manipulating-data-example-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "791f4c31-81d8-4861-9884-b0f810ab5929",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff1f67ef-9e56-4130-84c4-02965949c983",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_100303/3682680866.py:1: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('./tractmeasures.csv')\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>subjectID</th>\n",
       "      <th>classID</th>\n",
       "      <th>structureID</th>\n",
       "      <th>ad</th>\n",
       "      <th>fa</th>\n",
       "      <th>md</th>\n",
       "      <th>rd</th>\n",
       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
       "      <th>...</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
       "      <th>StreamlineLengthTotal</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>ndi</th>\n",
       "      <th>odi</th>\n",
       "      <th>isovf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P0259</td>\n",
       "      <td>ping-siemens</td>\n",
       "      <td>anterioFrontalCC</td>\n",
       "      <td>1.561799</td>\n",
       "      <td>0.577652</td>\n",
       "      <td>0.898518</td>\n",
       "      <td>0.566878</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>35671.0</td>\n",
       "      <td>73.175009</td>\n",
       "      <td>...</td>\n",
       "      <td>9.633515</td>\n",
       "      <td>204377.801361</td>\n",
       "      <td>0.056471</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>F</td>\n",
       "      <td>15.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P0259</td>\n",
       "      <td>ping-siemens</td>\n",
       "      <td>forcepsMajor</td>\n",
       "      <td>1.742048</td>\n",
       "      <td>0.664032</td>\n",
       "      <td>0.918391</td>\n",
       "      <td>0.506563</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>28226.0</td>\n",
       "      <td>105.453338</td>\n",
       "      <td>...</td>\n",
       "      <td>11.020375</td>\n",
       "      <td>136140.258911</td>\n",
       "      <td>0.044685</td>\n",
       "      <td>0.000861</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>F</td>\n",
       "      <td>15.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P0259</td>\n",
       "      <td>ping-siemens</td>\n",
       "      <td>forcepsMinor</td>\n",
       "      <td>1.445160</td>\n",
       "      <td>0.577602</td>\n",
       "      <td>0.823898</td>\n",
       "      <td>0.513267</td>\n",
       "      <td>2612.0</td>\n",
       "      <td>30618.0</td>\n",
       "      <td>83.529335</td>\n",
       "      <td>...</td>\n",
       "      <td>12.294189</td>\n",
       "      <td>218178.623823</td>\n",
       "      <td>0.048472</td>\n",
       "      <td>0.001741</td>\n",
       "      <td>0.002965</td>\n",
       "      <td>F</td>\n",
       "      <td>15.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P0259</td>\n",
       "      <td>ping-siemens</td>\n",
       "      <td>leftAnterioFrontoCerebellar</td>\n",
       "      <td>1.363159</td>\n",
       "      <td>0.459017</td>\n",
       "      <td>0.895156</td>\n",
       "      <td>0.661154</td>\n",
       "      <td>115.0</td>\n",
       "      <td>8839.0</td>\n",
       "      <td>117.470699</td>\n",
       "      <td>...</td>\n",
       "      <td>14.475128</td>\n",
       "      <td>13509.130436</td>\n",
       "      <td>0.013993</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>F</td>\n",
       "      <td>15.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P0259</td>\n",
       "      <td>ping-siemens</td>\n",
       "      <td>leftArc</td>\n",
       "      <td>1.185017</td>\n",
       "      <td>0.467973</td>\n",
       "      <td>0.769184</td>\n",
       "      <td>0.561267</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>42516.0</td>\n",
       "      <td>95.917415</td>\n",
       "      <td>...</td>\n",
       "      <td>16.192672</td>\n",
       "      <td>252742.387980</td>\n",
       "      <td>0.067307</td>\n",
       "      <td>0.001757</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>F</td>\n",
       "      <td>15.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>97246</th>\n",
       "      <td>CC723395</td>\n",
       "      <td>camcan</td>\n",
       "      <td>rightmotorThalamic</td>\n",
       "      <td>1.386857</td>\n",
       "      <td>0.468146</td>\n",
       "      <td>0.899936</td>\n",
       "      <td>0.656476</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>46753.0</td>\n",
       "      <td>69.164430</td>\n",
       "      <td>...</td>\n",
       "      <td>7.817672</td>\n",
       "      <td>442652.351570</td>\n",
       "      <td>0.064460</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.002518</td>\n",
       "      <td>F</td>\n",
       "      <td>86.00</td>\n",
       "      <td>0.652338</td>\n",
       "      <td>0.228999</td>\n",
       "      <td>0.119380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97247</th>\n",
       "      <td>CC723395</td>\n",
       "      <td>camcan</td>\n",
       "      <td>rightpArc</td>\n",
       "      <td>1.324807</td>\n",
       "      <td>0.481639</td>\n",
       "      <td>0.842081</td>\n",
       "      <td>0.600718</td>\n",
       "      <td>7218.0</td>\n",
       "      <td>26248.0</td>\n",
       "      <td>64.049600</td>\n",
       "      <td>...</td>\n",
       "      <td>10.251082</td>\n",
       "      <td>462310.013086</td>\n",
       "      <td>0.036189</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.002630</td>\n",
       "      <td>F</td>\n",
       "      <td>86.00</td>\n",
       "      <td>0.581033</td>\n",
       "      <td>0.224042</td>\n",
       "      <td>0.058370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97248</th>\n",
       "      <td>CC723395</td>\n",
       "      <td>camcan</td>\n",
       "      <td>rightparietoThalamic</td>\n",
       "      <td>1.525193</td>\n",
       "      <td>0.496708</td>\n",
       "      <td>0.960258</td>\n",
       "      <td>0.677791</td>\n",
       "      <td>4458.0</td>\n",
       "      <td>51337.0</td>\n",
       "      <td>77.050118</td>\n",
       "      <td>...</td>\n",
       "      <td>9.986447</td>\n",
       "      <td>343489.426510</td>\n",
       "      <td>0.070781</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>F</td>\n",
       "      <td>86.00</td>\n",
       "      <td>0.601043</td>\n",
       "      <td>0.189712</td>\n",
       "      <td>0.121911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97249</th>\n",
       "      <td>CC723395</td>\n",
       "      <td>camcan</td>\n",
       "      <td>rightspinoThalamic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.453529</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4756.0</td>\n",
       "      <td>25281.0</td>\n",
       "      <td>43.825718</td>\n",
       "      <td>...</td>\n",
       "      <td>12.258535</td>\n",
       "      <td>208435.113969</td>\n",
       "      <td>0.034856</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>F</td>\n",
       "      <td>86.00</td>\n",
       "      <td>0.639214</td>\n",
       "      <td>0.253739</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97250</th>\n",
       "      <td>CC723395</td>\n",
       "      <td>camcan</td>\n",
       "      <td>righttemporoThalamic</td>\n",
       "      <td>1.684437</td>\n",
       "      <td>0.464806</td>\n",
       "      <td>1.125351</td>\n",
       "      <td>0.845808</td>\n",
       "      <td>473.0</td>\n",
       "      <td>10146.0</td>\n",
       "      <td>66.137263</td>\n",
       "      <td>...</td>\n",
