n",
    "wdf['infant_dob'] = pd.to_datetime(wdf['InfantDOB_IfPreg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af290d40",
   "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>Taxon</th>\n",
       "      <th>Name</th>\n",
       "      <th>InfantLitSz_IfPreg</th>\n",
       "      <th>infant_dob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20485</th>\n",
       "      <td>ERUF</td>\n",
       "      <td>CALO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1972-03-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Taxon  Name  InfantLitSz_IfPreg infant_dob\n",
       "20485  ERUF  CALO                 1.0 1972-03-08"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = wdf[ (wdf['Preg_Status'] == 'P') & (wdf['conception_date'] == wdf['conception_date'].min()) ]\n",
    "g[['Taxon', 'Name' , 'InfantLitSz_IfPreg', 'infant_dob']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fa4d9d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6158d55c",
   "metadata": {},
   "source": [
    "### Birth Type, Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7de77cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = wdf[['DLC_ID', 'Taxon', 'Birth_Type']].drop_duplicates()\n",
    "g = g.groupby(['Taxon', 'Birth_Type']).size().reset_index()\n",
    "g.columns = ['Taxon', 'Birth_Type', 'count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71a0ced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 714.05x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = g[ g['Birth_Type'] != 'Unk' ]\n",
    "\n",
    "bar = sns.catplot(\n",
    "    data=g,\n",
    "    kind='bar',\n",
    "    x='count',\n",
    "    y='Taxon',\n",
    "    hue='Birth_Type',\n",
    "    height=6,\n",
    "    aspect=1.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "728cabec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2327a48a",
   "metadata": {},
   "source": [
    "### Merging with common name fact table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35f5b498",
   "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>common</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CMED</th>\n",
       "      <td>Fat-tailed dwarf lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMAD</th>\n",
       "      <td>Aye-aye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EALB</th>\n",
       "      <td>White-fronted brown lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECOL</th>\n",
       "      <td>Collared brown lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECOR</th>\n",
       "      <td>Crowned lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFLA</th>\n",
       "      <td>Blue-eyed black lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFUL</th>\n",
       "      <td>Common brown lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMAC</th>\n",
       "      <td>Black lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMON</th>\n",
       "      <td>Mongoose lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERUB</th>\n",
       "      <td>Red-bellied lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERUF</th>\n",
       "      <td>Red-fronted brown lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESAN</th>\n",
       "      <td>Sanfords brown lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EUL</th>\n",
       "      <td>Eulemur hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMOH</th>\n",
       "      <td>Mohol bushbaby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGG</th>\n",
       "      <td>Eastern lesser bamboo lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LCAT</th>\n",
       "      <td>Ring-tailed lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTAR</th>\n",
       "      <td>Slender loris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMUR</th>\n",
       "      <td>Gray mouse lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZAZ</th>\n",
       "      <td>Northern giant mouse lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCOU</th>\n",
       "      <td>Slow loris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPYG</th>\n",
       "      <td>Pygmy slow loris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OGG</th>\n",
       "      <td>Northern greater galago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCOQ</th>\n",
       "      <td>Coquerels sifaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPOT</th>\n",
       "      <td>Potto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAR</th>\n",
       "      <td>Varecia hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VRUB</th>\n",
       "      <td>Red ruffed lemur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VVV</th>\n",
       "      <td>Black-and-white ruffed lemur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            common\n",
       "CMED        Fat-tailed dwarf lemur\n",
       "DMAD                       Aye-aye\n",
       "EALB     White-fronted brown lemur\n",
