A_Gentle_Introduction_to_Data_Science_with_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Gentle Introduction to Data Science with Python\n",
    "Python is a capable and free (open source) programming language well-suited for analysis of data, including microbiome data. Together, we will go through a real-life example, re-analyzing microbiome - immune checkpoint inhibitor studies. We will touch on both the basics (loading in data, transforming, graphing, basic statistical analysis, and outputting data and figures), and use the examples to compare and contrast with some equivalents in R. This introduction should be suitable for those with no data analysis with code experience, but have some added dimensions for those familiar with R.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `jupyter` (much like an R Markdown document) allows for markdown text, code, and the output from code to all be in the same document.\n",
    "\n",
    "I used another feature of python to make this portable: `virtual environments`. Virtual environments are python environments kept seperate from those on the rest of your computer. In the environment can be a specific version of python and all the supporting libraries for a project. Need to go back and rerun an analysis? The virutal environment for a project can keep the data, code, _and_ packages for an analysis together and unchanged from when first run.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vanilla python has multiple useful data structures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 c False\n"
     ]
    }
   ],
   "source": [
    "# lists / arrays\n",
    "L = [1, 2, 3, 4]\n",
    "\n",
    "# dictionaries\n",
    "D = {\n",
    "    1: 'a', \n",
    "    2: 'b',\n",
    "    3: 'c',\n",
    "    4: 'd'\n",
    "}\n",
    "# sets\n",
    "S = {\n",
    "    1,\n",
    "    2,\n",
    "    3,\n",
    "    4\n",
    "}\n",
    "\n",
    "print(\n",
    "    L[2],\n",
    "    D[3],\n",
    "    5 in S\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python is organized with whitespace. \n",
    "- Spaces, tabs, and indentations in general are really important, and are how python scripts are organised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python has a large number of libraries that can be imported\n",
    "- `import` is the command to import a library.\n",
    "- One nice feature of python is one can change the name of the library on import\n",
    "- The parts of a library stay in the namespace of the package. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Pandas` is among the most useful of these for data analysis: The Python Data Analysis Library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample_ID</th>\n",
       "      <th>Publication_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>ICI_type</th>\n",
       "      <th>Sequencing_method</th>\n",
       "      <th>Seq_note</th>\n",
       "      <th>Project_ID</th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Colitis</th>\n",
       "      <th>ICI_Response</th>\n",
       "      <th>ICI_Response_RECIST</th>\n",
       "      <th>data_type</th>\n",
       "      <th>specimen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SRR6000888</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Melanoma</td>\n",
       "      <td>PD-1</td>\n",
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       "      <td>P29</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16S_miseq_p</td>\n",
       "      <td>SRR6000888</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SRR6000887</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Melanoma</td>\n",
       "      <td>PD-1</td>\n",
       "      <td>16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRP116709</td>\n",
       "      <td>P22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16S_miseq_p</td>\n",
       "      <td>SRR6000887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SRR6000897</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Melanoma</td>\n",
       "      <td>PD-1</td>\n",
       "      <td>16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRP116709</td>\n",
       "      <td>P40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16S_miseq_p</td>\n",
       "      <td>SRR6000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SRR6000883</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Melanoma</td>\n",
       "      <td>PD-1</td>\n",
       "      <td>16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRP116709</td>\n",
       "      <td>P26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16S_miseq_p</td>\n",
       "      <td>SRR6000883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SRR6000882</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Melanoma</td>\n",
       "      <td>PD-1</td>\n",
       "      <td>16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SRP116709</td>\n",
       "      <td>P25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16S_miseq_p</td>\n",
       "      <td>SRR6000882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample_ID Publication_ID Location cancer_type ICI_type Sequencing_method  \\\n",
       "0  SRR6000888         Matson  Chicago    Melanoma     PD-1               16S   \n",
       "1  SRR6000887         Matson  Chicago    Melanoma     PD-1               16S   \n",
       "2  SRR6000897         Matson  Chicago    Melanoma     PD-1               16S   \n",
       "3  SRR6000883         Matson  Chicago    Melanoma     PD-1               16S   \n",
       "4  SRR6000882         Matson  Chicago    Melanoma     PD-1               16S   \n",
       "\n",
       "   Seq_note Project_ID Subject_ID Timepoint Colitis ICI_Response  \\\n",
       "0       NaN  SRP116709        P29       NaN     NaN            R   \n",
       "1       NaN  SRP116709        P22       NaN     NaN           NR   \n",
       "2       NaN  SRP116709        P40       NaN     NaN           NR   \n",
       "3       NaN  SRP116709        P26       NaN     NaN           NR   \n",
       "4       NaN  SRP116709        P25       NaN     NaN           NR   \n",
       "\n",
       "  ICI_Response_RECIST    data_type    specimen  \n",
       "0                 NaN  16S_miseq_p  SRR6000888  \n",
       "1                 NaN  16S_miseq_p  SRR6000887  \n",
       "2                 NaN  16S_miseq_p  SRR6000897  \n",
       "3                 NaN  16S_miseq_p  SRR6000883  \n",
       "4                 NaN  16S_miseq_p  SRR6000882  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_csv('data/metadata.csv')\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas creates data frames\n",
    "- These are akin to data frames in R\n",
    "- These can be created from CSV, excel, TSV, and a multitude of other formats\n",
    "- One can also make a dataframe directly from python code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python dataframes have powerful methods to do things like group and summarize the data.\n",
    "- count(), sum(), describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sample_ID</th>\n",
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       "      <th>cancer_type</th>\n",
       "      <th>ICI_type</th>\n",
       "      <th>Sequencing_method</th>\n",
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       "      <th>Project_ID</th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Colitis</th>\n",
       "      <th>ICI_Response</th>\n",
       "      <th>ICI_Response_RECIST</th>\n",
       "      <th>data_type</th>\n",
       "      <th>specimen</th>\n",
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       "    <tr>\n",
       "      <th>Chaput</th>\n",
       "      <td>83</td>\n",
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       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>Dubin</th>\n",
       "      <td>34</td>\n",
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       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matson</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sample_ID  Location  cancer_type  ICI_type  Sequencing_method  \\\n",
       "Publication_ID                                                                  \n",
       "Chaput                 83        83           83        83                 83   \n",
       "Dubin                  34        34           34        34                 34   \n",
       "Matson                 42        42           42        42                 42   \n",
       "\n",
       "                Seq_note  Project_ID  Subject_ID  Timepoint  Colitis  \\\n",
       "Publication_ID                                                         \n",
       "Chaput                 0          83          83         83       83   \n",
       "Dubin                  0          34          34          0       34   \n",
       "Matson                 0          42          42          0        0   \n",
       "\n",
       "                ICI_Response  ICI_Response_RECIST  data_type  specimen  \n",
       "Publication_ID                                                          \n",
       "Chaput                    83                   83         83        83  \n",
       "Dubin                      0                    0         34        34  \n",
       "Matson                    42                    0         42        42  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.groupby('Publication_ID').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrames can be sliced to select for data that matches a condition\n",
    "- In this case, specimens for which we have response data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>ICI_type</th>\n",
       "      <th>Sequencing_method</th>\n",
       "      <th>Seq_note</th>\n",
       "      <th>Project_ID</th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>Timepoint</th>\n",
       "      <th>Colitis</th>\n",
       "      <th>ICI_Response</th>\n",
       "      <th>ICI_Response_RECIST</th>\n",
       "      <th>data_type</th>\n",
       "      <th>specimen</th>\n",
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       "    <tr>\n",
       "      <th>Publication_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chaput</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>Matson</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sample_ID  Location  cancer_type  ICI_type  Sequencing_method  \\\n",
       "Publication_ID                                                                  \n",
       "Chaput                 83        83           83        83                 83   \n",
       "Matson                 42        42           42        42                 42   \n",
       "\n",
       "                Seq_note  Project_ID  Subject_ID  Timepoint  Colitis  \\\n",
       "Publication_ID                                                         \n",
       "Chaput                 0          83          83         83       83   \n",
       "Matson                 0          42          42          0        0   \n",
       "\n",
       "                ICI_Response  ICI_Response_RECIST  data_type  specimen  \n",
       "Publication_ID                                                          \n",
       "Chaput                    83                   83         83        83  \n",
       "Matson                    42                    0         42        42  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata[\n",
    "    ~metadata.ICI_Response.isna()\n",
    "].groupby('Publication_ID').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also get _all_ the possible values for a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'baseline', 'before ICI 3', 'before ICI 2', 'tox',\n",
       "       'before ICI 4', 'end of ICI', 'post-tox'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.Timepoint.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### and fill missing values in a column very easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['baseline', 'before ICI 3', 'before ICI 2', 'tox', 'before ICI 4',\n",
       "       'end of ICI', 'post-tox'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.Timepoint.fillna('baseline', inplace=True)\n",
    "metadata.Timepoint.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see if baseline alpha diversity varied by response or non-response in each trial\n",
    "- Pandas can directly read in gzipped data without issue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placerun</th>\n",
       "      <th>phylo_entropy</th>\n",
       "      <th>quadratic</th>\n",
       "      <th>unrooted_pd</th>\n",
       "      <th>rooted_pd</th>\n",
       "      <th>bwpd</th>\n",
       "      <th>rooted_qd_0</th>\n",
       "      <th>rooted_qd_1</th>\n",
       "      <th>rooted_qd_1.00001</th>\n",
       "      <th>rooted_qd_2</th>\n",
       "      <th>rooted_qd_3</th>\n",
       "      <th>rooted_qd_4</th>\n",
       "      <th>rooted_qd_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>3.41531</td>\n",
       "      <td>1.36426</td>\n",
       "      <td>26.9724</td>\n",
       "      <td>27.3329</td>\n",
       "      <td>3.44274</td>\n",
       "      <td>12.48660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.75985</td>\n",
       "      <td>2.65419</td>\n",
       "      <td>2.00188</td>\n",
       "      <td>1.69356</td>\n",
       "      <td>1.51998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wgs__SRR6000947</td>\n",
       "      <td>3.67516</td>\n",
       "      <td>1.38211</td>\n",
       "      <td>33.2994</td>\n",
       "      <td>33.6599</td>\n",
       "      <td>3.42520</td>\n",
       "      <td>15.35990</td>\n",
       "      <td>inf</td>\n",