       "      <td>7.739838</td>\n",
       "      <td>31282.925241</td>\n",
       "      <td>0.013989</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>F</td>\n",
       "      <td>86.00</td>\n",
       "      <td>0.589534</td>\n",
       "      <td>0.219034</td>\n",
       "      <td>0.184972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97251 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subjectID       classID                  structureID        ad  \\\n",
       "0         P0259  ping-siemens             anterioFrontalCC  1.561799   \n",
       "1         P0259  ping-siemens                 forcepsMajor  1.742048   \n",
       "2         P0259  ping-siemens                 forcepsMinor  1.445160   \n",
       "3         P0259  ping-siemens  leftAnterioFrontoCerebellar  1.363159   \n",
       "4         P0259  ping-siemens                      leftArc  1.185017   \n",
       "...         ...           ...                          ...       ...   \n",
       "97246  CC723395        camcan           rightmotorThalamic  1.386857   \n",
       "97247  CC723395        camcan                    rightpArc  1.324807   \n",
       "97248  CC723395        camcan         rightparietoThalamic  1.525193   \n",
       "97249  CC723395        camcan           rightspinoThalamic       NaN   \n",
       "97250  CC723395        camcan         righttemporoThalamic  1.684437   \n",
       "\n",
       "             fa        md        rd  StreamlineCount   volume  \\\n",
       "0      0.577652  0.898518  0.566878           2793.0  35671.0   \n",
       "1      0.664032  0.918391  0.506563           1291.0  28226.0   \n",
       "2      0.577602  0.823898  0.513267           2612.0  30618.0   \n",
       "3      0.459017  0.895156  0.661154            115.0   8839.0   \n",
       "4      0.467973  0.769184  0.561267           2635.0  42516.0   \n",
       "...         ...       ...       ...              ...      ...   \n",
       "97246  0.468146  0.899936  0.656476           6400.0  46753.0   \n",
       "97247  0.481639  0.842081  0.600718           7218.0  26248.0   \n",
       "97248  0.496708  0.960258  0.677791           4458.0  51337.0   \n",
       "97249  0.453529       NaN       NaN           4756.0  25281.0   \n",
       "97250  0.464806  1.125351  0.845808            473.0  10146.0   \n",
       "\n",
       "       avgerageStreamlineLength  ...  fullDisplacementStdev  \\\n",
       "0                     73.175009  ...               9.633515   \n",
       "1                    105.453338  ...              11.020375   \n",
       "2                     83.529335  ...              12.294189   \n",
       "3                    117.470699  ...              14.475128   \n",
       "4                     95.917415  ...              16.192672   \n",
       "...                         ...  ...                    ...   \n",
       "97246                 69.164430  ...               7.817672   \n",
       "97247                 64.049600  ...              10.251082   \n",
       "97248                 77.050118  ...               9.986447   \n",
       "97249                 43.825718  ...              12.258535   \n",
       "97250                 66.137263  ...               7.739838   \n",
       "\n",
       "       StreamlineLengthTotal  TotalVolumeProportion  TotalCountProportion  \\\n",
       "0              204377.801361               0.056471              0.001862   \n",
       "1              136140.258911               0.044685              0.000861   \n",
       "2              218178.623823               0.048472              0.001741   \n",
       "3               13509.130436               0.013993              0.000077   \n",
       "4              252742.387980               0.067307              0.001757   \n",
       "...                      ...                    ...                   ...   \n",
       "97246          442652.351570               0.064460              0.002133   \n",
       "97247          462310.013086               0.036189              0.002406   \n",
       "97248          343489.426510               0.070781              0.001486   \n",
       "97249          208435.113969               0.034856              0.001585   \n",
       "97250           31282.925241               0.013989              0.000158   \n",
       "\n",
       "       TotalWiringProportion  gender    age       ndi       odi     isovf  \n",
       "0                   0.002777       F  15.75       NaN       NaN       NaN  \n",
       "1                   0.001850       F  15.75       NaN       NaN       NaN  \n",
       "2                   0.002965       F  15.75       NaN       NaN       NaN  \n",
       "3                   0.000184       F  15.75       NaN       NaN       NaN  \n",
       "4                   0.003435       F  15.75       NaN       NaN       NaN  \n",
       "...                      ...     ...    ...       ...       ...       ...  \n",
       "97246               0.002518       F  86.00  0.652338  0.228999  0.119380  \n",
       "97247               0.002630       F  86.00  0.581033  0.224042  0.058370  \n",
       "97248               0.001954       F  86.00  0.601043  0.189712  0.121911  \n",
       "97249               0.001186       F  86.00  0.639214  0.253739       NaN  \n",
       "97250               0.000178       F  86.00  0.589534  0.219034  0.184972  \n",
       "\n",
       "[97251 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./tractmeasures.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c9e00b2-00a0-4031-a3f8-5744c3018c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ad</th>\n",
       "      <th>fa</th>\n",
       "      <th>md</th>\n",
       "      <th>rd</th>\n",
       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
       "      <th>streamlineLengthStdev</th>\n",
       "      <th>averageFullDisplacement</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
       "      <th>StreamlineLengthTotal</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>age</th>\n",
       "      <th>ndi</th>\n",
       "      <th>odi</th>\n",
       "      <th>isovf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>92826.000000</td>\n",
       "      <td>92772.000000</td>\n",
       "      <td>92808.000000</td>\n",
       "      <td>92823.000000</td>\n",
       "      <td>92363.000000</td>\n",
       "      <td>92418.000000</td>\n",
       "      <td>92411.000000</td>\n",
       "      <td>92409.000000</td>\n",
       "      <td>92391.000000</td>\n",
       "      <td>92410.000000</td>\n",
       "      <td>9.235800e+04</td>\n",
       "      <td>92429.000000</td>\n",
       "      <td>92363.000000</td>\n",
       "      <td>92361.000000</td>\n",
       "      <td>97251.000000</td>\n",
       "      <td>90404.000000</td>\n",
       "      <td>90419.000000</td>\n",
       "      <td>90575.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.476497</td>\n",
       "      <td>0.517657</td>\n",
       "      <td>0.903298</td>\n",
       "      <td>0.616889</td>\n",
       "      <td>5516.901064</td>\n",
       "      <td>35410.325629</td>\n",
       "      <td>96.780277</td>\n",
       "      <td>19.768709</td>\n",
       "      <td>63.120079</td>\n",
       "      <td>13.398333</td>\n",
       "      <td>4.770732e+05</td>\n",
       "      <td>0.051857</td>\n",
       "      <td>0.001854</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>36.145893</td>\n",
       "      <td>0.684854</td>\n",
       "      <td>0.184330</td>\n",
       "      <td>0.124787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.117915</td>\n",