       "ECOL          Collared brown lemur\n",
       "ECOR                 Crowned lemur\n",
       "EFLA         Blue-eyed black lemur\n",
       "EFUL            Common brown lemur\n",
       "EMAC                   Black lemur\n",
       "EMON                Mongoose lemur\n",
       "ERUB             Red-bellied lemur\n",
       "ERUF       Red-fronted brown lemur\n",
       "ESAN          Sanfords brown lemur\n",
       "EUL                 Eulemur hybrid\n",
       "GMOH                Mohol bushbaby\n",
       "HGG    Eastern lesser bamboo lemur\n",
       "LCAT             Ring-tailed lemur\n",
       "LTAR                 Slender loris\n",
       "MMUR              Gray mouse lemur\n",
       "MZAZ    Northern giant mouse lemur\n",
       "NCOU                    Slow loris\n",
       "NPYG              Pygmy slow loris\n",
       "OGG        Northern greater galago\n",
       "PCOQ              Coquerels sifaka\n",
       "PPOT                         Potto\n",
       "VAR                 Varecia hybrid\n",
       "VRUB              Red ruffed lemur\n",
       "VVV   Black-and-white ruffed lemur"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_name = {\n",
    "    'CMED': ['Fat-tailed dwarf lemur'],\n",
    "    'DMAD' : ['Aye-aye'],\n",
    "    'EALB' : ['White-fronted brown lemur'],\n",
    "    'ECOL' : ['Collared brown lemur'],\n",
    "    'ECOR' : ['Crowned lemur'],\n",
    "    'EFLA' : ['Blue-eyed black lemur'],\n",
    "    'EFUL' : ['Common brown lemur'],\n",
    "    'EMAC' : ['Black lemur'],\n",
    "    'EMON' : ['Mongoose lemur'],\n",
    "    'ERUB' : ['Red-bellied lemur'],\n",
    "    'ERUF' : ['Red-fronted brown lemur'],\n",
    "    'ESAN' : ['Sanfords brown lemur'],\n",
    "    'EUL' : ['Eulemur hybrid'],\n",
    "    'GMOH' : ['Mohol bushbaby'],\n",
    "    'HGG' : ['Eastern lesser bamboo lemur'],\n",
    "    'LCAT' : ['Ring-tailed lemur'],\n",
    "    'LTAR' : ['Slender loris'],\n",
    "    'MMUR' : ['Gray mouse lemur'],\n",
    "    'MZAZ' : ['Northern giant mouse lemur'],\n",
    "    'NCOU' : ['Slow loris'],\n",
    "    'NPYG' : ['Pygmy slow loris'],\n",
    "    'OGG' : ['Northern greater galago'],\n",
    "    'PCOQ' : ['Coquerels sifaka'],\n",
    "    'PPOT' : ['Potto'],\n",
    "    'VAR' : ['Varecia hybrid'],\n",
    "    'VRUB' : ['Red ruffed lemur'],\n",
    "    'VVV' : ['Black-and-white ruffed lemur']\n",
    "}\n",
    "\n",
    "names = pd.DataFrame(common_name)\n",
    "names = names.T\n",
    "names.columns = ['common']\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6acdab2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "679d0cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "wdf = wdf.merge(names, how='inner', left_on='Taxon', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "baeb9944",
   "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>Taxon</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>DLC_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOB_Estimated</th>\n",
       "      <th>Weight_g</th>\n",
       "      <th>Weight_Date</th>\n",
       "      <th>MonthOfWeight</th>\n",
       "      <th>AgeAtWt_d</th>\n",
       "      <th>AgeAtWt_wk</th>\n",
       "      <th>AgeAtWt_mo</th>\n",
       "      <th>AgeAtWt_mo_NoDec</th>\n",
       "      <th>AgeAtWt_y</th>\n",
       "      <th>Change_Since_PrevWt_g</th>\n",
       "      <th>Days_Since_PrevWt</th>\n",
       "      <th>Avg_Daily_WtChange_g</th>\n",
       "      <th>DOD</th>\n",
       "      <th>DaysBeforeDeath</th>\n",
       "      <th>Birth_Type</th>\n",
       "      <th>Birth_Institution</th>\n",
       "      <th>Litter_Size</th>\n",
       "      <th>R_Min_Dam_AgeAtConcep_y</th>\n",
       "      <th>Age_Category</th>\n",
       "      <th>Current_Resident</th>\n",
       "      <th>Preg_Status</th>\n",
       "      <th>Expected_Gestation_d</th>\n",
       "      <th>ConcepDate_IfPreg</th>\n",
       "      <th>InfantDOB_IfPreg</th>\n",
       "      <th>DaysBeforeInfBirth_IfPreg</th>\n",
       "      <th>Pct_PregRemain_IfPreg</th>\n",
       "      <th>InfantLitSz_IfPreg</th>\n",
       "      <th>conception_date</th>\n",
       "      <th>infant_dob</th>\n",
       "      <th>common</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>KANGA</td>\n",
       "      <td>25Aug1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>16Feb1972</td>\n",
       "      <td>2</td>\n",
       "      <td>3827.0</td>\n",
       "      <td>546.71</td>\n",
       "      <td>125.82</td>\n",
       "      <td>125.0</td>\n",
       "      <td>10.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07Feb1977</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Northern greater galago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OGG</td>\n",
       "      <td>N</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>KANGA</td>\n",
       "      <td>25Aug1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>20Jun1972</td>\n",
