analysis_guide.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2b9176e-e759-4893-93b7-231e525d0986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in links: /usr/share/pip-wheels\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages (1.3.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages (from scikit-learn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages (from scikit-learn) (1.11.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages (from scikit-learn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "#load libraries\n",
    "!pip install scikit-learn\n",
    "import pandas as pd\n",
    "import numpy as py\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "894fc2fd-e1ab-43b2-ad12-18f655b6945d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  gun_deaths.csv.zip\n",
      "replace gun_deaths.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
     ]
    }
   ],
   "source": [
    "#data processing\n",
    "!unzip gun_deaths.csv.zip\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b4536a3-e7f7-4ec4-b111-8c240e5fffd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('gun_deaths.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e475f156-0573-4e71-995d-3aa47021610e",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>intent</th>\n",
       "      <th>police</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>place</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "      <td>Home</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Street</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>64.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>Less than HS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>48.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Accidental</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black</td>\n",
       "      <td>Home</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>43.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>Less than HS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Homicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month        intent  police sex   age  \\\n",
       "0   2012      1       Suicide       0   M  34.0   \n",
       "1   2012      1       Suicide       0   F  21.0   \n",
       "2   2012      1       Suicide       0   M  60.0   \n",
       "3   2012      2       Suicide       0   M  64.0   \n",
       "4   2012      2       Suicide       0   M  31.0   \n",
       "5   2012      2       Suicide       0   M  17.0   \n",
       "6   2012      2  Undetermined       0   M  48.0   \n",
       "7   2012      3       Suicide       0   M  41.0   \n",
       "8   2012      2    Accidental       0   M  50.0   \n",
       "9   2012      2       Suicide       0   M   NaN   \n",
       "10  2012      2       Suicide       0   M  30.0   \n",
       "11  2012      1       Suicide       0   M  21.0   \n",
       "12  2012      3       Suicide       0   M  43.0   \n",
       "13  2012      3       Suicide       0   M  34.0   \n",
       "14  2012      3      Homicide       0   M  27.0   \n",
       "\n",
       "                              race            place     education  \n",
       "0           Asian/Pacific Islander             Home           BA+  \n",
       "1                            White           Street  Some college  \n",
