example_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3a1566e-11ae-42e5-902c-16e00cf1ad71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b8627a7-fee2-43c2-95b9-624a71a58294",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_df = pd.read_csv('/ssd-data/MRKR_dataset/MRKR_clinical_data_tables/MRKR_image_metadata.csv')\n",
    "pain_df = pd.read_csv('/ssd-data/MRKR_dataset/MRKR_clinical_data_tables/MRKR_pain.csv')\n",
    "icd_df = pd.read_csv('/ssd-data/MRKR_dataset/MRKR_clinical_data_tables/MRKR_ICD.csv')\n",
    "demo_df = pd.read_csv('/ssd-data/MRKR_dataset/MRKR_clinical_data_tables/MRKR_demographics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba250122-87c7-4406-b260-e73b76634aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_df = image_df.rename(columns={'StudyDate_anon':'date_anon'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49c7e405-73ae-4d13-939b-0629f6b12e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_df = icd_df[(icd_df.joint_infection==1) | (icd_df.trauma_lower_extremity==1) | (icd_df.knee_osteomyelitis==1)][['empi_anon','date_anon']].drop_duplicates().reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc190a02-9c17-4d5c-bfa4-6d3e7783a00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125113, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7157b73b-2017-469d-b434-2707fec03e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure date_anon columns in both datasets are in datetime format\n",
    "image_df['date_anon'] = pd.to_datetime(image_df['date_anon'])\n",
    "remove_df['date_anon'] = pd.to_datetime(remove_df['date_anon'])\n",
    "\n",
    "# Sort both DataFrames by patient ID and date for pd.merge_asof to work\n",
    "image_df = image_df.sort_values(by=['empi_anon','date_anon'])\n",
    "remove_df = remove_df.sort_values(by=['empi_anon','date_anon'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f9a11d0-aef2-4cb7-8c01-02a9feb878f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a merge on 'empi_anon' and allow a condition for date difference\n",
    "merged_df = pd.merge(image_df, remove_df, on='empi_anon', suffixes=('_main', '_remove'))\n",
    "\n",
    "# Filter the merged DataFrame where the date difference is within 90 days\n",
    "merged_df['date_diff'] = (merged_df['date_anon_main'] - merged_df['date_anon_remove']).abs()\n",
    "\n",
    "# Keep only rows where the date difference is within 90 days\n",
    "matches_within_30_days = merged_df[merged_df['date_diff'] <= pd.Timedelta(days=90)]\n",
    "\n",
    "# Drop the date_diff column if it's not needed\n",
    "matches_within_30_days = matches_within_30_days.drop(columns=['date_diff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99eafaed-46e9-4d94-88d5-7cead0d8fa9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(503261, 19)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ef6f018-5264-47b4-9c11-738f1c854834",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_df = image_df[~image_df.dicom_path.isin(matches_within_30_days.dicom_path.tolist())].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e97c8fb8-6844-4995-8d3a-955c585097b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(383450, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee0d37ab-4c90-443d-92f7-e7ff83b9ec0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoimmune_patients = icd_df[(icd_df.autoimmune==1)].empi_anon.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6db3ac98-ac74-4b4a-83f6-db5fa7ac5afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_df = image_df[~image_df.empi_anon.isin(autoimmune_patients)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc6c977f-09cd-4dd9-86a1-8737012a3e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_df = image_df[(image_df.laterality==\"B\") & (image_df.weight_bearing==1) & (image_df.view_position==\"F\") & (image_df.age_at_exam<90)].reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6bbe1d6-820a-4ff2-b88f-cf14197ac97a",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_df = selected_df.copy()\n",