       "      <td>0.049495</td>\n",
       "      <td>0.058868</td>\n",
       "      <td>0.060686</td>\n",
       "      <td>6753.742082</td>\n",
       "      <td>19852.010263</td>\n",
       "      <td>34.297341</td>\n",
       "      <td>6.767724</td>\n",
       "      <td>23.963298</td>\n",
       "      <td>4.367884</td>\n",
       "      <td>6.199206e+05</td>\n",
       "      <td>0.028900</td>\n",
       "      <td>0.002256</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>15.922492</td>\n",
       "      <td>0.075725</td>\n",
       "      <td>0.037533</td>\n",
       "      <td>0.038376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.051704</td>\n",
       "      <td>0.310910</td>\n",
       "      <td>0.702391</td>\n",
       "      <td>0.365640</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1058.000000</td>\n",
       "      <td>33.919778</td>\n",
       "      <td>5.167135</td>\n",
       "      <td>22.952348</td>\n",
       "      <td>3.709262</td>\n",
       "      <td>4.408077e+02</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>0.429968</td>\n",
       "      <td>0.069162</td>\n",
       "      <td>0.020107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.395162</td>\n",
       "      <td>0.486183</td>\n",
       "      <td>0.865164</td>\n",
       "      <td>0.577790</td>\n",
       "      <td>837.000000</td>\n",
       "      <td>21532.000000</td>\n",
       "      <td>68.737595</td>\n",
       "      <td>13.979550</td>\n",
       "      <td>45.827789</td>\n",
       "      <td>10.289190</td>\n",
       "      <td>7.658337e+04</td>\n",
       "      <td>0.031713</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.630211</td>\n",
       "      <td>0.160295</td>\n",
       "      <td>0.096466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.466986</td>\n",
       "      <td>0.515330</td>\n",
       "      <td>0.897856</td>\n",
       "      <td>0.612615</td>\n",
       "      <td>3082.000000</td>\n",
       "      <td>31017.500000</td>\n",
       "      <td>89.133614</td>\n",
       "      <td>19.816734</td>\n",
       "      <td>56.267892</td>\n",
       "      <td>12.593058</td>\n",
       "      <td>2.402114e+05</td>\n",
       "      <td>0.045405</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.001304</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.689105</td>\n",
       "      <td>0.184583</td>\n",
       "      <td>0.124088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.546958</td>\n",
       "      <td>0.545252</td>\n",
       "      <td>0.934813</td>\n",
       "      <td>0.651376</td>\n",
       "      <td>7500.500000</td>\n",
       "      <td>45924.500000</td>\n",
       "      <td>125.651477</td>\n",
       "      <td>24.971466</td>\n",
       "      <td>82.710495</td>\n",
       "      <td>15.805394</td>\n",
       "      <td>5.907684e+05</td>\n",
       "      <td>0.067668</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.741428</td>\n",
       "      <td>0.208010</td>\n",
       "      <td>0.150818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.052108</td>\n",
       "      <td>0.754050</td>\n",
       "      <td>1.280872</td>\n",
       "      <td>0.973784</td>\n",
       "      <td>43416.000000</td>\n",
       "      <td>118129.000000</td>\n",
       "      <td>188.911053</td>\n",
       "      <td>66.056197</td>\n",
       "      <td>128.361884</td>\n",
       "      <td>30.001785</td>\n",
       "      <td>4.217022e+06</td>\n",
       "      <td>0.160582</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.021154</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.882528</td>\n",
       "      <td>0.383483</td>\n",
       "      <td>0.305197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ad            fa            md            rd  \\\n",
       "count  92826.000000  92772.000000  92808.000000  92823.000000   \n",
       "mean       1.476497      0.517657      0.903298      0.616889   \n",
       "std        0.117915      0.049495      0.058868      0.060686   \n",
       "min        1.051704      0.310910      0.702391      0.365640   \n",
       "25%        1.395162      0.486183      0.865164      0.577790   \n",
       "50%        1.466986      0.515330      0.897856      0.612615   \n",
       "75%        1.546958      0.545252      0.934813      0.651376   \n",
       "max        2.052108      0.754050      1.280872      0.973784   \n",
       "\n",
       "       StreamlineCount         volume  avgerageStreamlineLength  \\\n",
       "count     92363.000000   92418.000000              92411.000000   \n",
       "mean       5516.901064   35410.325629                 96.780277   \n",
       "std        6753.742082   19852.010263                 34.297341   \n",
       "min           6.000000    1058.000000                 33.919778   \n",
       "25%         837.000000   21532.000000                 68.737595   \n",
       "50%        3082.000000   31017.500000                 89.133614   \n",
       "75%        7500.500000   45924.500000                125.651477   \n",
       "max       43416.000000  118129.000000                188.911053   \n",
       "\n",
       "       streamlineLengthStdev  averageFullDisplacement  fullDisplacementStdev  \\\n",
       "count           92409.000000             92391.000000           92410.000000   \n",
       "mean               19.768709                63.120079              13.398333   \n",
       "std                 6.767724                23.963298               4.367884   \n",
       "min                 5.167135                22.952348               3.709262   \n",
       "25%                13.979550                45.827789              10.289190   \n",
       "50%                19.816734                56.267892              12.593058   \n",
       "75%                24.971466                82.710495              15.805394   \n",
       "max                66.056197               128.361884              30.001785   \n",
       "\n",
       "       StreamlineLengthTotal  TotalVolumeProportion  TotalCountProportion  \\\n",
       "count           9.235800e+04           92429.000000          92363.000000   \n",
       "mean            4.770732e+05               0.051857              0.001854   \n",
       "std             6.199206e+05               0.028900              0.002256   \n",
       "min             4.408077e+02               0.002117              0.000002   \n",
       "25%             7.658337e+04               0.031713              0.000283   \n",
       "50%             2.402114e+05               0.045405              0.001050   \n",
       "75%             5.907684e+05               0.067668              0.002529   \n",
       "max             4.217022e+06               0.160582              0.014472   \n",
       "\n",
       "       TotalWiringProportion           age           ndi           odi  \\\n",
       "count           92361.000000  97251.000000  90404.000000  90419.000000   \n",
       "mean                0.002534     36.145893      0.684854      0.184330   \n",
       "std                 0.003229     15.922492      0.075725      0.037533   \n",
       "min                 0.000002      3.416667      0.429968      0.069162   \n",
       "25%                 0.000419     27.000000      0.630211      0.160295   \n",
       "50%                 0.001304     31.000000      0.689105      0.184583   \n",
       "75%                 0.003160     37.000000      0.741428      0.208010   \n",
       "max                 0.021154     86.000000      0.882528      0.383483   \n",
       "\n",
       "              isovf  \n",