       "      <td>5.34982</td>\n",
       "      <td>2.70777</td>\n",
       "      <td>1.99378</td>\n",
       "      <td>1.68398</td>\n",
       "      <td>1.51448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wgs__SRR6000946</td>\n",
       "      <td>4.07237</td>\n",
       "      <td>1.51638</td>\n",
       "      <td>29.0650</td>\n",
       "      <td>29.0650</td>\n",
       "      <td>3.62707</td>\n",
       "      <td>13.24060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.39275</td>\n",
       "      <td>3.23404</td>\n",
       "      <td>2.22731</td>\n",
       "      <td>1.81212</td>\n",
       "      <td>1.59887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wgs__SRR6000944</td>\n",
       "      <td>3.05071</td>\n",
       "      <td>1.26369</td>\n",
       "      <td>23.3397</td>\n",
       "      <td>23.3397</td>\n",
       "      <td>3.42116</td>\n",
       "      <td>11.29600</td>\n",
       "      <td>inf</td>\n",
       "      <td>4.37751</td>\n",
       "      <td>2.57469</td>\n",
       "      <td>2.01532</td>\n",
       "      <td>1.73131</td>\n",
       "      <td>1.55971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wgs__SRR6000943</td>\n",
       "      <td>3.71150</td>\n",
       "      <td>1.52155</td>\n",
       "      <td>19.9175</td>\n",
       "      <td>20.2780</td>\n",
       "      <td>3.78097</td>\n",
       "      <td>9.00839</td>\n",
       "      <td>0.444244</td>\n",
       "      <td>5.20077</td>\n",
       "      <td>3.08585</td>\n",
       "      <td>2.19424</td>\n",
       "      <td>1.78275</td>\n",
       "      <td>1.56581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          placerun  phylo_entropy  quadratic  unrooted_pd  rooted_pd     bwpd  \\\n",
       "0  wgs__SRR6000949        3.41531    1.36426      26.9724    27.3329  3.44274   \n",
       "1  wgs__SRR6000947        3.67516    1.38211      33.2994    33.6599  3.42520   \n",
       "2  wgs__SRR6000946        4.07237    1.51638      29.0650    29.0650  3.62707   \n",
       "3  wgs__SRR6000944        3.05071    1.26369      23.3397    23.3397  3.42116   \n",
       "4  wgs__SRR6000943        3.71150    1.52155      19.9175    20.2780  3.78097   \n",
       "\n",
       "   rooted_qd_0  rooted_qd_1  rooted_qd_1.00001  rooted_qd_2  rooted_qd_3  \\\n",
       "0     12.48660     0.000000            4.75985      2.65419      2.00188   \n",
       "1     15.35990          inf            5.34982      2.70777      1.99378   \n",
       "2     13.24060     0.000000            6.39275      3.23404      2.22731   \n",
       "3     11.29600          inf            4.37751      2.57469      2.01532   \n",
       "4      9.00839     0.444244            5.20077      3.08585      2.19424   \n",
       "\n",
       "   rooted_qd_4  rooted_qd_5  \n",
       "0      1.69356      1.51998  \n",
       "1      1.68398      1.51448  \n",
       "2      1.81212      1.59887  \n",
       "3      1.73131      1.55971  \n",
       "4      1.78275      1.56581  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the alpha diversity data \n",
    "alpha_diversity = pd.read_csv('data/alpha_diversity.csv.gz')\n",
    "alpha_diversity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can create new columns by modifying old columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placerun</th>\n",
       "      <th>phylo_entropy</th>\n",
       "      <th>quadratic</th>\n",
       "      <th>unrooted_pd</th>\n",
       "      <th>rooted_pd</th>\n",
       "      <th>bwpd</th>\n",
       "      <th>rooted_qd_0</th>\n",
       "      <th>rooted_qd_1</th>\n",
       "      <th>rooted_qd_1.00001</th>\n",
       "      <th>rooted_qd_2</th>\n",
       "      <th>rooted_qd_3</th>\n",
       "      <th>rooted_qd_4</th>\n",
       "      <th>rooted_qd_5</th>\n",
       "      <th>Sample_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>3.41531</td>\n",
       "      <td>1.36426</td>\n",
       "      <td>26.9724</td>\n",
       "      <td>27.3329</td>\n",
       "      <td>3.44274</td>\n",
       "      <td>12.48660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.75985</td>\n",
       "      <td>2.65419</td>\n",
       "      <td>2.00188</td>\n",
       "      <td>1.69356</td>\n",
       "      <td>1.51998</td>\n",
       "      <td>SRR6000949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wgs__SRR6000947</td>\n",
       "      <td>3.67516</td>\n",
       "      <td>1.38211</td>\n",
       "      <td>33.2994</td>\n",
       "      <td>33.6599</td>\n",
       "      <td>3.42520</td>\n",
       "      <td>15.35990</td>\n",
       "      <td>inf</td>\n",
       "      <td>5.34982</td>\n",
       "      <td>2.70777</td>\n",
       "      <td>1.99378</td>\n",
       "      <td>1.68398</td>\n",
       "      <td>1.51448</td>\n",
       "      <td>SRR6000947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wgs__SRR6000946</td>\n",
       "      <td>4.07237</td>\n",
       "      <td>1.51638</td>\n",
       "      <td>29.0650</td>\n",
       "      <td>29.0650</td>\n",
       "      <td>3.62707</td>\n",
       "      <td>13.24060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.39275</td>\n",
       "      <td>3.23404</td>\n",
       "      <td>2.22731</td>\n",
       "      <td>1.81212</td>\n",
       "      <td>1.59887</td>\n",
       "      <td>SRR6000946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wgs__SRR6000944</td>\n",
       "      <td>3.05071</td>\n",
       "      <td>1.26369</td>\n",
       "      <td>23.3397</td>\n",
       "      <td>23.3397</td>\n",
       "      <td>3.42116</td>\n",
       "      <td>11.29600</td>\n",
       "      <td>inf</td>\n",
       "      <td>4.37751</td>\n",
       "      <td>2.57469</td>\n",
       "      <td>2.01532</td>\n",
       "      <td>1.73131</td>\n",
       "      <td>1.55971</td>\n",
       "      <td>SRR6000944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wgs__SRR6000943</td>\n",
       "      <td>3.71150</td>\n",
       "      <td>1.52155</td>\n",
       "      <td>19.9175</td>\n",
       "      <td>20.2780</td>\n",
       "      <td>3.78097</td>\n",
       "      <td>9.00839</td>\n",
       "      <td>0.444244</td>\n",
       "      <td>5.20077</td>\n",
       "      <td>3.08585</td>\n",
       "      <td>2.19424</td>\n",
       "      <td>1.78275</td>\n",
       "      <td>1.56581</td>\n",
       "      <td>SRR6000943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          placerun  phylo_entropy  quadratic  unrooted_pd  rooted_pd     bwpd  \\\n",
       "0  wgs__SRR6000949        3.41531    1.36426      26.9724    27.3329  3.44274   \n",
       "1  wgs__SRR6000947        3.67516    1.38211      33.2994    33.6599  3.42520   \n",
       "2  wgs__SRR6000946        4.07237    1.51638      29.0650    29.0650  3.62707   \n",
       "3  wgs__SRR6000944        3.05071    1.26369      23.3397    23.3397  3.42116   \n",
       "4  wgs__SRR6000943        3.71150    1.52155      19.9175    20.2780  3.78097   \n",
       "\n",
       "   rooted_qd_0  rooted_qd_1  rooted_qd_1.00001  rooted_qd_2  rooted_qd_3  \\\n",
       "0     12.48660     0.000000            4.75985      2.65419      2.00188   \n",
       "1     15.35990          inf            5.34982      2.70777      1.99378   \n",
       "2     13.24060     0.000000            6.39275      3.23404      2.22731   \n",
       "3     11.29600          inf            4.37751      2.57469      2.01532   \n",
       "4      9.00839     0.444244            5.20077      3.08585      2.19424   \n",
       "\n",
       "   rooted_qd_4  rooted_qd_5   Sample_ID  \n",
       "0      1.69356      1.51998  SRR6000949  \n",
       "1      1.68398      1.51448  SRR6000947  \n",
       "2      1.81212      1.59887  SRR6000946  \n",
       "3      1.73131      1.55971  SRR6000944  \n",
       "4      1.78275      1.56581  SRR6000943  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_diversity['Sample_ID'] = alpha_diversity.placerun.apply(lambda p: p.split(\"__\")[1])\n",
    "alpha_diversity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The combination of slicing and selecting columns can efficiently simplify complex dataframes to just what is needed for a question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Publication_ID</th>\n",
       "      <th>Sample_ID</th>\n",
       "      <th>ICI_Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000888</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000887</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000897</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000883</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000882</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Publication_ID   Sample_ID ICI_Response\n",
       "0         Matson  SRR6000888            R\n",
       "1         Matson  SRR6000887           NR\n",
       "2         Matson  SRR6000897           NR\n",
       "3         Matson  SRR6000883           NR\n",
       "4         Matson  SRR6000882           NR"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pare down our metadata to just what we need\n",
    "response_metadata = metadata[\n",
    "    ~metadata.ICI_Response.isna() &\n",
    "    (metadata.Timepoint == 'baseline')\n",
    "][[\n",
    "    'Publication_ID',\n",
    "    'Sample_ID',\n",
    "    'ICI_Response',\n",
    "\n",
    "]].drop_duplicates()\n",
    "\n",
    "response_metadata.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas can very efficiently merge together dataframes, matching on columns or the index\n",
    "- This is very akin to an SQL JOIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>quadratic</th>\n",
       "      <th>unrooted_pd</th>\n",
       "      <th>rooted_pd</th>\n",
       "      <th>bwpd</th>\n",
       "      <th>rooted_qd_0</th>\n",
       "      <th>rooted_qd_1</th>\n",
       "      <th>rooted_qd_1.00001</th>\n",
       "      <th>rooted_qd_2</th>\n",
       "      <th>rooted_qd_3</th>\n",
       "      <th>rooted_qd_4</th>\n",
       "      <th>rooted_qd_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000888</td>\n",
       "      <td>R</td>\n",
       "      <td>16s__SRR6000888</td>\n",
       "      <td>2.97914</td>\n",
       "      <td>1.18380</td>\n",
       "      <td>18.0552</td>\n",
       "      <td>18.4157</td>\n",
       "      <td>2.86628</td>\n",
       "      <td>9.37864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55940</td>\n",
       "      <td>2.51811</td>\n",
       "      <td>1.86687</td>\n",
       "      <td>1.59380</td>\n",
       "      <td>1.44927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000887</td>\n",
       "      <td>NR</td>\n",
       "      <td>16s__SRR6000887</td>\n",
       "      <td>2.43453</td>\n",
       "      <td>1.10099</td>\n",
       "      <td>10.1266</td>\n",
       "      <td>10.4871</td>\n",
       "      <td>2.76146</td>\n",
       "      <td>5.20900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.35094</td>\n",
       "      <td>2.20685</td>\n",
       "      <td>1.72022</td>\n",
       "      <td>1.49902</td>\n",
       "      <td>1.38014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000897</td>\n",
       "      <td>NR</td>\n",
       "      <td>16s__SRR6000897</td>\n",
       "      <td>3.85681</td>\n",
       "      <td>1.45508</td>\n",
       "      <td>25.5293</td>\n",
       "      <td>25.8898</td>\n",
       "      <td>3.45654</td>\n",
       "      <td>11.82560</td>\n",
       "      <td>inf</td>\n",
       "      <td>5.82206</td>\n",
       "      <td>2.98181</td>\n",
       "      <td>2.07909</td>\n",
       "      <td>1.71347</td>\n",
       "      <td>1.52589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000883</td>\n",
       "      <td>NR</td>\n",
       "      <td>16s__SRR6000883</td>\n",
       "      <td>3.60791</td>\n",
       "      <td>1.48953</td>\n",
       "      <td>19.4445</td>\n",
       "      <td>19.8050</td>\n",
       "      <td>3.66557</td>\n",
       "      <td>8.67830</td>\n",
       "      <td>inf</td>\n",
       "      <td>4.85948</td>\n",
       "      <td>2.87929</td>\n",
       "      <td>2.09376</td>\n",
       "      <td>1.73498</td>\n",
       "      <td>1.54282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000882</td>\n",
       "      <td>NR</td>\n",
       "      <td>16s__SRR6000882</td>\n",
       "      <td>2.96983</td>\n",
       "      <td>1.13609</td>\n",
       "      <td>19.9858</td>\n",
       "      <td>20.3463</td>\n",
       "      <td>2.68763</td>\n",
       "      <td>10.51950</td>\n",
       "      <td>inf</td>\n",
       "      <td>4.64346</td>\n",
       "      <td>2.42357</td>\n",
       "      <td>1.78834</td>\n",
       "      <td>1.53668</td>\n",
       "      <td>1.40750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Publication_ID   Sample_ID ICI_Response         placerun  phylo_entropy  \\\n",
       "0         Matson  SRR6000888            R  16s__SRR6000888        2.97914   \n",
       "1         Matson  SRR6000887           NR  16s__SRR6000887        2.43453   \n",
       "2         Matson  SRR6000897           NR  16s__SRR6000897        3.85681   \n",
       "3         Matson  SRR6000883           NR  16s__SRR6000883        3.60791   \n",
       "4         Matson  SRR6000882           NR  16s__SRR6000882        2.96983   \n",
       "\n",
       "   quadratic  unrooted_pd  rooted_pd     bwpd  rooted_qd_0  rooted_qd_1  \\\n",
       "0    1.18380      18.0552    18.4157  2.86628      9.37864          0.0   \n",
       "1    1.10099      10.1266    10.4871  2.76146      5.20900          0.0   \n",
       "2    1.45508      25.5293    25.8898  3.45654     11.82560          inf   \n",