    "left_df = selected_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd9d804e-df82-4d90-8998-15cbd5d207cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_df = right_df[(right_df.arthroplasty==\"0\") | (right_df.arthroplasty==\"L\")]\n",
    "left_df = left_df[(left_df.arthroplasty==\"0\") | (left_df.arthroplasty==\"R\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "efe21167-54ae-49d2-88ad-16c271faffaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "laterality\n",
       "NaN    4724556\n",
       "R        96492\n",
       "L        87153\n",
       "B        62668\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pain_df.laterality.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66fc575e-f996-43de-829f-90c80902f608",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_pain_df = pain_df[(pain_df.laterality==\"R\") | (pain_df.laterality==\"B\")]\n",
    "left_pain_df = pain_df[(pain_df.laterality==\"L\") | (pain_df.laterality==\"B\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f6af706-d88a-442a-a556-32d4880fe4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_df = right_df.rename(columns={'StudyDate_anon':'date_anon'})\n",
    "left_df = left_df.rename(columns={'StudyDate_anon':'date_anon'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00bf2c56-de84-4f14-bd4f-2bddd1bd9667",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3042315/2115231548.py: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",
      "  right_pain_df['date_anon'] = pd.to_datetime(right_pain_df['date_anon'])\n",
      "/tmp/ipykernel_3042315/2115231548.py:5: 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",
      "  left_pain_df['date_anon'] = pd.to_datetime(left_pain_df['date_anon'])\n"
     ]
    }
   ],
   "source": [
    "right_df['date_anon'] = pd.to_datetime(right_df['date_anon'])\n",
    "right_pain_df['date_anon'] = pd.to_datetime(right_pain_df['date_anon'])\n",
    "\n",
    "left_df['date_anon'] = pd.to_datetime(left_df['date_anon'])\n",
    "left_pain_df['date_anon'] = pd.to_datetime(left_pain_df['date_anon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "057af0ea-1e4f-4b60-98e3-b091b3d8b643",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_df = right_df.sort_values(by=['date_anon'])\n",
    "left_df = left_df.sort_values(by=['date_anon'])\n",
    "\n",
    "right_pain_df = right_pain_df.sort_values(by=['date_anon'])\n",
    "left_pain_df = left_pain_df.sort_values(by=['date_anon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d775b56-4a3d-4a7d-9195-ecfa691b22c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_merged_df = pd.merge_asof(right_df, right_pain_df[['empi_anon','pain_score','date_anon']], on='date_anon', by='empi_anon', tolerance=pd.Timedelta(days=3), direction='nearest')\n",
    "\n",
    "left_merged_df = pd.merge_asof(left_df, left_pain_df[['empi_anon','pain_score','date_anon']], on='date_anon', by='empi_anon', tolerance=pd.Timedelta(days=3), direction='nearest')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1fc07e17-612f-4d69-92d4-ef15ef2d236f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pain_score\n",
       "NaN     36377\n",
       "5.0      2660\n",
       "8.0      2564\n",
       "7.0      2236\n",
       "6.0      1836\n",
       "4.0      1801\n",
       "3.0      1363\n",
       "10.0     1304\n",
       "2.0      1010\n",
       "9.0       982\n",
       "0.0       603\n",