       "count  90575.000000  \n",
       "mean       0.124787  \n",
       "std        0.038376  \n",
       "min        0.020107  \n",
       "25%        0.096466  \n",
       "50%        0.124088  \n",
       "75%        0.150818  \n",
       "max        0.305197  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brief summary statistics of the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d6cc12e-dda7-498a-a9be-9572ff0f658d",
   "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>ad</th>\n",
       "      <th>fa</th>\n",
       "      <th>md</th>\n",
       "      <th>rd</th>\n",
       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
       "      <th>streamlineLengthStdev</th>\n",
       "      <th>averageFullDisplacement</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
       "      <th>StreamlineLengthTotal</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>age</th>\n",
       "      <th>ndi</th>\n",
       "      <th>odi</th>\n",
       "      <th>isovf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30219.000000</td>\n",
       "      <td>30221.000000</td>\n",
       "      <td>30224.000000</td>\n",
       "      <td>30221.000000</td>\n",
       "      <td>29988.000000</td>\n",
       "      <td>30043.000000</td>\n",
       "      <td>30013.000000</td>\n",
       "      <td>30007.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30017.000000</td>\n",
       "      <td>2.998800e+04</td>\n",
       "      <td>30046.000000</td>\n",
       "      <td>29988.000000</td>\n",
       "      <td>29989.000000</td>\n",
       "      <td>31583.000000</td>\n",
       "      <td>30101.000000</td>\n",
       "      <td>30065.000000</td>\n",
       "      <td>30191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.501721</td>\n",
       "      <td>0.510636</td>\n",
       "      <td>0.930800</td>\n",
       "      <td>0.646029</td>\n",
       "      <td>4589.527411</td>\n",
       "      <td>33987.174417</td>\n",
       "      <td>96.394595</td>\n",
       "      <td>20.469447</td>\n",
       "      <td>61.052913</td>\n",
       "      <td>13.211583</td>\n",
       "      <td>3.781381e+05</td>\n",
       "      <td>0.047634</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>52.833835</td>\n",
       "      <td>0.634185</td>\n",
       "      <td>0.205257</td>\n",
       "      <td>0.109847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.129155</td>\n",
       "      <td>0.050265</td>\n",
       "      <td>0.067781</td>\n",
       "      <td>0.066643</td>\n",
       "      <td>5682.136834</td>\n",
       "      <td>18137.704142</td>\n",
       "      <td>34.143430</td>\n",
       "      <td>7.380438</td>\n",
       "      <td>23.290699</td>\n",
       "      <td>3.877417</td>\n",
       "      <td>4.761070e+05</td>\n",
       "      <td>0.025170</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.002619</td>\n",
       "      <td>17.848405</td>\n",
       "      <td>0.076096</td>\n",
       "      <td>0.032999</td>\n",
       "      <td>0.039051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.133695</td>\n",
       "      <td>0.344676</td>\n",
       "      <td>0.744163</td>\n",
       "      <td>0.438491</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2676.000000</td>\n",
       "      <td>38.652989</td>\n",
       "      <td>7.263402</td>\n",
       "      <td>24.139289</td>\n",
       "      <td>4.937802</td>\n",
       "      <td>8.374765e+02</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.429968</td>\n",
       "      <td>0.105343</td>\n",
       "      <td>0.020107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.403475</td>\n",
       "      <td>0.477097</td>\n",
       "      <td>0.882822</td>\n",
       "      <td>0.598447</td>\n",
       "      <td>753.750000</td>\n",
       "      <td>20973.000000</td>\n",
       "      <td>69.071024</td>\n",
       "      <td>14.691697</td>\n",
       "      <td>44.401382</td>\n",
       "      <td>10.428907</td>\n",
       "      <td>7.204449e+04</td>\n",
       "      <td>0.029642</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.576625</td>\n",
       "      <td>0.186699</td>\n",
       "      <td>0.080022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.493233</td>\n",
       "      <td>0.509912</td>\n",
       "      <td>0.922344</td>\n",
       "      <td>0.640422</td>\n",
       "      <td>2608.500000</td>\n",
       "      <td>29771.000000</td>\n",
       "      <td>89.119506</td>\n",
       "      <td>19.798491</td>\n",
       "      <td>54.623806</td>\n",
       "      <td>12.575595</td>\n",
       "      <td>2.094145e+05</td>\n",
       "      <td>0.041735</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.001168</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.620409</td>\n",
       "      <td>0.206197</td>\n",
       "      <td>0.104488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.580560</td>\n",
       "      <td>0.544985</td>\n",
       "      <td>0.969543</td>\n",
       "      <td>0.687719</td>\n",
       "      <td>6342.250000</td>\n",
       "      <td>44591.500000</td>\n",
       "      <td>123.424553</td>\n",
       "      <td>25.332270</td>\n",
       "      <td>80.051351</td>\n",
       "      <td>15.502998</td>\n",
       "      <td>4.813575e+05</td>\n",
       "      <td>0.063711</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.002678</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.694902</td>\n",
       "      <td>0.223668</td>\n",
       "      <td>0.134246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.052108</td>\n",
       "      <td>0.692919</td>\n",
       "      <td>1.280872</td>\n",
       "      <td>0.973784</td>\n",
       "      <td>35906.000000</td>\n",
       "      <td>109803.000000</td>\n",
       "      <td>188.911053</td>\n",
       "      <td>66.056197</td>\n",
       "      <td>119.659114</td>\n",
       "      <td>27.065877</td>\n",
       "      <td>3.321925e+06</td>\n",
       "      <td>0.142380</td>\n",
       "      <td>0.011969</td>\n",
       "      <td>0.018117</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.856223</td>\n",
       "      <td>0.383483</td>\n",
       "      <td>0.305197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ad            fa            md            rd  \\\n",
       "count  30219.000000  30221.000000  30224.000000  30221.000000   \n",
       "mean       1.501721      0.510636      0.930800      0.646029   \n",
       "std        0.129155      0.050265      0.067781      0.066643   \n",
       "min        1.133695      0.344676      0.744163      0.438491   \n",
       "25%        1.403475      0.477097      0.882822      0.598447   \n",
       "50%        1.493233      0.509912      0.922344      0.640422   \n",
       "75%        1.580560      0.544985      0.969543      0.687719   \n",
       "max        2.052108      0.692919      1.280872      0.973784   \n",
       "\n",
       "       StreamlineCount         volume  avgerageStreamlineLength  \\\n",
       "count     29988.000000   30043.000000              30013.000000   \n",
       "mean       4589.527411   33987.174417                 96.394595   \n",
       "std        5682.136834   18137.704142                 34.143430   \n",
       "min           8.000000    2676.000000                 38.652989   \n",
       "25%         753.750000   20973.000000                 69.071024   \n",
       "50%        2608.500000   29771.000000                 89.119506   \n",
       "75%        6342.250000   44591.500000                123.424553   \n",
       "max       35906.000000  109803.000000                188.911053   \n",
       "\n",
       "       streamlineLengthStdev  averageFullDisplacement  fullDisplacementStdev  \\\n",
       "count           30007.000000             30000.000000           30017.000000   \n",
       "mean               20.469447                61.052913              13.211583   \n",
       "std                 7.380438                23.290699               3.877417   \n",
       "min                 7.263402                24.139289               4.937802   \n",
       "25%                14.691697                44.401382              10.428907   \n",
       "50%                19.798491                54.623806              12.575595   \n",
       "75%                25.332270                80.051351              15.502998   \n",
       "max                66.056197               119.659114              27.065877   \n",
       "\n",
       "       StreamlineLengthTotal  TotalVolumeProportion  TotalCountProportion  \\\n",
       "count           2.998800e+04           30046.000000          29988.000000   \n",
       "mean            3.781381e+05               0.047634              0.001530   \n",
       "std             4.761070e+05               0.025170              0.001894   \n",
       "min             8.374765e+02               0.003293              0.000003   \n",
       "25%             7.204449e+04               0.029642              0.000251   \n",
       "50%             2.094145e+05               0.041735              0.000869   \n",
       "75%             4.813575e+05               0.063711              0.002114   \n",
       "max             3.321925e+06               0.142380              0.011969   \n",
       "\n",
       "       TotalWiringProportion           age           ndi           odi  \\\n",
       "count           29989.000000  31583.000000  30101.000000  30065.000000   \n",
       "mean                0.002095     52.833835      0.634185      0.205257   \n",
       "std                 0.002619     17.848405      0.076096      0.032999   \n",
       "min                 0.000006     18.000000      0.429968      0.105343   \n",
       "25%                 0.000404     38.000000      0.576625      0.186699   \n",
       "50%                 0.001168     52.000000      0.620409      0.206197   \n",
       "75%                 0.002678     68.000000      0.694902      0.223668   \n",
       "max                 0.018117     86.000000      0.856223      0.383483   \n",
       "\n",
       "              isovf  \n",
       "count  30191.000000  \n",
       "mean       0.109847  \n",
       "std        0.039051  \n",
       "min        0.020107  \n",
       "25%        0.080022  \n",
       "50%        0.104488  \n",
       "75%        0.134246  \n",
       "max        0.305197  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at summary statistics only from camcan project\n",
    "camcan = df.loc[df['classID'] == 'camcan']\n",
    "camcan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16565127-acbf-4725-8bf9-280de5223c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ad</th>\n",
       "      <th>fa</th>\n",
       "      <th>md</th>\n",
       "      <th>rd</th>\n",
       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
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       "      <th>averageFullDisplacement</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
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       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>age</th>\n",
       "      <th>ndi</th>\n",
       "      <th>odi</th>\n",
       "      <th>isovf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>camcan</th>\n",
       "      <td>1.501721</td>\n",
       "      <td>0.510636</td>\n",
       "      <td>0.930800</td>\n",
       "      <td>0.646029</td>\n",
       "      <td>4589.527411</td>\n",
       "      <td>33987.174417</td>\n",
       "      <td>96.394595</td>\n",
       "      <td>20.469447</td>\n",
       "      <td>61.052913</td>\n",
       "      <td>13.211583</td>\n",
       "      <td>378138.078356</td>\n",
       "      <td>0.047634</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>52.833835</td>\n",
       "      <td>0.634185</td>\n",
       "      <td>0.205257</td>\n",
       "      <td>0.109847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hcp</th>\n",
       "      <td>1.470997</td>\n",
       "      <td>0.522995</td>\n",
       "      <td>0.892393</td>\n",
       "      <td>0.603072</td>\n",
       "      <td>6116.002761</td>\n",
       "      <td>36619.073814</td>\n",
       "      <td>97.659855</td>\n",
       "      <td>19.491670</td>\n",
       "      <td>64.438096</td>\n",
       "      <td>13.537222</td>\n",
       "      <td>539883.432300</td>\n",
       "      <td>0.054650</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>28.733427</td>\n",
       "      <td>0.710146</td>\n",
       "      <td>0.173905</td>\n",
       "      <td>0.132257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ping-siemens</th>\n",
       "      <td>1.286301</td>\n",
       "      <td>0.468524</td>\n",
       "      <td>0.826318</td>\n",
       "      <td>0.596133</td>\n",
       "      <td>1869.495780</td>\n",
       "      <td>22118.880053</td>\n",
       "      <td>78.428750</td>\n",
       "      <td>17.830513</td>\n",
       "      <td>55.456566</td>\n",
       "      <td>12.179022</td>\n",
       "      <td>117580.144653</td>\n",
       "      <td>0.033602</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>11.746311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ad        fa        md        rd  StreamlineCount  \\\n",
       "classID                                                                 \n",
       "camcan        1.501721  0.510636  0.930800  0.646029      4589.527411   \n",
       "hcp           1.470997  0.522995  0.892393  0.603072      6116.002761   \n",
       "ping-siemens  1.286301  0.468524  0.826318  0.596133      1869.495780   \n",
       "\n",
       "                    volume  avgerageStreamlineLength  streamlineLengthStdev  \\\n",
       "classID                                                                       \n",
       "camcan        33987.174417                 96.394595              20.469447   \n",
       "hcp           36619.073814                 97.659855              19.491670   \n",
       "ping-siemens  22118.880053                 78.428750              17.830513   \n",
       "\n",
       "              averageFullDisplacement  fullDisplacementStdev  \\\n",
       "classID                                                        \n",
       "camcan                      61.052913              13.211583   \n",
       "hcp                         64.438096              13.537222   \n",
       "ping-siemens                55.456566              12.179022   \n",
       "\n",
       "              StreamlineLengthTotal  TotalVolumeProportion  \\\n",
       "classID                                                      \n",
       "camcan                378138.078356               0.047634   \n",
       "hcp                   539883.432300               0.054650   \n",
       "ping-siemens          117580.144653               0.033602   \n",
       "\n",
       "              TotalCountProportion  TotalWiringProportion        age  \\\n",
       "classID                                                                \n",
       "camcan                    0.001530               0.002095  52.833835   \n",
       "hcp                       0.002039               0.002787  28.733427   \n",
