clustering_upgma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2baa9c1b-4fbb-42c2-9470-abca0c91e798",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "174eebe1-a0ad-446a-99f0-2780cdd3f19a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      A     B     C     D     E     F     G\n",
       "A   0.0  19.0  27.0   8.0  33.0  18.0  13.0\n",
       "B  19.0   0.0  31.0  18.0  36.0   1.0  13.0\n",
       "C  27.0  31.0   0.0  26.0  41.0  32.0  29.0\n",
       "D   8.0  18.0  26.0   0.0  31.0  17.0  14.0\n",
       "E  33.0  36.0  41.0  31.0   0.0  35.0  28.0\n",
       "F  18.0   1.0  32.0  17.0  35.0   0.0  12.0\n",
       "G  13.0  13.0  29.0  14.0  28.0  12.0   0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    " ('A', 'A'): np.nan,\n",
    "\n",
    " ('B', 'A'): 19,\n",
    " ('C', 'A'): 27,\n",
    " ('D', 'A'): 8,\n",
    " ('E', 'A'): 33,\n",
    " ('F', 'A'): 18,\n",
    " ('G', 'A'): 13,\n",
    "\n",
    " ('C', 'B'): 31,\n",
    " ('D', 'B'): 18,\n",
    " ('E', 'B'): 36,\n",
    " ('F', 'B'): 1,\n",
    " ('G', 'B'): 13,\n",
    "\n",
    " ('D', 'C'): 26,\n",
    " ('E', 'C'): 41,\n",
    " ('F', 'C'): 32,\n",
    " ('G', 'C'): 29,\n",
    "\n",
    " ('E', 'D'): 31,\n",
    " ('F', 'D'): 17,\n",
    " ('G', 'D'): 14,\n",
    "\n",
    " ('F', 'E'): 35,\n",
    " ('G', 'E'): 28,\n",
    "\n",
    " ('G', 'F'): 12,\n",
    "\n",
    " ('G', 'G'): np.nan\n",
    " }\n",
    "data = pd.Series(data).unstack()\n",
    "data = data.fillna(0) + data.T.fillna(0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b521da3-3dd2-4a22-8303-3b8e49365cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/sjanssen/miniconda3/envs/notebookServer/lib/python3.10/site-packages/seaborn/matrix.py:530: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  linkage = hierarchy.linkage(self.array, method=self.method,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x150ef044bd60>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f8d54a-c685-43d2-ad9f-89d464ec2978",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ggmap",
   "language": "python",
   "name": "ggmap"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}