04-CellTypes_solutions.ipynb

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    "# Allen Cell Types Database\n",
    "\n",
    "This notebook will serve as an introduction to the Allen Cell Types database. We'll work with the AllenSDK to see what information we can learn about the electrophysiology of various cells.\n",
    "\n",
    "First, we'll `import` the CellTypesCache module. This module provides tools to allow us to get information from the cell types database. We're giving it a manifest filename as well -- `CellTypesCache` will create this manifest file, which contains metadata about the cache. If you're curious, you can look under cell_types in your directory, and take a look at the file.\n",
    "\n",
    "(See the full documentation for the core package <a href=\"https://allensdk.readthedocs.io/en/latest/allensdk.core.html\">here</a>.)\n",
    "\n",
    "## Step Zero: Setup"
   ]
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   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the \"Cell Types Cache\" from the AllenSDK core package\n",
    "from allensdk.core.cell_types_cache import CellTypesCache\n",
    "\n",
    "#Import CellTypesApi, which will allow us to query the database.\n",
    "from allensdk.api.queries.cell_types_api import CellTypesApi\n",
    "\n",
    "# We'll then initialize the cache as 'ctc' (cell types cache)\n",
    "ctc = CellTypesCache(manifest_file='cell_types/manifest.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<allensdk.core.cell_types_cache.CellTypesCache at 0x7fa4562d5310>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step One: Get Cells & Manipulate Dataframe\n",
    "\n",
    "Look through <a href=\"https://allensdk.readthedocs.io/en/latest/allensdk.core.cell_types_cache.html\">the documentation for the CellTypesCache</a> for information on the `get_cells` method.\n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Use the <code>get_cells</code> method in the cell below to get information about all of the human cells in the database. Assign the output of this to `human_cells`, and look at the output when it is done.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "  'name': 'H16.06.007.01.05.02',\n",
       "  'structure_layer_name': '3',\n",
       "  'structure_area_id': 12113,\n",
       "  'structure_area_abbrev': 'FroL',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
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       "  'donor_id': 518229880,\n",
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       "  'normalized_depth': 0.161618547158325},\n",
       " {'reporter_status': None,\n",
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       "  'id': 529833429,\n",
       "  'name': 'H16.06.010.01.03.08.02',\n",
       "  'structure_layer_name': '5',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'truncated',\n",
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       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 529678110,\n",
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       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [317.0, 268.0, 88.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 539542640,\n",
       "  'name': 'H16.06.011.01.01.10.04',\n",
       "  'structure_layer_name': '3',\n",
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       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
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       "  'donor_id': 539522943,\n",
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       " {'reporter_status': None,\n",
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       "  'species': 'Homo Sapiens',\n",
       "  'id': 541540615,\n",
       "  'name': 'H16.03.007.01.01.08.02',\n",
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       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
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       "  'species': 'Homo Sapiens',\n",
       "  'id': 529889679,\n",
       "  'name': 'H16.06.010.01.01.10.01',\n",
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       "  'structure_area_abbrev': 'ITG',\n",
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       " {'reporter_status': None,\n",
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       "  'species': 'Homo Sapiens',\n",
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       "  'normalized_depth': 0.261770055284043},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [313.0, 280.0, 94.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 569844159,\n",
       "  'name': 'H17.03.002.11.04.04',\n",
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       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
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       " {'reporter_status': None,\n",
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       "  'species': 'Homo Sapiens',\n",
       "  'id': 528625591,\n",
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       "  'donor_id': 528574320,\n",
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       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [81.0, 149.0, 216.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 545188095,\n",
       "  'name': 'H16.03.009.11.14.01',\n",
       "  'structure_layer_name': '5',\n",
       "  'structure_area_id': 12136,\n",
       "  'structure_area_abbrev': 'AnG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'truncated',\n",
       "  'reconstruction_type': None,\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 545106104,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [70.0, 249.0, 103.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 505690787,\n",
       "  'name': 'H16.06.003.01.08.02',\n",
       "  'structure_layer_name': '3',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': None,\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 504919864,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [69.0, 254.0, 96.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 528636794,\n",
       "  'name': 'H16.06.009.01.02.03.01',\n",
       "  'structure_layer_name': '5',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'truncated',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 528574320,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': 0.558691608635989},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [274.0, 331.0, 228.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 576107598,\n",
       "  'name': 'H17.06.007.11.08.02',\n",
       "  'structure_layer_name': '2',\n",
       "  'structure_area_id': 12116,\n",
       "  'structure_area_abbrev': 'MFG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'tumor',\n",
       "  'donor_id': 576060516,\n",
       "  'structure_hemisphere': 'right',\n",
       "  'normalized_depth': 0.118443475606138},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [270.0, 351.0, 219.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 517689107,\n",
       "  'name': 'H16.06.006.01.06.03',\n",
       "  'structure_layer_name': '3',\n",
       "  'structure_area_id': 12113,\n",
       "  'structure_area_abbrev': 'FroL',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': None,\n",
       "  'disease_state': 'tumor',\n",
       "  'donor_id': 517325289,\n",
       "  'structure_hemisphere': 'right',\n",