       "1.0       494\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_merged_df.pain_score.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4bc81e5-eafd-4a72-807f-89befe1359ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pain_score\n",
       "NaN     37710\n",
       "5.0      2542\n",
       "8.0      2477\n",
       "7.0      2212\n",
       "4.0      1786\n",
       "6.0      1781\n",
       "3.0      1337\n",
       "10.0     1204\n",
       "9.0       974\n",
       "2.0       958\n",
       "0.0       583\n",
       "1.0       454\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_merged_df.pain_score.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c54b591d-59fc-4209-a0f8-b660478b79eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_final_df = right_merged_df.dropna(subset=['pain_score'])\n",
    "left_final_df = left_merged_df.dropna(subset=['pain_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64f14412-62f7-42c5-8cbc-3af77a1c8b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_final_df = right_final_df.rename(columns={'R_KLG_inference':'KLG_inference'})\n",
    "left_final_df = left_final_df.rename(columns={'L_KLG_inference':'KLG_inference'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b359bf78-67c5-401e-8cad-7486ddab3f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "right_final_df['laterality']=\"R\"\n",
    "left_final_df['laterality']=\"L\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da734754-e22a-4cf8-9a6d-02002539801f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.concat([right_final_df, left_final_df]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ba336af-9995-4bf0-b792-2ca547ad2efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R_KLG_inference\n",
       "2.0    17472\n",
       "0.0    13347\n",
       "3.0     9305\n",
       "4.0     5176\n",
       "1.0     2539\n",
       "NaN       84\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df[(image_df.laterality==\"B\") & (image_df.view_position==\"F\")  & (image_df.weight_bearing==1) & (image_df.arthroplasty==\"0\")].R_KLG_inference.value_counts(dropna=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77d64dd8-69be-4760-b5f9-3af96626418a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33161, 21)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48703f87-a212-44f2-89ad-a0c9d0e498d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KLG_inference\n",
       "2.0    11652\n",
       "3.0     8505\n",
       "0.0     6684\n",
       "4.0     4628\n",
       "1.0     1622\n",
       "NaN       70\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.KLG_inference.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4aa57617-7b87-4759-b122-cf253f8e518d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.dropna(subset=['KLG_inference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c49469cf-9b88-4805-945c-c4127d305cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.merge(data_df, demo_df[['empi_anon','sex','race']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ac2df040-2140-4b04-b4e2-b99d285bd91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pUUlJidLT05WZmanFixfr2rVrIWP279+vSZMmyWKxaOTIkaqpqbn5PQQAAHGnVwFm2LBheu6559TU1KRjx45pxowZ+ta3vqVTp05JksrLy7Vz507t2LFD9fX1OnfunGbPnh28fXt7u0pKStTW1qZDhw7p9ddfV01NjVauXBkcc/bsWZWUlGj69Olqbm5WWVmZHnvsMe3duzdMuwwAAEzXrzeDH3rooZDlZ599Vps2bVJjY6OGDRumLVu2aNu2bZoxY4YkaevWrRo9erQaGxtVUFCg2tpanT59Wu+9955sNpsmTJigtWvXaunSpVq1apVSUlK0efNm5ebmat26dZKk0aNH6+DBg1q/fr2Ki4vDtNsAAMBkvQowX9Te3q4dO3bo8uXLstvtampqks/nU2FhYXDMqFGjlJOTo4aGBhUUFKihoUFjx46VzWYLjikuLtbChQt16tQpTZw4UQ0NDSFzdIwpKyvrth6v1yuv1xtc9ng8kiSfzyefz3ezu9lJx1zhnBNdo9c9Y0kK3NrtEwMhfyIy6HN09KTPHFNuXSSPzz2ds9cB5sSJE7Lb7bp69aoGDBigt99+W3l5eWpublZKSooGDRoUMt5ms8nlckmSXC5XSHjp2N6xrbsxHo9Hn3/+udLS0rqsq7KyUqtXr+60vra2Vunp6b3dzRtyOp1hnxNdo9fdq5