       "3    1.48953      19.4445    19.8050  3.66557      8.67830          inf   \n",
       "4    1.13609      19.9858    20.3463  2.68763     10.51950          inf   \n",
       "\n",
       "   rooted_qd_1.00001  rooted_qd_2  rooted_qd_3  rooted_qd_4  rooted_qd_5  \n",
       "0            4.55940      2.51811      1.86687      1.59380      1.44927  \n",
       "1            3.35094      2.20685      1.72022      1.49902      1.38014  \n",
       "2            5.82206      2.98181      2.07909      1.71347      1.52589  \n",
       "3            4.85948      2.87929      2.09376      1.73498      1.54282  \n",
       "4            4.64346      2.42357      1.78834      1.53668      1.40750  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_ad = pd.merge(\n",
    "    response_metadata,\n",
    "    alpha_diversity,\n",
    "    on='Sample_ID',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "response_ad.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn is an advanced python graphing library\n",
    "- Again, we import with `as` to make the name shorter given how often we use it\n",
    "- matplotlib is the (still powerful but) basic graphing library for python, inspired by graphing in MATLAB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Publication_ID', ylabel='quadratic'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xZk+dMd69hhsUCn0uqfvYfMTTPoJvgPOA0meowrGmhTjLG0H5i5CgQObdeCZr9hzBAIldWlDbmAlH6nwm3PCt9f7Lu2DN69b7rH3w3lXw+yS/G8lZHw3rHlll0kO/Xw9j+VPwzd9dGwK/fRnir6j9vFbdYMrn8IPruZchN1llDZSniSDUGFP2IK0x5rjWCDwjIiRWqBWoSgrzICe9vCNuR6XBOxnb4eheaNHF97E5TI+2TXnh6gE8v9hqHpo2PJqh3VrZHZb3FBdZiaCMgaWPeZYIwJoKpeMrXgnN1zxNBNkiMsAYsxZARAYCubWco1TNNnwIn//RmsulfQJMngvt4yG9QnNQ49bQtPpBearuXRjXngtPMAjS75iSqg8rFOXXft6eldbTbt1GW+MM/ICniWAm8IGI7AcEaAdM8lZQygHyj8Fnt5eP2DyQDN8+DOc/aNUAfvkBmkXBhOesIf9K1bWgRtZqZD+8WF52Zi0PKrx3NWxZaL1vFgXT/+v+WGkD5VEiMMb8KCK9gV6uoq3GmKorsSvlqcx97sP2AXYth/0/wZQvrH0hTXUWUuVdFz5iNfEcSIaYc6zZRU9k7w/lSQCsPqwfXrIGQDZwNSYCERlljPlWRH5baVdPEcEYM9+LsSl/FtnTWiz8yK7yssy9Vudwt1FwbcN9Jls1ICLW6Pa+HqyLlZdZtSz3aJ2HZIfaHh89x/Xn+Gpe47wYl/J3AQFw9QfQa6w1f0tFO76FX1bbEpZSJ9T1HIio8NCCBEL/a+yLpw7VWCMwxtzvevugMWZXxX0iElPNKUp5LrIHTH7XmoDup3fc9xXl2ROTqiK3oJiDWXl0aRXu7Mefg0Jg+v9g9cvW4vaNW0POYSgpbvBNmJ6OLP6omrIP6zIQ5WADp0FghQ7hdn2hyzD74lFlPlu3n8H/+JqRTyzhvKeWsifD4dN/NG1r9SNs/gy+fwHemwwfXW93VKettj6C3kAs0LxSP0EzILT6s9TJyi0oZvnPabRqEsLALi3sDsf3Og6EGUsh5UPrW1bC1Q3+G5Y/yCss5u6PN3AsvwiAHWnZPLZoK89fPcDmyGy28jn3GuvG+TDqXr8eUNYLqy8gAqtfoNQx4AYvxeQoqUdymPjiKn7Nsj5Y4+LbM/sqB/6ite0Dbf1vnveGLO1YPsfyitzKdqQdP8HRDlJS5FlZA1JbH8ECYIGIDDXGrPJRTI7y6opdZUkAYOH6A9x4diZ9O/r5ugVZ+62xAu0ToKV2N9VHnVqG07tdU7b8Wj7f0Hln+N/qXCdtyI3w8//AuGYW7jYaWveq+Zx6ztMBZT+JyK1YzURlTULGmGleicpBMnOqDsc4mlvN1Mz+ZPNC+GAKlBRaM4+OfxYGXGd3VKoar/wukccWbWX7oeOcd0Ybbhvdw+6Q7NdtFMxYYvUTRHT2fEqKeszTRPA2sAUYAzwIXA1s9lZQTnJ5Yic+Sd5XNtFXdKtwzuzqx/O7gDXJV4krAZoS+PoB6H+tTixXD3VsEc6syf3tDqP+aR9vvfyEp4mguzHmchG5xBjzpoi8Cyz3ZmBOMbRbK96bMZSPf0qlVeMQrjurC8GBfr5MROVBOHlZ1iN4gV5bME8pVQNPf/NK2y+Oikgc8CvQxjshOc/gmJYMjnHQDKUDroPlT5RvJ1ylSUApG3n62/eyiLQA7gU+BZoA/+e1qJR/G3Wv1UG8e4W1IM3AqXZHpJSj1ZoIXCuTZRljjgDLgK5ej0r5NxFraL6fDM9XqqGrtTHaGFMC/MUHsSillLKBp01DX4vIn4B5QNkYc2PMYa9EpZRS9VHqGti8wHpsNOFqCA6zO6I64WkiKF2E5tYKZQZtJlJKOcXP/4N3r7AeeQbY9Cn87lN7Y6ojni5Mo0M/lVLOUVIC2/9nrZbX80KI6ASr/1OeBAB2LYVlT8DwP1rTqjdgtU06V3lBGje6MI1Syu9sXQSf3wlZqdb2f//P+uYfFFL12G8fgux0uOhfvo2xjtVWIyidaK4NcBbwrWv7XGAloInAQ99uOcjDCzdzMCuPS/tHcf/4WBoFNexvEUr5na1fwtwr3cuKcq0ZR4fdDtu/hsIc9/1rXocx/2zQtYIaIzfGTDXGTAWCgT7GmMuMMZdhzTkU7IsA/cHRnAJumbOWnenZZBcUM+eHvbz23a7aT1RK+da696ovLy6w1jb+fRKEV5oCplHjBp0EwPOFaToZYw5U2D4IdPZCPH5pw75M8gpL3MqSdusDV0rVO02qmV1VAmHwDOt98yi48FFrssRS597jm9i8yNOnhr4Rka+Aua7tScDX3gnJ/8R2aE5IUAD5ReXJoG2zUL7YcIARPSJpGqqVK6XqhbNus5qHMvda2+0TrNlxOySUHxN/OUQNgL3fWyPj2/S2I9I6JcYYzw60Oo5HuDaXGWM+9lpUNUhMTDRJSUl23Pq0fLXxVx5auIlDWflEtQhjV7o1HKNFeDAf3DSU7m2a2hyhjYoKYOPHkLUP+lzSoFd6Un6gqAD2rrRqB23OsDuaOiMia4wxidXu8zQRnMJNX8Na3eyQMSaumv1XA3cBgrXi2c3GmHW1XbehJoJSO9OOM+rJpW5lEwd25InL+9kUUT3w1iWwc4n1PjAEpiyEToNtDUmpMqX/RzbwadJrSgQe9RGIyJki8qOIHBeRAhEpFpGsWk57A7iwhv27gHOMMX2Bh4CXPYmlocgrLOa77ensTndf7Dsrr+qSdpm5VRencYwD68qTAEBxPvzwkm3hKOVm8SPwSCd4tAuseMbuaLzG0z6C2cCVwAdAInAd0LOmE4wxy0Qkuob9Kytsfg909DCWem/jvkymvLGatGMFiMCtI7vzpzHWUnb9OjanT/tmbDpg5VERuHJQJzvDtVl137Ia9jcv5Se2/ReWVhgf8PX9Vk21y1n2xeQlHj/zZIzZDgQaY4qNMa9T87f9kzUd+LIOr2ebZ77exvjZK0g7Zi03aQy8sGQ7BzJzARAR3r1hCH88vyeTB3dizvQhjHatA7vvaC4PfraJO+Yl8932dNt+Bp9qH28t/VcqKBTOvNm+eJQqlfpjNWUNt1m6Jp7WCHJEpBGQLCKPAQc4iSRSExE5FysRDK/hmBnADIDOnevvU6sbUjN55uufq5SXGMg4XkD75tYEVSn7smgcEsS0YTH0aGt1EucVFjPxxZUcyLQWsv8keR/vXn8mQ7v5+bKVAJPnweZPrc7i3uO0s1jVD12GVlPmf7UB8DwRXAsEAr8H7gA6AZed7s1FJB54BbjIGJNxouOMMS/j6kNITEz0Tu92Hdjya/XdJr3aNiW2QzMAHl64iVdWWIPJAgOEF64ewJjYdny3Pb0sCYBVk5i/NtUZiSCoEfSdaHcUSrnrNgrOfwhWzbbGEoz4ozWozA95OuncHtfbXODvdXFjEemMNUXFtcaYbXVxTbud1T2SQBGKKzyJFSDw9KQERISsvELeXLW7bF9xieHFJTsYE9uOVk2qzmNSXZlSyoeG/cF6+TlPnxraJSI7K79qOWcusAroJSKpIjJdRG4SkZtch9wHtAJeEJFkEWnwjW9REWEM6+7+Db7EwM+HjgHWxIXFJe4VmsJia5BZQqcILk3oUFbeuWU404ZFezdgpZTC86ahivWhUOByoMbV1o0xk2vZfz1wvYf3bzBG9W7Dsp/dO3rPaG81CzUPD2biwI68n5Ratm/68PIZvp+5sj/Xj+jKkZwChsS00knplFI+4WnTUOX2+2dEZA3Wt3pVweQhnVmz9yifr99PaHAgM8/rQc+25aOGH/ltPMN7tGbbr8cY2as1idHu+TQuqrmvQ1YKgG0Hj/HL4RzO7NqKxiGefkdU/sCjkcUiMqDCZgBWDeFmY4zPh8M2lJHFWXmFNAoMIDQ40ONz0o/n8+bK3aQdy+fS/lGc2dWPOorzsmDV85C2GXqMgf5X2x2RquCRLzfz76VWa2/Lxo2Ye8OZ9Grn4GlP/FBNI4s9TftPYi1NCVAE7MZqHlIn0OwkJ5IrKi7hin+vYmeaNRJ5XtIvvD1tCMN7RHojPN97/zrYudh6v2kB5B62JvhStvs1M4//LCvv8jucXcDsxdt5bnJ/G6NSvuRpIliIlQhKh3waYJy45t4wxjxV96E5y+rdh8uSAFiPj76f9It/JIJjv5YngVLJc90TQc5h2PI5hLWwlgYM1KYJXzmaW0ClZxg4nJ1vTzDKFp7+tg0EBgELsJLBeGA1UHX0lDolEWGNqpQ1D/OT6akbNYHgcPeVnZq0Ln9/eCf8Z7RVSwCIHgG/+6zBT/LVUPRu14z4js1Zn5pZVnZFopOnPalGUb61PvGupdC+H4z8G4TX+LxMg+JpH8EyYKwx5phruynwuTHmbC/HV0VD6SM4FXfMS+bjn/YB0LppCB/ddBadW4XbHFUdWfUC/Pce6xnakOZw7Xz4dQOsecNa8zUr1f346BHWHPBDb4Om1SwWourU0ZwCXluxi72Hc7i4b3suiG1nd0j1y5d3uU+G2G209RluQE57GmoR2QrEG2PyXdshwHpjTK86jdQD/pwIANbuPULasXxG9IgkvJGfNY8c2Q3pP0OnIbB7Obx3Ve3ntO4NN69q8EsBqgbuyd5w7IB72d9SIaThdKjXRWfxW8BqESldjOZSrGmmVR0b0LmF3SF4T4to6wWwbZFn56Rtgf0/QceB3opKqdq1iHZPBE3aWc2dfsKjr1nGmH8AU4EjrtdUY8wj3gxM+bnIaiqT/a+1lgB0I9DYDzrMVcM25p/l6xk3agpjn4QAzx8Nr+88bnswxqwF1noxFuUkidOs5qFtiyCwEQy/A869GzJ2wBtjy799Db0VWnSxN1alogbAzBRrHEzLrg2qScgTXluq0lv8vY/AcbIOQHAYhEWUl6WusZ7O6DEG2sXaFppS/qQu+giU8o5m7d23P74Z1r1rvV/7FkxbBE31CRalvEkfxVC+sXsFLH0Mtn9z4mP2/1SeBACO7ILvX/B+bEo5nNYIlPetegG++lv59rn3wDl/qXrc8TTPytRpSzuWzwtLtrM7PZsL49oxaVD9XflPeZ8mAuV9K2dV2n4Ozv5z1ZHDMWdDs44VBpcJ9JvkkxCdZsrrq9m431pRb/HWNPKLSrhuaLS9QSnbaNOQsse+NbDkUdj0KZRYi/MQHArTvoQhN0HcRGvkZteRtobpj7YfOlaWBEotSN5vUzSqPtAagfK+4XfAlxWagrqNglfOo2xC24FTYfwz1vuIznDRo76O0FFaNQ6hUWAABa7V8QDaNQ+1MSJlN60RKO8bciNMXQSj74PrPrU6ganw2PLatyD3qF3ROU6Lxo2484KeBAZYTXPtmoVyx3k9bY5K2UlrBMo3ugy1XgBS6fuHiM406mM3ntONCQkd+OVwLgmdInRZVIfTf33lPYW5sOJp+GCKNcto6eDFYTPdk0HidAjVJTp9rX3zMAbHtNQkoLRGoLzo45tg0yfW+40fW6OIz/0bxF4KrbpZYwra9IEe59sZpVKOp4lAeUf+cdj8qXtZ8rvWqmTFBdCur/VSStlO64TKO4JCqk7MVVIIj3eDx7rCB1OtVZ+UUrbTRKC8IzAYzvt7eV9AUKg1o2hRHmBg43zraSGllO20aUh5T+JU6D4aDm6EzFT44k/u+w9tsicupZQbrREo74roDL0ugl4XW+sOVNRdO4mVqg80ESjfaB4Fk9+z1itu0wcufgJ6X2x3VEoptGlI+VL30dZLKVWvaI1AKaUcTmsEyrcKsuHHVyHjZ6vfoNdFdkeklONpIlC+9f51sP1r6/3at2DCczDgOntjUsrhtGlI+U5mankSKLXmDVtCUUqV81oiEJHXROSQiKScYL+IyCwR2S4i60VkgLdiUfVEcDgEBLuX6WRzStnOmzWCN4ALa9h/EdDD9ZoBvOjFWFR9EN4Sht1evh3cGM65y754lFKAF/sIjDHLRCS6hkMuAd4yxhjgexGJEJH2xpgD3opJ1QOj/w/6XALp26xlKBtH2h2RUo5nZ2dxFPBLhe1UV5kmAn/XPt56KaXqhQbRWSwiM0QkSUSS0tLS7A5HKaX8ip2JYB/QqcJ2R1dZFcaYl40xicaYxNatW/skOKWUcgo7m4Y+BX4vIu8BQ4DMU+0fKCwsJDU1lby8vDoN0J+EhobSsWNHgoODaz+4Lu1ZBStnWWsPDLkReo7x7f2VUrXyWiIQkbnASCBSRFKB+4FgAGPMS8AXwMXAdiAHmHqq90pNTaVp06ZER0cjugh6FcYYMjIySE1NJSYmxnc3ProX3r7UtQYBsHMxXP81RA2s+bzCXGum0oBAr4eolPLuU0OTa9lvgFvr4l55eXmaBGogIrRq1Qqf969s+6o8CQCYEti88MSJoCAHFtwCmxZAWEsY80/oN8k3sSrlYA2is9gTmgRqZsvfT4toz8pKrZptLXJvSiAnHRbcCscOeis6pZSL3yQCVQ91Gw3xV5Zvdz8f4mv4hr8/2X27pBAOVjswXSlVh3TSOeU9AQHw23/DuXdDcSFEdq/5+JizYevn5duNmtTen6CUOm1+WyNo0qRJ2ftt27Zx8cUX06NHDwYMGMAVV1zBwYMHWbJkCePGjTvhNd544w1at25NQkICvXv35umnn/ZF6P6nRZfakwDA4BusKSiatIP2/eDKORAW4fXwlHI6v68R5OXlMXbsWJ566inGjx8PwJIlSzzuOJ00aRKzZ88mIyODXr16MXHiRDp16lT7ierkBQTC+Q9aL6WUz/htjaDUu+++y9ChQ8uSAMDIkSOJi4s7qeu0atWK7t27c+CANdThnXfeYfDgwSQkJHDjjTdSXFxMcXExU6ZMIS4ujr59+5bVIEaOHMntt99OQkICcXFxrF69GoDDhw9z6aWXEh8fz5lnnsn69esBeOCBB5g2bRojR46ka9euzJo1C4Ds7GzGjh1Lv379iIuLY968eQCsWbOGc845h4EDBzJmzJiyGJVSyhN+XyNISUlh4MDTb2feu3cveXl5xMfHs3nzZubNm8d3331HcHAwt9xyC3PmzCE2NpZ9+/aRkmJ1cB49erTs/JycHJKTk1m2bBnTpk0jJSWF+++/n/79+/PJJ5/w7bffct1115GcnAzAli1bWLx4MceOHaNXr17cfPPNLFq0iA4dOvD551Y7emZmJoWFhdx2220sWLCA1q1bM2/ePO655x5ee+210/6ZlVLO4PeJ4HTNmzePZcuWsWXLFmbPnk1oaCjffPMNa9asYdCgQQDk5ubSpk0bxo8fz86dO7ntttsYO3YsF1xwQdl1Jk+2hlWcffbZZGVlcfToUVasWMFHH30EwKhRo8jIyCArKwuAsWPHEhISQkhICG3atOHgwYP07duXO++8k7vuuotx48YxYsQIUlJSSElJ4fzzzweguLiY9u3b+/KvSDVwGcfzuW/BRn7YlUF8xwgevCSWji3C7Q5L+ZDfJ4LY2FiWLl16yueX9hEkJSVxwQUXMGHCBIwx/O53v+ORRx6pcvy6dev46quveOmll3j//ffLvplXfo6/tuf6Q0JCyt4HBgZSVFREz549Wbt2LV988QX33nsvo0eP5je/+Q2xsbGsWrXqlH9G5Wz3fJzCoo2/AvDtlkNk5Rby4c1n2RyV8iW/7yO46qqrWLlyZVlzCsCyZcvKmm88lZiYyLXXXsuzzz7L6NGj+fDDDzl06BBgtfXv2bOH9PR0SkpKuOyyy3j44YdZu3Zt2fml7fkrVqygefPmNG/enBEjRjBnzhzA6sCOjIykWbNmJ4xh//79hIeHc8011/DnP/+ZtWvX0qtXL9LS0soSQWFhIRs3bjypn00528od6W7bSXuOkF9UbFM0yg5+XyMICwtj4cKFzJw5k5kzZxIcHEx8fDzPPvss6enptV+ggrvuuosBAwZw99138/DDD3PBBRdQUlJCcHAwzz//PGFhYUydOpWSkhIAtxpDaGgo/fv3p7CwsKyWUNopHB8fT3h4OG+++WaN99+wYQN//vOfCQgIIDg4mBdffJFGjRrx4Ycf8oc//IHMzEyKioqYOXMmsbGxJ/k3pZwqLqo5K3dklG33bNuEkCCd58lJxJryp+FITEw0SUlJbmWbN2/mjDPOsCmi2o0cOZInnniCxMREW+Oo739Pyh670rO5be5aUvZl0a11Y569sj9xUbqWtL8RkTXGmGr/E/L7GoFSqmYxkY1ZeNsIjucX0SRE/0twIv1XB15//XWeffZZt7Jhw4bx/PPP18n1lyxZUifXUcqbNAk4l/7LA1OnTmXq1FNeDkEppRo0v39qSCmlVM00ESillMNpIlBKKYfTPoJalJQYPl23n1dX7OJAZi7tm4cxfXgME/p1ICDg1Ff9CgwMpG/fvhQVFRETE8Pbb79NRERE3QWulFIe0hpBDUpKDDe9s4a7P97Ahn2ZpB8vYMO+TP42fwM3vbOGkpJTH4MRFhZGcnIyKSkptGzZss6eUFJKqZOliaAGn67bz4rt6eQUuA+3zy0sZvnP6Xy2fn+d3Gfo0KHs27evTq6llFInSxNBDV5dsatKEiiVW1jMK8t3nfY9iouL+eabb5gwYcJpX0sppU6FJoIaHMjMPa39NcnNzSUhIYF27dpx8ODBsmmklVLK1zQR1KB987DT2l+T0j6CPXv2YIzRPgKllG00EdRg+vAYwoKrn4UxLDiQ60fEnPY9wsPDmTVrFk8++SRFRUWnfT2llDpZmghqMKFfB0b0iKySDMKCAxnRI5Lx8R3q5D79+/cnPj6euXPn1sn1lFLqZOg4ghoEBAgvXTOQz9bv55Xl5eMIrh8Rw/j40xtHcPz4cbftzz777HTDVUqpU6KJoBYBAcIlCVFckhBldyhKKeUV2jSklFIOp4lAKaUcThOBUko5nCYCpZRyOK8mAhG5UES2ish2EflrNfs7i8hiEflJRNaLyMXejEcppVRVXksEIhIIPA9cBPQBJotIn0qH3Qu8b4zpD1wJvOCteE5ZSQmsfx/+fQ483t36c/37VvlpEBHuvPPOsu0nnniCBx54AIAHHniAqKgoEhIS6NOnj44vUEp5lTdrBIOB7caYncaYAuA94JJKxxigmet9c6BupvOsKyUlMO8a+Ox2OJAM2WnWn5/dDu9fe1rJICQkhPnz55Oenl7t/jvuuIPk5GQWLFjAjTfeSGFh4SnfSymlauLNRBAF/FJhO9VVVtEDwDUikgp8AdzmxXhOXsqHsHMxFOa4lxfmwI5vIeWjU750UFAQM2bM4Omnn67xuB49ehAeHs6RI0dO+V5KKVUTuzuLJwNvGGM6AhcDb4tIlZhEZIaIJIlIUlpamu+iW/V81SRQqjAHVs0+rcvfeuutzJkzh8zMzBMes3btWnr06EGbNm1O615KKXUi3kwE+4BOFbY7usoqmg68D2CMWQWEApGVL2SMedkYk2iMSWzdurWXwq1GVi2LxdS2vxbNmjXjuuuuY9asWVX2Pf3008TGxjJkyBDuueee07qPUkrVxJuJ4Eegh4jEiEgjrM7gTysdsxcYDSAiZ2AlAh9+5a9Fs1qmlahtvwdmzpzJq6++SnZ2tlv5HXfcwcaNG/noo4+YPn06eXl5p30vpU5FcYlhUcqvvLR0Bz8fPGZ3OMoLvJYIjDFFwO+Br4DNWE8HbRSRB0WkdDmuO4EbRGQdMBeYYow59YWA69rQWyE4vPp9weEw9PenfYuWLVtyxRVX8Oqrr1a7f8KECSQmJvLmm2+e9r2UOhV3vp/MTe+s4V9fbuGiZ5ezdFv9+a6m6oZX+wiMMV8YY3oaY7oZY/7hKrvPGPOp6/0mY8wwY0w/Y0yCMea/3oznpMVNhK7nVk0GweHQbRTEXVYnt7nzzjtP+PQQwH333cdTTz1FyWk+sqrUyTqQmcsnyeUP8xWVGF5ZvtPGiJQ36OyjNQkIgEnvWE8HrZpt9Qk0i7JqAnGXWftPUcVpqNu2bUtOTnmndOl4glIDBw5k69atp3wvpU5VdfXzknpUaVd1QxNBbQICIP5y66WUw3SICGN8vw58ts6qFQQGCNcP72pzVKquaSJQStXo6Sv6MSa2LbvSsjmvT1vOaN+s9pNUg+I3icAYg8iprxjm7+pTH7xqWIICAxhXR8uyqvrJ7gFldSI0NJSMjAz9z+4EjDFkZGQQGhpqdyhKqXrIL2oEHTt2JDU1FZ+OOm5gQkND6dixo91hKKXqIb9IBMHBwcTExNgdhlJKNUh+0TSklFLq1GkiUEoph9NEoJRSDicN7UkbEUkD9tgdhx+JBE48v4VS9tHPZt3qYoypdvrmBpcIVN0SkSRjTKLdcShVmX42fUebhpRSyuE0ESillMNpIlAv2x2AUiegn00f0T4CpZRyOK0RKKWUw2kiUEoph9NE0ICJiBGRdypsB4lImogsrOW8BBG52PsRKicTkXYi8p6I7BCRNSLyhYjMqO3zWQf3jRCRW7x5D3+jiaBhywbiRCTMtX0+sM+D8xIATQTKa8RaHORjYIlrzfKBwN+Atj64fQSgieAkaCJo+L4AxrreTwbmlu4QkcEiskpEfhKRlSLSS0QaAQ8Ck0QkWUQmicg5rvfJrmObiuVxEUkRkQ0iMsl1zZEiskREPhSRLSIyR3RFIFXVuUChMeal0gJjzDpgOdCkus+PiNwnIj+6PnMvVyhfIiLPuj6fKSIy2FX+gIj8qfT6rn3RwL+Abq7jH/fdj9xwaSJo+N4DrhSRUCAe+KHCvi3ACGNMf+A+4J/GmALX+3nGmARjzDzgT8CtxpgEYASQC/wWq+bQDzgPeFxE2ruu2x+YCfQBugLDvPkDqgYpDlhzgn0n+vzMNsYMMsbEAWHAuArnhLs+n7cAr9Vy778CO1yf7z+fWvjOoomggTPGrAeisWoDX1Ta3Rz4QERSgKeB2BNc5jvgKRH5AxBhjCkChgNzjTHFxpiDwFJgkOv41caYVGNMCZDsur9SnjrR5+dcEflBRDYAo3D/vM4FMMYsA5qJSITvwvV/mgj8w6fAE1RoFnJ5CFjs+oY1Hqh2rUpjzL+A67G+hX0nIr1ruV9+hffF+MkCR6pObQQGnmBflc+Pq0b7AjDRGNMX+A/un9fKA54MUIT7/2G6Fusp0kTgH14D/m6M2VCpvDnlncdTKpQfA5qWbohIN2PMBmPMo8CPQG+sttxJIhIoIq2Bs4HVXopf+Z9vgRARmVFaICLxWE2P1Sn9TzxdRJoAEyvtL+2jGg5kGmMygd3AAFf5AKB0mUK3z7eqnSYCP+CqZs+qZtdjwCMi8hPu39oXA31KO4uBma6OtvVAIfAl1hMf64F1WL/UfzHG/OrVH0T5DWNNWfAb4DzX46MbgUeAaj9DxpijWLWAFOArrC8kFeW5PscvAdNdZR8BLV3X/j2wzXWtDKyabYp2FntGp5hQStVrIrIE+JMxJsnuWPyV1giUUsrhtEaglFIOpzUCpZRyOE0ESinlcJoIlFLK4TQRKKWUw2kiUA2eiBRXmJDsAxEJr+X43SISWU152SRmIvKgiJx3CrFEi8hVFbYTRaS6MR6nTESOV7hXrmuiwM0islpEptTlvZQz6NQAyh/kuiYkQ0TmADcBT53OBY0x953iqdHAVcC7ruskAd58/n2Ha1JBRKQrMF9ExBjzuhfvqfyM1giUv1kOdHdNl122AIqIzK70bfkvrum1V4tI98oXEZE3RGSi6/0g1zTe61zHN3V9G18uImtdr7Ncp/4LGOGqodxRMQ4RaSkin4jIehH53jXlQmlN5DXXdMs7XZP/nTRjzE7gj8Apna+cSxOB8hsiEgRcBFSec6k6ma7JzWYDz9RwzUbAPOB2Y0zplNy5wCHgfGPMAKx5cEqbf/4KLHdNgfx0pcv9HfjJGBMP3A28VWFfb2AMMBi4X0SCPfgZqrPWdS2lPKaJQPmDMBFJxmqC2Qu86sE5cyv8ObSG43oBB4wxPwIYY7Jc03QHA/9xTZn8Adbc+rUZDrztus63QCsRaeba97kxJt8Yk46VZE51JS9dJEidNO0jUP6grI+glIjUNkWxOcF7T90BHMRauCcAyDuFa1RUV1N79wc2n2YsymG0RqD81R6sGVZDXIuYjK60f1KFP1fVcJ2tQHsRGQTg6h8Iwpri+4BrcZVrgUDX8TVNgbwcuNp1nZFAujEm6yR+phq5lml8Aniurq6pnEFrBMovGWN+EZH3saY13gX8VOmQFq5pt/OxVnc70XUKXFN1PyciYVj9A+dhLaLykYhcBywCsl2nrAeKRWQd8Eal+z4AvOa6bw7wu9P6IS3dXNMzh2IloVnGmDfq4LrKQXTSOaWUcjhtGlJKKYfTpiGl6iERaQV8U82u0a4VuJSqM9o0pJRSDqdNQ0op5XCaCJRSyuE0ESillMNpIlBKKYf7f55aLWYftFFdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(\n",
    "    data=response_ad,\n",