       "      <td>6</td>\n",
       "      <td>3952.0</td>\n",
       "      <td>564.57</td>\n",
       "      <td>129.93</td>\n",
       "      <td>129.0</td>\n",
       "      <td>10.83</td>\n",
       "      <td>104.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>07Feb1977</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Duke Lemur Center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>adult</td>\n",
       "      <td>N</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Northern greater galago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Taxon Hybrid  DLC_ID Sex   Name        DOB DOB_Estimated  Weight_g  \\\n",
       "0   OGG      N       5   M  KANGA  25Aug1961           NaN    1086.0   \n",
       "1   OGG      N       5   M  KANGA  25Aug1961           NaN    1190.0   \n",
       "\n",
       "  Weight_Date  MonthOfWeight  AgeAtWt_d  AgeAtWt_wk  AgeAtWt_mo  \\\n",
       "0   16Feb1972              2     3827.0      546.71      125.82   \n",
       "1   20Jun1972              6     3952.0      564.57      129.93   \n",
       "\n",
       "   AgeAtWt_mo_NoDec  AgeAtWt_y  Change_Since_PrevWt_g  Days_Since_PrevWt  \\\n",
       "0             125.0      10.48                    NaN                NaN   \n",
       "1             129.0      10.83                  104.0              125.0   \n",
       "\n",
       "   Avg_Daily_WtChange_g        DOD  DaysBeforeDeath Birth_Type  \\\n",
       "0                   NaN  07Feb1977           1818.0         CB   \n",
       "1                  0.83  07Feb1977           1693.0         CB   \n",
       "\n",
       "   Birth_Institution  Litter_Size  R_Min_Dam_AgeAtConcep_y Age_Category  \\\n",
       "0  Duke Lemur Center          1.0                     0.59        adult   \n",
       "1  Duke Lemur Center          1.0                     0.59        adult   \n",
       "\n",
       "  Current_Resident Preg_Status  Expected_Gestation_d ConcepDate_IfPreg  \\\n",
       "0                N          NP                   NaN               NaN   \n",
       "1                N          NP                   NaN               NaN   \n",
       "\n",
       "  InfantDOB_IfPreg  DaysBeforeInfBirth_IfPreg  Pct_PregRemain_IfPreg  \\\n",
       "0              NaN                        NaN                    NaN   \n",
       "1              NaN                        NaN                    NaN   \n",
       "\n",
       "   InfantLitSz_IfPreg conception_date infant_dob                   common  \n",
       "0                 NaN             NaT        NaT  Northern greater galago  \n",
       "1                 NaN             NaT        NaT  Northern greater galago  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5257e123",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4862155b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "52ed4ee9",
   "metadata": {},
   "source": [
    "### Weight, Species, Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b7e4098",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = wdf[['DLC_ID', 'common', 'Sex', 'Weight_g']].drop_duplicates()\n",
    "g = g.groupby(['common', 'Sex']).mean().reset_index()\n",
    "g = g[['common', 'Sex', 'Weight_g']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd0fc205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = sns.catplot(\n",
    "    data=g,\n",
    "    x=\"Weight_g\",\n",
    "    y='common',\n",
    "    kind='box',\n",
    "    height=7,\n",
    "    aspect=1.5\n",
    ").set(\n",
    "    title=\"Lemur Weight (g) by Species\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "59a243c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = g[g['Sex']!='ND']\n",
    "\n",
    "box = sns.catplot(\n",
    "    data=g,\n",
    "    x=\"Weight_g\",\n",
    "    y='Sex',\n",
    "    kind='box',\n",
    "    height=7,\n",
    "    aspect=1.5\n",
    ").set(\n",
    "    title=\"Lemur Weight (g) by Sex\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d97d27e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a160d1c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "95fd41c5",
   "metadata": {},
   "source": [
    "# Load Life History Data\n",
    "The **Life History** table holds aggregate information about the\n",
    "species of lemurs in residence at the DLC. Each row is a different\n",
    "species. The columns are described in the\n",
    "**Descriptions and Usage Notes** file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eea8b656",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf = pd.read_csv('../data/lemur_history.csv', index_col='Variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e8a466e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf = hdf.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10df45ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'R_Ratio_MtoF_DLCBirths',\n",
    "    'R_Mean_LitterSize',\n",
    "    'R_Expected_Gestation_d',\n",