oSnnnW5vvDMxG6RZ+jo7s+79mzJ4qVxLdIHJ+vXLnSo3G9DjD33HOPmpubdeHCBf3bv/2b5s2bp/r6+l4XGG7Lli1TRUVFcNnj8Wj48OEqKiqS1WoN2/34fD45nU7NnDlTycnJYZsXndHrnhmz6tauD7MkBrQ2368VxxLl9SeEqSp8GX2Ojp70+eQqLke4VZE8Pne8gnIjvQ4wKSkpGjlypCRp8uTJOnr0qDZs2KDvfOc7amtr0/nz50POwrjdbmVlZUmSsrKydOTIkZD5Ot6l9MUxX37nktvtltVqve7ZF0myWCyyWCyd1icnJ0fkyS9S86Izet09b3t4ngy9/oSwzYXro8/R0V2fOZ6ETySOzz2d75Y/B8bv98vr9Wry5MlKTk5WXV1dcNuZM2fU0tIiu90uSbLb7Tpx4oRaW1uDY5xOp6xWq/Ly8oJjvjhHx5iOOQAAAHp1BmbZsmWaNWuWcnJydPHiRW3btk379+/X3r17lZGRoQULFqiiokKDBw+W1WrVE088IbvdroKCAklSUVGR8vLyNHfuXFVVVcnlcmn58uVyOBzBsyePP/64Nm7cqCVLlmj+/Pnat2+ftm/frt27d4d/7wEAgJF6FWBaW1v1gx/8QJ9++qkyMjI0btw47d27VzNnzpQkrV+/XomJiSotLZXX61VxcbFeeeWV4O2TkpK0a9cuLVy4UHa7Xf3799e8efO0Zs2a4Jjc3Fzt3r1b5eXl2rBhg4YNG6bXXnuNt1ADAICgXgWYLVu2dLs9NTVV1dXVqq6uvu6YESNG3PAK8GnTpun48eO9KQ0AANxG+C4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMbp1bdRA7ebu57aHesSAABd4AwMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOPwbdQAAPQxdz21O9YldMuSFFDVlNjWwBkYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADG6VWAqays1Fe/+lUNHDhQmZmZevjhh3XmzJmQMVevXpXD4dCQIUM0YMAAlZaWyu12h4xpaWlRSUmJ0tPTlZmZqcWLF+vatWshY/bv369JkybJYrFo5MiRqqmpubk9BAAAcadXAaa+vl4Oh0ONjY1yOp3y+XwqKirS5cuXg2PKy8u1c+dO7dixQ/X19Tp37pxmz54d3N7e3q6SkhK1tbXp0KFDev3111VTU6OVK1cGx5w9e1YlJSWaPn26mpubVVZWpscee0x79+4Nwy4DAADT9evN4HfffTdkuaamRpmZmWpqatIDDzygCxcuaMuWLdq2bZtmzJghSdq6datGjx6txsZGFRQUqLa2VqdPn9Z7770nm82mCRMmaO3atVq6dKlWrVqllJQUbd68Wbm5uVq3bp0kafTo0Tp48KDWr1+v4uLiMO06AAAwVa8CzJdduHBBkjR48GBJUlNTk3w+nwoLC4NjRo0apZycHDU0NKigoEANDQ0aO3asbDZbcExxcbEWLlyoU6dOaeLEiWpoaAiZo2NMWVnZdWvxer3yer3BZY/HI0ny+Xzy+Xy3spshOuYK55zoWl/otSUpELP7jhZLYiDkT0QGfY6OnvTZhON3Xz/2dPQ3Er3s6Zw3HWD8fr/Kysp03333acyYMZIkl8ullJQUDRo0KGSszWaTy+UKjvlieOnY3rGtuzEej0eff/650tLSOtVTWVmp1atXd1pfW1ur9PT0m9vJbjidzrDPia7FstdVU2J211G3Nt8f6xJuC/Q5Orrr8549e6JYyc0x5dgTiePzlStXejTupgOMw+HQyZMndfDgwZudIqyWLVumioqK4LLH49Hw4cNVVFQkq9Uatvvx+XxyOp2aOXOmkpOTwzYvOusLvR6zKv6vu7IkBrQ2368VxxLl9SfEupy4RZ+joyd9Prmq71+K0NePPR19jsTxueMVlBu5qQCzaNEi7dq1SwcOHNCwYcOC67OystTW1qbz58+HnIVxu93KysoKjjly5EjIfB3vUvrimC+/c8ntdstqtXZ59kWSLBaLLBZLp/XJyckRefKL1Ly3k7ue2t3tdktSQFVTpInP7pO3PVYH/NvnicbrT4hhn28f9Dk6uuuzCcduUx4jkXgu7Ol8vQowgUBATzzxhN5++23t379fubm5IdsnT56s5ORk1dXVqbS0VJJ05swZtbS0yG63S5LsdrueffZZtba2KjMzU9KfTkFZrVbl5eUFx3z5FJ/T6QzOAQDAzbrRf55ghl4FGIfDoW3btukXv/iFBg4cGLxmJSMjQ2lpacrIyNCCBQtUUVGhwYMHy2q16oknnpDdbldBQYEkqaioSHl5eZo7d66qqqrkcrm0fPlyORyO4BmUxx9/XBs3btSSJUs0f/587du3T9u3b9fu3TzoAABALz8HZtOmTbpw4YKmTZumoUOHBn/eeuut4Jj169frm9/8pkpLS/XAAw8oKytL//7v/x7cnpSUpF27dikpKUl2u13f//739YMf/EBr1qwJjsnNzdXu3bvldDo1fvx4rVu3Tq+99hpvoQYAAJJu4iWkG0lNTVV1dbWqq6uvO2bEiBE3vAp82rRpOn78eG/KAwAAtwm+CwkAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOv1gXYKoxq/bK254Q6zKu63fPlcS6BAAAIoYzMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4/Q6wBw4cEAPPfSQsrOzlZCQoHfeeSdkeyAQ0MqVKzV06FClpaWpsLBQH330UciYzz77THPmzJHVatWgQYO0YMECXbp0KWTMf/3Xf+nrX/+6UlNTNXz4cFVVVfV+7wAAQFzqdYC5fPmyxo8fr+rq6i63V1VV6aWXXtLmzZt1+PBh9e/fX8XFxbp69WpwzJw5c3Tq1Ck5nU7t2rVLBw4c0I9+9KPgdo/Ho6KiIo0YMUJNTU36yU9+olWrVunVV1+9iV0EAADxptefxDtr1izNmjWry22BQEAvvviili9frm9961uSpH/+53+WzWbTO++8o0ceeUS//vWv9e677+ro0aPKz8+XJL388sv6xje+oX/6p39Sdna23njjDbW1tennP/+5UlJSdO+996q5uVkvvPBCSNABAAC3p7B+lcDZs2flcrlUWFgYXJeRkaGpU6eqoaFBjzzyiBoaGjRo0KBgeJGkwsJCJSYm6vDhw/r2t7+thoYGPfDAA0pJSQmOKS4u1vPPP68//vGPuuOOOzrdt9frldfrDS57PB5Jks/nk8/nC9s+dsxlSQyEbc5ICOc+R4olqfsedvS4r/fadPQ5OuhzdNDn6OjobySea3o6Z1gDjMvlkiTZbLaQ9TabLbjN5XIpMzMztIh+/TR48OCQMbm5uZ3m6NjWVYCprKzU6tWrO62vra1Venr6Te7R9a3N94d9znDas2dPrEu4oaopPRvX13sdL+hzdNDn6KDP0eF0OsM+55UrV3o0Lm6+zHHZsmWqqKgILns8Hg0fPlxFRUWyWq1hux+fzyen06kVxxLl9ffdL3M8uao41iXc0JhVe7vdbkkMaG2+v8/32nT0OTroc3TQ5+jo6PPMmTOVnJwc1rk7XkG5kbAGmKysLEmS2+3W0KFDg+vdbrcmTJgQHNPa2hpyu2vXrumzzz4L3j4rK0tutztkTMdyx5gvs1gsslgsndYnJyeHvbmS5