    "    x='Publication_ID',\n",
    "    y='quadratic',\n",
    "    hue='ICI_Response',\n",
    "    dodge=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas has extremely powerful methods to convert from wide to long (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Publication_ID</th>\n",
       "      <th>Sample_ID</th>\n",
       "      <th>ICI_Response</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000888</td>\n",
       "      <td>R</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.97914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000887</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.43453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000897</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>3.85681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000883</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>3.60791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000882</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.96983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Publication_ID   Sample_ID ICI_Response       variable    value\n",
       "0         Matson  SRR6000888            R  phylo_entropy  2.97914\n",
       "1         Matson  SRR6000887           NR  phylo_entropy  2.43453\n",
       "2         Matson  SRR6000897           NR  phylo_entropy  3.85681\n",
       "3         Matson  SRR6000883           NR  phylo_entropy  3.60791\n",
       "4         Matson  SRR6000882           NR  phylo_entropy  2.96983"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt converts to long\n",
    "response_ad_long = response_ad.melt(\n",
    "    id_vars=[\n",
    "        'Publication_ID',\n",
    "        'Sample_ID',\n",
    "        'ICI_Response',\n",
    "    ],\n",
    "    value_vars=[\n",
    "        'phylo_entropy',\n",
    "        'quadratic',\n",
    "        'unrooted_pd',\n",
    "        'rooted_pd',\n",
    "        'bwpd',\n",
    "        'rooted_qd_1.00001',\n",
    "        'rooted_qd_2'\n",
    "    ]\n",
    ")\n",
    "response_ad_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn works particularly well with data in long format and can quickly plot all the permutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x125754be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 433.375x2520 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    data=response_ad_long,\n",
    "    x='Publication_ID',\n",
    "    y='value',\n",
    "    hue='ICI_Response',\n",
    "    dodge=True,\n",
    "    kind='strip',\n",
    "    row='variable',\n",
    "    sharey=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python also has very good (if a bit less cutting edge than R) statistical and machine learning libraries.\n",
    "- statsmodels has a formula syntax borrowed from R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.multitest import multipletests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Publication_ID</th>\n",
       "      <th>Sample_ID</th>\n",
       "      <th>ICI_Response</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>ICI_Response_TF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000888</td>\n",
       "      <td>R</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.97914</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000887</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.43453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000897</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>3.85681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000883</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>3.60791</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matson</td>\n",
       "      <td>SRR6000882</td>\n",
       "      <td>NR</td>\n",
       "      <td>phylo_entropy</td>\n",
       "      <td>2.96983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Publication_ID   Sample_ID ICI_Response       variable    value  \\\n",
       "0         Matson  SRR6000888            R  phylo_entropy  2.97914   \n",
       "1         Matson  SRR6000887           NR  phylo_entropy  2.43453   \n",
       "2         Matson  SRR6000897           NR  phylo_entropy  3.85681   \n",
       "3         Matson  SRR6000883           NR  phylo_entropy  3.60791   \n",
       "4         Matson  SRR6000882           NR  phylo_entropy  2.96983   \n",
       "\n",
       "   ICI_Response_TF  \n",
       "0                1  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_ad_long['ICI_Response_TF'] = response_ad_long.ICI_Response.apply(lambda v: 1 if v=='R' else 0)\n",
    "response_ad_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.633822\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>ICI_Response_TF</td> <th>  No. Observations:  </th>  <td>    26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>    24</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Aug 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.01738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:46:48</td>     <th>  Log-Likelihood:    </th> <td> -16.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -16.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td>0.4451</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.6412</td> <td>    1.744</td> <td>    0.368</td> <td> 0.713</td> <td>   -2.776</td> <td>    4.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value</th>     <td>   -0.3157</td> <td>    0.424</td> <td>   -0.744</td> <td> 0.457</td> <td>   -1.147</td> <td>    0.516</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:        ICI_Response_TF   No. Observations:                   26\n",
       "Model:                          Logit   Df Residuals:                       24\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Thu, 20 Aug 2020   Pseudo R-squ.:                 0.01738\n",
       "Time:                        07:46:48   Log-Likelihood:                -16.479\n",
       "converged:                       True   LL-Null:                       -16.771\n",
       "Covariance Type:            nonrobust   LLR p-value:                    0.4451\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.6412      1.744      0.368      0.713      -2.776       4.059\n",
       "value         -0.3157      0.424     -0.744      0.457      -1.147       0.516\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = smf.logit(\n",
    "    \"ICI_Response_TF ~ value\",\n",
    "    data=response_ad_long[\n",
    "        (response_ad_long.Publication_ID == 'Chaput') &\n",
    "        (response_ad_long.variable == 'rooted_qd_1.00001')\n",
    "    ]\n",
    ").fit()\n",
    "\n",
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.605356\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>ICI_Response_TF</td> <th>  No. Observations:  </th>  <td>    26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>    24</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Aug 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.06151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:46:48</td>     <th>  Log-Likelihood:    </th> <td> -15.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -16.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td>0.1509</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.8250</td> <td>    2.558</td> <td>    1.104</td> <td> 0.270</td> <td>   -2.189</td> <td>    7.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value</th>     <td>   -1.0228</td> <td>    0.758</td> <td>   -1.349</td> <td> 0.177</td> <td>   -2.509</td> <td>    0.464</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:        ICI_Response_TF   No. Observations:                   26\n",
       "Model:                          Logit   Df Residuals:                       24\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Thu, 20 Aug 2020   Pseudo R-squ.:                 0.06151\n",
       "Time:                        07:46:48   Log-Likelihood:                -15.739\n",
       "converged:                       True   LL-Null:                       -16.771\n",
       "Covariance Type:            nonrobust   LLR p-value:                    0.1509\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.8250      2.558      1.104      0.270      -2.189       7.839\n",
       "value         -1.0228      0.758     -1.349      0.177      -2.509       0.464\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = smf.logit(\n",
    "    \"ICI_Response_TF ~ value\",\n",
    "    data=response_ad_long[\n",
    "        (response_ad_long.Publication_ID == 'Chaput') &\n",
    "        (response_ad_long.variable == 'bwpd')\n",
    "    ]\n",
    ").fit()\n",
    "\n",
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.7316085898000334, pvalue=0.47149134131420956)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(\n",
    "    a=response_ad_long[\n",
    "        (response_ad_long.Publication_ID == 'Chaput') &\n",
    "        (response_ad_long.variable == 'rooted_qd_1.00001') &\n",
    "        (response_ad_long.ICI_Response == 'R')\n",
    "    ].value,\n",
    "    b=response_ad_long[\n",
    "        (response_ad_long.Publication_ID == 'Chaput') &\n",
    "        (response_ad_long.variable == 'rooted_qd_1.00001') &\n",
    "        (response_ad_long.ICI_Response == 'NR')\n",
    "    ].value\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for loops are easy to create in python\n",
    "\n",
    "As an example, let's work on some tSNE plots, varying the perplexity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas can read in whitespace delimited data files as well.\n",
    "And, in fact, can read in arbitrarily delimited files, including with regular expressions\n",
    "- pythex.org is a very useful site to test python-compatible regular expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>Z_1</th>\n",
       "      <th>unit_tax_Z_1</th>\n",
       "      <th>inv_tax_Z_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>wgs__SRR6000947</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>2.83368</td>\n",
       "      <td>0.225979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>wgs__SRR6000946</td>\n",
       "      <td>1.065470</td>\n",
       "      <td>4.27888</td>\n",
       "      <td>0.364500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>wgs__SRR6000944</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>3.79864</td>\n",
       "      <td>0.359135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>wgs__SRR6000943</td>\n",
       "      <td>1.494210</td>\n",
       "      <td>5.85040</td>\n",
       "      <td>0.532006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wgs__SRR6000949</td>\n",
       "      <td>wgs__SRR6000942</td>\n",
       "      <td>1.423440</td>\n",
       "      <td>5.47316</td>\n",
       "      <td>0.508411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sample_1         sample_2       Z_1  unit_tax_Z_1  inv_tax_Z_1\n",
       "0  wgs__SRR6000949  wgs__SRR6000947  0.789809       2.83368     0.225979\n",
       "1  wgs__SRR6000949  wgs__SRR6000946  1.065470       4.27888     0.364500\n",
       "2  wgs__SRR6000949  wgs__SRR6000944  0.905309       3.79864     0.359135\n",
       "3  wgs__SRR6000949  wgs__SRR6000943  1.494210       5.85040     0.532006\n",
       "4  wgs__SRR6000949  wgs__SRR6000942  1.423440       5.47316     0.508411"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance = pd.read_csv('data/kr_distance.csv.gz', delimiter='\\s+')\n",
    "distance.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This pairwise distance data is in long format. We need it into a distance matrix to run tSNE. \n",
    "First step is to clean up the sample names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>Z_1</th>\n",
       "      <th>unit_tax_Z_1</th>\n",
       "      <th>inv_tax_Z_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SRR6000949</td>\n",
       "      <td>SRR6000947</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>2.83368</td>\n",
       "      <td>0.225979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SRR6000949</td>\n",
       "      <td>SRR6000946</td>\n",
       "      <td>1.065470</td>\n",
       "      <td>4.27888</td>\n",
       "      <td>0.364500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SRR6000949</td>\n",
       "      <td>SRR6000944</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>3.79864</td>\n",
       "      <td>0.359135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SRR6000949</td>\n",
       "      <td>SRR6000943</td>\n",
       "      <td>1.494210</td>\n",
       "      <td>5.85040</td>\n",
       "      <td>0.532006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SRR6000949</td>\n",
       "      <td>SRR6000942</td>\n",
       "      <td>1.423440</td>\n",
       "      <td>5.47316</td>\n",
       "      <td>0.508411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample_1    sample_2       Z_1  unit_tax_Z_1  inv_tax_Z_1\n",
       "0  SRR6000949  SRR6000947  0.789809       2.83368     0.225979\n",
       "1  SRR6000949  SRR6000946  1.065470       4.27888     0.364500\n",
       "2  SRR6000949  SRR6000944  0.905309       3.79864     0.359135\n",
       "3  SRR6000949  SRR6000943  1.494210       5.85040     0.532006\n",
       "4  SRR6000949  SRR6000942  1.423440       5.47316     0.508411"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance['sample_1'] = distance.sample_1.apply(lambda s: s.split('__')[1])\n",
    "distance['sample_2'] = distance.sample_2.apply(lambda s: s.split('__')[1])\n",
    "distance.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert from long to wide format (i.e. a matrix) and see what the issue is\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sample_2</th>\n",