       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [325.0, 257.0, 102.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 508298270,\n",
       "  'name': 'H16.06.004.01.04.05',\n",
       "  'structure_layer_name': '3',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 504921484,\n",
       "  'structure_hemisphere': 'right',\n",
       "  'normalized_depth': 0.0954777859793282},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [312.0, 280.0, 89.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 545612828,\n",
       "  'name': 'H16.03.010.13.05.03',\n",
       "  'structure_layer_name': '3',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 545510854,\n",
       "  'structure_hemisphere': 'right',\n",
       "  'normalized_depth': 0.223071224985407},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [67.0, 256.0, 110.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 527952884,\n",
       "  'name': 'H16.06.008.01.31.06',\n",
       "  'structure_layer_name': '4',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'truncated',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 527747035,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': 0.576931185341878},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [89.0, 227.0, 87.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 488701127,\n",
       "  'name': 'H15.03.006.01.10.01',\n",
       "  'structure_layer_name': '5',\n",
       "  'structure_area_id': 12142,\n",
       "  'structure_area_abbrev': 'ITG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'truncated',\n",
       "  'reconstruction_type': None,\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 487505044,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': None},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [79.0, 273.0, 86.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 561469082,\n",
       "  'name': 'H16.06.013.12.08.06',\n",
       "  'structure_layer_name': '5',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'aspiny',\n",
       "  'apical': 'NA',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 561414332,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': 0.673116370394225},\n",
       " {'reporter_status': None,\n",
       "  'cell_soma_location': [68.0, 252.0, 106.0],\n",
       "  'species': 'Homo Sapiens',\n",
       "  'id': 601958555,\n",
       "  'name': 'H17.03.010.11.13.06',\n",
       "  'structure_layer_name': '4',\n",
       "  'structure_area_id': 12141,\n",
       "  'structure_area_abbrev': 'MTG',\n",
       "  'transgenic_line': '',\n",
       "  'dendrite_type': 'spiny',\n",
       "  'apical': 'intact',\n",
       "  'reconstruction_type': 'full',\n",
       "  'disease_state': 'epilepsy',\n",
       "  'donor_id': 601901227,\n",
       "  'structure_hemisphere': 'left',\n",
       "  'normalized_depth': 0.514497947267724}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_cells = ctc.get_cells(species=[CellTypesApi.HUMAN])\n",
    "human_cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chances are, your output looks a bit messy. This is where pandas can really come in handy!\n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Convert <code>human_cells</code> into a Pandas Dataframe by:\n",
    "    \n",
    "1. Importing `pandas` as `pd`\n",
    "2. Creating a dataframe with `pd.DataFrame()`\n",
    "3. Assigning that dataframe to `human_df`\n",
    "4. Showing the first five rows of the df using the `.head()` method.\n",
    "    \n",
    "</div>\n",
    "\n",
    "Note: If you're having trouble with Pandas, it can help to look at <a href=\"https://pandas.pydata.org/pandas-docs/stable/user_guide/\">the user guide</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reporter_status</th>\n",
       "      <th>cell_soma_location</th>\n",
       "      <th>species</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>structure_layer_name</th>\n",
       "      <th>structure_area_id</th>\n",
       "      <th>structure_area_abbrev</th>\n",
       "      <th>transgenic_line</th>\n",
       "      <th>dendrite_type</th>\n",
       "      <th>apical</th>\n",
       "      <th>reconstruction_type</th>\n",
       "      <th>disease_state</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>structure_hemisphere</th>\n",
       "      <th>normalized_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>[273.0, 354.0, 216.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>525011903</td>\n",
       "      <td>H16.03.003.01.14.02</td>\n",
       "      <td>3</td>\n",
       "      <td>12113</td>\n",
       "      <td>FroL</td>\n",
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       "      <td>spiny</td>\n",
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       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>524848408</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>[69.0, 254.0, 96.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>528642047</td>\n",
       "      <td>H16.06.009.01.02.06.05</td>\n",
       "      <td>5</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>528574320</td>\n",
       "      <td>left</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>[322.0, 255.0, 92.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>537256313</td>\n",
       "      <td>H16.03.006.01.05.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>truncated</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>536912860</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>[79.0, 273.0, 91.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>519832676</td>\n",
       "      <td>H16.03.001.01.09.01</td>\n",
       "      <td>3</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>truncated</td>\n",
       "      <td>full</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>518641172</td>\n",
       "      <td>left</td>\n",
       "      <td>0.290951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>[66.0, 220.0, 105.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>596020931</td>\n",
       "      <td>H17.06.009.11.04.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>full</td>\n",
       "      <td>tumor</td>\n",
       "      <td>595954915</td>\n",
       "      <td>left</td>\n",
       "      <td>0.497825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  reporter_status     cell_soma_location       species         id  \\\n",
       "0            None  [273.0, 354.0, 216.0]  Homo Sapiens  525011903   \n",
       "1            None    [69.0, 254.0, 96.0]  Homo Sapiens  528642047   \n",
       "2            None   [322.0, 255.0, 92.0]  Homo Sapiens  537256313   \n",
       "3            None    [79.0, 273.0, 91.0]  Homo Sapiens  519832676   \n",
       "4            None   [66.0, 220.0, 105.0]  Homo Sapiens  596020931   \n",
       "\n",
       "                     name structure_layer_name  structure_area_id  \\\n",
       "0     H16.03.003.01.14.02                    3              12113   \n",
       "1  H16.06.009.01.02.06.05                    5              12141   \n",
       "2     H16.03.006.01.05.02                    4              12141   \n",
       "3     H16.03.001.01.09.01                    3              12141   \n",
       "4     H17.06.009.11.04.02                    4              12141   \n",
       "\n",
       "  structure_area_abbrev transgenic_line dendrite_type     apical  \\\n",
       "0                  FroL                         spiny     intact   \n",