PUn9Olvo47EPodbT/vX13sdL+hzdNDn6KDP0RGJ59iezhfWz4HJzc1VVlaW6urqgus8Ho8OHz4su90uSbLb7Tp//ryampqCY/bt2ye/36+pU6cGxxw4cCDkdTCn06l77rmny5ePAADA7aXXZ2AuXbqkjz/+OLh89uxZNTc3a/DgwcrJyVFZWZmeeeYZ3X333crNzdWKFSuUnZ2thx9+WJI0evRo/dVf/ZV++MMfavPmzfL5fFq0aJEeeeQRZWdnS5K+973vafXq1VqwYIGWLl2qkydPasOGDVq/fn149vo2cNdTu2NdAgAAEdPrAHPs2DFNnz49uNxx3cm8efNUU1OjJUuW6PLly/rRj36k8+fP6/7779e7776r1NTU4G3eeOMNLVq0SA8++KASExNVWlqql156Kbg9IyNDtbW1cjgcmjx5su68806tXLmSt1ADAABJNxFgpk2bpkDg+m9PS0hI0Jo1a7RmzZrrjhk8eLC2bdvW7f2MGzdO//mf/9nb8gAAwG2A70ICAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMbp0wGmurpad911l1JTUzV16lQdOXIk1iUBAIA+oM8GmLfeeksVFRV6+umn9cEHH2j8+PEqLi5Wa2trrEsDAAAx1mcDzAsvvKAf/vCHevTRR5WXl6fNmzcrPT1dP//5z2NdGgAAiLF+sS6gK21tbWpqatKyZcuC6xITE1VYWKiGhoYub+P1euX1eoPLFy5ckCR99tln8vl8YavN5/PpypUr6udLVLs/IWzzorN+/oCuXPHT6wijz9FBn6ODPkdHR5//8Ic/KDk5OaxzX7x4UZIUCAS6ryGs9xomv//979Xe3i6bzRay3maz6cMPP+zyNpWVlVq9enWn9bm5uRGpEdHxvVgXcJugz9FBn6ODPkdHpPt88eJFZWRkXHd7nwwwN2PZsmWqqKgILvv9fn322WcaMmSIEhLCl8I9Ho+GDx+uTz75RFarNWzzojN6HR30OTroc3TQ5+iIZJ8DgYAuXryo7Ozsbsf1yQBz5513KikpSW63O2S92+1WVlZWl7exWCyyWCwh6wYNGhSpEmW1WvnHESX0Ojroc3TQ5+igz9ERqT53d+alQ5+8iDclJUWTJ09WXV1dcJ3f71ddXZ3sdnsMKwMAAH1BnzwDI0kVFRWaN2+e8vPzNWXKFL344ou6fPmyHn300ViXBgAAYqzPBpjvfOc7+r//+z+tXLlSLpdLEyZM0Lvvvtvpwt5os1gsevrppzu9XIXwo9fRQZ+jgz5HB32Ojr7Q54TAjd6nBAAA0Mf0yWtgAAAAukOAAQAAxiHAAAAA4xBgAACAcQgwvVRdXa277rpLqampmjp1qo4cORLrkuJKZWWlvvrVr2rgwIHKzMzUww8/rDNnzsS6rLj33HPPKSEhQWVlZbEuJS797//+r77//e9ryJAhSktL09ixY3Xs2LFYlxVX2tvbtWLFCuXm5iotLU1//ud/rrVr197w+3TQvQMHDuihhx5Sdna2EhIS9M4774RsDwQCWrlypYYOHaq0tDQVFhbqo48+ikptBJheeOutt1RRUaGnn35aH3zwgcaPH6/i4mK1trbGurS4UV9fL4fDocbGRjmdTvl8PhUVFeny5cuxLi1uHT16VD/96U81bty4WJcSl/74xz/qvvvuU3Jysn75y1/q9OnTWrdune64445YlxZXnn/+eW3atEkbN27Ur3/9az3//POqqqrSyy+/HOvSjHb58mWNHz9e1dXVXW6vqqrSSy+9pM2bN+vw4cPq37+/iouLdfXq1cgXF0CPTZkyJeBwOILL7e3tgezs7EBlZWUMq4pvra2tAUmB+vr6WJcSly5evBi4++67A06nM/CXf/mXgSeffDLWJcWdpUuXBu6///5YlxH3SkpKAvPnzw9ZN3v27MCcOXNiVFH8kRR4++23g8t+vz+QlZUV+MlPfhJcd/78+YDFYgn867/+a8Tr4QxMD7W1tampqUmFhYXBdYmJiSosLFRDQ0MMK4tvFy5ckCQNHjw4xpXEJ4fDoZKSkpDHNcLrP/7jP5Sfn6+/+Zu/UWZmpiZOnKif/exnsS4r7nzta19TXV2dfvOb30iSfvWrX+ngwYOaNWtWjCuLX2fPnpXL5Qo5fmRkZGjq1KlReV7ss5/E29f8/ve/V3t7e6dPArbZbPrwww9jVFV88/v9Kisr03333acxY8bEupy48+abb+qDDz7Q0aNHY11KXPvtb3+rTZs2qaKiQv/wD/+go0eP6u/+7u+UkpKiefPmxbq8uPHUU0/J4/Fo1KhRSkpKUnt7u5599lnNmTMn1qXFLZfLJUldPi92bIskAgz6LIfDoZMnT+rgwYOxLiXufPLJJ3ryySfldDqVmpoa63Limt/vV35+vn784x9LkiZOnKiTJ09q8+bNBJgw2r59u9544w1t27ZN9957r5qbm1VWVqbs7Gz6HKd4CamH7rzzTiUlJcntdoesd7vdysrKilFV8WvRokXatWuX3n//fQ0bNizW5cSdpqYmtba2atKkSerXr5/69eun+vp6vfTSS+rXr5/a29tjXWLcGDp0qPLy8kLWjR49Wi0tLTGqKD4tXrxYTz31lB555BGNHTtWc+fOVXl5uSorK2NdWtzqeO6L1fMiAaaHUlJSNHnyZNXV1QXX+f1+1dXVyW63x7Cy+BIIBLRo0SK9/fbb2rdvn3Jzc2NdUlx68MEHdeLECTU3Nwd/8vPzNWfOHDU3NyspKSnWJcaN++67r9NHAfzmN7/RiBEjYlRRfLpy5YoSE0Of0pKSkuT3+2NUUfzLzc1VVlZWyPOix+PR4cOHo/K8yEtIvVBRUaF58+YpPz9fU6ZM0YsvvqjLly/r0UcfjXVpccPhcGjbtm36xS9+oYEDBwZfR83IyFBaWlqMq4sfAwcO7HRdUf/+/TVkyBCuNwqz8vJyfe1rX9OPf/xj/e3f/q2OHDmiV199Va+++mqsS4srDz30kJ599lnl5OTo3nvv1fHjx/XCCy9o/vz5sS7NaJcuXdLHH38cXD579qyam5s1ePBg5eTkqKysTM8884zuvvtu5ebmasWKFcrOztbDDz8c+eIi/j6nOPPyyy8HcnJyAikpKYEpU6YEGhsbY11SXJHU5c/WrVtjXVrc423UkbNz587AmDFjAhaLJTBq1KjAq6++GuuS4o7H4wk8+eSTgZycnEBqamrgz/7szwL/+I//GPB6vbEuzWjvv/9+l8fkefPmBQKBP72VesWKFQGbzRawWCyBBx98MHDmzJmo1JYQCPAxhQAAwCxcAwMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcf4faAaPQAaK344AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df.pain_score.hist(bins=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "423213fa-d4c5-44bd-97b5-bd474c6b2aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df[data_df.sex==\"Male\"].pain_score.hist(bins=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1de57f5e-3283-4003-a183-b293c373cbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