       "      <th>ERR2213660</th>\n",
       "      <th>ERR2213661</th>\n",
       "      <th>ERR2213662</th>\n",
       "      <th>ERR2213664</th>\n",
       "      <th>ERR2213665</th>\n",
       "      <th>ERR2213666</th>\n",
       "      <th>ERR2213668</th>\n",
       "      <th>ERR2213669</th>\n",
       "      <th>ERR2213670</th>\n",
       "      <th>ERR2213671</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR6000937</th>\n",
       "      <th>SRR6000938</th>\n",
       "      <th>SRR6000939</th>\n",
       "      <th>SRR6000941</th>\n",
       "      <th>SRR6000942</th>\n",
       "      <th>SRR6000943</th>\n",
       "      <th>SRR6000944</th>\n",
       "      <th>SRR6000946</th>\n",
       "      <th>SRR6000947</th>\n",
       "      <th>SRR6000948</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ERR2213660</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.32085</td>\n",
       "      <td>1.59989</td>\n",
       "      <td>1.66444</td>\n",
       "      <td>1.09514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.83951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213661</th>\n",
       "      <td>0.486953</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.29963</td>\n",
       "      <td>1.66830</td>\n",
       "      <td>1.69018</td>\n",
       "      <td>1.18227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.84154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213662</th>\n",
       "      <td>0.548831</td>\n",
       "      <td>0.522689</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.12501</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.41874</td>\n",
       "      <td>1.08449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.58488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213664</th>\n",
       "      <td>1.334530</td>\n",
       "      <td>1.302760</td>\n",
       "      <td>1.132210</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.14566</td>\n",
       "      <td>1.58071</td>\n",
       "      <td>1.54082</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.62503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213665</th>\n",
       "      <td>1.505220</td>\n",
       "      <td>1.744790</td>\n",
       "      <td>1.459870</td>\n",
       "      <td>1.64348</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>...</td>\n",
       "      <td>1.68118</td>\n",
       "      <td>1.31029</td>\n",
       "      <td>1.37075</td>\n",
       "      <td>1.53813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.45827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000944</th>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>1.73466</td>\n",
       "      <td>1.82551</td>\n",
       "      <td>0.920229</td>\n",
       "      <td>1.148420</td>\n",
       "      <td>1.44540</td>\n",
       "      <td>0.961573</td>\n",
       "      <td>1.086690</td>\n",
       "      <td>...</td>\n",
       "      <td>1.40790</td>\n",
       "      <td>1.67260</td>\n",
       "      <td>1.65346</td>\n",
       "      <td>1.22469</td>\n",
       "      <td>1.14886</td>\n",
       "      <td>1.47594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000946</th>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>1.32427</td>\n",
       "      <td>1.91523</td>\n",
       "      <td>1.033770</td>\n",
       "      <td>1.088430</td>\n",
       "      <td>1.66105</td>\n",
       "      <td>1.153230</td>\n",
       "      <td>1.079130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.46043</td>\n",
       "      <td>1.70953</td>\n",
       "      <td>1.75242</td>\n",
       "      <td>1.16576</td>\n",
       "      <td>1.58920</td>\n",
       "      <td>1.66868</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.86690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000947</th>\n",
       "      <td>0.921134</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.51614</td>\n",
       "      <td>1.47730</td>\n",
       "      <td>0.817039</td>\n",
       "      <td>0.873793</td>\n",
       "      <td>1.18746</td>\n",
       "      <td>1.002040</td>\n",
       "      <td>0.702643</td>\n",
       "      <td>...</td>\n",
       "      <td>1.28955</td>\n",
       "      <td>1.52723</td>\n",
       "      <td>1.49865</td>\n",
       "      <td>1.10213</td>\n",
       "      <td>1.58229</td>\n",
       "      <td>1.32211</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.14510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000948</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.18278</td>\n",
       "      <td>1.09926</td>\n",
       "      <td>0.83870</td>\n",
       "      <td>1.39858</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>SRR6000949</th>\n",
       "      <td>0.981527</td>\n",
       "      <td>0.832912</td>\n",
       "      <td>0.877248</td>\n",
       "      <td>1.59739</td>\n",
       "      <td>1.75270</td>\n",
       "      <td>0.986046</td>\n",
       "      <td>1.143350</td>\n",
       "      <td>1.57957</td>\n",
       "      <td>1.123470</td>\n",
       "      <td>0.867411</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25668</td>\n",
       "      <td>1.62427</td>\n",
       "      <td>1.33523</td>\n",
       "      <td>1.08177</td>\n",
       "      <td>1.42344</td>\n",
       "      <td>1.49421</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>1.06547</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>1.40359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>454 rows × 454 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_2    ERR2213660  ERR2213661  ERR2213662  ERR2213664  ERR2213665  \\\n",
       "sample_1                                                                 \n",
       "ERR2213660         NaN         NaN         NaN         NaN         NaN   \n",
       "ERR2213661    0.486953         NaN         NaN         NaN         NaN   \n",
       "ERR2213662    0.548831    0.522689         NaN         NaN         NaN   \n",
       "ERR2213664    1.334530    1.302760    1.132210         NaN         NaN   \n",
       "ERR2213665    1.505220    1.744790    1.459870     1.64348         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944    0.946156    0.903185    0.990397     1.73466     1.82551   \n",
       "SRR6000946    0.917878    0.927294    0.998259     1.32427     1.91523   \n",
       "SRR6000947    0.921134    0.954903    0.751172     1.51614     1.47730   \n",
       "SRR6000948         NaN         NaN         NaN         NaN         NaN   \n",
       "SRR6000949    0.981527    0.832912    0.877248     1.59739     1.75270   \n",
       "\n",
       "sample_2    ERR2213666  ERR2213668  ERR2213669  ERR2213670  ERR2213671  ...  \\\n",
       "sample_1                                                                ...   \n",
       "ERR2213660         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "ERR2213661         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "ERR2213662         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "ERR2213664         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "ERR2213665         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "...                ...         ...         ...         ...         ...  ...   \n",
       "SRR6000944    0.920229    1.148420     1.44540    0.961573    1.086690  ...   \n",
       "SRR6000946    1.033770    1.088430     1.66105    1.153230    1.079130  ...   \n",
       "SRR6000947    0.817039    0.873793     1.18746    1.002040    0.702643  ...   \n",
       "SRR6000948         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "SRR6000949    0.986046    1.143350     1.57957    1.123470    0.867411  ...   \n",
       "\n",
       "sample_2    SRR6000937  SRR6000938  SRR6000939  SRR6000941  SRR6000942  \\\n",
       "sample_1                                                                 \n",
       "ERR2213660     1.32085     1.59989     1.66444     1.09514         NaN   \n",
       "ERR2213661     1.29963     1.66830     1.69018     1.18227         NaN   \n",
       "ERR2213662     1.12501     1.49692     1.41874     1.08449         NaN   \n",
       "ERR2213664     1.14566     1.58071     1.54082     1.49692         NaN   \n",
       "ERR2213665     1.68118     1.31029     1.37075     1.53813         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944     1.40790     1.67260     1.65346     1.22469     1.14886   \n",
       "SRR6000946     1.46043     1.70953     1.75242     1.16576     1.58920   \n",
       "SRR6000947     1.28955     1.52723     1.49865     1.10213     1.58229   \n",
       "SRR6000948     1.18278     1.09926     0.83870     1.39858         NaN   \n",
       "SRR6000949     1.25668     1.62427     1.33523     1.08177     1.42344   \n",
       "\n",
       "sample_2    SRR6000943  SRR6000944  SRR6000946  SRR6000947  SRR6000948  \n",
       "sample_1                                                                \n",
       "ERR2213660         NaN         NaN         NaN         NaN     1.83951  \n",
       "ERR2213661         NaN         NaN         NaN         NaN     1.84154  \n",
       "ERR2213662         NaN         NaN         NaN         NaN     1.58488  \n",
       "ERR2213664         NaN         NaN         NaN         NaN     1.62503  \n",
       "ERR2213665         NaN         NaN         NaN         NaN     1.45827  \n",
       "...                ...         ...         ...         ...         ...  \n",
       "SRR6000944     1.47594         NaN         NaN         NaN     1.78076  \n",
       "SRR6000946     1.66868    1.128770         NaN         NaN     1.86690  \n",
       "SRR6000947     1.32211    1.027680     1.14510         NaN     1.50010  \n",
       "SRR6000948         NaN         NaN         NaN         NaN         NaN  \n",
       "SRR6000949     1.49421    0.905309     1.06547    0.789809     1.40359  \n",
       "\n",
       "[454 rows x 454 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance.pivot(\n",
    "    index='sample_1',\n",
    "    columns='sample_2',\n",
    "    values='Z_1'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When A -> B exists, B -> A does not. To fill in the matrix we need to include both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>Z_1</th>\n",
       "      <th>unit_tax_Z_1</th>\n",
       "      <th>inv_tax_Z_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103280</th>\n",
       "      <td>SRR2940912</td>\n",
       "      <td>SRR2940914</td>\n",
       "      <td>1.098710</td>\n",
       "      <td>4.23255</td>\n",
       "      <td>0.377396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103281</th>\n",
       "      <td>SRR2940911</td>\n",
       "      <td>SRR2940914</td>\n",
       "      <td>0.767321</td>\n",
       "      <td>2.91430</td>\n",
       "      <td>0.249968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103282</th>\n",
       "      <td>SRR2940912</td>\n",
       "      <td>SRR2940913</td>\n",
       "      <td>1.735470</td>\n",
       "      <td>5.07238</td>\n",
       "      <td>0.512887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103283</th>\n",
       "      <td>SRR2940911</td>\n",
       "      <td>SRR2940913</td>\n",
       "      <td>1.495510</td>\n",
       "      <td>4.45899</td>\n",
       "      <td>0.448165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103284</th>\n",
       "      <td>SRR2940911</td>\n",
       "      <td>SRR2940912</td>\n",
       "      <td>1.087490</td>\n",
       "      <td>3.85116</td>\n",
       "      <td>0.304186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sample_1    sample_2       Z_1  unit_tax_Z_1  inv_tax_Z_1\n",
       "103280  SRR2940912  SRR2940914  1.098710       4.23255     0.377396\n",
       "103281  SRR2940911  SRR2940914  0.767321       2.91430     0.249968\n",
       "103282  SRR2940912  SRR2940913  1.735470       5.07238     0.512887\n",
       "103283  SRR2940911  SRR2940913  1.495510       4.45899     0.448165\n",
       "103284  SRR2940911  SRR2940912  1.087490       3.85116     0.304186"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_full = pd.concat([\n",
    "    distance,\n",
    "    distance.rename({'sample_1': 'sample_2', 'sample_2': 'sample_1'}, axis=1)\n",
    "]).drop_duplicates()\n",
    "distance_full.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pivot is a fast and power pandas function to convert from long-formatted data to wide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>sample_2</th>\n",
       "      <th>ERR2213660</th>\n",
       "      <th>ERR2213661</th>\n",
       "      <th>ERR2213662</th>\n",
       "      <th>ERR2213664</th>\n",
       "      <th>ERR2213665</th>\n",
       "      <th>ERR2213666</th>\n",
       "      <th>ERR2213668</th>\n",
       "      <th>ERR2213669</th>\n",
       "      <th>ERR2213670</th>\n",
       "      <th>ERR2213671</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR6000938</th>\n",
       "      <th>SRR6000939</th>\n",
       "      <th>SRR6000941</th>\n",
       "      <th>SRR6000942</th>\n",
       "      <th>SRR6000943</th>\n",
       "      <th>SRR6000944</th>\n",
       "      <th>SRR6000946</th>\n",
       "      <th>SRR6000947</th>\n",
       "      <th>SRR6000948</th>\n",
       "      <th>SRR6000949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ERR2213660</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.486953</td>\n",
       "      <td>0.548831</td>\n",
       "      <td>1.33453</td>\n",
       "      <td>1.50522</td>\n",
       "      <td>0.669113</td>\n",
       "      <td>0.816424</td>\n",
       "      <td>1.219760</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>...</td>\n",
       "      <td>1.59989</td>\n",
       "      <td>1.66444</td>\n",