       "1                   MTG                        aspiny         NA   \n",
       "2                   MTG                         spiny  truncated   \n",
       "3                   MTG                         spiny  truncated   \n",
       "4                   MTG                        aspiny         NA   \n",
       "\n",
       "  reconstruction_type disease_state   donor_id structure_hemisphere  \\\n",
       "0                None      epilepsy  524848408                right   \n",
       "1                None      epilepsy  528574320                 left   \n",
       "2                None      epilepsy  536912860                right   \n",
       "3                full      epilepsy  518641172                 left   \n",
       "4                full         tumor  595954915                 left   \n",
       "\n",
       "   normalized_depth  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3          0.290951  \n",
       "4          0.497825  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create your dataframe here\n",
    "import pandas as pd\n",
    "\n",
    "human_df = pd.DataFrame(human_cells)\n",
    "human_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some information about our cells. We can use `len()` on a dataframe to get the number of rows. Alternatively, we can use the `count()` method on our dataframe to get detailed information for each column. For our purposes today, let's just get the number of rows, which is equivalent to the number of observations. \n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Get information about the dataset by:\n",
    "\n",
    "1. Using the `count()` method to see how many observations there are in each of the columns. Why might some be missing?\n",
    "2. Using `len()` to see the length of the whole dataframe and assign the output to `n_human_cells`.\n",
    "    \n",
    "   </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "413 cells in database\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "reporter_status            0\n",
       "cell_soma_location       413\n",
       "species                  413\n",
       "name                     413\n",
       "structure_layer_name     413\n",
       "structure_area_id        413\n",
       "structure_area_abbrev    413\n",
       "transgenic_line          413\n",
       "dendrite_type            413\n",
       "apical                   413\n",
       "reconstruction_type      158\n",
       "disease_state            413\n",
       "donor_id                 413\n",
       "structure_hemisphere     413\n",
       "normalized_depth         162\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check count and len here\n",
    "n_human_cells = len(human_df)\n",
    "print(f\"{n_human_cells} cells in database\")\n",
    "human_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment, our rows don't have any useful information -- they're simply a list of indices. We can reassign the row values by using the method `set_index`. \n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Execute the <code>set_index</code> method to set the 'id' column as the index, and reassign your dataframe as `human_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>reporter_status</th>\n",
       "      <th>cell_soma_location</th>\n",
       "      <th>species</th>\n",
       "      <th>name</th>\n",
       "      <th>structure_layer_name</th>\n",
       "      <th>structure_area_id</th>\n",
       "      <th>structure_area_abbrev</th>\n",
       "      <th>transgenic_line</th>\n",
       "      <th>dendrite_type</th>\n",
       "      <th>apical</th>\n",
       "      <th>reconstruction_type</th>\n",
       "      <th>disease_state</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>structure_hemisphere</th>\n",
       "      <th>normalized_depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>525011903</th>\n",
       "      <td>None</td>\n",
       "      <td>[273.0, 354.0, 216.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.03.003.01.14.02</td>\n",
       "      <td>3</td>\n",
       "      <td>12113</td>\n",
       "      <td>FroL</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>intact</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>524848408</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528642047</th>\n",
       "      <td>None</td>\n",
       "      <td>[69.0, 254.0, 96.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.06.009.01.02.06.05</td>\n",
       "      <td>5</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>528574320</td>\n",
       "      <td>left</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537256313</th>\n",
       "      <td>None</td>\n",
       "      <td>[322.0, 255.0, 92.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.03.006.01.05.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>truncated</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>536912860</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519832676</th>\n",
       "      <td>None</td>\n",
       "      <td>[79.0, 273.0, 91.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.03.001.01.09.01</td>\n",
       "      <td>3</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>truncated</td>\n",
       "      <td>full</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>518641172</td>\n",
       "      <td>left</td>\n",
       "      <td>0.290951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596020931</th>\n",
       "      <td>None</td>\n",
       "      <td>[66.0, 220.0, 105.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H17.06.009.11.04.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>full</td>\n",
       "      <td>tumor</td>\n",
       "      <td>595954915</td>\n",
       "      <td>left</td>\n",
       "      <td>0.497825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reporter_status     cell_soma_location       species  \\\n",
       "id                                                               \n",
       "525011903            None  [273.0, 354.0, 216.0]  Homo Sapiens   \n",
       "528642047            None    [69.0, 254.0, 96.0]  Homo Sapiens   \n",
       "537256313            None   [322.0, 255.0, 92.0]  Homo Sapiens   \n",
       "519832676            None    [79.0, 273.0, 91.0]  Homo Sapiens   \n",
       "596020931            None   [66.0, 220.0, 105.0]  Homo Sapiens   \n",
       "\n",
       "                             name structure_layer_name  structure_area_id  \\\n",
       "id                                                                          \n",
       "525011903     H16.03.003.01.14.02                    3              12113   \n",
       "528642047  H16.06.009.01.02.06.05                    5              12141   \n",
       "537256313     H16.03.006.01.05.02                    4              12141   \n",
       "519832676     H16.03.001.01.09.01                    3              12141   \n",
       "596020931     H17.06.009.11.04.02                    4              12141   \n",
       "\n",
       "          structure_area_abbrev transgenic_line dendrite_type     apical  \\\n",
       "id                                                                         \n",
       "525011903                  FroL                         spiny     intact   \n",
       "528642047                   MTG                        aspiny         NA   \n",