    "    'R_Peak_Birth_Month',\n",
    "    'M_Mean_All_AdultWeight_g',\n",
    "    'M_Mean_All_NeonateWeight_g',\n",
    "    'M_Mean_All_YngAdultWeight_g',\n",
    "    'L_Median_All_Longevity_gt30d_y',\n",
    "    'L_Pct_All_InfMort_lt30d'\n",
    "]\n",
    "\n",
    "hdf = hdf[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1880fda0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 9)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "09424acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# coercing errors will convert anything unparsable to NaN. We can then exclude these values later. \n",
    "for col in columns:\n",
    "    hdf[col] = pd.to_numeric(hdf[col], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07714518",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dcff71d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 9)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc08ec80",
   "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>Variable</th>\n",
       "      <th>R_Ratio_MtoF_DLCBirths</th>\n",
       "      <th>R_Mean_LitterSize</th>\n",
       "      <th>R_Expected_Gestation_d</th>\n",
       "      <th>R_Peak_Birth_Month</th>\n",
       "      <th>M_Mean_All_AdultWeight_g</th>\n",
       "      <th>M_Mean_All_NeonateWeight_g</th>\n",
       "      <th>M_Mean_All_YngAdultWeight_g</th>\n",
       "      <th>L_Median_All_Longevity_gt30d_y</th>\n",
       "      <th>L_Pct_All_InfMort_lt30d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CMED</th>\n",
       "      <td>1.267</td>\n",
       "      <td>2.18</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>237.60</td>\n",
       "      <td>13.21</td>\n",
       "      <td>237.21</td>\n",
       "      <td>15.07</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMAD</th>\n",
       "      <td>0.706</td>\n",
       "      <td>1.00</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>2690.07</td>\n",
       "      <td>106.55</td>\n",
       "      <td>2588.45</td>\n",
       "      <td>25.10</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECOL</th>\n",
       "      <td>1.269</td>\n",
       "      <td>1.22</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>2333.61</td>\n",
       "      <td>62.49</td>\n",
       "      <td>2245.34</td>\n",
       "      <td>21.12</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECOR</th>\n",
       "      <td>1.667</td>\n",
       "      <td>1.22</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "      <td>1644.84</td>\n",
       "      <td>55.81</td>\n",
       "      <td>1550.73</td>\n",
       "      <td>18.71</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFLA</th>\n",
       "      <td>0.917</td>\n",
       "      <td>1.05</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>2433.89</td>\n",
       "      <td>78.51</td>\n",
       "      <td>2227.93</td>\n",
       "      <td>19.57</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variable  R_Ratio_MtoF_DLCBirths  R_Mean_LitterSize  R_Expected_Gestation_d  \\\n",
       "CMED                       1.267               2.18                      62   \n",
       "DMAD                       0.706               1.00                     165   \n",
       "ECOL                       1.269               1.22                     123   \n",
       "ECOR                       1.667               1.22                     124   \n",
       "EFLA                       0.917               1.05                     124   \n",
       "\n",
       "Variable  R_Peak_Birth_Month  M_Mean_All_AdultWeight_g  \\\n",
       "CMED                       7                    237.60   \n",
       "DMAD                       0                   2690.07   \n",
       "ECOL                       4                   2333.61   \n",
       "ECOR                       5                   1644.84   \n",
       "EFLA                       3                   2433.89   \n",
       "\n",
       "Variable  M_Mean_All_NeonateWeight_g  M_Mean_All_YngAdultWeight_g  \\\n",
       "CMED                           13.21                       237.21   \n",
       "DMAD                          106.55                      2588.45   \n",
       "ECOL                           62.49                      2245.34   \n",
       "ECOR                           55.81                      1550.73   \n",
       "EFLA                           78.51                      2227.93   \n",
       "\n",
       "Variable  L_Median_All_Longevity_gt30d_y  L_Pct_All_InfMort_lt30d  \n",
       "CMED                               15.07                     0.29  \n",
       "DMAD                               25.10                     0.10  \n",
       "ECOL                               21.12                     0.25  \n",
       "ECOR                               18.71                     0.33  \n",