       "      <td>1.09514</td>\n",
       "      <td>1.31687</td>\n",
       "      <td>1.31749</td>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.921134</td>\n",
       "      <td>1.83951</td>\n",
       "      <td>0.981527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213661</th>\n",
       "      <td>0.486953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>1.30276</td>\n",
       "      <td>1.74479</td>\n",
       "      <td>0.774207</td>\n",
       "      <td>0.939362</td>\n",
       "      <td>1.399830</td>\n",
       "      <td>0.916139</td>\n",
       "      <td>0.662176</td>\n",
       "      <td>...</td>\n",
       "      <td>1.66830</td>\n",
       "      <td>1.69018</td>\n",
       "      <td>1.18227</td>\n",
       "      <td>1.17848</td>\n",
       "      <td>1.38759</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>1.84154</td>\n",
       "      <td>0.832912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213662</th>\n",
       "      <td>0.548831</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13221</td>\n",
       "      <td>1.45987</td>\n",
       "      <td>0.653719</td>\n",
       "      <td>0.757618</td>\n",
       "      <td>1.193140</td>\n",
       "      <td>0.895867</td>\n",
       "      <td>0.590189</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.41874</td>\n",
       "      <td>1.08449</td>\n",
       "      <td>1.37505</td>\n",
       "      <td>1.29689</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.58488</td>\n",
       "      <td>0.877248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213664</th>\n",
       "      <td>1.334530</td>\n",
       "      <td>1.302760</td>\n",
       "      <td>1.132210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>1.430940</td>\n",
       "      <td>1.332070</td>\n",
       "      <td>1.595750</td>\n",
       "      <td>1.440650</td>\n",
       "      <td>1.353890</td>\n",
       "      <td>...</td>\n",
       "      <td>1.58071</td>\n",
       "      <td>1.54082</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.77473</td>\n",
       "      <td>1.85132</td>\n",
       "      <td>1.734660</td>\n",
       "      <td>1.324270</td>\n",
       "      <td>1.516140</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.597390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213665</th>\n",
       "      <td>1.505220</td>\n",
       "      <td>1.744790</td>\n",
       "      <td>1.459870</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.323250</td>\n",
       "      <td>1.296960</td>\n",
       "      <td>0.768458</td>\n",
       "      <td>1.250180</td>\n",
       "      <td>1.649970</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31029</td>\n",
       "      <td>1.37075</td>\n",
       "      <td>1.53813</td>\n",
       "      <td>2.31225</td>\n",
       "      <td>1.10122</td>\n",
       "      <td>1.825510</td>\n",
       "      <td>1.915230</td>\n",
       "      <td>1.477300</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.752700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000944</th>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>1.73466</td>\n",
       "      <td>1.82551</td>\n",
       "      <td>0.920229</td>\n",
       "      <td>1.148420</td>\n",
       "      <td>1.445400</td>\n",
       "      <td>0.961573</td>\n",
       "      <td>1.086690</td>\n",
       "      <td>...</td>\n",
       "      <td>1.67260</td>\n",
       "      <td>1.65346</td>\n",
       "      <td>1.22469</td>\n",
       "      <td>1.14886</td>\n",
       "      <td>1.47594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.78076</td>\n",
       "      <td>0.905309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000946</th>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>1.32427</td>\n",
       "      <td>1.91523</td>\n",
       "      <td>1.033770</td>\n",
       "      <td>1.088430</td>\n",
       "      <td>1.661050</td>\n",
       "      <td>1.153230</td>\n",
       "      <td>1.079130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.70953</td>\n",
       "      <td>1.75242</td>\n",
       "      <td>1.16576</td>\n",
       "      <td>1.58920</td>\n",
       "      <td>1.66868</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>1.86690</td>\n",
       "      <td>1.065470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000947</th>\n",
       "      <td>0.921134</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.51614</td>\n",
       "      <td>1.47730</td>\n",
       "      <td>0.817039</td>\n",
       "      <td>0.873793</td>\n",
       "      <td>1.187460</td>\n",
       "      <td>1.002040</td>\n",
       "      <td>0.702643</td>\n",
       "      <td>...</td>\n",
       "      <td>1.52723</td>\n",
       "      <td>1.49865</td>\n",
       "      <td>1.10213</td>\n",
       "      <td>1.58229</td>\n",
       "      <td>1.32211</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50010</td>\n",
       "      <td>0.789809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000948</th>\n",
       "      <td>1.839510</td>\n",
       "      <td>1.841540</td>\n",
       "      <td>1.584880</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.743780</td>\n",
       "      <td>1.676860</td>\n",
       "      <td>1.619420</td>\n",
       "      <td>1.756280</td>\n",
       "      <td>1.772610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.09926</td>\n",
       "      <td>0.83870</td>\n",
       "      <td>1.39858</td>\n",
       "      <td>2.11238</td>\n",
       "      <td>1.93712</td>\n",
       "      <td>1.780760</td>\n",
       "      <td>1.866900</td>\n",
       "      <td>1.500100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.403590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000949</th>\n",
       "      <td>0.981527</td>\n",
       "      <td>0.832912</td>\n",
       "      <td>0.877248</td>\n",
       "      <td>1.59739</td>\n",
       "      <td>1.75270</td>\n",
       "      <td>0.986046</td>\n",
       "      <td>1.143350</td>\n",
       "      <td>1.579570</td>\n",
       "      <td>1.123470</td>\n",
       "      <td>0.867411</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62427</td>\n",
       "      <td>1.33523</td>\n",
       "      <td>1.08177</td>\n",
       "      <td>1.42344</td>\n",
       "      <td>1.49421</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>1.065470</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>1.40359</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_2    ERR2213660  ERR2213661  ERR2213662  ERR2213664  ERR2213665  \\\n",
       "sample_1                                                                 \n",
       "ERR2213660         NaN    0.486953    0.548831     1.33453     1.50522   \n",
       "ERR2213661    0.486953         NaN    0.522689     1.30276     1.74479   \n",
       "ERR2213662    0.548831    0.522689         NaN     1.13221     1.45987   \n",
       "ERR2213664    1.334530    1.302760    1.132210         NaN     1.64348   \n",
       "ERR2213665    1.505220    1.744790    1.459870     1.64348         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944    0.946156    0.903185    0.990397     1.73466     1.82551   \n",
       "SRR6000946    0.917878    0.927294    0.998259     1.32427     1.91523   \n",
       "SRR6000947    0.921134    0.954903    0.751172     1.51614     1.47730   \n",
       "SRR6000948    1.839510    1.841540    1.584880     1.62503     1.45827   \n",
       "SRR6000949    0.981527    0.832912    0.877248     1.59739     1.75270   \n",
       "\n",
       "sample_2    ERR2213666  ERR2213668  ERR2213669  ERR2213670  ERR2213671  ...  \\\n",
       "sample_1                                                                ...   \n",
       "ERR2213660    0.669113    0.816424    1.219760    0.694386    0.678244  ...   \n",
       "ERR2213661    0.774207    0.939362    1.399830    0.916139    0.662176  ...   \n",
       "ERR2213662    0.653719    0.757618    1.193140    0.895867    0.590189  ...   \n",
       "ERR2213664    1.430940    1.332070    1.595750    1.440650    1.353890  ...   \n",
       "ERR2213665    1.323250    1.296960    0.768458    1.250180    1.649970  ...   \n",
       "...                ...         ...         ...         ...         ...  ...   \n",
       "SRR6000944    0.920229    1.148420    1.445400    0.961573    1.086690  ...   \n",
       "SRR6000946    1.033770    1.088430    1.661050    1.153230    1.079130  ...   \n",
       "SRR6000947    0.817039    0.873793    1.187460    1.002040    0.702643  ...   \n",
       "SRR6000948    1.743780    1.676860    1.619420    1.756280    1.772610  ...   \n",
       "SRR6000949    0.986046    1.143350    1.579570    1.123470    0.867411  ...   \n",
       "\n",
       "sample_2    SRR6000938  SRR6000939  SRR6000941  SRR6000942  SRR6000943  \\\n",
       "sample_1                                                                 \n",
       "ERR2213660     1.59989     1.66444     1.09514     1.31687     1.31749   \n",
       "ERR2213661     1.66830     1.69018     1.18227     1.17848     1.38759   \n",
       "ERR2213662     1.49692     1.41874     1.08449     1.37505     1.29689   \n",
       "ERR2213664     1.58071     1.54082     1.49692     1.77473     1.85132   \n",
       "ERR2213665     1.31029     1.37075     1.53813     2.31225     1.10122   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944     1.67260     1.65346     1.22469     1.14886     1.47594   \n",
       "SRR6000946     1.70953     1.75242     1.16576     1.58920     1.66868   \n",
       "SRR6000947     1.52723     1.49865     1.10213     1.58229     1.32211   \n",
       "SRR6000948     1.09926     0.83870     1.39858     2.11238     1.93712   \n",
       "SRR6000949     1.62427     1.33523     1.08177     1.42344     1.49421   \n",
       "\n",
       "sample_2    SRR6000944  SRR6000946  SRR6000947  SRR6000948  SRR6000949  \n",
       "sample_1                                                                \n",
       "ERR2213660    0.946156    0.917878    0.921134     1.83951    0.981527  \n",
       "ERR2213661    0.903185    0.927294    0.954903     1.84154    0.832912  \n",
       "ERR2213662    0.990397    0.998259    0.751172     1.58488    0.877248  \n",
       "ERR2213664    1.734660    1.324270    1.516140     1.62503    1.597390  \n",
       "ERR2213665    1.825510    1.915230    1.477300     1.45827    1.752700  \n",
       "...                ...         ...         ...         ...         ...  \n",
       "SRR6000944         NaN    1.128770    1.027680     1.78076    0.905309  \n",
       "SRR6000946    1.128770         NaN    1.145100     1.86690    1.065470  \n",
       "SRR6000947    1.027680    1.145100         NaN     1.50010    0.789809  \n",
       "SRR6000948    1.780760    1.866900    1.500100         NaN    1.403590  \n",
       "SRR6000949    0.905309    1.065470    0.789809     1.40359         NaN  \n",
       "\n",
       "[455 rows x 455 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_mat = distance_full.pivot(\n",
    "    index='sample_1',\n",
    "    columns='sample_2',\n",
    "    values='Z_1'\n",
    ")\n",
    "distance_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now let's make sure the index (row labels) is in the same order as the columns\n",
    ".loc[] is a way of accessing and reordering the index (row labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sample_2</th>\n",
       "      <th>ERR2213660</th>\n",
       "      <th>ERR2213661</th>\n",
       "      <th>ERR2213662</th>\n",
       "      <th>ERR2213664</th>\n",
       "      <th>ERR2213665</th>\n",
       "      <th>ERR2213666</th>\n",
       "      <th>ERR2213668</th>\n",
       "      <th>ERR2213669</th>\n",
       "      <th>ERR2213670</th>\n",
       "      <th>ERR2213671</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR6000938</th>\n",
       "      <th>SRR6000939</th>\n",
       "      <th>SRR6000941</th>\n",
       "      <th>SRR6000942</th>\n",
       "      <th>SRR6000943</th>\n",
       "      <th>SRR6000944</th>\n",
       "      <th>SRR6000946</th>\n",
       "      <th>SRR6000947</th>\n",
       "      <th>SRR6000948</th>\n",
       "      <th>SRR6000949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ERR2213660</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.486953</td>\n",
       "      <td>0.548831</td>\n",
       "      <td>1.33453</td>\n",
       "      <td>1.50522</td>\n",
       "      <td>0.669113</td>\n",
       "      <td>0.816424</td>\n",
       "      <td>1.219760</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>...</td>\n",
       "      <td>1.59989</td>\n",
       "      <td>1.66444</td>\n",
       "      <td>1.09514</td>\n",
       "      <td>1.31687</td>\n",
       "      <td>1.31749</td>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.921134</td>\n",
       "      <td>1.83951</td>\n",
       "      <td>0.981527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213661</th>\n",
       "      <td>0.486953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>1.30276</td>\n",
       "      <td>1.74479</td>\n",
       "      <td>0.774207</td>\n",
       "      <td>0.939362</td>\n",
       "      <td>1.399830</td>\n",
       "      <td>0.916139</td>\n",
       "      <td>0.662176</td>\n",
       "      <td>...</td>\n",
       "      <td>1.66830</td>\n",
       "      <td>1.69018</td>\n",
       "      <td>1.18227</td>\n",
       "      <td>1.17848</td>\n",
       "      <td>1.38759</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>1.84154</td>\n",
       "      <td>0.832912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213662</th>\n",
       "      <td>0.548831</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13221</td>\n",