       "537256313                   MTG                         spiny  truncated   \n",
       "519832676                   MTG                         spiny  truncated   \n",
       "596020931                   MTG                        aspiny         NA   \n",
       "\n",
       "          reconstruction_type disease_state   donor_id structure_hemisphere  \\\n",
       "id                                                                            \n",
       "525011903                None      epilepsy  524848408                right   \n",
       "528642047                None      epilepsy  528574320                 left   \n",
       "537256313                None      epilepsy  536912860                right   \n",
       "519832676                full      epilepsy  518641172                 left   \n",
       "596020931                full         tumor  595954915                 left   \n",
       "\n",
       "           normalized_depth  \n",
       "id                           \n",
       "525011903               NaN  \n",
       "528642047               NaN  \n",
       "537256313               NaN  \n",
       "519832676          0.290951  \n",
       "596020931          0.497825  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reassign the index here -- note that you can't run this twice! (Why?)\n",
    "human_df = human_df.set_index('id')\n",
    "human_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would help to know what information is in our dataset. In other words, what is across the columns at the top? We can get a list by accessing the attribute `.columns`. Assign the output of this method to `human_df_columns`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['reporter_status', 'cell_soma_location', 'species', 'name', 'structure_layer_name', 'structure_area_id', 'structure_area_abbrev', 'transgenic_line', 'dendrite_type', 'apical', 'reconstruction_type', 'disease_state', 'donor_id', 'structure_hemisphere', 'normalized_depth']\n"
     ]
    }
   ],
   "source": [
    "human_df_columns = human_df.columns\n",
    "human_df_columns = list(human_df_columns)\n",
    "print(human_df_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access individual columns with the notation `dataframe['column name']`. \n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Check out the <code>dendrite_type</code> column by using this notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['spiny', 'aspiny', 'sparsely spiny'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at dendrite_type column\n",
    "human_df['dendrite_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like numpy arrays, we can use boolean indexing to filter our pandas dataframe. Our dataframe has data on two different dendrite types. Filter your dataframe by using the following syntax:\n",
    "```\n",
    "new_df = original_df[original_df['Column of Interest'] == 'Desired Value']\n",
    "```\n",
    "In plain english: save a dataframe from the original dataframe, where the original dataframe values in my Column of Interest are equal to my Desired Value.\n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Subset your dataframe by:\n",
    "\n",
    "1. Assigning your new dataframe and give it a reasonable name (e.g., `spiny_df`)\n",
    "2. Creating a second dataframe for aspiny.\n",
    "    \n",
    "    </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>reporter_status</th>\n",
       "      <th>cell_soma_location</th>\n",
       "      <th>species</th>\n",
       "      <th>name</th>\n",
       "      <th>structure_layer_name</th>\n",
       "      <th>structure_area_id</th>\n",
       "      <th>structure_area_abbrev</th>\n",
       "      <th>transgenic_line</th>\n",
       "      <th>dendrite_type</th>\n",
       "      <th>apical</th>\n",
       "      <th>reconstruction_type</th>\n",
       "      <th>disease_state</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>structure_hemisphere</th>\n",
       "      <th>normalized_depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>528642047</th>\n",
       "      <td>None</td>\n",
       "      <td>[69.0, 254.0, 96.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.06.009.01.02.06.05</td>\n",
       "      <td>5</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>None</td>\n",
       "      <td>epilepsy</td>\n",
       "      <td>528574320</td>\n",
       "      <td>left</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596020931</th>\n",
       "      <td>None</td>\n",
       "      <td>[66.0, 220.0, 105.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H17.06.009.11.04.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>full</td>\n",
       "      <td>tumor</td>\n",
       "      <td>595954915</td>\n",
       "      <td>left</td>\n",
       "      <td>0.497825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576095991</th>\n",
       "      <td>None</td>\n",
       "      <td>[274.0, 331.0, 228.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H17.06.007.11.08.01</td>\n",
       "      <td>5</td>\n",
       "      <td>12116</td>\n",
       "      <td>MFG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>full</td>\n",
       "      <td>tumor</td>\n",
       "      <td>576060516</td>\n",
       "      <td>right</td>\n",
       "      <td>0.673428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531520637</th>\n",
       "      <td>None</td>\n",
       "      <td>[127.0, 354.0, 219.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.06.007.01.05.03</td>\n",
       "      <td>3</td>\n",
       "      <td>12113</td>\n",
       "      <td>FroL</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>full</td>\n",
       "      <td>tumor</td>\n",
       "      <td>518229880</td>\n",
       "      <td>left</td>\n",
       "      <td>0.388676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596049209</th>\n",
       "      <td>None</td>\n",
       "      <td>[66.0, 220.0, 105.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H17.06.009.11.04.05</td>\n",
       "      <td>1</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>None</td>\n",
       "      <td>tumor</td>\n",
       "      <td>595954915</td>\n",
       "      <td>left</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reporter_status     cell_soma_location       species  \\\n",
       "id                                                               \n",
       "528642047            None    [69.0, 254.0, 96.0]  Homo Sapiens   \n",
       "596020931            None   [66.0, 220.0, 105.0]  Homo Sapiens   \n",
       "576095991            None  [274.0, 331.0, 228.0]  Homo Sapiens   \n",
       "531520637            None  [127.0, 354.0, 219.0]  Homo Sapiens   \n",
       "596049209            None   [66.0, 220.0, 105.0]  Homo Sapiens   \n",
       "\n",
       "                             name structure_layer_name  structure_area_id  \\\n",
       "id                                                                          \n",
       "528642047  H16.06.009.01.02.06.05                    5              12141   \n",
       "596020931     H17.06.009.11.04.02                    4              12141   \n",
       "576095991     H17.06.007.11.08.01                    5              12116   \n",
       "531520637     H16.06.007.01.05.03                    3              12113   \n",
       "596049209     H17.06.009.11.04.05                    1              12141   \n",