       "2                            White  Other specified           BA+  \n",
       "3                            White             Home           BA+  \n",
       "4                            White  Other specified        HS/GED  \n",
       "5   Native American/Native Alaskan             Home  Less than HS  \n",
       "6                            White             Home        HS/GED  \n",
       "7   Native American/Native Alaskan             Home        HS/GED  \n",
       "8                            White  Other specified  Some college  \n",
       "9                            Black             Home           NaN  \n",
       "10                           White             Home  Some college  \n",
       "11  Native American/Native Alaskan             Home        HS/GED  \n",
       "12                           White             Home  Less than HS  \n",
       "13  Native American/Native Alaskan             Home        HS/GED  \n",
       "14                           White             Home        HS/GED  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84e077c4-f812-41b2-9176-dbd0d3f09cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100798 entries, 0 to 100797\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   year       100798 non-null  int64  \n",
      " 1   month      100798 non-null  int64  \n",
      " 2   intent     100797 non-null  object \n",
      " 3   police     100798 non-null  int64  \n",
      " 4   sex        100798 non-null  object \n",
      " 5   age        100780 non-null  float64\n",
      " 6   race       100798 non-null  object \n",
      " 7   place      99414 non-null   object \n",
      " 8   education  99376 non-null   object \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f72bdedf-fc04-44c2-8a16-e1700a1fda37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = df['age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f1004ea5-38df-4fce-8b92-ac979c3125a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100798 entries, 0 to 100797\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   year       100798 non-null  int64 \n",
      " 1   month      100798 non-null  int64 \n",
      " 2   intent     100798 non-null  object\n",
      " 3   police     100798 non-null  int64 \n",
      " 4   sex        100798 non-null  object\n",
      " 5   age        100798 non-null  int64 \n",
      " 6   race       100798 non-null  object\n",
      " 7   place      100798 non-null  object\n",
      " 8   education  100798 non-null  object\n",
      "dtypes: int64(4), object(5)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "103cdc34-fd33-419f-9064-a47cbbf8594b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(py.nan,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "264f4d39-ec06-4980-87c6-8d9133bb7f75",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>intent</th>\n",
