handling_data_types.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b00643dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ce412b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8c57583",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bfab382",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels_data = pd.read_csv(\"SMM_s1_DR_Seg_3Dcount_75slices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da91a6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "intensities_map = pd.read_csv(\"KNK regions to exclude - template.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bd12eb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LabelID</th>\n",
       "      <th>LabelAbrv</th>\n",
       "      <th>LabelName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>TMv</td>\n",
       "      <td>Tuberomammillary nucleus ventral part</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SSp-m6b</td>\n",
       "      <td>Primary somatosensory area mouth layer 6b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>IC</td>\n",
       "      <td>Inferior colliculus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>int</td>\n",
       "      <td>internal capsule</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>PSV</td>\n",
       "      <td>Principal sensory nucleus of the trigeminal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>21127</td>\n",
       "      <td>TEa2/3</td>\n",
       "      <td>Temporal association areas layer 2/3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>21128</td>\n",
       "      <td>SSp-ll5</td>\n",
       "      <td>Primary somatosensory area lower limb layer 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>21139</td>\n",
       "      <td>NLOT3</td>\n",
       "      <td>Nucleus of the lateral olfactory tract layer 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>30703</td>\n",
       "      <td>DG-mo</td>\n",
       "      <td>Dentate gyrus, molecular layer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>30704</td>\n",
       "      <td>DG-po</td>\n",
       "      <td>Dentate gyrus, polymorph layer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LabelID LabelAbrv                                       LabelName\n",
       "0           1       TMv           Tuberomammillary nucleus ventral part\n",
       "1           2   SSp-m6b       Primary somatosensory area mouth layer 6b\n",
       "2           4        IC                             Inferior colliculus\n",
       "3           6       int                                internal capsule\n",
       "4           7       PSV     Principal sensory nucleus of the trigeminal\n",
       "...       ...       ...                                             ...\n",
       "1106    21127    TEa2/3            Temporal association areas layer 2/3\n",
       "1107    21128   SSp-ll5   Primary somatosensory area lower limb layer 5\n",
       "1108    21139     NLOT3  Nucleus of the lateral olfactory tract layer 3\n",
       "1109    30703     DG-mo                  Dentate gyrus, molecular layer\n",
       "1110    30704     DG-po                  Dentate gyrus, polymorph layer\n",
       "\n",
       "[1111 rows x 3 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensities_map[[\"LabelID\", \"LabelAbrv\", \"LabelName\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d21ecdc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PIXEL_COUNT</th>\n",
       "      <th>INTENSITY_1</th>\n",
       "      <th>INTENSITY_1_PERC</th>\n",
       "      <th>INTENSITY_2</th>\n",
       "      <th>INTENSITY_2_PERC</th>\n",
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       "      <th>INTENSITY_3_PERC</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>806</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>1035</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>873</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>1058</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>234077</th>\n",
       "      <td>1</td>\n",
       "      <td>20749</td>\n",
       "      <td>1.000</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>20574</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>234079</th>\n",
       "      <td>2</td>\n",
       "      <td>20784</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234080</th>\n",
       "      <td>2</td>\n",
       "      <td>20771</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>234081</th>\n",
       "      <td>1</td>\n",
       "      <td>20697</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>234082 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PIXEL_COUNT INTENSITY_1 INTENSITY_1_PERC INTENSITY_2 INTENSITY_2_PERC  \\\n",
       "0                5         806            1.000           0            0.000   \n",
       "1               50        1035            1.000           0            0.000   \n",
       "2                4         120            1.000           0            0.000   \n",
       "3                1         873            1.000           0            0.000   \n",
       "4               26        1058            1.000           0            0.000   \n",
       "...            ...         ...              ...         ...              ...   \n",
       "234077           1       20749            1.000           0            0.000   \n",
       "234078           2       20574            1.000           0            0.000   \n",
       "234079           2       20784            1.000           0            0.000   \n",
       "234080           2       20771            1.000           0            0.000   \n",
       "234081           1       20697            1.000           0            0.000   \n",
       "\n",
       "       INTENSITY_3 INTENSITY_3_PERC  \n",
       "0                0                0  \n",
       "1                0                0  \n",
       "2                0                0  \n",
       "3                0                0  \n",
       "4                0                0  \n",
       "...            ...              ...  \n",
       "234077           0            0.000  \n",
       "234078           0            0.000  \n",
       "234079           0            0.000  \n",
       "234080           0            0.000  \n",
       "234081           0            0.000  \n",
       "\n",