       "\n",
       "          structure_area_abbrev transgenic_line dendrite_type apical  \\\n",
       "id                                                                     \n",
       "528642047                   MTG                        aspiny     NA   \n",
       "596020931                   MTG                        aspiny     NA   \n",
       "576095991                   MFG                        aspiny     NA   \n",
       "531520637                  FroL                        aspiny     NA   \n",
       "596049209                   MTG                        aspiny     NA   \n",
       "\n",
       "          reconstruction_type disease_state   donor_id structure_hemisphere  \\\n",
       "id                                                                            \n",
       "528642047                None      epilepsy  528574320                 left   \n",
       "596020931                full         tumor  595954915                 left   \n",
       "576095991                full         tumor  576060516                right   \n",
       "531520637                full         tumor  518229880                 left   \n",
       "596049209                None         tumor  595954915                 left   \n",
       "\n",
       "           normalized_depth  \n",
       "id                           \n",
       "528642047               NaN  \n",
       "596020931          0.497825  \n",
       "576095991          0.673428  \n",
       "531520637          0.388676  \n",
       "596049209               NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spiny_df = human_df[human_df['dendrite_type']=='spiny']\n",
    "aspiny_df = human_df[human_df['dendrite_type']=='aspiny']\n",
    "aspiny_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Two: Get Electrophysiology Data\n",
    "\n",
    "At this point, you might have realized that this dataframe doesn't contain any data about the electrophysiology -- it's just metadata about the cells. In order to get information about the electrophysiological properties of these cells, we need to use the `get_ephys_features()` method on our instance of the cell types cache.\n",
    "\n",
    "1. Execute the `get_ephys_features` method on our cell types instance and assign the output of this to `ephys_features`.\n",
    "2. Convert `ephys_features` into a pandas dataframe.\n",
    "3. Re-assign the index to be the column labeled 'specimen_id' of the cell (and reassign to `ephys_features`). 'specimen_id' is the label that can link this dataframe to our metadata (human_df) dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-54.828129</td>\n",
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      "text/plain": [
       "             adaptation     avg_isi  electrode_0_pa  f_i_curve_slope  \\\n",
       "specimen_id                                                            \n",
       "529878215           NaN  134.700000       22.697498     8.335459e-02   \n",
       "548459652           NaN         NaN      -24.887498    -3.913630e-19   \n",
       "579978640      0.009770   39.044800      -46.765002     5.267857e-01   \n",
       "439024551     -0.007898  117.816429        5.996250     1.542553e-01   \n",
       "515188639      0.022842   68.321429       14.910000     1.714041e-01   \n",
       "\n",
       "             fast_trough_t_long_square  fast_trough_t_ramp  \\\n",
       "specimen_id                                                  \n",
       "529878215                     1.187680           13.295200   \n",
       "548459652                     1.099840           20.650105   \n",
       "579978640                     1.157840            2.551310   \n",
       "439024551                     1.989165            9.572025   \n",
       "515188639                     1.081980            2.462880   \n",
       "\n",
       "             fast_trough_t_short_square  fast_trough_v_long_square  \\\n",
       "specimen_id                                                          \n",
       "529878215                      1.025916                 -56.375004   \n",
       "548459652                      1.025460                 -54.000000   \n",
       "579978640                      1.025387                 -59.500000   \n",
       "439024551                      1.028733                 -47.531250   \n",
       "515188639                      1.025620                 -48.437504   \n",
       "\n",
       "             fast_trough_v_ramp  fast_trough_v_short_square  ...  \\\n",
       "specimen_id                                                  ...   \n",
       "529878215            -57.385420                  -57.431251  ...   \n",
       "548459652            -54.828129                  -54.656254  ...   \n",
       "579978640            -58.234378                  -59.940975  ...   \n",
       "439024551            -50.359375                  -65.500000  ...   \n",
       "515188639            -46.520837                  -51.406253  ...   \n",
       "\n",
       "             trough_t_ramp  trough_t_short_square  trough_v_long_square  \\\n",
       "specimen_id                                                               \n",
       "529878215        13.295680               1.134780            -56.593754   \n",
       "548459652        20.650735               1.160940            -55.406254   \n",
       "579978640         2.551960               1.089851            -60.062500   \n",
       "439024551         9.576308               1.423229            -49.406254   \n",
       "515188639         2.490433               1.479690            -53.000004   \n",
       "\n",
       "             trough_v_ramp  trough_v_short_square  \\\n",
       "specimen_id                                         \n",
       "529878215       -57.739586             -74.143753   \n",
       "548459652       -55.242191             -73.500000   \n",
       "579978640       -58.570314             -61.371531   \n",
       "439024551       -52.718752             -75.273443   \n",
       "515188639       -54.645837             -64.250003   \n",
       "\n",
       "             upstroke_downstroke_ratio_long_square  \\\n",
       "specimen_id                                          \n",
       "529878215                                 3.029695   \n",
       "548459652                                 2.441895   \n",
       "579978640                                 2.023762   \n",
       "439024551                                 3.105931   \n",
       "515188639                                 3.285760   \n",
       "\n",
       "             upstroke_downstroke_ratio_ramp  \\\n",
       "specimen_id                                   \n",
       "529878215                          3.061646   \n",
       "548459652                          2.245653   \n",
       "579978640                          2.162878   \n",
       "439024551                          3.491663   \n",
       "515188639                          3.363504   \n",
       "\n",
       "             upstroke_downstroke_ratio_short_square  vm_for_sag      vrest  \n",
       "specimen_id                                                                 \n",
       "529878215                                  2.969821  -80.468750 -73.553391  \n",
       "548459652                                  2.231575  -84.406258 -73.056595  \n",