       "      <th>police</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>place</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "      <td>Home</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Street</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>64.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>BA+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>Less than HS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>48.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Accidental</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Other specified</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Home</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>Some college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>43.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>Less than HS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Native American/Native Alaskan</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Homicide</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Home</td>\n",
       "      <td>HS/GED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month        intent  police sex   age  \\\n",
       "0   2012      1       Suicide       0   M  34.0   \n",
       "1   2012      1       Suicide       0   F  21.0   \n",
       "2   2012      1       Suicide       0   M  60.0   \n",
       "3   2012      2       Suicide       0   M  64.0   \n",
       "4   2012      2       Suicide       0   M  31.0   \n",
       "5   2012      2       Suicide       0   M  17.0   \n",
       "6   2012      2  Undetermined       0   M  48.0   \n",
       "7   2012      3       Suicide       0   M  41.0   \n",
       "8   2012      2    Accidental       0   M  50.0   \n",
       "9   2012      2       Suicide       0   M   0.0   \n",
       "10  2012      2       Suicide       0   M  30.0   \n",
       "11  2012      1       Suicide       0   M  21.0   \n",
       "12  2012      3       Suicide       0   M  43.0   \n",
       "13  2012      3       Suicide       0   M  34.0   \n",
       "14  2012      3      Homicide       0   M  27.0   \n",
       "\n",
       "                              race            place     education  \n",
       "0           Asian/Pacific Islander             Home           BA+  \n",
       "1                            White           Street  Some college  \n",
       "2                            White  Other specified           BA+  \n",
       "3                            White             Home           BA+  \n",
       "4                            White  Other specified        HS/GED  \n",
       "5   Native American/Native Alaskan             Home  Less than HS  \n",
       "6                            White             Home        HS/GED  \n",
       "7   Native American/Native Alaskan             Home        HS/GED  \n",
       "8                            White  Other specified  Some college  \n",
       "9                            Black             Home             0  \n",
       "10                           White             Home  Some college  \n",
       "11  Native American/Native Alaskan             Home        HS/GED  \n",
       "12                           White             Home  Less than HS  \n",
       "13  Native American/Native Alaskan             Home        HS/GED  \n",
       "14                           White             Home        HS/GED  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79a71521-2874-40e4-aea3-0dd5a96ee364",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_police=df.iloc[:,[2,3,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "03024b5c-f595-4ccf-8655-7354339b0f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100798 entries, 0 to 100797\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   intent  100798 non-null  object\n",