       "ping-siemens              0.001246               0.001604  11.746311   \n",
       "\n",
       "                   ndi       odi     isovf  \n",
       "classID                                     \n",
       "camcan        0.634185  0.205257  0.109847  \n",
       "hcp           0.710146  0.173905  0.132257  \n",
       "ping-siemens       NaN       NaN       NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at per-tract average for everything in camcan\n",
    "camcan.groupby('structureID').mean()\n",
    "camcan.groupby('structureID').min()\n",
    "camcan.groupby('structureID').max()\n",
    "camcan.groupby('structureID').describe()\n",
    "df.groupby('classID').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3169bf24-241a-4ba2-acab-9828f9b53e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ad</th>\n",
       "      <th>fa</th>\n",
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       "      <th>StreamlineCount</th>\n",
       "      <th>volume</th>\n",
       "      <th>avgerageStreamlineLength</th>\n",
       "      <th>streamlineLengthStdev</th>\n",
       "      <th>averageFullDisplacement</th>\n",
       "      <th>fullDisplacementStdev</th>\n",
       "      <th>StreamlineLengthTotal</th>\n",
       "      <th>TotalVolumeProportion</th>\n",
       "      <th>TotalCountProportion</th>\n",
       "      <th>TotalWiringProportion</th>\n",
       "      <th>age</th>\n",
       "      <th>ndi</th>\n",
       "      <th>odi</th>\n",
       "      <th>isovf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classID</th>\n",
       "      <th>structureID</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">camcan</th>\n",
       "      <th>anterioFrontalCC</th>\n",
       "      <td>1.591386</td>\n",
       "      <td>0.550839</td>\n",
       "      <td>0.928908</td>\n",
       "      <td>0.597832</td>\n",
       "      <td>5822.934560</td>\n",
       "      <td>47016.937120</td>\n",
       "      <td>90.557248</td>\n",
       "      <td>16.854352</td>\n",
       "      <td>33.183012</td>\n",
       "      <td>10.869053</td>\n",
       "      <td>5.316225e+05</td>\n",
       "      <td>0.065942</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>52.689922</td>\n",
       "      <td>0.584451</td>\n",
       "      <td>0.168822</td>\n",
       "      <td>0.085504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forcepsMajor</th>\n",
       "      <td>1.869817</td>\n",
       "      <td>0.646278</td>\n",
       "      <td>0.990990</td>\n",
       "      <td>0.551832</td>\n",
       "      <td>5957.557613</td>\n",
       "      <td>60021.251521</td>\n",
       "      <td>133.071392</td>\n",
       "      <td>27.651130</td>\n",
       "      <td>46.061907</td>\n",
       "      <td>14.088605</td>\n",
       "      <td>7.938651e+05</td>\n",
       "      <td>0.084067</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.004386</td>\n",
       "      <td>52.576998</td>\n",
       "      <td>0.687201</td>\n",
       "      <td>0.123027</td>\n",
       "      <td>0.142209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forcepsMinor</th>\n",
       "      <td>1.548952</td>\n",
       "      <td>0.535826</td>\n",
       "      <td>0.916553</td>\n",
       "      <td>0.599525</td>\n",
       "      <td>10617.795918</td>\n",
       "      <td>43949.539715</td>\n",
       "      <td>94.072706</td>\n",
       "      <td>12.730785</td>\n",
       "      <td>39.887078</td>\n",
       "      <td>14.898807</td>\n",
       "      <td>1.012129e+06</td>\n",
       "      <td>0.061328</td>\n",
       "      <td>0.003539</td>\n",
       "      <td>0.005538</td>\n",
       "      <td>52.644660</td>\n",
       "      <td>0.575006</td>\n",
       "      <td>0.177738</td>\n",
       "      <td>0.076176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leftAnterioFrontoCerebellar</th>\n",
       "      <td>1.514104</td>\n",
       "      <td>0.550078</td>\n",
       "      <td>0.905396</td>\n",
       "      <td>0.601802</td>\n",
       "      <td>414.133603</td>\n",
       "      <td>31334.639676</td>\n",
       "      <td>148.189476</td>\n",
       "      <td>27.095231</td>\n",
       "      <td>97.561732</td>\n",
       "      <td>16.566771</td>\n",
       "      <td>6.171169e+04</td>\n",
       "      <td>0.043847</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>53.009597</td>\n",
       "      <td>0.726897</td>\n",
       "      <td>0.210631</td>\n",
       "      <td>0.124038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leftArc</th>\n",
       "      <td>1.372990</td>\n",
       "      <td>0.506068</td>\n",
       "      <td>0.855262</td>\n",
       "      <td>0.596752</td>\n",
       "      <td>5085.274949</td>\n",
       "      <td>56416.162272</td>\n",
       "      <td>115.247431</td>\n",
       "      <td>22.461162</td>\n",
       "      <td>63.108002</td>\n",
       "      <td>15.311031</td>\n",
       "      <td>5.890919e+05</td>\n",
       "      <td>0.079270</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>52.805019</td>\n",
       "      <td>0.617685</td>\n",
       "      <td>0.197451</td>\n",
       "      <td>0.068920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ping-siemens</th>\n",
       "      <th>rightmotorThalamic</th>\n",
       "      <td>1.155852</td>\n",
       "      <td>0.446905</td>\n",
       "      <td>0.757400</td>\n",
       "      <td>0.555707</td>\n",
       "      <td>5842.324324</td>\n",
       "      <td>44409.513514</td>\n",
       "      <td>58.941077</td>\n",
       "      <td>13.512853</td>\n",
       "      <td>46.787632</td>\n",
       "      <td>8.845682</td>\n",
       "      <td>3.337254e+05</td>\n",
       "      <td>0.067178</td>\n",
       "      <td>0.003895</td>\n",
       "      <td>0.004687</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rightpArc</th>\n",
       "      <td>1.214483</td>\n",
       "      <td>0.440179</td>\n",
       "      <td>0.796038</td>\n",
       "      <td>0.592477</td>\n",
       "      <td>1014.513514</td>\n",
       "      <td>16642.432432</td>\n",
       "      <td>64.975160</td>\n",
       "      <td>10.868907</td>\n",
       "      <td>47.344864</td>\n",
       "      <td>8.684519</td>\n",
       "      <td>6.597449e+04</td>\n",
       "      <td>0.025674</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rightparietoThalamic</th>\n",
       "      <td>1.209149</td>\n",
       "      <td>0.415720</td>\n",
       "      <td>0.826612</td>\n",
       "      <td>0.627638</td>\n",
       "      <td>3477.540541</td>\n",
       "      <td>40684.540541</td>\n",
       "      <td>56.090361</td>\n",
       "      <td>16.984725</td>\n",
       "      <td>42.638446</td>\n",
       "      <td>11.584540</td>\n",
       "      <td>1.945038e+05</td>\n",
       "      <td>0.060327</td>\n",
       "      <td>0.002318</td>\n",
       "      <td>0.002659</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rightspinoThalamic</th>\n",
       "      <td>1.210720</td>\n",
       "      <td>0.388679</td>\n",
       "      <td>0.845113</td>\n",
       "      <td>0.661433</td>\n",
       "      <td>3444.945946</td>\n",
       "      <td>26298.513514</td>\n",
       "      <td>41.638665</td>\n",
       "      <td>15.938460</td>\n",
       "      <td>32.135673</td>\n",
       "      <td>11.898093</td>\n",
       "      <td>1.441125e+05</td>\n",
       "      <td>0.039963</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.001972</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>righttemporoThalamic</th>\n",
       "      <td>1.240990</td>\n",
       "      <td>0.419822</td>\n",
       "      <td>0.845064</td>\n",