       "579978640                                  2.006406  -93.375008 -60.277321  \n",
       "439024551                                  1.733896  -87.656250 -75.205559  \n",
       "515188639                                  4.234701  -81.625008 -63.474991  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ephys_features = ctc.get_ephys_features()\n",
    "ephys_features = pd.DataFrame(ephys_features)\n",
    "ephys_features = ephys_features.set_index('specimen_id')\n",
    "ephys_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join our dataframes\n",
    "\n",
    "Now we have two dataframes, one with the metadata for human cells (indexed by id) and another with the electrophysiology data for all cells, also indexed by id. Usefully, these ids are unique to each cell, meaning we can match them across dataframes.\n",
    "\n",
    "We can use either the `merge` or `join` pandas methods in order to pull all of this data into one dataframe. \n",
    "\n",
    "![](http://www.datasciencemadesimple.com/wp-content/uploads/2017/09/join-or-merge-in-python-pandas-1.png)\n",
    "\n",
    "There are different types of joins/merges you can do in pandas, illustrated <a href=\"http://www.datasciencemadesimple.com/join-merge-data-frames-pandas-python/\">above</a>. Here, we want to do an **inner** merge, where we're only keeping entries with indices that are in both dataframes. We could do this merge based on columns, alternatively.\n",
    "\n",
    "**Inner** is the default kind of join, so we do not need to specify it. And by default, join will use the 'left' dataframe, in other words, the dataframe that is executing the `join` method.\n",
    "\n",
    "If you need more information, look at the <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html\">join</a> and <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html\">merge</a> documentation: you can use either of these to unite your dataframes, though join will be simpler!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-67.468758</td>\n",
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       "      <td>1.891881</td>\n",
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       "      <td>1.989616</td>\n",
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       "      <td>-69.209610</td>\n",
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       "      <td>-87.531250</td>\n",
       "      <td>-72.628105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519832676</th>\n",
       "      <td>None</td>\n",
       "      <td>[79.0, 273.0, 91.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H16.03.001.01.09.01</td>\n",
       "      <td>3</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>spiny</td>\n",
       "      <td>truncated</td>\n",
       "      <td>...</td>\n",
       "      <td>9.962780</td>\n",
       "      <td>1.211020</td>\n",
       "      <td>-53.875004</td>\n",
       "      <td>-52.416668</td>\n",
       "      <td>-73.693753</td>\n",
       "      <td>4.574865</td>\n",
       "      <td>3.817988</td>\n",
       "      <td>4.980603</td>\n",
       "      <td>-84.218758</td>\n",
       "      <td>-72.547661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596020931</th>\n",
       "      <td>None</td>\n",
       "      <td>[66.0, 220.0, 105.0]</td>\n",
       "      <td>Homo Sapiens</td>\n",
       "      <td>H17.06.009.11.04.02</td>\n",
       "      <td>4</td>\n",
       "      <td>12141</td>\n",
       "      <td>MTG</td>\n",
       "      <td></td>\n",
       "      <td>aspiny</td>\n",
       "      <td>NA</td>\n",
       "      <td>...</td>\n",
       "      <td>14.667340</td>\n",
       "      <td>1.336668</td>\n",
       "      <td>-63.593754</td>\n",
       "      <td>-63.239583</td>\n",
       "      <td>-75.518753</td>\n",
       "      <td>1.452890</td>\n",
       "      <td>1.441754</td>\n",
       "      <td>1.556087</td>\n",
       "      <td>-82.531250</td>\n",
       "      <td>-74.260269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          reporter_status     cell_soma_location       species  \\\n",
       "id                                                               \n",
       "525011903            None  [273.0, 354.0, 216.0]  Homo Sapiens   \n",
       "528642047            None    [69.0, 254.0, 96.0]  Homo Sapiens   \n",
       "537256313            None   [322.0, 255.0, 92.0]  Homo Sapiens   \n",
       "519832676            None    [79.0, 273.0, 91.0]  Homo Sapiens   \n",
       "596020931            None   [66.0, 220.0, 105.0]  Homo Sapiens   \n",
       "\n",
       "                             name structure_layer_name  structure_area_id  \\\n",
       "id                                                                          \n",
       "525011903     H16.03.003.01.14.02                    3              12113   \n",
       "528642047  H16.06.009.01.02.06.05                    5              12141   \n",
       "537256313     H16.03.006.01.05.02                    4              12141   \n",
       "519832676     H16.03.001.01.09.01                    3              12141   \n",
       "596020931     H17.06.009.11.04.02                    4              12141   \n",
       "\n",
       "          structure_area_abbrev transgenic_line dendrite_type     apical  ...  \\\n",
       "id                                                                        ...   \n",
       "525011903                  FroL                         spiny     intact  ...   \n",
       "528642047                   MTG                        aspiny         NA  ...   \n",
       "537256313                   MTG                         spiny  truncated  ...   \n",
       "519832676                   MTG                         spiny  truncated  ...   \n",
       "596020931                   MTG                        aspiny         NA  ...   \n",
       "\n",
       "          trough_t_ramp trough_t_short_square  trough_v_long_square  \\\n",
       "id                                                                    \n",
       "525011903      4.134987              1.375253            -53.968754   \n",
       "528642047           NaN              1.051160            -67.468758   \n",
       "537256313      5.694547              1.389900            -52.125004   \n",
       "519832676      9.962780              1.211020            -53.875004   \n",
       "596020931     14.667340              1.336668            -63.593754   \n",
       "\n",
       "          trough_v_ramp  trough_v_short_square  \\\n",
       "id                                               \n",
       "525011903    -59.510420             -71.197919   \n",
       "528642047           NaN             -70.875002   \n",
       "537256313    -51.520836             -72.900002   \n",
       "519832676    -52.416668             -73.693753   \n",
       "596020931    -63.239583             -75.518753   \n",
       "\n",
       "           upstroke_downstroke_ratio_long_square  \\\n",
       "id                                                 \n",
       "525011903                               2.895461   \n",
       "528642047                               1.891881   \n",
       "537256313                               3.121182   \n",
       "519832676                               4.574865   \n",
       "596020931                               1.452890   \n",
       "\n",
       "           upstroke_downstroke_ratio_ramp  \\\n",