       "EFLA                               19.57                     0.23  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6d7e60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "07c8e6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 54 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.pairplot(\n",
    "    data=hdf,\n",
    "    corner=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "592f0a40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b46275eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e2d43bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43dffdeb",
   "metadata": {},
   "source": [
    "### Clustering Species - How many clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "218e27ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(hdf.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4ad68360",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for k in range(1, 20):\n",
    "    result = KMeans(k).fit(x)\n",
    "    inertia.append([k, result.inertia_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73fa68a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = sns.relplot(\n",
    "    data=pd.DataFrame(inertia, columns=['k', 'inertia']),\n",
    "    x='k',\n",
    "    y='inertia',\n",
    "    kind='line'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db05bf9",
   "metadata": {},
   "source": [
    "### Clustering Species - What if we performed PCA first?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac7c99b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=6).fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "97385145",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcadf = pd.DataFrame({\n",
    "    'component': ['PCA-1', 'PCA-2', 'PCA-3', 'PCA-4', 'PCA-5', 'PCA-6'],\n",
    "    'variance': pca.explained_variance_\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f02d253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = sns.catplot(\n",
    "    data=pcadf,\n",
    "    kind='bar',\n",
    "    x='component',\n",
    "    y='variance',\n",
    "    height=6,\n",
    "    aspect=1.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ba04fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "32415b47",
   "metadata": {},
   "source": [
    "### Clustering Species (n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d7e5478",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = PCA(n_components=2).fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "669b1204",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for k in range(1, 20):\n",
    "    result = KMeans(k).fit(x)\n",
    "    inertia.append([k, result.inertia_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ef7bdfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = sns.relplot(\n",
    "    data=pd.DataFrame(inertia, columns=['k', 'inertia']),\n",
    "    x='k',\n",
    "    y='inertia',\n",
    "    kind='line'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3926faa3",
   "metadata": {},
   "source": [
    "### Clustering Species (k=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cd5c1ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = KMeans(6).fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f841f534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.22885336, -0.11183808],\n",
       "       [-0.6063008 , -0.32963998],\n",
       "       [ 1.61695795,  3.97138819],\n",
       "       [ 3.96033596, -3.03815975],\n",
       "       [-2.20427705, -2.03067556],\n",
       "       [-2.37237729,  0.86345069]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3792cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdf['cluster'] = result.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fa2e1e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcadf = pd.DataFrame(x, columns=['PCA-1', 'PCA-2'])\n",
    "pcadf['cluster'] = result.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c14b6fc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "281e1aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAABsR9AAAAAAYDuCBgAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbEfQAAAAAGA7ggYAAAAA2xE0AAAAANiOoAEAAADAdgQNAAAAALYjaAAAAACwHUEDAAAAgO0IGmiUotGwQqEqGYbhdikAAAA4CT63CwC+y7JMffn5Fi2c9wcd2H9Qw4YP1SWXX6T05s3dLg0AAAD1QNBAo/L19h26f+T/J9M0JUnTfzVP1lRLP7n2J7Isy+XqAAAAUFcsnUKjYRjSpx99HgsZRz37+xdVVVnhUlUAAAA4GQQNNBqWJTVrnlZrPDMrQ14fF98AAAASCUEDjcoPe/5ALVu1iL02DEP3PDBSPp/fxaoAAABQX7xNjEYlmJOjBU//Vh9v+FSHSw+rx4XddXrnTm6XBQAAgHoiaKDRCbZtqx/ntZVhGGwABwAASFAsnUKjRcgAAABIXAQNAAAAALYjaAAAAAAJaO3atbrmmmtOev6yZcv07LPP2lhRTQQNAAAAoAnasGGDqqqqHDs+m8EBAACABPDiiy9q8eLF8ng8atmypYYOHRr73MSJE9W1a1f9/Oc/r/X6ueee05IlS+T3+5WSkqJf//rX+uqrr/Tmm2/q/fffV2pqqm6++WYtWLBAr732mkzTVPv27TV16lTl5OTo1ltvVYsWLbR9+3bddNNNuvXWW+tUL0EDAAAAaOQ2b96sWbNmafny5WrXrp2eeuopPfHEE/Kd4KHG0WhUjz76qN58801lZ2drxYoV2rBhg376059q9erV6tq1q26++WatWLFCX375pZYtWyafz6cXXnhBkydP1qJFiyRJmZmZeuWVV+pVM0EDAAAAaOQKCgp0ySWXqF27dpKkESNG6Oyzz9ZvfvOb487zer36yU9+omHDhunyyy/XJZdcotzc3Fpf99Zbb+nTTz/V9ddfL0kyTVOVlZWxz19wwQX1rpmgAQAAADRyXq9XhmHEXldVVWn79u2x1//+/LFwOBz7eNasWfryyy+1Zs0aLVy