       "[234082 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixels_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80fce39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels_data = pixels_data[pixels_data.PIXEL_COUNT != \"PIXEL_COUNT\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0cf45ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels_data_1 = pixels_data[[\"PIXEL_COUNT\", \"INTENSITY_1\", \"INTENSITY_1_PERC\"]]\n",
    "pixels_data_2 = pixels_data[[\"PIXEL_COUNT\", \"INTENSITY_2\", \"INTENSITY_2_PERC\"]]\n",
    "pixels_data_3 = pixels_data[[\"PIXEL_COUNT\", \"INTENSITY_3\", \"INTENSITY_3_PERC\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "051c4477",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-d087bd0d7ce0>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pixels_data_1[\"TOTAL_PIXELS\"] = pixels_data_1[\"PIXEL_COUNT\"].astype(int) * pixels_data_1[\"INTENSITY_1_PERC\"].astype(float)\n",
      "<ipython-input-32-d087bd0d7ce0>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pixels_data_2[\"TOTAL_PIXELS\"] = pixels_data_2[\"PIXEL_COUNT\"].astype(int) * pixels_data_2[\"INTENSITY_2_PERC\"].astype(float)\n",
      "<ipython-input-32-d087bd0d7ce0>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pixels_data_3[\"TOTAL_PIXELS\"] = pixels_data_3[\"PIXEL_COUNT\"].astype(int) * pixels_data_3[\"INTENSITY_3_PERC\"].astype(float)\n"
     ]
    }
   ],
   "source": [
    "pixels_data_1[\"TOTAL_PIXELS\"] = pixels_data_1[\"PIXEL_COUNT\"].astype(int) * pixels_data_1[\"INTENSITY_1_PERC\"].astype(float)\n",
    "pixels_data_2[\"TOTAL_PIXELS\"] = pixels_data_2[\"PIXEL_COUNT\"].astype(int) * pixels_data_2[\"INTENSITY_2_PERC\"].astype(float)\n",
    "pixels_data_3[\"TOTAL_PIXELS\"] = pixels_data_3[\"PIXEL_COUNT\"].astype(int) * pixels_data_3[\"INTENSITY_3_PERC\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e64d7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels_data_1 = pixels_data_1[[\"TOTAL_PIXELS\",\"INTENSITY_1\"]]\n",
    "pixels_data_1 = pixels_data_1.rename(columns={\"INTENSITY_1\": \"INTENSITY\"})\n",
    "pixels_data_2 = pixels_data_2[[\"TOTAL_PIXELS\",\"INTENSITY_2\"]]\n",
    "pixels_data_2 = pixels_data_2.rename(columns={\"INTENSITY_2\": \"INTENSITY\"})\n",
    "pixels_data_3 = pixels_data_3[[\"TOTAL_PIXELS\",\"INTENSITY_3\"]]\n",
    "pixels_data_3 = pixels_data_3.rename(columns={\"INTENSITY_3\": \"INTENSITY\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cdc37889",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_sum_data = pixels_data_1.append(pixels_data_2, ignore_index=True).append(pixels_data_3, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58ed45a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOTAL_PIXELS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTENSITY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>39064.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2136.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>582.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>6281.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>395.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2717.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1018.856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1314.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>758 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOTAL_PIXELS\n",
       "INTENSITY              \n",
       "0                 0.000\n",
       "1               209.958\n",
       "10            39064.188\n",
       "100            2136.103\n",
       "1004            582.927\n",
       "...                 ...\n",
       "988            6281.054\n",
       "989             395.003\n",
       "990            2717.235\n",
       "996            1018.856\n",
       "998            1314.023\n",
       "\n",
       "[758 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sum_data = total_sum_data.groupby(['INTENSITY']).agg('sum')\n",
    "total_sum_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "667136d7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "You are trying to merge on int32 and object columns. If you wish to proceed you should use pd.concat",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-3fefd00c2b5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mintensities_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotal_sum_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'LabelID'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'INTENSITY'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m   8193\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 8195\u001b[1;33m         return merge(\n\u001b[0m\u001b[0;32m   8196\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8197\u001b[0m             \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m     72\u001b[0m     \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m ) -> \"DataFrame\":\n\u001b[1;32m---> 74\u001b[1;33m     op = _MergeOperation(\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    670\u001b[0m         \u001b[1;31m# validate the merge keys dtypes. We may need to coerce\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    671\u001b[0m         \u001b[1;31m# to avoid incompatible dtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 672\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_coerce_merge_keys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    673\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    674\u001b[0m         \u001b[1;31m# If argument passed to validate,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m_maybe_coerce_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1191\u001b[0m                     \u001b[0minferred_right\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstring_types\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0minferred_left\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1192\u001b[0m                 ):\n\u001b[1;32m-> 1193\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m             \u001b[1;31m# datetimelikes must match exactly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: You are trying to merge on int32 and object columns. If you wish to proceed you should use pd.concat"
     ]
    }
   ],
   "source": [
    "intensities_map.merge(total_sum_data, how='left', left_on='LabelID', right_on='INTENSITY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b37ef2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>IDPath</th>\n",
       "      <th>LabelID</th>\n",
       "      <th>LabelAbrv</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Right hemisphere</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>LabelIDs to exclude</th>\n",
       "      <th>LabelAbrv</th>\n",