       "id                                          \n",
       "525011903                        2.559876   \n",
       "528642047                             NaN   \n",
       "537256313                        3.464528   \n",
       "519832676                        3.817988   \n",
       "596020931                        1.441754   \n",
       "\n",
       "           upstroke_downstroke_ratio_short_square  vm_for_sag      vrest  \n",
       "id                                                                        \n",
       "525011903                                3.099787  -88.843758 -70.561035  \n",
       "528642047                                1.989616 -101.000000 -69.209610  \n",
       "537256313                                3.054681  -87.531250 -72.628105  \n",
       "519832676                                4.980603  -84.218758 -72.547661  \n",
       "596020931                                1.556087  -82.531250 -74.260269  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "human_ephys_features = human_df.join(ephys_features)\n",
    "human_ephys_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Three: Confirm the data and take a look!\n",
    "\n",
    "As a result, you should have a dataframe called `human_ephys_features` that contains metadata about your cells, as well as their electrophysiological properties.\n",
    "\n",
    "<div class=\"alert alert-success\"><b>Task</b>: Confirm and inspect your data.\n",
    "\n",
    "1. Confirm that you have right amount of data by checking it's length using a Boolean to test whether it is equal to `n_human_cells` that you assigned above.\n",
    "2. Confirm that you have all of the columns from *both* the human_df and ephys_features dataframes programmatically. Remember that you can get the columns by accessing the `columns` attribute, and that you already assigned the human_df columns to a variable above. There are a few different ways to do this!\n",
    "3. Confirm that the only 'species' in your `human_ephys_features` dataframe is 'Homo Sapiens'. You can use the `unique()` method to show unique values in a column.\n",
    "    </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check your data structure here\n",
    "assert len(human_ephys_features) == n_human_cells\n",
    "assert list(human_ephys_features.columns) == list(human_df.columns) + list(ephys_features.columns)\n",
    "assert human_ephys_features['species'].unique() == 'Homo Sapiens'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Finally*, let's take a look at the data. You can use the `describe()` method to show the basic statistics for your cells. We'll start plotting these metrics next week!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>structure_area_id</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>normalized_depth</th>\n",
       "      <th>adaptation</th>\n",
       "      <th>avg_isi</th>\n",
       "      <th>electrode_0_pa</th>\n",
       "      <th>f_i_curve_slope</th>\n",
       "      <th>fast_trough_t_long_square</th>\n",
       "      <th>fast_trough_t_ramp</th>\n",
       "      <th>fast_trough_t_short_square</th>\n",
       "      <th>...</th>\n",
       "      <th>trough_t_ramp</th>\n",
       "      <th>trough_t_short_square</th>\n",
       "      <th>trough_v_long_square</th>\n",
       "      <th>trough_v_ramp</th>\n",
       "      <th>trough_v_short_square</th>\n",
       "      <th>upstroke_downstroke_ratio_long_square</th>\n",
       "      <th>upstroke_downstroke_ratio_ramp</th>\n",
       "      <th>upstroke_downstroke_ratio_short_square</th>\n",
       "      <th>vm_for_sag</th>\n",
       "      <th>vrest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>413.000000</td>\n",
       "      <td>4.130000e+02</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>4.130000e+02</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12137.322034</td>\n",
       "      <td>5.456465e+08</td>\n",
       "      <td>0.343733</td>\n",
       "      <td>0.131967</td>\n",
       "      <td>109.279349</td>\n",
       "      <td>-6.635370</td>\n",
       "      <td>1.750440e-01</td>\n",
       "      <td>1.183481</td>\n",
       "      <td>8.149731</td>\n",
       "      <td>1.025559</td>\n",
       "      <td>...</td>\n",
       "      <td>8.174076</td>\n",
       "      <td>1.257313</td>\n",
       "      <td>-56.099201</td>\n",
       "      <td>-56.355718</td>\n",
       "      <td>-67.837462</td>\n",
       "      <td>3.244010</td>\n",
       "      <td>3.205314</td>\n",
       "      <td>3.156902</td>\n",
       "      <td>-85.237973</td>\n",
       "      <td>-69.620829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.044412</td>\n",
       "      <td>3.212552e+07</td>\n",
       "      <td>0.213355</td>\n",
       "      <td>0.205747</td>\n",
       "      <td>123.453767</td>\n",
       "      <td>45.107432</td>\n",
       "      <td>1.771202e-01</td>\n",
       "      <td>0.122147</td>\n",
       "      <td>5.726134</td>\n",
       "      <td>0.001772</td>\n",
       "      <td>...</td>\n",
       "      <td>5.731079</td>\n",
       "      <td>0.229787</td>\n",
       "      <td>5.187391</td>\n",
       "      <td>4.581063</td>\n",
       "      <td>7.993370</td>\n",
       "      <td>0.967127</td>\n",
       "      <td>0.982746</td>\n",
       "      <td>0.880730</td>\n",
       "      <td>7.485094</td>\n",
       "      <td>4.582908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12113.000000</td>\n",
       "      <td>4.869091e+08</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>-0.056414</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>-536.389988</td>\n",
       "      <td>-2.001092e-18</td>\n",
       "      <td>1.033480</td>\n",
       "      <td>1.289113</td>\n",
       "      <td>1.023585</td>\n",
       "      <td>...</td>\n",
       "      <td>1.296327</td>\n",
       "      <td>1.023744</td>\n",
       "      <td>-70.750008</td>\n",
       "      <td>-69.864586</td>\n",
       "      <td>-83.132818</td>\n",
       "      <td>1.127131</td>\n",
       "      <td>0.885542</td>\n",
       "      <td>1.227955</td>\n",
       "      <td>-108.812500</td>\n",
       "      <td>-82.766602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12141.000000</td>\n",
       "      <td>5.277470e+08</td>\n",
       "      <td>0.164336</td>\n",
       "      <td>0.018315</td>\n",
       "      <td>32.614167</td>\n",
       "      <td>-26.175625</td>\n",
       "      <td>5.454545e-02</td>\n",
       "      <td>1.118860</td>\n",
       "      <td>3.721813</td>\n",
       "      <td>1.025007</td>\n",
       "      <td>...</td>\n",
       "      <td>3.730220</td>\n",
       "      <td>1.100360</td>\n",
       "      <td>-59.093750</td>\n",
       "      <td>-58.338544</td>\n",
       "      <td>-72.786463</td>\n",
       "      <td>2.609401</td>\n",
       "      <td>2.658864</td>\n",
       "      <td>2.630396</td>\n",
       "      <td>-90.250008</td>\n",
       "      <td>-72.547661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12141.000000</td>\n",
       "      <td>5.395229e+08</td>\n",
       "      <td>0.289369</td>\n",
       "      <td>0.055110</td>\n",
       "      <td>73.993333</td>\n",
       "      <td>-0.776250</td>\n",
       "      <td>1.087121e-01</td>\n",
       "      <td>1.155080</td>\n",
       "      <td>6.797963</td>\n",