4UC+99JLmzp1b4/imaeqOO+7Qz372M0lSKBRSaWlp7PPp6en1rpnN4AAAAEAj16dPHxUUFGjv3r2SpCVLlmjmzJmxz7ds2VKbNm2SJBUVFWndunWSpP379ys3N1dZWVkaMWKEfvGLX+jTTz+VdCS8RCIRSdIll1yiF198UWVlZZKkuXPnasKECQ2qmSsadWKq9MBB+Xw+Nc9swYPkAABwkc9nKOCxZFqGqiM84BVNQ7du3TR+/HjdcccdkqRgMKiHHnpIv/vd7yRJt956q8aNG6err75ap556qvr27StJatWqle655x6NGDFCqamp8nq9evjhhyVJl112maZNmyZJuvPOO1VUVKQbb7xRhmGoXbt2sc+dLMNKsL+dJSVlMs34lVx64ICeW7xMK5b+RRmZzfWLiaPU99K+8vn9DTpuMJih4uLDNlWJf0d/nUV/nUePnUV/neVkf9MDUvRAkSKHDsrw+pTS9lRVGymKRB05XaPE/7/Oind/g8GMuJ0r3lg6dRyGIb2y4q968bmXFYlEdWB/qaZOmK7tW7e5XRoAAE2Oz2vILN2nyKGDkiQrGlHVzh1K8TShlAEkEILGcVSWl+vlF1+tNf75p1v0nb04AAAgDvxeKVx6oPYnwtXxLwbACRE0jsOfElDHTh1qjQdz2iixFpwBAJD4TEvyBAK1P+FhyynQGBE0jsPr9evuMcMVSPnXD7Wu3Trp3O5nuVgVAABNU3XYUkpOe313WYEnvZmi3mOEDwCuc+0tgLKyMg0bNkxPPPGETj31VLfKOKFOZ3bR4mXztWPb10pNTVXnM89QRosst8sCAKBJqoj4lNahqxSpljxeRT1+VYVPPA9A/LkSND755BNNnjxZO3bscOP09WJZUttTTlHbU05xuxQAAJo805LKw4akVCmqI38ANEquLJ1aunSppk6dquzsbDdODwAAAKCOVq5cqYEDB6p///569tln6zzPlSsajzzyiBunBQAAAFAPRUVFmj17tvLz8xUIBDRs2DD16dNHXbp0OeHchLtNQ+vWzd0uwTbJ/ICWxoD+Oov+Oo8eO4v+Oov+Oov+OisR+1t9oESVe3bKDIfk8QeU1ra9Ulq2bvBx16xZo759+yorK0uSdPXVV+vVV1/V/ffff8K5CRc04v1kcKfwVE9n0V9n0V/n0WNn0V9n0V9n0V9nJeKTwasPlKi88GvJMiVJZjh05LXU4LCxd+9eBYPB2Ovs7Gxt3LixTnO5vS0AAACQwCr37IyFjBjLPDLeQKZpyvjOLaUty6rx+ngIGgAAAEACM8Oheo3XR9u2bVVcXBx7XVxcXOcbOrkaNN58881G/QwNAAAAoLHz+I/90MrvG6+Piy66SAUFBdq/f78qKyv12muv6bLLLqvT3ITbowEAAADgX9Latq+xR0OSZHiU1rZ9g4+dk5OjsWPH6rbbblM4HNYNN9yg7t2712kuQQMAAABIYEc3fDtx1ylJGjRokAYNGlTveQQNAAAAIMGltGxtW7CwC5vBAQAAANjuhEEjPz9f8+fP1+bNm2uM/+53v3OsKAAAAACJ7bhBY+bMmcrPz1dJSYluu+02rVy5Mva5V1991fHiAAAAACSm4+7R+Nvf/qb8/HwFAgH9x3/8h37+858rGAyqb9++sqzEfzo3GgePx5DHYygSMU/8xQAAAEgIx72i4fF4FAgcuf/uOeecoxkzZui//uu/VFhYWOcnAgLHU7xnj55b/IJ+OeZXenf1Oyo/fNjtkgAAAGCD417R6Nixo+bOnatbbrlFrVu31qWXXqo77rhDI0eOVCjU8CcNomk7dOCAHrhzkop275UkrXlnnW6786cafvctkgiyAAAAiey4VzQeeughFRYW6sMPP4yNjRw5UiNGjNBh3nlGA321/etYyDjquaf+pAMlJS5VBAAAgGMpKyvTNddco8LCwjrPOe4VjVatWmnmzJk1xoqLi3XzzTfrxhtvPLkqgaO+Z5sP+38AAADq59BX36jk402KVFTKl56m1j1+oMwzTrPl2J988okmT56sHTt21GtevZ+jcdddd0mS/H5/facCNZzRpaNy2mXXGPvZ8KFq1aaNSxUBAAAknkNffaO9az9UpKJSkhSpqNTetR/q0Fff2HL8pUuXaurUqcrOzj7xF39HvZ8MzrvNsEtmVkvNWfSIVq96R59t/EL9865Qrz49xf4MAACAuiv5eJOsaLTGmBWNquTjTbZc1XjkkUdOal69gwZgp+y27XTzz4dxe1sAAICTdPRKRl3H46XeS6cWLlwo6cgzNgA7mKZFyAAAADhJvvS0eo3HS72CRlVVlVavXq2f/OQnGjdunFM1AQAAAKij1j1+IMPrrTFmeL1q3eMHLlV0RJ2WThUVFemPf/yjli5dqvLyct11110aMWKEw6UBAAAAOJGj+zCcuuvUyTpu0Ni4caOeeuoprV69Wv369dPUqVM1a9YsjRkzJl71AQAAADiBzDNOczxYvPnmm/X6+uMunbrxxhvl8/n0xhtv6IknntDAgQNlGNwRCAAAAMDxHTdo/Pd//7c+//xzDR06VDNnztTWrVvjVRcAAACABHbcoHHrrbfqz3/+s2bNmqWdO3fquuuuU3FxsVasWKFIJBKvGgEAAAAkmDptBu/Tp4/69Omjffv26YUXXtDcuXM1Z84cvf322w6XBwAAACAR1euBfW3atNF9992ne+65R2+99ZZTNQEAAABIcCd8jsa8efNUUFAQez1hwgT93//9n370ox85WhgAAACAxHXcoLFo0SK9//77at++fWzslltu0bvvvqvFixc7XhwAAAAAd82fP195eXnKy8vTjBkz6jzvuEFj5cqVWrhwoU477V/35O3evbv+93//V/n5+SdfLQAAAIBGb82aNXrvvfe0fPlyrVixQp999plef/31Os097h4Nr9erFi1a1Bpv06aNvP/2mHMAAAAA7tjz8VZte32dqg+WKSWruTr/uLfa9uja4OMGg0FNnDhRgUBAktS5c2ft2rWrTnNPuBk8FArFDvzdMW5vCwAAALhvz8dbtXnFOzLDR34/rz5Yps0r3pGkBoeNrl3/NX/Hjh1atWqVnn/++TrNPe7Sqcsvv1wzZ86UZVmxMcuyNGvWLPXt2/ckywUAAABgl22vr4uFjKPMcETbXl9n2zm2bt2q22+/XRMmTNDpp59epznHvaJxzz336N5779WPfvQjnXfeeTJNU59++qk6deqkxx9/3I6aAQAAADRA9cGyeo3X14YNGzRmzBhNmjRJeXl5dZ533KARCAQ0btw4vfHGGzIMQ82aNdPIkSPVo0ePhtYLAEDcpPolbzQkWabkC8gyTbdLAgDbpGQ1P2aoSMlq3uBj7969W/fdd59mz56tfv361WvucYPGn/70J02fPl0dO3bUN998o1mzZhEyAAAJJdUvmSU7FS7/1z/Cvo6dZRg+fWdlMNCkpPoNeRSVJa+qIqqxTB6Jp/OPe9fYoyFJHr9PnX/cu8HHfvLJJ1VdXa1p06bFxoYNG6abbrrphHOPGzSeeeYZrVy5Ujk5Ofroo480e/ZsXXrppQ0uGACAePGaoRohQ5LKd32jlFM6qyrEL1doepoHTFXt+lpWOCTD61N6u9NUafpl8tchYR3d8O3EXacmT56syZMnn9TcE951KicnR5LUs2dPHThw4KROAgCAa46xTMoKh49/NxQgSaX6DVXv2iErHJYkWdGIqnZ+pdTTzlRFyOXi0CBte3S1JVjY6bg/Zw3DqPGaZ2cAABKOL1BrKNCipUJs00AT5LGiMv8ZMmIsS4qQMmC/er2h8+/BAwCAxq4y6lHqqZ1k+I8EDl9mS6W1ba9IhHUiaHosj0fy1P71z/CecJELUG/H/b9qy5Yt6tWrV+x1VVWVevXqJcuyZBiGPvzwQ8cLBJxSdqhU27Z+pfKycp3eqaNO6dBeEmEaSDamKVUYfqW06ySPYSkUNeRNSZUUPuFcINlURwyltTtNVTt3xMZSgu1UHfVKInzDXscNGq+//nq86gDi6tDBg5oy7hF9+vEXkiSfz6t5T05Tt3PPdrkyAE6wLKkqfPSXKH6ZQtNlmpaqPKlKPe1MWdGQDK9f1aZHkSh/L2C/4waN9u3bx6sOIK7+sWVbLGRIUiQS1ezHFmje72coEEh1sTIAAJwVNS2Vm4akFIm9SnAQN91Ak1R68FCtscJvditUVe1CNQAAAMmHoIEm6YzOHWuNXXPdj5WRmelCNQAAAI3X3LlzNXDgQOXl5Wnx4sV1nsctBtAknXbGaZr++FTN+s187S85qGuG9tew4dfLYjM4AABIQF++t0kFS95WWckhNW+dqX7DLteZl/ygwcddt26dPvjgA7388suKRCIaOHCgcnNz1alTpxPOJWigSfJ4fbrwogv1+2X/q3AorMysFjIMLvAh8RnGkY3PAICm48v3Numtha8oEopIksr2HdJbC1+RpAaHjd69e+vpp5+Wz+dTUVGRotGo0tPT6zSX36zQZFmWlN6suVq0bEnIQMLzGFJKuFLGnp3ylBQpxQyJRx8BQNNQsOTtWMg4KhKKqGDJ27Yc3+/3a968ecrLy1O/fv2Uk5NTp3n8dgUAScBfVa6it9/T/k8+0/4Nn6j4nfcViPKkXwBoCspKat/k5njjJ2PMmDEqKCjQ7t27tXTp0jrNIWgATYBhSD6fRwZvcScln9dQ6eYva6yZMkNhhfft46oGADQBzVsf+2Y23zdeH9u2bdMXXxx5JEBaWpr69++vLVu21GkuQQNIcgFFZBTvUfmHH0q7C5WiyIknIaEYsmRW1741c7Q6RLgEgCag37DL5QvU3HrtC/jUb9jlDT52YWGhJk+erFAopFAopNWrV+v888+v01xXNoOvXLlSCxYsUCQS0fDhw3XzzTe7UQaQ9HweqWzTF6oo3C1JqtyzV4HCncq68AKFLN5nSBYRU8ro0kkl6z+uMZ6aHVS1yc5wAEh2Rzd8O3HXqdzcXG3cuFFDhgyR1+tV//79lZeXV6e5cQ8aRUVFmj17tvLz8xUIBDRs2DD16dNHXbp0iXcpQNLzhKpiIeOo0P6DUlWllNLMnaIaAcMwFLDCUiQiyx9I+NBlWZKnVRu1Pv88HdryDxl+v7LOPUuR1HSJnAEATcKZl/zAlmBxLKNHj9bo0aPrPS/uQWPNmjXq27evsrKyJElXX321Xn31Vd1///3xLgVouprw/U8NQ/IfPqDi9R/LDIXka95MbXqfrypfqtulNUhYHhltctQymC1LhkKmCBkAAFfF/W28vXv3KhgMxl5nZ2erqKgo3mUATYIZSFV6+3Y1xgItW0hpdbv/dTIKREPaW/B3maEjd2SKlJVr39r1Chimy5U1nGVJIdNQOPG/FQBAEoj7FQ3TNGtsTrQsq16bFVu3bu5EWa4IBjPcLiGp0d8jml9wnsrb5aiscJfS22areYdTFMhseG8Stb9lhbtqXdGJlFcoxTDVok0Ll6o6tkTtcaKgv86iv86iv86iv/aIe9Bo27at1q9fH3tdXFys7OzsOs8vKSmTmQSbG4PBDBUXH3a7jKRFf2syWmerWXZbRaOWSqstqYG9SeT+pnhq/9jzBPwKmYYON6LvKZF7nAjor7Por7Por7Pi3d9kDjVxXzp10UUXqaCgQPv371dlZaVee+01XXbZZfEuA2hSLEuKRExZTXhvxlGRQKqyzj3rXwOGodbn91DIcOUmfAAAJK24/8uak5OjsWPH6rbbblM4HNYNN9yg7t27x7sMAE1U1DLkO7WDcrKDilZXy5uerrA3QAgDAMBmrryFN2jQIA0aNMiNUwOwmd9jyWOZihpeRRJkE3LEMhTxp0n+tCOPLyRjAABwQtOnT9eBAwc0bdq0On19Yt88HoBrDMNQarhCpR+sU9Frq1W+8ROlWmG3ywIAAA4oKCjQ8uXL6zWHRckATkrADKno3QJZkagkqXJXkcyqamX2vlBhs+53kgMAAA1X8Nd1yn/iZZXs3a/W2a00dNS16nd1b1uOffDgQc2ePVujRo3S5s2b6zyPoAHgpJgVFbGQcZQVNeW3TEUMb1N+JiAAAHFV8Nd1+sO05xSqPvKMqJKi/frDtOckyZawMWXKFI0dO1a7d++u1zyWTgE4KYbvX+9TeHw+ten5QwWyMrV//Ucy9u2VX9HjzAYAAHbJf+LlWMg4KlQdUv4TLzf42MuWLVO7du3Ur1+/es/ligaAk2KlpqtZxw4q//pbtTiri/Z/tllm6MgejaqS/Wpx9pnydjwjKZ57AwBAY1ayd3+9xuvjlVdeUXFxsQYPHqzS0lJVVFTo0Ucf1aRJk044l6AB4KSELUPpZ3VTeof2Uqg6FjKOOrTlH8rucKqq+TEDAICjWme3UklR7VDROrtVg4+9ePHi2Mf5+flat25dnUKGxNIpAA0QtjwKp2dKPn/tT3o8kuK7KdxrWEqJVMlfcUgpVlgGe9IBAE3A0FHXKpASqDEWSAlo6KhrXaroCN5qBHBSfB7JGwlJhiEjo7m8aamKVlbFPp91TjeFDG/cnlHhNSxFC79VyWdH7oZheL3Kvri3QmkZbEwHACS1oxu+nbrr1FFDhw7V0KFD6/z1BA0A9RYuq1DV5s0q2/GNZBjK7NpJ2Zf0VVVRscKHDintlHZS80yF4/gLvre6MhYyJMmKRrXv7x+p9WUXKyRv/AoBAMAF/a7ubXuwaCiCBoB6MQypvHDXkZAhSZalQ19uUyCrhdTuVKW0NxSOxv8R4VZ1da2xaGWVjEhE8hI0AACIN/ZoAKgXr9fQ4a+/rTVeuadIHo+hqAshQ5I8aWm1xvwZzWUda/8IAABwHEEDQL2YppQabF1rPKVVS1dvZRv2p6hN714y/nn1wpeeptYX9lLI4sccAABuYOkUgHoxDCnzjI6q2rdfVcUlkiR/Zob8wWxVu7jr2rQMWS3bKPvKXFnhkJSSqipCBgAAriFoAKizVLNahzdvU2nJfqW3b6eWPzxb0XBUSk9XteX+PgjLkqoNnxTwxe1uVwAA4NgIGgDqJEVRFb/3gaJVRzZdH/pym0IHS5XRq5fC7mzLAAAAjRhBA0CdmBXlsZBxVNXefcoMVUm+VJeqAgAATrv11lu1f/9++XxHosOvf/1rnXfeeSecR9AAUCfGsW4RaxgyPMmxD8LnkbzRsOTxKCQvD/kDACSUv6x4XfNmLNKeXXvV9pRsjZlwp/KG/LjBx7UsSzt27NBbb70VCxp1RdAAUDepaUrNCaqqqDg21OKsLor4AlKCL51KtcI6+OGnqtq7T97UFLXudZ4iGVly8SZaAADU2V9WvK6HJs5UVeWRlQe7dxbpoYkzJanBYWP79u2SpNtvv10HDx7UjTfeqFtuuaVOcwkaQBIzDMnr9SgaNRv8Dn3I8ijzvB+q+aFDipaVyd+ihazmGQm/P8PnkQ5+tElVe/dJkqJV1dpbsE45V16mai9LwgAAjd+8GYtiIeOoqspqzZuxqMFB49ChQ+rXr58efPBBhcNh3XbbbTrjjDN08cUXn3AuQQNIUimKKFy8TxV79iq1bbYCbdqouoF/5UPySZmtFOzcUcXFh5Pizk7eaLjGVRpJkiVFyyukTIIGAKDx27Nrb73G66Nnz57q2bNn7PUNN9ygv/3tbwQNoKnyeyyVfrxJVXuO/ICp2LVHqTlBZfTsobBpuFxd42J5vPKmpSpaWVVj3OPnieIAgMTQ9pRs7d5ZdMzxhlq/fr3C4bD69esn6ciejbru1UiOXZwAavBUV8VCxlFVRcXyVFd9z4ymKyyPWp/f48g6s39q1qG9rLRm7hUFAEA9jJlwp1LTUmqMpaalaMyEOxt87MOHD2vGjBmqrq5WWVmZli9frh//uG7LsbiiASQh63s2ZCTBSifbWZYUaZapnCsvU7S8XJ5AQFZausI8VRwAkCCO7sNw4q5TV1xxhT755BMNGTJEpmnqZz/7WY2lVMdjWN/3G0kjVVJSJjMJbgUTDGYcWeMORzT1/voNS4c+/FDVxSWxsZRga2X26qWw1fClU029v/FAj51Ff51Ff51Ff50V7/4GgxlxO1e8cUUDSEJhy1BWz/MU2lOkyj1FSmubo0DbHFXbEDIAAADqgqABJKlq+WS0O1XNT+2gaNRSdWJdvAQAAAmOoAEkMcuyFIkQMAAAQPyx2xEAAACA7QgaAAAAAGxH0AAAAABgO/ZoAAAAnASvx1DAZ8mQVB01FI2yJw74LoIGAACNnGFIfq8hGYZCYdPtciAp4JO8laUK7dpz5HWroKz0lqqKuFwY0IiwdAoAgEbM75PSVSVr7w5Ze3eouTckn9ftquA3qxXat1uyLMmyFCrZK0+4XAaPKwJiCBoAADRiATOkql1fy6yukllVqcrCr5RihN0uq0nzej2KHC6tNR49dEA+L79aAUfxtwEAgEbK7/MofHBfrfHooYPy8guta0zTkjclrda4kZouk4ejAjH8lAIAoJGyLEuGr/Z2SsPnk8UvtK6xLEtKz5DhD8TGDK9P3oyWbAgHvoPN4AAANFKRqKXUrDaKHDp4ZC+AJBkeeZq1kBnmF1o3VYSktFPOkCcSkiVLli9F5SG3qwIaF4IGAACNWEXYo7QOXWRVlUuGISOlmSoiHkkEDbdVhiTpn1c1CBlALQQNAAAaMdOSysMeGb4MSZIVlggZABIBQQMAgATAlgwAiYbN4AAAAABsR9AAAAAAYDuCBgAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtuP2tgAAAPXk9RhK8VqSFZXp8akqxP2HgX/nWtCYM2eOvF6vRo8e7VYJAAAA9ebzGApEy1S9a6dkWTL8ATU/5XSVhQy3SwMalbgvnTp8+LAmTZqkxYsXx/vUAAAADZbijah6T2HsKYpWOKTqom8V8BE0gO+Ke9BYvXq1Tj/9dI0cOTLepwYAAGi4SLjWkFlVKZ/B8ingu+IeNIYMGaK77rpLXq833qcGAABoOK+/1pARSFFUXNEAvsuxPRqrVq3SY489VmOsU6dOeuqppxp03NatmzdofmMSDGa4XUJSo7/Oor/Oo8fOor/OSub+mtGIFMxRVXHRkQGPR81P7Sh/8/j9jpLM/W0M6K89HAsaAwYM0IABA2w/bklJmUwz8S9NBoMZKi4+7HYZSYv+Oov+Oo8eO4v+Oqsp9NefkqXU0zJlmVHJ61dplSGrMj7fc1Por5vi3d9kDjXc3hYAAKCewlEpHPVI8khRSUr8N0EBu/HAPgAAAAC2c+2KBs/PAAAAAJIXVzQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbEfQAAAAAGA7ggYAAAAA2xE0AAAAANiOoAEAAADAdgQNAAAAALYjaAAAAACwHUEDAAAAgO0IGgAAAABsR9AAACQEn2EpJVIlf8UhpSgiwzDcLgkAcBw+twsAAOBEfIal8Nc7tG/zVkmSx+dT8JI+qg40c7kyAMD34YoGAKDR81RVqPSfIUOSzEhE+zd8Ir/HcrEqAMDxEDQAAI2eWVVVayx8uEyeaMSFagAAdUHQAAA0et70tFpjgZZZMr2sAAaAxoqgAQBo9CKBNLXq1V2G58g/W75m6WrV6zyFTTaEA0BjxVtBAIBGL2oZ8mS3U/aPWssKh6WUVFVZvFcGAI0ZQQMAkBBM01K14ZcCfok94ADQ6PF2EAAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbEfQAAAAAGA7ggYAAAAA28U9aGzYsEE33HCDBg8erOHDh2vnzp3xLgEAAACAw+IeNMaPH6+HH35YL730kgYNGqSHH3443iUgQRmGJTMcluF2IQAAADihuAaNUCikBx54QGeddZYkqVu3btq9e3c8S0CCOrzvgP78/6/U7Afm6q9/XKXyA6VulwQAAIDj8MXzZIFAQIMHD5Ykmaap+fPn66qrropnCXBJdVm59hbulcfrUbB9tgLpaXWeG6qs1MKpT2pvYbEkafWyt7Vt01e669d3yBsIOFUyAAAAGsCxoLFq1So99thjNcY6deqkp556SqFQSBMnTlQkEtHdd99dr+O2bt3czjJdFQxmuF1CXOzcvktzx/2fSvbslyR16Hqq7n34DuV0yK7T/M0f7o6FjKN2fPG1Kg4eUqdzz/jeeU2lv26hv86jx86iv86iv86iv86iv/ZwLGgMGDBAAwYMqDVeXl6ue+65R1lZWVqwYIH8fn+9jltSUibTtOwq0zXBYIaKiw+7XYbjPB5D76xcEwsZkvTt1kJ9/P4mnX/VhbLq8J/S/J7xqKXv7WFT6a9b6K/z6LGz6K+z6K+z6K+z4t3fZA41rmwG79ixo+bMmaMAy16Sn2Vp68ZttYa/3vy1PJ66/e/Xqm0b9bz0vBpjlw66WC3atLSlRAAAANgvrns0Pv/8c61evVpdunTRddddJ0nKzs7WokWL4lkG4siSofMv76kdX3xdY/zcPucoGv2+axU1ef1+XTdqiM6/oqe+/UehOnY7TR3OPE2G1+tEyQAAALBBXIPGOeecoy1btsTzlHCZZVn64UU/1Ddbv9Xf39ggw2Poiusu0+nnfP/eimNJaZ6uruefrW4XnpMUS+cAAACSXVyDBpqmtMzmuuHeG9R/2FUyPIYyWrWQjJNbtUfIAAAASAwEDcSH16PMYCu3qwAAAECcxH0zOAAAAIDkR9AAAAAAYDuCBgAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbEfQAAAAAGA7ggYAAAAA2xE0AAAAANiOoAEAAADAdgQNAAAAALYjaAAAAACwHUEDAAAAgO0IGgAAAABsR9AAAAAAYDuCBgAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbEfQAAAAAGA7ggYAAAAA2xE0AAAAANiOoAEAAADAdnEPGuvXr9fQoUM1aNAgjRo1SqWlpfEuAQAAAIDD4h40fvnLX2rGjBlauXKlunTpoieffDLeJQAAAABwmC/eJ3zllVfk9/sVDodVVFSkbt26xbsEAAAAQJLk8RgyTcvtMpJS3K9o+P1+bdmyRbm5uVq7dq3y8vLiXQIAAACaOCsU0oGt3+rTFe9p90dbZZZXul1S0jEsy3Ikwq1atUqPPfZYjbFOnTrpqaeeir1esmSJVqxYoSVLljhRAgAAAFBLNBLVumXv6MOXCmJjwU5tlTf+P9SsZYaLlSUXx4LGsVRXV+vdd9/VVVddJUmqqKjQxRdfrI8++qjOxygpKUuKy1vBYIaKiw+7XUbSor/Oor/Oo8fOor/Oor/Oor8NFzlcrhUPPiUzatYYHzDhRnXq1SWu/Q0GkzfYxHXplM/n00MPPaRNmzZJOnLVo1evXvEsAQAAAE2cZZq1QoYkmZGoC9Ukr7huBvd6vZo9e7amTJmiaDSqnJwcPfLII/EsAQAAAE1cILOZOvU5S9vXbo6NpbVopoycVi5WlXziftepCy64QPn5+fE+LQAAACBJsgyPeg69RK06BLX9gy+U3aW9zv5RD3nSU90uLanEPWgAAAAAbvOmp6nLFT3V+bLuMrwembVXUqGBCBoAAABokkzTkgyPLEKGI+L+HA0AAAAAyY+gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqABAAAAwHYEDQAAAAC2I2gAAAAAsB1BAwAAAIDtCBoAAAAAbOdzu4D68ngMt0uwTTJ9L40R/XUW/XUePXYW/XUW/XUW/XUW/bWHYVmW5XYRAAAAAJILS6cAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGAAAAANsRNAAAAADYjqDhkvXr12vo0KEaNGiQRo0apdLSUrdLSjobNmzQDTfcoMGDB2v48OHauXOn2yUlpTlz5ujxxx93u4yksXLlSg0cOFD9+/fXs88+63Y5SamsrEzXXHONCgsL3S4l6cyfP195eXnKy8vTjBkz3C4n6cydO1cDBw5UXl6eFi9e7HY5SWv69OmaOHGi22UkBYKGS375y19qxowZWrlypbp06aInn3zS7ZKSzvjx4/Xwww/rpZde0qBBg/Twww+7XVJSOXz4sCZNmsQ/djYqKirS7Nmz9dxzz2nFihV64YUX9I9//MPtspLKJ598optuukk7duxwu5Sks2bNGr333ntavny5VqxYoc8++0yvv/6622UljXXr1umDDz7Qyy+/rD/96U965plntH37drfLSjoFBQVavny522UkDYKGS1555RV16dJF4XBYRUVFyszMdLukpBIKhfTAAw/orLPOkiR169ZNu3fvdrmq5LJ69WqdfvrpGjlypNulJI01a9aob9++ysrKUnp6uq6++mq9+uqrbpeVVJYuXaqpU6cqOzvb7VKSTjAY1MSJExUIBOT3+9W5c2ft2rXL7bKSRu/evfX000/L5/OppKRE0WhU6enpbpeVVA4ePKjZs2dr1KhRbpeSNAgaLvH7/dqyZYtyc3O1du1a5eXluV1SUgkEAho8eLAkyTRNzZ8/X1dddZXLVSWXIUOG6K677pLX63W7lKSxd+9eBYPB2Ovs7GwVFRW5WFHyeeSRR3TBBRe4XUZS6tq1q3r06CFJ2rFjh1atWqXc3Fx3i0oyfr9f8+bNU15envr166ecnBy3S0oqU6ZM0dixY3nz10YEDYetWrVKl112WY0/I0aMkHTkXfY1a9bo3nvv1dixY90tNIEdr8ehUEjjxo1TJBLR3Xff7W6hCep4/YW9TNOUYRix15Zl1XgNJIKtW7fq9ttv14QJE3T66ae7XU7SGTNmjAoKCrR7924tXbrU7XKSxrJly9SuXTv169fP7VKSis/tApLdgAEDNGDAgBpj1dXVeuONN2LvsF977bWaPn26G+UlhWP1WJLKy8t1zz33KCsrSwsWLJDf73ehusT3ff2F/dq2bav169fHXhcXF7PEBwllw4YNGjNmjCZNmsSVeptt27ZNoVBIZ599ttLS0tS/f39t2bLF7bKSxiuvvKLi4mINHjxYpaWlqqio0KOPPqpJkya5XVpC44qGC3w+nx566CFt2rRJ0pF3jHv16uVyVcln/Pjx6tixo+bMmaNAIOB2OcAJXXTRRSooKND+/ftVWVmp1157TZdddpnbZQF1snv3bt13332aNWsWIcMBhYWFmjx5skKhkEKhkFavXq3zzz/f7bKSxuLFi/XnP/9ZL730ksaMGaMrr7ySkGEDrmi4wOv1avbs2ZoyZYqi0ahycnL0yCOPuF1WUvn888+1evVqdenSRdddd52kI+vdFy1a5HJlwPfLycnR2LFjddtttykcDuuGG25Q9+7d3S4LqJMnn3xS1dXVmjZtWmxs2LBhuummm1ysKnnk5uZq48aNGjJkiLxer/r370+gQ6NnWJZluV0EAAAAgOTC0ikAAAAAtiNoAAAAALAdQQMAAACA7QgaAAAAAGxH0AAAAABgO4IGACShwsJCnX322Ro8eHDsz7XXXqsXX3xR0pEHh86ZM0dDhgzR4MGDNWjQIC1cuFD/fiPCZ555Rt26ddPHH39cp/Nu3rxZl1xyid3fDgAgAfEcDQBIUqmpqXrppZdir4uKinTNNdfoBz/4gWbOnKkzzjhDL7zwglJSUnTgwAHdfffdqqio0C9+8YvYnCVLlmjQoEH6wx/+oB49enzvuSKRiP74xz9q0aJFqqiocPC7AgAkCq5oAEATkZOTo44dO+qNN97Q9u3b9ctf/lIpKSmSpJYtW2rGjBm68MILY1+/du1alZaWavz48Vq9erV27979vcf+/PPPtWXLFs2fP9/x7wMAkBgIGgDQRHz00Uf65ptvJEndu3eX1+ut8fnTTz9dF198cez1c889p0GDBiknJ0d9+/bVH//4x+89dvfu3fXYY48pGAw6UzwAIOGwdAoAklRVVZUGDx4sSYpGo2rZsqVmzpypr776qtZejH9XXFys1atX609/+pMkaciQIfrVr36l++67T+np6Y7XDgBIfAQNAEhS/75H46jMzEz94Q9/UDQarXFVY+PGjXrmmWc0c+ZMLV26VJJ0zz33SJJM01RZWZmWL1+uq666SnfddVds3sKFC5WTk+PwdwMASDQEDQBoYnr27KlOnTrpscce0/jx45WSkqJ9+/bp4Ycf1sUXX6xoNKply5bpoYce0vXXXx+bN3fuXD399NP62c9+dswAAwDAd7FHAwCaoHnz5kmShg4dqmuvvVYjRoxQ//79NWbMGL311lsyTVODBg2qMWfEiBHat2+f/va3v7lRMgAgwRjWiRbqAgAAAEA9cUUDAAAAgO0IGgAAAABsR9AAAAAAYDuCBgAAAADbETQAAAAA2I6gAQAAAMB2BA0AAAAAtiNoAAAAALDd/wMx90viwi1ecwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 801.725x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter = sns.relplot(\n",
    "    data=pcadf,\n",
    "    x='PCA-1',\n",
    "    y='PCA-2',\n",
    "    hue='cluster',\n",
    "    height=7,\n",
    "    aspect=1.5\n",
    ").set(\n",
    "    title=\"Lemur Clusters\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5866af46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}