       "      <th>LabelName.1</th>\n",
       "      <th>Already Added?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/997/8/343/1129/1097/467/331/557/1/</td>\n",
       "      <td>1</td>\n",
       "      <td>TMv</td>\n",
       "      <td>Tuberomammillary nucleus ventral part</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TMv</td>\n",
       "      <td>Tuberomammillary nucleus ventral part</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/997/8/567/688/695/315/453/322/345/2/</td>\n",
       "      <td>2</td>\n",
       "      <td>SSp-m6b</td>\n",
       "      <td>Primary somatosensory area mouth layer 6b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/997/8/343/313/339/4/</td>\n",
       "      <td>4</td>\n",
       "      <td>IC</td>\n",
       "      <td>Inferior colliculus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/997/1009/983/784/6/</td>\n",
       "      <td>6</td>\n",
       "      <td>int</td>\n",
       "      <td>internal capsule</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/997/8/343/1065/771/1132/7/</td>\n",
       "      <td>7</td>\n",
       "      <td>PSV</td>\n",
       "      <td>Principal sensory nucleus of the trigeminal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>/997/8/567/688/695/315/541/1127/</td>\n",
       "      <td>21127</td>\n",
       "      <td>TEa2/3</td>\n",
       "      <td>Temporal association areas layer 2/3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>/997/8/567/688/695/315/453/322/337/1128/</td>\n",
       "      <td>21128</td>\n",
       "      <td>SSp-ll5</td>\n",
       "      <td>Primary somatosensory area lower limb layer 5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>/997/8/567/688/695/698/619/1139/</td>\n",
       "      <td>21139</td>\n",
       "      <td>NLOT3</td>\n",
       "      <td>Nucleus of the lateral olfactory tract layer 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>/997/8/567/688/695/1089/1080/726/10703/</td>\n",
       "      <td>30703</td>\n",
       "      <td>DG-mo</td>\n",
       "      <td>Dentate gyrus, molecular layer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>/997/8/567/688/695/1089/1080/726/10704/</td>\n",
       "      <td>30704</td>\n",
       "      <td>DG-po</td>\n",
       "      <td>Dentate gyrus, polymorph layer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        IDPath  LabelID LabelAbrv  \\\n",
       "0          /997/8/343/1129/1097/467/331/557/1/        1       TMv   \n",
       "1        /997/8/567/688/695/315/453/322/345/2/        2   SSp-m6b   \n",
       "2                        /997/8/343/313/339/4/        4        IC   \n",
       "3                         /997/1009/983/784/6/        6       int   \n",
       "4                  /997/8/343/1065/771/1132/7/        7       PSV   \n",
       "...                                        ...      ...       ...   \n",
       "1106          /997/8/567/688/695/315/541/1127/    21127    TEa2/3   \n",
       "1107  /997/8/567/688/695/315/453/322/337/1128/    21128   SSp-ll5   \n",
       "1108          /997/8/567/688/695/698/619/1139/    21139     NLOT3   \n",
       "1109   /997/8/567/688/695/1089/1080/726/10703/    30703     DG-mo   \n",
       "1110   /997/8/567/688/695/1089/1080/726/10704/    30704     DG-po   \n",
       "\n",
       "                                           LabelName Right hemisphere  \\\n",
       "0              Tuberomammillary nucleus ventral part              NaN   \n",
       "1          Primary somatosensory area mouth layer 6b              NaN   \n",
       "2                                Inferior colliculus              NaN   \n",
       "3                                   internal capsule              NaN   \n",
       "4        Principal sensory nucleus of the trigeminal              NaN   \n",
       "...                                              ...              ...   \n",
       "1106            Temporal association areas layer 2/3              NaN   \n",
       "1107   Primary somatosensory area lower limb layer 5              NaN   \n",
       "1108  Nucleus of the lateral olfactory tract layer 3              NaN   \n",
       "1109                  Dentate gyrus, molecular layer              NaN   \n",
       "1110                  Dentate gyrus, polymorph layer              NaN   \n",
       "\n",
       "      Unnamed: 5  Unnamed: 6  Unnamed: 7  LabelIDs to exclude   \\\n",
       "0            NaN         NaN         NaN                   1.0   \n",
       "1            NaN         NaN         NaN                   NaN   \n",
       "2            NaN         NaN         NaN                   NaN   \n",
       "3            NaN         NaN         NaN                   NaN   \n",
       "4            NaN         NaN         NaN                   NaN   \n",
       "...          ...         ...         ...                   ...   \n",
       "1106         NaN         NaN         NaN                   NaN   \n",
       "1107         NaN         NaN         NaN                   NaN   \n",
       "1108         NaN         NaN         NaN                   NaN   \n",
       "1109         NaN         NaN         NaN                   NaN   \n",
       "1110         NaN         NaN         NaN                   NaN   \n",
       "\n",
       "     LabelAbrv                                    LabelName.1 Already Added?  \n",
       "0                  TMv  Tuberomammillary nucleus ventral part          False  \n",
       "1                  NaN                                    NaN          False  \n",
       "2                  NaN                                    NaN          False  \n",
       "3                  NaN                                    NaN          False  \n",
       "4                  NaN                                    NaN          False  \n",
       "...                ...                                    ...            ...  \n",
       "1106               NaN                                    NaN            NaN  \n",
       "1107               NaN                                    NaN            NaN  \n",
       "1108               NaN                                    NaN            NaN  \n",
       "1109               NaN                                    NaN            NaN  \n",
       "1110               NaN                                    NaN            NaN  \n",
       "\n",
       "[1111 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intensities_map"
   ]
  }
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