       "      <td>1.025408</td>\n",
       "      <td>...</td>\n",
       "      <td>6.838143</td>\n",
       "      <td>1.164032</td>\n",
       "      <td>-55.406254</td>\n",
       "      <td>-55.494795</td>\n",
       "      <td>-70.100002</td>\n",
       "      <td>3.301126</td>\n",
       "      <td>3.263123</td>\n",
       "      <td>3.194418</td>\n",
       "      <td>-84.781250</td>\n",
       "      <td>-70.213211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12141.000000</td>\n",
       "      <td>5.698006e+08</td>\n",
       "      <td>0.505001</td>\n",
       "      <td>0.140285</td>\n",
       "      <td>138.436667</td>\n",
       "      <td>17.738125</td>\n",
       "      <td>2.462715e-01</td>\n",
       "      <td>1.209740</td>\n",
       "      <td>10.944773</td>\n",
       "      <td>1.025828</td>\n",
       "      <td>...</td>\n",
       "      <td>10.996197</td>\n",
       "      <td>1.416013</td>\n",
       "      <td>-52.968750</td>\n",
       "      <td>-53.488284</td>\n",
       "      <td>-64.968754</td>\n",
       "      <td>3.954461</td>\n",
       "      <td>3.863596</td>\n",
       "      <td>3.792753</td>\n",
       "      <td>-79.718750</td>\n",
       "      <td>-66.577446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12147.000000</td>\n",
       "      <td>6.434887e+08</td>\n",
       "      <td>0.869488</td>\n",
       "      <td>0.975859</td>\n",
       "      <td>905.200000</td>\n",
       "      <td>149.337501</td>\n",
       "      <td>9.746466e-01</td>\n",
       "      <td>1.967520</td>\n",
       "      <td>29.809180</td>\n",
       "      <td>1.047168</td>\n",
       "      <td>...</td>\n",
       "      <td>29.809360</td>\n",
       "      <td>1.935630</td>\n",
       "      <td>-41.531254</td>\n",
       "      <td>-46.635417</td>\n",
       "      <td>-43.023439</td>\n",
       "      <td>5.808203</td>\n",
       "      <td>5.514112</td>\n",
       "      <td>5.281676</td>\n",
       "      <td>-65.531250</td>\n",
       "      <td>-55.762165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       structure_area_id      donor_id  normalized_depth  adaptation  \\\n",
       "count         413.000000  4.130000e+02        162.000000  286.000000   \n",
       "mean        12137.322034  5.456465e+08          0.343733    0.131967   \n",
       "std             9.044412  3.212552e+07          0.213355    0.205747   \n",
       "min         12113.000000  4.869091e+08          0.005808   -0.056414   \n",
       "25%         12141.000000  5.277470e+08          0.164336    0.018315   \n",
       "50%         12141.000000  5.395229e+08          0.289369    0.055110   \n",
       "75%         12141.000000  5.698006e+08          0.505001    0.140285   \n",
       "max         12147.000000  6.434887e+08          0.869488    0.975859   \n",
       "\n",
       "          avg_isi  electrode_0_pa  f_i_curve_slope  fast_trough_t_long_square  \\\n",
       "count  357.000000      410.000000     4.130000e+02                 413.000000   \n",
       "mean   109.279349       -6.635370     1.750440e-01                   1.183481   \n",
       "std    123.453767       45.107432     1.771202e-01                   0.122147   \n",
       "min      4.666667     -536.389988    -2.001092e-18                   1.033480   \n",
       "25%     32.614167      -26.175625     5.454545e-02                   1.118860   \n",
       "50%     73.993333       -0.776250     1.087121e-01                   1.155080   \n",
       "75%    138.436667       17.738125     2.462715e-01                   1.209740   \n",
       "max    905.200000      149.337501     9.746466e-01                   1.967520   \n",
       "\n",
       "       fast_trough_t_ramp  fast_trough_t_short_square  ...  trough_t_ramp  \\\n",
       "count          412.000000                  413.000000  ...     412.000000   \n",
       "mean             8.149731                    1.025559  ...       8.174076   \n",
       "std              5.726134                    0.001772  ...       5.731079   \n",
       "min              1.289113                    1.023585  ...       1.296327   \n",
       "25%              3.721813                    1.025007  ...       3.730220   \n",
       "50%              6.797963                    1.025408  ...       6.838143   \n",
       "75%             10.944773                    1.025828  ...      10.996197   \n",
       "max             29.809180                    1.047168  ...      29.809360   \n",
       "\n",
       "       trough_t_short_square  trough_v_long_square  trough_v_ramp  \\\n",
       "count             413.000000            413.000000     412.000000   \n",
       "mean                1.257313            -56.099201     -56.355718   \n",
       "std                 0.229787              5.187391       4.581063   \n",
       "min                 1.023744            -70.750008     -69.864586   \n",
       "25%                 1.100360            -59.093750     -58.338544   \n",
       "50%                 1.164032            -55.406254     -55.494795   \n",
       "75%                 1.416013            -52.968750     -53.488284   \n",
       "max                 1.935630            -41.531254     -46.635417   \n",
       "\n",
       "       trough_v_short_square  upstroke_downstroke_ratio_long_square  \\\n",
       "count             413.000000                             413.000000   \n",
       "mean              -67.837462                               3.244010   \n",
       "std                 7.993370                               0.967127   \n",
       "min               -83.132818                               1.127131   \n",
       "25%               -72.786463                               2.609401   \n",
       "50%               -70.100002                               3.301126   \n",
       "75%               -64.968754                               3.954461   \n",
       "max               -43.023439                               5.808203   \n",
       "\n",
       "       upstroke_downstroke_ratio_ramp  upstroke_downstroke_ratio_short_square  \\\n",
       "count                      412.000000                              413.000000   \n",
       "mean                         3.205314                                3.156902   \n",
       "std                          0.982746                                0.880730   \n",
       "min                          0.885542                                1.227955   \n",
       "25%                          2.658864                                2.630396   \n",
       "50%                          3.263123                                3.194418   \n",
       "75%                          3.863596                                3.792753   \n",
       "max                          5.514112                                5.281676   \n",
       "\n",
       "       vm_for_sag       vrest  \n",
       "count  413.000000  413.000000  \n",
       "mean   -85.237973  -69.620829  \n",
       "std      7.485094    4.582908  \n",
       "min   -108.812500  -82.766602  \n",
       "25%    -90.250008  -72.547661  \n",
       "50%    -84.781250  -70.213211  \n",
       "75%    -79.718750  -66.577446  \n",
       "max    -65.531250  -55.762165  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the data here\n",
    "human_ephys_features.describe()"
   ]
  }
 ],
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