       "      <td>1.45987</td>\n",
       "      <td>0.653719</td>\n",
       "      <td>0.757618</td>\n",
       "      <td>1.193140</td>\n",
       "      <td>0.895867</td>\n",
       "      <td>0.590189</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.41874</td>\n",
       "      <td>1.08449</td>\n",
       "      <td>1.37505</td>\n",
       "      <td>1.29689</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.58488</td>\n",
       "      <td>0.877248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213664</th>\n",
       "      <td>1.334530</td>\n",
       "      <td>1.302760</td>\n",
       "      <td>1.132210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>1.430940</td>\n",
       "      <td>1.332070</td>\n",
       "      <td>1.595750</td>\n",
       "      <td>1.440650</td>\n",
       "      <td>1.353890</td>\n",
       "      <td>...</td>\n",
       "      <td>1.58071</td>\n",
       "      <td>1.54082</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.77473</td>\n",
       "      <td>1.85132</td>\n",
       "      <td>1.734660</td>\n",
       "      <td>1.324270</td>\n",
       "      <td>1.516140</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.597390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213665</th>\n",
       "      <td>1.505220</td>\n",
       "      <td>1.744790</td>\n",
       "      <td>1.459870</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.323250</td>\n",
       "      <td>1.296960</td>\n",
       "      <td>0.768458</td>\n",
       "      <td>1.250180</td>\n",
       "      <td>1.649970</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31029</td>\n",
       "      <td>1.37075</td>\n",
       "      <td>1.53813</td>\n",
       "      <td>2.31225</td>\n",
       "      <td>1.10122</td>\n",
       "      <td>1.825510</td>\n",
       "      <td>1.915230</td>\n",
       "      <td>1.477300</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.752700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000944</th>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>1.73466</td>\n",
       "      <td>1.82551</td>\n",
       "      <td>0.920229</td>\n",
       "      <td>1.148420</td>\n",
       "      <td>1.445400</td>\n",
       "      <td>0.961573</td>\n",
       "      <td>1.086690</td>\n",
       "      <td>...</td>\n",
       "      <td>1.67260</td>\n",
       "      <td>1.65346</td>\n",
       "      <td>1.22469</td>\n",
       "      <td>1.14886</td>\n",
       "      <td>1.47594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.78076</td>\n",
       "      <td>0.905309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000946</th>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>1.32427</td>\n",
       "      <td>1.91523</td>\n",
       "      <td>1.033770</td>\n",
       "      <td>1.088430</td>\n",
       "      <td>1.661050</td>\n",
       "      <td>1.153230</td>\n",
       "      <td>1.079130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.70953</td>\n",
       "      <td>1.75242</td>\n",
       "      <td>1.16576</td>\n",
       "      <td>1.58920</td>\n",
       "      <td>1.66868</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>1.86690</td>\n",
       "      <td>1.065470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000947</th>\n",
       "      <td>0.921134</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.51614</td>\n",
       "      <td>1.47730</td>\n",
       "      <td>0.817039</td>\n",
       "      <td>0.873793</td>\n",
       "      <td>1.187460</td>\n",
       "      <td>1.002040</td>\n",
       "      <td>0.702643</td>\n",
       "      <td>...</td>\n",
       "      <td>1.52723</td>\n",
       "      <td>1.49865</td>\n",
       "      <td>1.10213</td>\n",
       "      <td>1.58229</td>\n",
       "      <td>1.32211</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50010</td>\n",
       "      <td>0.789809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000948</th>\n",
       "      <td>1.839510</td>\n",
       "      <td>1.841540</td>\n",
       "      <td>1.584880</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.743780</td>\n",
       "      <td>1.676860</td>\n",
       "      <td>1.619420</td>\n",
       "      <td>1.756280</td>\n",
       "      <td>1.772610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.09926</td>\n",
       "      <td>0.83870</td>\n",
       "      <td>1.39858</td>\n",
       "      <td>2.11238</td>\n",
       "      <td>1.93712</td>\n",
       "      <td>1.780760</td>\n",
       "      <td>1.866900</td>\n",
       "      <td>1.500100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.403590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000949</th>\n",
       "      <td>0.981527</td>\n",
       "      <td>0.832912</td>\n",
       "      <td>0.877248</td>\n",
       "      <td>1.59739</td>\n",
       "      <td>1.75270</td>\n",
       "      <td>0.986046</td>\n",
       "      <td>1.143350</td>\n",
       "      <td>1.579570</td>\n",
       "      <td>1.123470</td>\n",
       "      <td>0.867411</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62427</td>\n",
       "      <td>1.33523</td>\n",
       "      <td>1.08177</td>\n",
       "      <td>1.42344</td>\n",
       "      <td>1.49421</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>1.065470</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>1.40359</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_2    ERR2213660  ERR2213661  ERR2213662  ERR2213664  ERR2213665  \\\n",
       "sample_2                                                                 \n",
       "ERR2213660         NaN    0.486953    0.548831     1.33453     1.50522   \n",
       "ERR2213661    0.486953         NaN    0.522689     1.30276     1.74479   \n",
       "ERR2213662    0.548831    0.522689         NaN     1.13221     1.45987   \n",
       "ERR2213664    1.334530    1.302760    1.132210         NaN     1.64348   \n",
       "ERR2213665    1.505220    1.744790    1.459870     1.64348         NaN   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944    0.946156    0.903185    0.990397     1.73466     1.82551   \n",
       "SRR6000946    0.917878    0.927294    0.998259     1.32427     1.91523   \n",
       "SRR6000947    0.921134    0.954903    0.751172     1.51614     1.47730   \n",
       "SRR6000948    1.839510    1.841540    1.584880     1.62503     1.45827   \n",
       "SRR6000949    0.981527    0.832912    0.877248     1.59739     1.75270   \n",
       "\n",
       "sample_2    ERR2213666  ERR2213668  ERR2213669  ERR2213670  ERR2213671  ...  \\\n",
       "sample_2                                                                ...   \n",
       "ERR2213660    0.669113    0.816424    1.219760    0.694386    0.678244  ...   \n",
       "ERR2213661    0.774207    0.939362    1.399830    0.916139    0.662176  ...   \n",
       "ERR2213662    0.653719    0.757618    1.193140    0.895867    0.590189  ...   \n",
       "ERR2213664    1.430940    1.332070    1.595750    1.440650    1.353890  ...   \n",
       "ERR2213665    1.323250    1.296960    0.768458    1.250180    1.649970  ...   \n",
       "...                ...         ...         ...         ...         ...  ...   \n",
       "SRR6000944    0.920229    1.148420    1.445400    0.961573    1.086690  ...   \n",
       "SRR6000946    1.033770    1.088430    1.661050    1.153230    1.079130  ...   \n",
       "SRR6000947    0.817039    0.873793    1.187460    1.002040    0.702643  ...   \n",
       "SRR6000948    1.743780    1.676860    1.619420    1.756280    1.772610  ...   \n",
       "SRR6000949    0.986046    1.143350    1.579570    1.123470    0.867411  ...   \n",
       "\n",
       "sample_2    SRR6000938  SRR6000939  SRR6000941  SRR6000942  SRR6000943  \\\n",
       "sample_2                                                                 \n",
       "ERR2213660     1.59989     1.66444     1.09514     1.31687     1.31749   \n",
       "ERR2213661     1.66830     1.69018     1.18227     1.17848     1.38759   \n",
       "ERR2213662     1.49692     1.41874     1.08449     1.37505     1.29689   \n",
       "ERR2213664     1.58071     1.54082     1.49692     1.77473     1.85132   \n",
       "ERR2213665     1.31029     1.37075     1.53813     2.31225     1.10122   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944     1.67260     1.65346     1.22469     1.14886     1.47594   \n",
       "SRR6000946     1.70953     1.75242     1.16576     1.58920     1.66868   \n",
       "SRR6000947     1.52723     1.49865     1.10213     1.58229     1.32211   \n",
       "SRR6000948     1.09926     0.83870     1.39858     2.11238     1.93712   \n",
       "SRR6000949     1.62427     1.33523     1.08177     1.42344     1.49421   \n",
       "\n",
       "sample_2    SRR6000944  SRR6000946  SRR6000947  SRR6000948  SRR6000949  \n",
       "sample_2                                                                \n",
       "ERR2213660    0.946156    0.917878    0.921134     1.83951    0.981527  \n",
       "ERR2213661    0.903185    0.927294    0.954903     1.84154    0.832912  \n",
       "ERR2213662    0.990397    0.998259    0.751172     1.58488    0.877248  \n",
       "ERR2213664    1.734660    1.324270    1.516140     1.62503    1.597390  \n",
       "ERR2213665    1.825510    1.915230    1.477300     1.45827    1.752700  \n",
       "...                ...         ...         ...         ...         ...  \n",
       "SRR6000944         NaN    1.128770    1.027680     1.78076    0.905309  \n",
       "SRR6000946    1.128770         NaN    1.145100     1.86690    1.065470  \n",
       "SRR6000947    1.027680    1.145100         NaN     1.50010    0.789809  \n",
       "SRR6000948    1.780760    1.866900    1.500100         NaN    1.403590  \n",
       "SRR6000949    0.905309    1.065470    0.789809     1.40359         NaN  \n",
       "\n",
       "[455 rows x 455 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_mat = distance_mat.loc[\n",
    "    distance_mat.columns\n",
    "]\n",
    "distance_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sample_2</th>\n",
       "      <th>ERR2213660</th>\n",
       "      <th>ERR2213661</th>\n",
       "      <th>ERR2213662</th>\n",
       "      <th>ERR2213664</th>\n",
       "      <th>ERR2213665</th>\n",
       "      <th>ERR2213666</th>\n",
       "      <th>ERR2213668</th>\n",
       "      <th>ERR2213669</th>\n",
       "      <th>ERR2213670</th>\n",
       "      <th>ERR2213671</th>\n",
       "      <th>...</th>\n",
       "      <th>SRR6000938</th>\n",
       "      <th>SRR6000939</th>\n",
       "      <th>SRR6000941</th>\n",
       "      <th>SRR6000942</th>\n",
       "      <th>SRR6000943</th>\n",
       "      <th>SRR6000944</th>\n",
       "      <th>SRR6000946</th>\n",
       "      <th>SRR6000947</th>\n",
       "      <th>SRR6000948</th>\n",
       "      <th>SRR6000949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ERR2213660</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.486953</td>\n",
       "      <td>0.548831</td>\n",
       "      <td>1.33453</td>\n",
       "      <td>1.50522</td>\n",
       "      <td>0.669113</td>\n",
       "      <td>0.816424</td>\n",
       "      <td>1.219760</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>...</td>\n",
       "      <td>1.59989</td>\n",
       "      <td>1.66444</td>\n",
       "      <td>1.09514</td>\n",
       "      <td>1.31687</td>\n",
       "      <td>1.31749</td>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.921134</td>\n",
       "      <td>1.83951</td>\n",
       "      <td>0.981527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213661</th>\n",
       "      <td>0.486953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>1.30276</td>\n",
       "      <td>1.74479</td>\n",
       "      <td>0.774207</td>\n",
       "      <td>0.939362</td>\n",
       "      <td>1.399830</td>\n",
       "      <td>0.916139</td>\n",
       "      <td>0.662176</td>\n",
       "      <td>...</td>\n",
       "      <td>1.66830</td>\n",
       "      <td>1.69018</td>\n",
       "      <td>1.18227</td>\n",
       "      <td>1.17848</td>\n",
       "      <td>1.38759</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>1.84154</td>\n",
       "      <td>0.832912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213662</th>\n",
       "      <td>0.548831</td>\n",
       "      <td>0.522689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.13221</td>\n",
       "      <td>1.45987</td>\n",
       "      <td>0.653719</td>\n",
       "      <td>0.757618</td>\n",
       "      <td>1.193140</td>\n",
       "      <td>0.895867</td>\n",
       "      <td>0.590189</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.41874</td>\n",
       "      <td>1.08449</td>\n",
       "      <td>1.37505</td>\n",
       "      <td>1.29689</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.58488</td>\n",
       "      <td>0.877248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213664</th>\n",
       "      <td>1.334530</td>\n",
       "      <td>1.302760</td>\n",
       "      <td>1.132210</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>1.430940</td>\n",
       "      <td>1.332070</td>\n",
       "      <td>1.595750</td>\n",
       "      <td>1.440650</td>\n",
       "      <td>1.353890</td>\n",
       "      <td>...</td>\n",
       "      <td>1.58071</td>\n",
       "      <td>1.54082</td>\n",
       "      <td>1.49692</td>\n",
       "      <td>1.77473</td>\n",
       "      <td>1.85132</td>\n",
       "      <td>1.734660</td>\n",
       "      <td>1.324270</td>\n",
       "      <td>1.516140</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.597390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213665</th>\n",