3iYcLAACiQYf2DF67dm3A8rJly5Samqra2lqNGDHCvz45OVk2m63VOaqqqrR//36tX79eaWlpGjRokMrKyjRz5kyVlpYqISFBS5cuVXZ2thYsWCBJ6tOnj7Zs2aKFCxeqoKCgvccIAACiTLsCzD86fvy4JKlLly4B65cvX6633npLNptNY8eO1Zw5c5ScnCxJqqmpUf/+/ZWWluYfX1BQoKlTp2rfvn0aPHiwampqlJeXFzBnQUGBiouLz1mL2+2W2+32L7tcLkmSx+ORx+O5lMMM0DxXMOdE6+h1eISjz5Y4X8jmDpZQ/50Fo88m9DHSLLG+gH9bwznl0oXyvNHWOS86wHi9XhUXF+vGG29Uv379/OvvueceZWVlKSMjQ7t379bMmTN14MABvfPOO5Ikp9MZEF4k+ZedTud5x7hcLn3//fdKSkpqUU95ebnmzZvXYn1VVZU/PAWTw+EI+pxoHb0Oj1D2uWJoyKYOmg8++CAs+7mUPpvQx8tFWa73nNvC9bu+EoTivHHq1Kk2jbvoAFNUVKS9e/dqy5YtAesfeugh/3/3799f6enpGjVqlA4ePKgePXpc7O4uaNasWSopKfEvu1wuZWZmKj8/X1arNWj78Xg8cjgcGj16tOLj44M2L1qi1+ERjj73K73871/bWxral6eD0WcT+hhpllifynK9mrMzVm5vTKtjQv27vhKE8rzR/ArKhVxUgJk2bZpWr16tzZs3q1u3bucdO2zYMEnSl19+qR49eshms2n79u0BY+rr6yXJf9+MzWbzr/vhGKvV2urVF0myWCyyWCwt1sfHx4fkpByqedESvQ6PUPbZ3dT6E8nlJFx/Y5fSZxP6eLlwe2PO2S/OJ8ETivNGW+dr17uQfD6fpk2bplWrVmnDhg3Kzs6+4GPq6uokSenp6ZIku92uPXv2qKGhwT/G4XDIarUqJyfHP6a6ujpgHofDIbvd3p5yAQBAlGpXgCkqKtJbb72lyspKderUSU6nU06nU99//70k6eDBgyorK1Ntba2++uorvffee5o4caJGjBihAQMGSJLy8/OVk5OjCRMm6E9/+pPWrVun2bNnq6ioyH8FZcqUKfrLX/6iRx99VJ9//rlefvllrVy5UtOnTw/y4QMAABO1K8AsWbJEx48f18iRI5Wenu7/WbFihSQpISFB69evV35+vnr37q1f/OIXGjdunN5//33/HHFxcVq9erXi4uJkt9t17733auLEiZo/f75/THZ2ttasWSOHw6GBAwdqwYIFevXVV3kLNQAAkNTOe2B8vvO/hS8zM1ObNm264DxZWVkXvAt85MiR2rVrV3vKAwAAVwi+CwkAABiHAAMAAIxDgAEAAMa5pK8SAIBoce1ja0I6vyXOp4qhf/8wOj7PBbh0XIEBAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHz4EBDHepn1/C55MAMBFXYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOO0K8CUl5frhhtuUKdOnZSamqo777xTBw4cCBhz+vRpFRUVqWvXrrrqqqs0btw41dfXB4w5fPiwCgsLlZycrNTUVM2YMUNnz54NGLNx40Zdf/31slgs6tmzp5YtW3ZxRwgAAKJOuwLMpk2bVFRUpK1bt8rhcMjj8Sg/P1+NjY3+MdOnT9f777+vt99+W5s2bdI333yju+66y7+9qalJhYWFOnPmjD755BO9+eabWrZsmebOnesfc+jQIRUWFuqWW25RXV2diouL9cADD2jdunVBOGQAAGC6Du0ZvHbt2oDlZcuWKTU1VbW1tRoxYoSOHz+u1157TZWVlbr11lslSW+88Yb69OmjrVu3avjw4aqqqtL+/fu1fv16paWladCgQSorK9PMmTNVWlqqhIQELV26VNnZ2VqwYIEkqU+fPtqyZYsWLlyogoKCIB06AAAwVbsCzD86fvy4JKlLly6SpNraWnk8HuXl5fnH9O7dW927d1dNTY2GDx+umpoa9e/fX2lpaf4xBQUFmjp1qvbt26fBgwerpqYmYI7mMcXFxeesxe12y+12+5ddLpckyePxyOPxXMphBmieK5hzonX0um0scb5Le3ysL+BfhAZ9Do+29JlzyqUL5fm5rXNedIDxer0qLi7WjTfeqH79+kmSnE6nEhIS1Llz54CxaWlpcjqd/jE/DC/N25u3nW+My+XS999/r6SkpBb1lJeXa968eS3WV1VVKTk5+eIO8jwcDkfQ50Tr6PX5VQwNzjxlud7gTITzos/hcb4+f/DBB2GsJLqF4vx86tSpNo276ABTVFSkvXv3asuWLRc7RVDNmjVLJSUl/mWXy6XMzEzl5+fLarUGbT8ej0cOh0OjR49WfHx80OZFS/S6bfqVXtq9YZZYn8pyvZqzM1Zub0yQqsI/os/h0ZY+7y3lVoRLFcrzc/MrKBdyUQFm2rRpWr16tTZv3qxu3br519tsNp05c0bHjh0LuApTX18vm83mH7N9+/aA+ZrfpfTDMf/4zqX6+npZrdZWr75IksVikcViabE+Pj4+JE9+oZoXLdHr83M3BefJ0O2NCdpcODf6HB7n6zPnk+AJxfm5rfO1611IPp9P06ZN06pVq7RhwwZlZ2cHbB8yZIji4+NVXV3tX3fgwAEdPnxYdrtdkmS327Vnzx41NDT4xzgcDlmtVuXk5PjH/HCO5jHNcwAAgCtbu67AFBUVqbKyUn/4wx/UqVMn/z0rKSkpSkpKUkpKiiZPnqySkhJ16dJFVqtVDz/8sOx2u4YPHy5Jys/PV05OjiZMmKCKigo5nU7Nnj1bRUVF/isoU6ZM0UsvvaRHH31U999/vzZs2KCVK1dqzZo1QT58AABgonZdgVmyZImOHz+ukSNHKj093f+zYsUK/5iFCxfqn//5nzVu3DiNGDFCNptN77zzjn97XFycVq9erbi4ONntdt17772aOHGi5s+f7x+TnZ2tNWvWyOFwaODAgVqwYIFeffVV3kINAAAktfMKjM934bf/JSYmavHixVq8ePE5x2RlZV3wLvCRI0dq165d7SkPAICocO1jl/crDpY4X9DeAXmx+C4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACM0yHSBQCXs2sfWxPpEgAAreAKDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOO0OMJs3b9bYsWOVkZGhmJgYvfvuuwHb/+M//kMxMTEBP7fddlvAmKNHj2r8+PGyWq3q3LmzJk+erJMnTwaM2b17t26++WYlJiYqMzNTFRUV7T86AAAQldodYBobGzVw4EAtXrz4nGNuu+02HTlyxP/z29/+NmD7+PHjtW/fPjkcDq1evVqbN2/WQw895N/ucrmUn5+vrKws1dbW6tlnn1VpaaleeeWV9pYLAACiULs/iXfMmDEaM2bMecdYLBbZbLZWt3322Wdau3atduzYodzcXEnSiy++qNtvv12//vWvlZGRoeXLl+vMmTN6/fXXlZCQoL59+6qurk7PPfdcQNABAABXppB8lcDGjRuVmpqqq6++WrfeequeeOIJde3aVZJUU1Ojzp07+8OLJOXl5Sk2Nlbbtm3Tv/7rv6qmpkYjRoxQQkKCf0xBQYGeeeYZfffdd7r66qtb7NPtdsvtdvuXXS6XJMnj8cjj8QTt2JrnCuacaN3