       "      <td>0.648919</td>\n",
       "      <td>918.945946</td>\n",
       "      <td>13451.027027</td>\n",
       "      <td>51.334750</td>\n",
       "      <td>13.492847</td>\n",
       "      <td>41.081212</td>\n",
       "      <td>9.102270</td>\n",
       "      <td>4.584474e+04</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                ad        fa        md  \\\n",
       "classID      structureID                                                 \n",
       "camcan       anterioFrontalCC             1.591386  0.550839  0.928908   \n",
       "             forcepsMajor                 1.869817  0.646278  0.990990   \n",
       "             forcepsMinor                 1.548952  0.535826  0.916553   \n",
       "             leftAnterioFrontoCerebellar  1.514104  0.550078  0.905396   \n",
       "             leftArc                      1.372990  0.506068  0.855262   \n",
       "...                                            ...       ...       ...   \n",
       "ping-siemens rightmotorThalamic           1.155852  0.446905  0.757400   \n",
       "             rightpArc                    1.214483  0.440179  0.796038   \n",
       "             rightparietoThalamic         1.209149  0.415720  0.826612   \n",
       "             rightspinoThalamic           1.210720  0.388679  0.845113   \n",
       "             righttemporoThalamic         1.240990  0.419822  0.845064   \n",
       "\n",
       "                                                rd  StreamlineCount  \\\n",
       "classID      structureID                                              \n",
       "camcan       anterioFrontalCC             0.597832      5822.934560   \n",
       "             forcepsMajor                 0.551832      5957.557613   \n",
       "             forcepsMinor                 0.599525     10617.795918   \n",
       "             leftAnterioFrontoCerebellar  0.601802       414.133603   \n",
       "             leftArc                      0.596752      5085.274949   \n",
       "...                                            ...              ...   \n",
       "ping-siemens rightmotorThalamic           0.555707      5842.324324   \n",
       "             rightpArc                    0.592477      1014.513514   \n",
       "             rightparietoThalamic         0.627638      3477.540541   \n",
       "             rightspinoThalamic           0.661433      3444.945946   \n",
       "             righttemporoThalamic         0.648919       918.945946   \n",
       "\n",
       "                                                volume  \\\n",
       "classID      structureID                                 \n",
       "camcan       anterioFrontalCC             47016.937120   \n",
       "             forcepsMajor                 60021.251521   \n",
       "             forcepsMinor                 43949.539715   \n",
       "             leftAnterioFrontoCerebellar  31334.639676   \n",
       "             leftArc                      56416.162272   \n",
       "...                                                ...   \n",
       "ping-siemens rightmotorThalamic           44409.513514   \n",
       "             rightpArc                    16642.432432   \n",
       "             rightparietoThalamic         40684.540541   \n",
       "             rightspinoThalamic           26298.513514   \n",
       "             righttemporoThalamic         13451.027027   \n",
       "\n",
       "                                          avgerageStreamlineLength  \\\n",
       "classID      structureID                                             \n",
       "camcan       anterioFrontalCC                            90.557248   \n",
       "             forcepsMajor                               133.071392   \n",
       "             forcepsMinor                                94.072706   \n",
       "             leftAnterioFrontoCerebellar                148.189476   \n",
       "             leftArc                                    115.247431   \n",
       "...                                                            ...   \n",
       "ping-siemens rightmotorThalamic                          58.941077   \n",
       "             rightpArc                                   64.975160   \n",
       "             rightparietoThalamic                        56.090361   \n",
       "             rightspinoThalamic                          41.638665   \n",
       "             righttemporoThalamic                        51.334750   \n",
       "\n",
       "                                          streamlineLengthStdev  \\\n",
       "classID      structureID                                          \n",
       "camcan       anterioFrontalCC                         16.854352   \n",
       "             forcepsMajor                             27.651130   \n",
       "             forcepsMinor                             12.730785   \n",
       "             leftAnterioFrontoCerebellar              27.095231   \n",
       "             leftArc                                  22.461162   \n",
       "...                                                         ...   \n",
       "ping-siemens rightmotorThalamic                       13.512853   \n",
       "             rightpArc                                10.868907   \n",
       "             rightparietoThalamic                     16.984725   \n",
       "             rightspinoThalamic                       15.938460   \n",
       "             righttemporoThalamic                     13.492847   \n",
       "\n",
       "                                          averageFullDisplacement  \\\n",
       "classID      structureID                                            \n",
       "camcan       anterioFrontalCC                           33.183012   \n",
       "             forcepsMajor                               46.061907   \n",
       "             forcepsMinor                               39.887078   \n",
       "             leftAnterioFrontoCerebellar                97.561732   \n",
       "             leftArc                                    63.108002   \n",
       "...                                                           ...   \n",
       "ping-siemens rightmotorThalamic                         46.787632   \n",
       "             rightpArc                                  47.344864   \n",
       "             rightparietoThalamic                       42.638446   \n",
       "             rightspinoThalamic                         32.135673   \n",
       "             righttemporoThalamic                       41.081212   \n",
       "\n",
       "                                          fullDisplacementStdev  \\\n",
       "classID      structureID                                          \n",
       "camcan       anterioFrontalCC                         10.869053   \n",
       "             forcepsMajor                             14.088605   \n",
       "             forcepsMinor                             14.898807   \n",
       "             leftAnterioFrontoCerebellar              16.566771   \n",
       "             leftArc                                  15.311031   \n",
       "...                                                         ...   \n",
       "ping-siemens rightmotorThalamic                        8.845682   \n",
       "             rightpArc                                 8.684519   \n",
       "             rightparietoThalamic                     11.584540   \n",
       "             rightspinoThalamic                       11.898093   \n",