       "      <td>1.505220</td>\n",
       "      <td>1.744790</td>\n",
       "      <td>1.459870</td>\n",
       "      <td>1.64348</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.323250</td>\n",
       "      <td>1.296960</td>\n",
       "      <td>0.768458</td>\n",
       "      <td>1.250180</td>\n",
       "      <td>1.649970</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31029</td>\n",
       "      <td>1.37075</td>\n",
       "      <td>1.53813</td>\n",
       "      <td>2.31225</td>\n",
       "      <td>1.10122</td>\n",
       "      <td>1.825510</td>\n",
       "      <td>1.915230</td>\n",
       "      <td>1.477300</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.752700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000944</th>\n",
       "      <td>0.946156</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.990397</td>\n",
       "      <td>1.73466</td>\n",
       "      <td>1.82551</td>\n",
       "      <td>0.920229</td>\n",
       "      <td>1.148420</td>\n",
       "      <td>1.445400</td>\n",
       "      <td>0.961573</td>\n",
       "      <td>1.086690</td>\n",
       "      <td>...</td>\n",
       "      <td>1.67260</td>\n",
       "      <td>1.65346</td>\n",
       "      <td>1.22469</td>\n",
       "      <td>1.14886</td>\n",
       "      <td>1.47594</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.78076</td>\n",
       "      <td>0.905309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000946</th>\n",
       "      <td>0.917878</td>\n",
       "      <td>0.927294</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>1.32427</td>\n",
       "      <td>1.91523</td>\n",
       "      <td>1.033770</td>\n",
       "      <td>1.088430</td>\n",
       "      <td>1.661050</td>\n",
       "      <td>1.153230</td>\n",
       "      <td>1.079130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.70953</td>\n",
       "      <td>1.75242</td>\n",
       "      <td>1.16576</td>\n",
       "      <td>1.58920</td>\n",
       "      <td>1.66868</td>\n",
       "      <td>1.128770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>1.86690</td>\n",
       "      <td>1.065470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000947</th>\n",
       "      <td>0.921134</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.751172</td>\n",
       "      <td>1.51614</td>\n",
       "      <td>1.47730</td>\n",
       "      <td>0.817039</td>\n",
       "      <td>0.873793</td>\n",
       "      <td>1.187460</td>\n",
       "      <td>1.002040</td>\n",
       "      <td>0.702643</td>\n",
       "      <td>...</td>\n",
       "      <td>1.52723</td>\n",
       "      <td>1.49865</td>\n",
       "      <td>1.10213</td>\n",
       "      <td>1.58229</td>\n",
       "      <td>1.32211</td>\n",
       "      <td>1.027680</td>\n",
       "      <td>1.145100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.50010</td>\n",
       "      <td>0.789809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000948</th>\n",
       "      <td>1.839510</td>\n",
       "      <td>1.841540</td>\n",
       "      <td>1.584880</td>\n",
       "      <td>1.62503</td>\n",
       "      <td>1.45827</td>\n",
       "      <td>1.743780</td>\n",
       "      <td>1.676860</td>\n",
       "      <td>1.619420</td>\n",
       "      <td>1.756280</td>\n",
       "      <td>1.772610</td>\n",
       "      <td>...</td>\n",
       "      <td>1.09926</td>\n",
       "      <td>0.83870</td>\n",
       "      <td>1.39858</td>\n",
       "      <td>2.11238</td>\n",
       "      <td>1.93712</td>\n",
       "      <td>1.780760</td>\n",
       "      <td>1.866900</td>\n",
       "      <td>1.500100</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.403590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000949</th>\n",
       "      <td>0.981527</td>\n",
       "      <td>0.832912</td>\n",
       "      <td>0.877248</td>\n",
       "      <td>1.59739</td>\n",
       "      <td>1.75270</td>\n",
       "      <td>0.986046</td>\n",
       "      <td>1.143350</td>\n",
       "      <td>1.579570</td>\n",
       "      <td>1.123470</td>\n",
       "      <td>0.867411</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62427</td>\n",
       "      <td>1.33523</td>\n",
       "      <td>1.08177</td>\n",
       "      <td>1.42344</td>\n",
       "      <td>1.49421</td>\n",
       "      <td>0.905309</td>\n",
       "      <td>1.065470</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>1.40359</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sample_2    ERR2213660  ERR2213661  ERR2213662  ERR2213664  ERR2213665  \\\n",
       "sample_2                                                                 \n",
       "ERR2213660    0.000000    0.486953    0.548831     1.33453     1.50522   \n",
       "ERR2213661    0.486953    0.000000    0.522689     1.30276     1.74479   \n",
       "ERR2213662    0.548831    0.522689    0.000000     1.13221     1.45987   \n",
       "ERR2213664    1.334530    1.302760    1.132210     0.00000     1.64348   \n",
       "ERR2213665    1.505220    1.744790    1.459870     1.64348     0.00000   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944    0.946156    0.903185    0.990397     1.73466     1.82551   \n",
       "SRR6000946    0.917878    0.927294    0.998259     1.32427     1.91523   \n",
       "SRR6000947    0.921134    0.954903    0.751172     1.51614     1.47730   \n",
       "SRR6000948    1.839510    1.841540    1.584880     1.62503     1.45827   \n",
       "SRR6000949    0.981527    0.832912    0.877248     1.59739     1.75270   \n",
       "\n",
       "sample_2    ERR2213666  ERR2213668  ERR2213669  ERR2213670  ERR2213671  ...  \\\n",
       "sample_2                                                                ...   \n",
       "ERR2213660    0.669113    0.816424    1.219760    0.694386    0.678244  ...   \n",
       "ERR2213661    0.774207    0.939362    1.399830    0.916139    0.662176  ...   \n",
       "ERR2213662    0.653719    0.757618    1.193140    0.895867    0.590189  ...   \n",
       "ERR2213664    1.430940    1.332070    1.595750    1.440650    1.353890  ...   \n",
       "ERR2213665    1.323250    1.296960    0.768458    1.250180    1.649970  ...   \n",
       "...                ...         ...         ...         ...         ...  ...   \n",
       "SRR6000944    0.920229    1.148420    1.445400    0.961573    1.086690  ...   \n",
       "SRR6000946    1.033770    1.088430    1.661050    1.153230    1.079130  ...   \n",
       "SRR6000947    0.817039    0.873793    1.187460    1.002040    0.702643  ...   \n",
       "SRR6000948    1.743780    1.676860    1.619420    1.756280    1.772610  ...   \n",
       "SRR6000949    0.986046    1.143350    1.579570    1.123470    0.867411  ...   \n",
       "\n",
       "sample_2    SRR6000938  SRR6000939  SRR6000941  SRR6000942  SRR6000943  \\\n",
       "sample_2                                                                 \n",
       "ERR2213660     1.59989     1.66444     1.09514     1.31687     1.31749   \n",
       "ERR2213661     1.66830     1.69018     1.18227     1.17848     1.38759   \n",
       "ERR2213662     1.49692     1.41874     1.08449     1.37505     1.29689   \n",
       "ERR2213664     1.58071     1.54082     1.49692     1.77473     1.85132   \n",
       "ERR2213665     1.31029     1.37075     1.53813     2.31225     1.10122   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "SRR6000944     1.67260     1.65346     1.22469     1.14886     1.47594   \n",
       "SRR6000946     1.70953     1.75242     1.16576     1.58920     1.66868   \n",
       "SRR6000947     1.52723     1.49865     1.10213     1.58229     1.32211   \n",
       "SRR6000948     1.09926     0.83870     1.39858     2.11238     1.93712   \n",
       "SRR6000949     1.62427     1.33523     1.08177     1.42344     1.49421   \n",
       "\n",
       "sample_2    SRR6000944  SRR6000946  SRR6000947  SRR6000948  SRR6000949  \n",
       "sample_2                                                                \n",
       "ERR2213660    0.946156    0.917878    0.921134     1.83951    0.981527  \n",
       "ERR2213661    0.903185    0.927294    0.954903     1.84154    0.832912  \n",
       "ERR2213662    0.990397    0.998259    0.751172     1.58488    0.877248  \n",
       "ERR2213664    1.734660    1.324270    1.516140     1.62503    1.597390  \n",
       "ERR2213665    1.825510    1.915230    1.477300     1.45827    1.752700  \n",
       "...                ...         ...         ...         ...         ...  \n",
       "SRR6000944    0.000000    1.128770    1.027680     1.78076    0.905309  \n",
       "SRR6000946    1.128770    0.000000    1.145100     1.86690    1.065470  \n",
       "SRR6000947    1.027680    1.145100    0.000000     1.50010    0.789809  \n",
       "SRR6000948    1.780760    1.866900    1.500100     0.00000    1.403590  \n",
       "SRR6000949    0.905309    1.065470    0.789809     1.40359    0.000000  \n",
       "\n",
       "[455 rows x 455 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And fill in the NaN with zero (as they are A->A) distances\n",
    "distance_mat.fillna(0, inplace=True)\n",
    "distance_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now we can load the TSNE module for python and use it to calculate the ordination while varying the perplexity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### list and dictionary comprehension are powerful techniques in vanilla python for handling lists of items\n",
    "This is a way of converting each item in a list in a quick and parallel manner. The general format is:\n",
    "```\n",
    "[\n",
    "    do_something\n",
    "    for item in list\n",
    "]\n",
    "```\n",
    "As in:\n",
    "```\n",
    "[\n",
    "    name.lower()\n",
    "    for name in\n",
    "    class_roster\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ERR2213660</th>\n",
       "      <td>-12.573725</td>\n",
       "      <td>13.747034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213661</th>\n",
       "      <td>-15.248500</td>\n",
       "      <td>14.626090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213662</th>\n",
       "      <td>-3.808824</td>\n",
       "      <td>3.242126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213664</th>\n",
       "      <td>18.756884</td>\n",
       "      <td>-9.432217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERR2213665</th>\n",
       "      <td>17.521534</td>\n",
       "      <td>9.204677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000944</th>\n",
       "      <td>-29.748484</td>\n",
       "      <td>19.440628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000946</th>\n",
       "      <td>-13.302332</td>\n",
       "      <td>-15.247581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000947</th>\n",
       "      <td>7.764101</td>\n",
       "      <td>4.072323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000948</th>\n",
       "      <td>19.844276</td>\n",
       "      <td>-35.691326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRR6000949</th>\n",
       "      <td>-7.111975</td>\n",
       "      <td>-8.030645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0          1\n",
       "sample_2                        \n",
       "ERR2213660 -12.573725  13.747034\n",
       "ERR2213661 -15.248500  14.626090\n",
       "ERR2213662  -3.808824   3.242126\n",
       "ERR2213664  18.756884  -9.432217\n",
       "ERR2213665  17.521534   9.204677\n",
       "...               ...        ...\n",
       "SRR6000944 -29.748484  19.440628\n",
       "SRR6000946 -13.302332 -15.247581\n",
       "SRR6000947   7.764101   4.072323\n",
       "SRR6000948  19.844276 -35.691326\n",
       "SRR6000949  -7.111975  -8.030645\n",
       "\n",
       "[455 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a dictionary where the key is a perplexity and the value is a dataframe of the TSNE coordinates for each sample\n",
    "tsne_perplexity = {\n",
    "    perplexity: pd.DataFrame(\n",
    "        TSNE(\n",
    "            metric='precomputed',\n",
    "            perplexity=perplexity\n",
    "        ).fit_transform(distance_mat),\n",
    "        index=distance_mat.index\n",
    "    )\n",
    "    for perplexity in range(15, 41,5)\n",
    "}\n",
    "tsne_perplexity[15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use the dictionary we created to make a plot for each perplexity value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x1152 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=6)\n",
    "fig.set_figheight(16)\n",
    "fig.set_figwidth(3)\n",
    "ax_i = 0\n",
    "for perplexity, tsne_result in tsne_perplexity.items():\n",
    "    ax = axes[ax_i]\n",
    "    ax.scatter(\n",
    "        tsne_result[0],\n",
    "        tsne_result[1],\n",
    "        alpha=0.3\n",
    "    )\n",
    "    ax.set_title(\n",
    "        \"Perplexity {}\".format(perplexity)\n",
    "    )\n",
    "    ax_i += 1\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use slicing to color specimens by publication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "tsne_result = tsne_perplexity[20]\n",
    "for publication, p_metadata in metadata.groupby('Publication_ID'):\n",
    "    tsne_publication = tsne_result[\n",
    "        tsne_result.index.map(lambda sp: sp in set(p_metadata.Sample_ID))\n",
    "    ]\n",
    "    ax.scatter(\n",
    "        x=tsne_publication[0],\n",
    "        y=tsne_publication[1],\n",
    "        label=publication,\n",
    "        alpha=0.3\n",
    "    )\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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