l0GtLnC9i+w4XS6wv4F+EBn0Oj7b0udevVoernItmiYt0BefX3N9QnJ/bOmfQA8xtt92mu+66S9nZ2Tp48KB++ctfasyYMaqpqVFcXJycTqdSU1MDi+jQQV26dJHT6ZQkOZ1OZWdnB4xJS0vzb2stwJSXl2vevHkt1ldVVSk5OTlYh+fncDiCPidaF8leVwyN2K7DrizXG+kSrgj0OTzoc3iE4vx86tSpNo0LeoC5++67/f/dv39/DRgwQD169NDGjRs1atSoYO/Ob9asWSopKfEvu1wuZWZmKj8/X1arNWj78Xg8cjgcGj16tOLj44M2L1q6HHrdr3RdRPYbTpZYn8pyvZqzM1Zub0yky4la9Dk86HN4NPc5FOfn5ldQLiTk30b94x//WNdcc42+/PJLjRo1SjabTQ0NDQFjzp49q6NHj/rvm7HZbKqvrw8Y07x8rntrLBaLLBZLi/Xx8fEhefIL1bxoKZK9djddOSdAtzfmijreSKHP4UGfwyMU5+e2zhfyz4H561//qm+//Vbp6emSJLvdrmPHjqm2ttY/ZsOGDfJ6vRo2bJh/zObNmwNeB3M4HOrVq1erLx8BAIArS7sDzMmTJ1VXV6e6ujpJ0qFDh1RXV6fDhw/r5MmTmjFjhrZu3aqvvvpK1dXVuuOOO9SzZ08VFBRIkvr06aPbbrtNDz74oLZv366PP/5Y06ZN0913362MjAxJ0j333KOEhARNnjxZ+/bt04oVK7Ro0aKAl4gAAMCVq90BZufOnRo8eLAGDx4sSSopKdHgwYM1d+5cxcXFaffu3fqXf/kX/eQnP9HkyZM1ZMgQffTRRwEv7yxfvly9e/fWqFGjdPvtt+umm24K+IyXlJQUVVVV6dChQxoyZIh+8YtfaO7cubyFGgAASLqIe2BGjhwpn+/cb09bt+7CNz126dJFlZWV5x0zYMAAffTRR+0tDwAAXAH4LiQAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGCcDpEuAFeuax9bc97tljifKoZK/UrXyd0UE6aqAAAm4AoMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOuwPM5s2bNXbsWGVkZCgmJkbvvvtuwHafz6e5c+cqPT1dSUlJysvL0xdffBEw5ujRoxo/frysVqs6d+6syZMn6+TJkwFjdu/erZtvvlmJiYnKzMxURUVF+48OAABEpXYHmMbGRg0cOFCLFy9udXtFRYVeeOEFLV26VNu2bVPHjh1VUFCg06dP+8eMHz9e+/btk8Ph0OrVq7V582Y99NBD/u0ul0v5+fnKyspSbW2tnn32WZWWluqVV165iEMEAADRpt3fhTRmzBiNGTOm1W0+n0/PP/+8Zs+erTvuuEOS9D//8z9KS0vTu+++q7vvvlufffaZ1q5dqx07dig3N1eS9OKLL+r222/Xr3/9a2VkZGj58uU6c+aMXn/9dSUkJKhv376qq6vTc889FxB0AADAlSmo98AcOnRITqdTeXl5/nUpKSkaNmyYampqJEk1NTXq3LmzP7xIUl5enmJjY7Vt2zb/mBEjRighIcE/pqCgQAcOHNB3330XzJIBAICBgvpt1E6nU5KUlpYWsD4tLc2/zel0KjU1NbCIDh3UpUuXgDHZ2dkt5mjedvXVV7fYt9vtltvt9i+7XC5JksfjkcfjuZTDCtA8VzDnvFJZ4nzn3x7rC/gXoUGfw4M+hwd9Do/m/obiubCtcwY1wERSeXm55s2b12J9VVWVkpOTg74/h8MR9DmvNBVD2zauLNcb2kIgiT6HC30OD/ocHqF4Ljx16lSbxgU1wNhsNklSfX290tPT/evr6+s1aNAg/5iGhoaAx509e1ZHjx71P95ms6m+vj5gTPNy85h/NGvWLJWUlPiXXS6XMjMzlZ+fL6vVemkH9gMej0cOh0OjR49WfHx80Oa9EvUrXXfe7ZZYn8pyvZqzM1Zub0yYqrry0OfwoM/hQZ/Do7nPoXgubH4F5UKCGmCys7Nls9lUXV3tDywul0vbtm3T1KlTJUl2u13Hjh1TbW2thgwZIknasGGDvF6vhg0b5h/zq1/9Sh6Px98Yh8OhXr16tfrykSRZLBZZLJYW6+Pj40MSNEI175XE3dS2k4vbG9Pmsbh49Dk86HN40OfwCMVzYVvna/dNvCdPnlRdXZ3q6uok/f3G3bq6Oh0+fFgxMTEqLi7WE088offee0979uzRxIkTlZGRoTvvvFOS1KdPH91222168MEHtX37dn388ceaNm2a7r77bmVkZEiS7rnnHiUkJGjy5Mnat2+fVqxYoUWLFgVcYQEAAFeudl+B2blzp2655Rb/cnOomDRpkpYtW6ZHH31UjY2Neuihh3Ts2DHddNNNWrt2rRITE/2PWb58uaZNm6ZRo0YpNjZW48aN0wsvvODfnpKSoqqqKhUVFWnIkCG65pprNHfuXN5CDQAAJF1EgBk5cqR8vnPf3R0TE6P58+dr/vz55xzTpUsXVVZWnnc/AwYM0EcffdTe8gAAwBWA70ICAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxukQ6QJM1a90ndxNMZEu45y+erow0iUAABAyXIEBAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4wQ9wJSWliomJibgp3fv3v7tp0+fVlFRkbp27aqrrrpK48aNU319fcAchw8fVmFhoZKTk5WamqoZM2bo7NmzwS4VAAAYqkMoJu3bt6/Wr1///3fS4f/vZvr06VqzZo3efvttpaSkaNq0abrrrrv08ccfS5KamppUWFgom82mTz75REeOHNHEiRMVHx+vp556KhTlRqVrH1sT6RIAAAiZkASYDh06yGaztVh//Phxvfbaa6qsrNStt94qSXrjjTfUp08fbd26VcOHD1dVVZX279+v9evXKy0tTYMGDVJZWZlmzpyp0tJSJSQkhKJkAABgkJAEmC+++EIZGRlKTEyU3W5XeXm5unfvrtraWnk8HuXl5fnH9u7dW927d1dNTY2GDx+umpoa9e/fX2lpaf4xBQUFmjp1qvbt26fBgwe3uk+32y232+1fdrlckiSPxyOPxxO0Y2ueyxLrC9qcaF1zj+l1aNHn8KDP4UGfw6O5v8F8fm3W1jmDHmCGDRumZcuWqVevXjpy5IjmzZunm2++WXv37pXT6VRCQoI6d+4c8Ji0tDQ5nU5JktPpDAgvzdubt51LeXm55s2b12J9VVWVkpOTL/GoWirL9QZ9TrSOXocHfQ4P+hwe9Dk8HA5H0Oc8depUm8YFPcCMGTPG/98DBgzQsGHDlJWVpZUrVyopKSnYu/ObNWuWSkpK/Msul0uZmZnKz8+X1WoN2n48Ho8cDofm7IyV2xsTtHnRkiXWp7JcL70OMfocHvQ5POhzeDT3efTo0YqPjw/q3M2voFxISF5C+qHOnTvrJz/5ib788kuNHj1aZ86c0bFjxwKuwtTX1/vvmbHZbNq+fXvAHM3vUmrtvppmFotFFoulxfr4+PigN1eS3N4YuZv4nyMc6HV40OfwoM/hQZ/DIxTPsW2dL+SfA3Py5EkdPHhQ6enpGjJkiOLj41VdXe3ffuDAAR0+fFh2u12SZLfbtWfPHjU0NPjHOBwOWa1W5eTkhLpcAABggKBfgfmv//ovjR07VllZWfrmm2/0+OOPKy4uTj//+c+VkpKiyZMnq6SkRF26dJHVatXDDz8su92u4cOHS5Ly8/OVk5OjCRMmqKKiQk6nU7Nnz1ZRUVGrV1gAAMCVJ+gB5q9//at+/vOf69tvv9WPfvQj3XTTTdq6dat+9KMfSZIWLlyo2NhYjRs3Tm63WwUFBXr55Zf9j4+Li9Pq1as1depU2e12dezYUZMmTdL8+fODXSoAADBU0APM7373u/NuT0xM1OLFi7V48eJzjsnKytIHH3wQ7NIAAECU4LuQAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADDOZR1gFi9erGuvvVaJiYkaNmyYtm/fHumSAADAZeCyDTArVqxQSUmJHn/8cX366acaOHCgCgoK1NDQEOnSAABAhF22Aea5557Tgw8+qPvuu085OTlaunSpkpOT9frrr0e6NAAAEGEdIl1Aa86cOaPa2lrNmjXLvy42NlZ5eXmqqalp9TFut1tut9u/fPz4cUnS0aNH5fF4glabx+PRqVOn1METqyZvTNDmRUsdvD6dOuWl1yFGn8ODPocHfQ6P5j5/++23io+PD+rcJ06ckCT5fL7z1xDUvQbJ3/72NzU1NSktLS1gfVpamj7//PNWH1NeXq558+a1WJ+dnR2SGhEe90S6gCsEfQ4P+hwe9Dk8Qt3nEydOKCUl5ZzbL8sAczFmzZqlkpIS/7LX69XRo0fVtWtXxcQEL4W7XC5lZmbq66+/ltVqDdq8aIlehwd9Dg/6HB70OTxC2Wefz6cTJ04oIyPjvOMuywBzzTXXKC4uTvX19QHr6+vrZbPZWn2MxWKRxWIJWNe5c+dQlSir1cr/HGFCr8ODPocHfQ4P+hweoerz+a68NLssb+JNSEjQkCFDVF1d7V/n9XpVXV0tu90ewcoAAMDl4LK8AiNJJSUlmjRpknJzczV06FA9//zzamxs1H333Rfp0gAAQIRdtgHmZz/7mf7v//5Pc+fOldPp1KBBg7R27doWN/aGm8Vi0eOPP97i5SoEH70OD/ocHvQ5POhzeFwOfY7xXeh9SgAAAJeZy/IeGAAAgPMhwAAAAOMQYAAAgHEIMAAAwDgEmHZavHixrr32WiUmJmrYsGHavn17pEuKKuXl5brhhhvUqVMnpaam6s4779SBAwciXVbUe/rppxUTE6Pi4uJIlxKV/vd//1f33nuvunbtqqSkJPXv3187d+6MdFlRpampSXPmzFF2draSkpLUo0cPlZWVXfD7dHB+mzdv1tixY5WRkaGYmBi9++67Adt9Pp/mzp2r9PR0JSUlKS8vT1988UVYaiPAtMOKFStUUlKixx9/XJ9++qkGDhyogoICNTQ0RLq0qLFp0yYVFRVp69atcjgc8ng8ys/PV2NjY6RLi1o7duzQb37zGw0YMCDSpUSl7777TjfeeKPi4+P14Ycfav/+/VqwYIGuvvrqSJcWVZ555hktWbJEL730kj777DM988wzqqio0Isvvhjp0ozW2NiogQMHavHixa1ur6io0AsvvKClS5dq27Zt6tixowoKCnT69OnQF+dDmw0dOtRXVFTkX25qavJlZGT4ysvLI1hVdGtoaPBJ8m3atCnSpUSlEydO+K677jqfw+Hw/dM//ZPvkUceiXRJUWfmzJm+m266KdJlRL3CwkLf/fffH7Durrvu8o0fPz5CFUUfSb5Vq1b5l71er89ms/meffZZ/7pjx475LBaL77e//W3I6+EKTBudOXNGtbW1ysvL86+LjY1VXl6eampqIlhZdDt+/LgkqUuXLhGuJDoVFRWpsLAw4O8awfXee+8pNzdX//Zv/6bU1FQNHjxY//3f/x3psqLOT3/6U1VXV+vPf/6zJOlPf/qTtmzZojFjxkS4suh16NAhOZ3OgPNHSkqKhg0bFpbnxcv2k3gvN3/729/U1NTU4pOA09LS9Pnnn0eoqujm9XpVXFysG2+8Uf369Yt0OVHnd7/7nT799FPt2LEj0qVEtb/85S9asmSJSkpK9Mtf/lI7duzQf/7nfyohIUGTJk2KdHlR47HHHpPL5VLv3r0VFxenpqYmPfnkkxo/fnykS4taTqdTklp9XmzeFkoEGFy2ioqKtHfvXm3ZsiXSpUSdr7/+Wo888ogcDocSExMjXU5U83q9ys3N1VNPPSVJGjx4sPbu3aulS5cSYIJo5cqVWr58uSorK9W3b1/V1dWpuLhYGRkZ9DlK8RJSG11zzTWKi4tTfX19wPr6+nrZbLYIVRW9pk2bptWrV+uPf/yjunXrFulyok5tba0aGhp0/fXXq0OHDurQoYM2bdqkF154QR06dFBTU1OkS4wa6enpysnJCVjXp08fHT58OEIVRacZM2boscce0913363+/ftrwoQJmj59usrLyyNdWtRqfu6L1PMiAaaNEhISNGTIEFVXV/vXeb1eVVdXy263R7Cy6OLz+TRt2jStWrVKGzZsUHZ2dqRLikqjRo3Snj17VFdX5//Jzc3V+PHjVVdXp7i4uEiXGDVuvPHGFh8F8Oc//1lZWVkRqig6nTp1SrGxgU9pcXFx8nq9Eaoo+mVnZ8tmswU8L7pcLm3bti0sz4u8hNQOJSUlmjRpknJzczV06FA9//zzamxs1H333Rfp0qJGUVGRKisr9Yc//EGdOnXyv46akpKipKSkCFcXPTp16tTivqKOHTuqa9eu3G8UZNOnT9dPf/pTPfXUU/r3f/93bd++Xa+88opeeeWVSJcWVcaOHasnn3xS3bt3V9++fbVr1y4999xzuv/++yNdmtFOnjypL7/80r986NAh1dXVqUuXLurevbuKi4v1xBNP6LrrrlN2drbmzJmjjIwM3XnnnaEvLuTvc4oyL774oq979+6+hIQE39ChQ31bt26NdElRRVKrP2+88UakS4t6vI06dN5//31fv379fBaLxde7d2/fK6+8EumSoo7L5fI98sgjvu7du/sSExN9P/7xj32/+tWvfG63O9KlGe2Pf/xjq+fkSZMm+Xy+v7+Ves6cOb60tDSfxWLxjRo1ynfgwIGw1Bbj8/ExhQAAwCzcAwMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcf4fhupkXtt+0gYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df[data_df.sex==\"Female\"].pain_score.hist(bins=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a9c784d4-1379-4a35-8434-320fe6d8ea75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df[data_df.race==\"Caucasian or White\"].pain_score.hist(bins=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1a9efddd-f3d7-4a3f-b750-824dd7e56375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df[data_df.race==\"African American or Black\"].pain_score.hist(bins=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf225ead-4417-4d42-8af2-73ad23d30146",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}