       "             righttemporoThalamic                      9.102270   \n",
       "\n",
       "                                          StreamlineLengthTotal  \\\n",
       "classID      structureID                                          \n",
       "camcan       anterioFrontalCC                      5.316225e+05   \n",
       "             forcepsMajor                          7.938651e+05   \n",
       "             forcepsMinor                          1.012129e+06   \n",
       "             leftAnterioFrontoCerebellar           6.171169e+04   \n",
       "             leftArc                               5.890919e+05   \n",
       "...                                                         ...   \n",
       "ping-siemens rightmotorThalamic                    3.337254e+05   \n",
       "             rightpArc                             6.597449e+04   \n",
       "             rightparietoThalamic                  1.945038e+05   \n",
       "             rightspinoThalamic                    1.441125e+05   \n",
       "             righttemporoThalamic                  4.584474e+04   \n",
       "\n",
       "                                          TotalVolumeProportion  \\\n",
       "classID      structureID                                          \n",
       "camcan       anterioFrontalCC                          0.065942   \n",
       "             forcepsMajor                              0.084067   \n",
       "             forcepsMinor                              0.061328   \n",
       "             leftAnterioFrontoCerebellar               0.043847   \n",
       "             leftArc                                   0.079270   \n",
       "...                                                         ...   \n",
       "ping-siemens rightmotorThalamic                        0.067178   \n",
       "             rightpArc                                 0.025674   \n",
       "             rightparietoThalamic                      0.060327   \n",
       "             rightspinoThalamic                        0.039963   \n",
       "             righttemporoThalamic                      0.020528   \n",
       "\n",
       "                                          TotalCountProportion  \\\n",
       "classID      structureID                                         \n",
       "camcan       anterioFrontalCC                         0.001941   \n",
       "             forcepsMajor                             0.001986   \n",
       "             forcepsMinor                             0.003539   \n",
       "             leftAnterioFrontoCerebellar              0.000138   \n",
       "             leftArc                                  0.001695   \n",
       "...                                                        ...   \n",
       "ping-siemens rightmotorThalamic                       0.003895   \n",
       "             rightpArc                                0.000676   \n",
       "             rightparietoThalamic                     0.002318   \n",
       "             rightspinoThalamic                       0.002297   \n",
       "             righttemporoThalamic                     0.000613   \n",
       "\n",
       "                                          TotalWiringProportion        age  \\\n",
       "classID      structureID                                                     \n",
       "camcan       anterioFrontalCC                          0.002931  52.689922   \n",
       "             forcepsMajor                              0.004386  52.576998   \n",
       "             forcepsMinor                              0.005538  52.644660   \n",
       "             leftAnterioFrontoCerebellar               0.000342  53.009597   \n",
       "             leftArc                                   0.003261  52.805019   \n",
       "...                                                         ...        ...   \n",
       "ping-siemens rightmotorThalamic                        0.004687  11.750000   \n",
       "             rightpArc                                 0.000896  11.750000   \n",
       "             rightparietoThalamic                      0.002659  11.750000   \n",
       "             rightspinoThalamic                        0.001972  11.750000   \n",
       "             righttemporoThalamic                      0.000648  11.750000   \n",
       "\n",
       "                                               ndi       odi     isovf  \n",
       "classID      structureID                                                \n",
       "camcan       anterioFrontalCC             0.584451  0.168822  0.085504  \n",
       "             forcepsMajor                 0.687201  0.123027  0.142209  \n",
       "             forcepsMinor                 0.575006  0.177738  0.076176  \n",
       "             leftAnterioFrontoCerebellar  0.726897  0.210631  0.124038  \n",
       "             leftArc                      0.617685  0.197451  0.068920  \n",
       "...                                            ...       ...       ...  \n",
       "ping-siemens rightmotorThalamic                NaN       NaN       NaN  \n",
       "             rightpArc                         NaN       NaN       NaN  \n",
       "             rightparietoThalamic              NaN       NaN       NaN  \n",
       "             rightspinoThalamic                NaN       NaN       NaN  \n",
       "             righttemporoThalamic              NaN       NaN       NaN  \n",
       "\n",
       "[183 rows x 18 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at per-tract, per classID average\n",
    "df.groupby(['classID','structureID']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e1ea2d0-2320-4cfe-b696-94feceb3002a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# make plots\n",
    "import seaborn as sns\n",
    "\n",
    "###### make a scatterplot of tract FA between ping-siemens and camcan\n",
    "# subset dataframe so we only have camcan and ping-siemens classes\n",
    "ping_camcan = df.loc[(df['classID'] == 'ping-siemens') | (df['classID'] == 'camcan')]\n",
    "ping_camcan\n",
    "\n",
    "# groupby data by structureID and compute mean\n",
    "ping_camcan_mean = ping_camcan.groupby(['classID','structureID']).mean().reset_index()\n",
    "\n",
    "# generate scatterplot\n",
    "sns.scatterplot(x=ping_camcan_mean.loc[ping_camcan_mean['classID'] == 'camcan']['fa'].tolist(),\n",
    "                y=ping_camcan_mean.loc[ping_camcan_mean['classID'] == 'ping-siemens']['fa'].tolist(),\n",
    "                hue=ping_camcan_mean.loc[ping_camcan_mean['classID'] == 'ping-siemens']['structureID'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4a36fa4-1780-488d-b84f-450370093f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='fa', ylabel='md'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# generate a scatterplot of FA vs MD\n",
    "sns.scatterplot(x='fa',y='md',data=ping_camcan_mean,hue='structureID',legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbe3504-2dd7-45b9-aa00-48e112464e50",
   "metadata": {},
   "source": [
    "# GOAL FOR NEXT MEETING: write a function that will generate a new dataframe to compute laterality index (LI)\n",
    "\n",
    "\n",
    "# HOMEWORK:\n",
    "1. set up your own notebook and fork of the github repository that way we can all work individually without messing up any one's repos\n",
    "2. play around with the data on your own time\n",
    "    a. generate some figures like the ones we generated together\n",
    "    b. subset different levels of datafram\n",
    "        i. grab one tract"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}