      " 1   police  100798 non-null  int64 \n",
      " 2   race    100798 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_police.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da2d35aa-5826-4c62-9c99-13239b5112b1",
   "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>intent</th>\n",
       "      <th>police</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Suicide</td>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    intent  police                    race\n",
       "0  Suicide       0  Asian/Pacific Islander\n",
       "1  Suicide       0                   White\n",
       "2  Suicide       0                   White\n",
       "3  Suicide       0                   White\n",
       "4  Suicide       0                   White"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_police.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bdb880d2-62ef-477e-9c0b-7a6efe485a4d",
   "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>police</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.013909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.117114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              police\n",
       "count  100798.000000\n",
       "mean        0.013909\n",
       "std         0.117114\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max         1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_police.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d30ef5e4-212a-4151-bde2-9f551703ea6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8eb89d7c-eebf-4699-9adf-13d3aeac8bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/numpy/lib/histograms.py:883: RuntimeWarning: invalid value encountered in divide\n",
      "  return n/db/n.sum(), bin_edges\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "         nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "         nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan],\n",
       "        [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "         nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]]),\n",
       " array([0.  , 0.04, 0.08, 0.12, 0.16, 0.2 , 0.24, 0.28, 0.32, 0.36, 0.4 ,\n",
       "        0.44, 0.48, 0.52, 0.56, 0.6 , 0.64, 0.68, 0.72, 0.76, 0.8 , 0.84,\n",
       "        0.88, 0.92, 0.96, 1.  ]),\n",
       " <a list of 3 BarContainer objects>)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGdCAYAAAAfTAk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAf30lEQVR4nO3dfWzV5f3/8deRllPR9ohUWqoFijPcBE2khNIuFbdgKd7BZJEb7ZxxjM4oAjEC4gLBhAIzjJlyM2vdNHHAFHD8wQh1CGH2AEIAO6gkarmZ9IhFOKcTV+6u7x/8OD+PpxRw/bQ9b56P5PzR61yf0+v6BO2TTz/n4HPOOQEAABhyXXsvAAAAoLUROAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADAnqb0X0B7Onz+vo0ePKjU1VT6fr72XAwAAroBzTo2NjcrKytJ117V8jeaaDJyjR48qOzu7vZcBAAB+gCNHjui2225rcc41GTipqamSLpygtLS0dl4NAAC4EpFIRNnZ2dGf4y25JgPn4q+l0tLSCBwAABLMldxewk3GAADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABz2iRwli5dqpycHKWkpCg3N1dbt25tcf6WLVuUm5urlJQU9enTR8uXL7/k3JUrV8rn82n06NGtvGoAAJCoPA+cVatWacqUKZo1a5Z2796twsJCjRw5UocPH252fl1dne6//34VFhZq9+7devHFFzV58mStXr06bu6hQ4f0/PPPq7Cw0OttAACABOJzzjkvv0FeXp4GDRqkZcuWRcf69++v0aNHq6ysLG7+9OnTtW7dOtXW1kbHSktLtXfvXgWDwejYuXPnNGzYMD355JPaunWrTp48qffee++K1hSJRBQIBBQOh5WWlvbDNwcAANrM1fz89vQKzunTp7Vr1y4VFRXFjBcVFam6urrZY4LBYNz8ESNGaOfOnTpz5kx0bO7cubrlllv01FNPXXYdTU1NikQiMQ8AAGCXp4HT0NCgc+fOKSMjI2Y8IyNDoVCo2WNCoVCz88+ePauGhgZJ0ocffqjKykpVVFRc0TrKysoUCASij+zs7B+wGwAAkCja5CZjn88X87VzLm7scvMvjjc2Nurxxx9XRUWF0tPTr+j7z5w5U+FwOPo4cuTIVe4AAAAkkiQvXzw9PV2dOnWKu1pz7NixuKs0F2VmZjY7PykpSd26ddO+fft08OBBPfTQQ9Hnz58/L0lKSkrSgQMHdPvtt8cc7/f75ff7W2NLAAAgAXh6Badz587Kzc1VVVVVzHhVVZUKCgqaPSY/Pz9u/saNGzV48GAlJyerX79+qqmp0Z49e6KPhx9+WD/5yU+0Z88efv0EAAC8vYIjSdOmTVNJSYkGDx6s/Px8vfbaazp8+LBKS0slXfj10RdffKG33npL0oV3TJWXl2vatGmaOHGigsGgKisrtWLFCklSSkqKBg4cGPM9brrpJkmKGwcAANcmzwNn7NixOn78uObOnav6+noNHDhQ69evV69evSRJ9fX1MZ+Jk5OTo/Xr12vq1KlasmSJsrKy9Oqrr2rMmDFeLxUAABjh+efgdER8Dg4AAImnw3wODgAAQHsgcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGBOmwTO0qVLlZOTo5SUFOXm5mrr1q0tzt+yZYtyc3OVkpKiPn36aPny5THPV1RUqLCwUF27dlXXrl01fPhw7dixw8stAACABOJ54KxatUpTpkzRrFmztHv3bhUWFmrkyJE6fPhws/Pr6up0//33q7CwULt379aLL76oyZMna/Xq1dE5mzdv1vjx4/XBBx8oGAyqZ8+eKioq0hdffOH1dgAAQALwOeecl98gLy9PgwYN0rJly6Jj/fv31+jRo1VWVhY3f/r06Vq3bp1qa2ujY6Wlpdq7d6+CwWCz3+PcuXPq2rWrysvL9Ytf/OKya4pEIgoEAgqHw0pLS/sBuwIAAG3tan5+e3oF5/Tp09q1a5eKiopixouKilRdXd3sMcFgMG7+iBEjtHPnTp05c6bZY06dOqUzZ87o5ptvbvb5pqYmRSKRmAcAALDL08BpaGjQuXPnlJGRETOekZGhUCjU7DGhUKjZ+WfPnlVDQ0Ozx8yYMUO33nqrhg8f3uzzZWVlCgQC0Ud2dvYP2A0AAEgUbXKTsc/ni/naORc3drn5zY1L0sKFC7VixQqtWbNGKSkpzb7ezJkzFQ6Ho48jR45c7RYAAEACSfLyxdPT09WpU6e4qzXHjh2Lu0pzUWZmZrPzk5KS1K1bt5jxV155RfPmzdP777+vu+6665Lr8Pv98vv9P3AXAAAg0Xh6Badz587Kzc1VVVVVzHhVVZUKCgqaPSY/Pz9u/saNGzV48GAlJydHx373u9/p5Zdf1oYNGzR48ODWXzwAAEhYnv+Katq0aXr99df1xhtvqLa2VlOnTtXhw4dVWloq6cKvj777zqfS0lIdOnRI06ZNU21trd544w1VVlbq+eefj85ZuHChXnrpJb3xxhvq3bu3QqGQQqGQ/vOf/3i9HQAAkAA8/RWVJI0dO1bHjx/X3LlzVV9fr4EDB2r9+vXq1auXJKm+vj7mM3FycnK0fv16TZ06VUuWLFFWVpZeffVVjRkzJjpn6dKlOn36tH7+85/HfK/Zs2drzpw5Xm8JAAB0cJ5/Dk5HxOfgAACQeDrM5+AAAAC0BwIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5rRJ4CxdulQ5OTlKSUlRbm6utm7d2uL8LVu2KDc3VykpKerTp4+WL18eN2f16tUaMGCA/H6/BgwYoLVr13q1fAAAkGA8D5xVq1ZpypQpmjVrlnbv3q3CwkKNHDlShw8fbnZ+XV2d7r//fhUWFmr37t168cUXNXnyZK1evTo6JxgMauzYsSopKdHevXtVUlKiRx99VNu3b/d6OwAAIAH4nHPOy2+Ql5enQYMGadmyZdGx/v37a/To0SorK4ubP336dK1bt061tbXRsdLSUu3du1fBYFCSNHbsWEUiEf3973+PzikuLlbXrl21YsWKy64pEokoEAgoHA4rLS3tf9keAABoI1fz89vTKzinT5/Wrl27VFRUFDNeVFSk6urqZo8JBoNx80eMGKGdO3fqzJkzLc651Gs2NTUpEonEPAAAgF2eBk5DQ4POnTunjIyMmPGMjAyFQqFmjwmFQs3OP3v2rBoaGlqcc6nXLCsrUyAQiD6ys7N/6JYAAEACaJObjH0+X8zXzrm4scvN//741bzmzJkzFQ6Ho48jR45c1foBAEBiSfLyxdPT09WpU6e4KyvHjh2LuwJzUWZmZrPzk5KS1K1btxbnXOo1/X6//H7/D90GAABIMJ5ewencubNyc3NVVVUVM15VVaWCgoJmj8nPz4+bv3HjRg0ePFjJycktzrnUawIAgGuLp1dwJGnatGkqKSnR4MGDlZ+fr9dee02HDx9WaWmppAu/Pvriiy/01ltvSbrwjqny8nJNmzZNEydOVDAYVGVlZcy7o5577jndc889WrBggUaNGqW//e1vev/99/XPf/7T6+0AAIAE4HngjB07VsePH9fcuXNVX1+vgQMHav369erVq5ckqb6+PuYzcXJycrR+/XpNnTpVS5YsUVZWll599VWNGTMmOqegoEArV67USy+9pN/+9re6/fbbtWrVKuXl5Xm9HQAAkAA8/xycjojPwQEAIPF0mM/BAQAAaA8EDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMwhcAAAgDkEDgAAMIfAAQAA5hA4AADAHAIHAACYQ+AAAABzCBwAAGAOgQMAAMzxNHBOnDihkpISBQIBBQIBlZSU6OTJky0e45zTnDlzlJWVpeuvv1733nuv9u3bF33+66+/1rPPPqu+ffuqS5cu6tmzpyZPnqxwOOzlVgAAQALxNHAmTJigPXv2aMOGDdqwYYP27NmjkpKSFo9ZuHChFi1apPLycn300UfKzMzUfffdp8bGRknS0aNHdfToUb3yyiuqqanRn//8Z23YsEFPPfWUl1sBAAAJxOecc168cG1trQYMGKBt27YpLy9PkrRt2zbl5+frk08+Ud++feOOcc4pKytLU6ZM0fTp0yVJTU1NysjI0IIFCzRp0qRmv9c777yjxx9/XN98842SkpIuu7ZIJKJAIKBwOKy0tLT/YZcAAKCtXM3Pb8+u4ASDQQUCgWjcSNLQoUMVCARUXV3d7DF1dXUKhUIqKiqKjvn9fg0bNuySx0iKbvRK4gYAANjnWRGEQiF17949brx79+4KhUKXPEaSMjIyYsYzMjJ06NChZo85fvy4Xn755Ute3ZEuXAVqamqKfh2JRC67fgAAkLiu+grOnDlz5PP5Wnzs3LlTkuTz+eKOd841O/5d33/+UsdEIhE98MADGjBggGbPnn3J1ysrK4ve6BwIBJSdnX0lWwUAAAnqqq/gPPPMMxo3blyLc3r37q2PP/5YX375ZdxzX331VdwVmosyMzMlXbiS06NHj+j4sWPH4o5pbGxUcXGxbrzxRq1du1bJycmXXM/MmTM1bdq06NeRSITIAQDAsKsOnPT0dKWnp192Xn5+vsLhsHbs2KEhQ4ZIkrZv365wOKyCgoJmj8nJyVFmZqaqqqp09913S5JOnz6tLVu2aMGCBdF5kUhEI0aMkN/v17p165SSktLiWvx+v/x+/5VuEQAAJDjPbjLu37+/iouLNXHiRG3btk3btm3TxIkT9eCDD8a8g6pfv35au3atpAu/mpoyZYrmzZuntWvX6l//+pd++ctfqkuXLpowYYKkC1duioqK9M0336iyslKRSEShUEihUEjnzp3zajsAACCBePq2o7fffluTJ0+Ovivq4YcfVnl5ecycAwcOxHxI3wsvvKBvv/1WTz/9tE6cOKG8vDxt3LhRqampkqRdu3Zp+/btkqQf/ehHMa9VV1en3r17e7gjAACQCDz7HJyOjM/BAQAg8XSIz8EBAABoLwQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOZ4GzokTJ1RSUqJAIKBAIKCSkhKdPHmyxWOcc5ozZ46ysrJ0/fXX695779W+ffsuOXfkyJHy+Xx67733Wn8DAAAgIXkaOBMmTNCePXu0YcMGbdiwQXv27FFJSUmLxyxcuFCLFi1SeXm5PvroI2VmZuq+++5TY2Nj3NzFixfL5/N5tXwAAJCgkrx64draWm3YsEHbtm1TXl6eJKmiokL5+fk6cOCA+vbtG3eMc06LFy/WrFmz9Mgjj0iS3nzzTWVkZOgvf/mLJk2aFJ27d+9eLVq0SB999JF69Ojh1TYAAEAC8uwKTjAYVCAQiMaNJA0dOlSBQEDV1dXNHlNXV6dQKKSioqLomN/v17Bhw2KOOXXqlMaPH6/y8nJlZmZedi1NTU2KRCIxDwAAYJdngRMKhdS9e/e48e7duysUCl3yGEnKyMiIGc/IyIg5ZurUqSooKNCoUaOuaC1lZWXR+4ACgYCys7OvdBsAACABXXXgzJkzRz6fr8XHzp07JanZ+2Occ5e9b+b7z3/3mHXr1mnTpk1avHjxFa955syZCofD0ceRI0eu+FgAAJB4rvoenGeeeUbjxo1rcU7v3r318ccf68svv4x77quvvoq7QnPRxV83hUKhmPtqjh07Fj1m06ZN+uyzz3TTTTfFHDtmzBgVFhZq8+bNca/r9/vl9/tbXDMAALDjqgMnPT1d6enpl52Xn5+vcDisHTt2aMiQIZKk7du3KxwOq6CgoNljcnJylJmZqaqqKt19992SpNOnT2vLli1asGCBJGnGjBn61a9+FXPcnXfeqd///vd66KGHrnY7AADAIM/eRdW/f38VFxdr4sSJ+uMf/yhJ+vWvf60HH3ww5h1U/fr1U1lZmX72s5/J5/NpypQpmjdvnu644w7dcccdmjdvnrp06aIJEyZIunCVp7kbi3v27KmcnByvtgMAABKIZ4EjSW+//bYmT54cfVfUww8/rPLy8pg5Bw4cUDgcjn79wgsv6Ntvv9XTTz+tEydOKC8vTxs3blRqaqqXSwUAAIb4nHOuvRfR1iKRiAKBgMLhsNLS0tp7OQAA4Apczc9v/i0qAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMIXAAAIA5BA4AADCHwAEAAOYQOAAAwBwCBwAAmEPgAAAAcwgcAABgDoEDAADMSWrvBbQH55wkKRKJtPNKAADAlbr4c/viz/GWXJOB09jYKEnKzs5u55UAAICr1djYqEAg0OIcn7uSDDLm/PnzOnr0qFJTU+Xz+dp7Oe0uEokoOztbR44cUVpaWnsvxyzOc9vgPLcdznXb4Dz/f845NTY2KisrS9dd1/JdNtfkFZzrrrtOt912W3svo8NJS0u75v/jaQuc57bBeW47nOu2wXm+4HJXbi7iJmMAAGAOgQMAAMwhcCC/36/Zs2fL7/e391JM4zy3Dc5z2+Fctw3O8w9zTd5kDAAAbOMKDgAAMIfAAQAA5hA4AADAHAIHAACYQ+BcA06cOKGSkhIFAgEFAgGVlJTo5MmTLR7jnNOcOXOUlZWl66+/Xvfee6/27dt3ybkjR46Uz+fTe++91/obSBBenOevv/5azz77rPr27asuXbqoZ8+emjx5ssLhsMe76ViWLl2qnJwcpaSkKDc3V1u3bm1x/pYtW5Sbm6uUlBT16dNHy5cvj5uzevVqDRgwQH6/XwMGDNDatWu9Wn7CaO3zXFFRocLCQnXt2lVdu3bV8OHDtWPHDi+3kBC8+PN80cqVK+Xz+TR69OhWXnUCcjCvuLjYDRw40FVXV7vq6mo3cOBA9+CDD7Z4zPz5811qaqpbvXq1q6mpcWPHjnU9evRwkUgkbu6iRYvcyJEjnSS3du1aj3bR8Xlxnmtqatwjjzzi1q1b5z799FP3j3/8w91xxx1uzJgxbbGlDmHlypUuOTnZVVRUuP3797vnnnvO3XDDDe7QoUPNzv/8889dly5d3HPPPef279/vKioqXHJysnv33Xejc6qrq12nTp3cvHnzXG1trZs3b55LSkpy27Zta6ttdThenOcJEya4JUuWuN27d7va2lr35JNPukAg4P7973+31bY6HC/O80UHDx50t956qyssLHSjRo3yeCcdH4Fj3P79+52kmP9xB4NBJ8l98sknzR5z/vx5l5mZ6ebPnx8d++9//+sCgYBbvnx5zNw9e/a42267zdXX11/TgeP1ef6uv/71r65z587uzJkzrbeBDmzIkCGutLQ0Zqxfv35uxowZzc5/4YUXXL9+/WLGJk2a5IYOHRr9+tFHH3XFxcUxc0aMGOHGjRvXSqtOPF6c5+87e/asS01NdW+++eb/vuAE5dV5Pnv2rPvxj3/sXn/9dffEE08QOM45fkVlXDAYVCAQUF5eXnRs6NChCgQCqq6ubvaYuro6hUIhFRUVRcf8fr+GDRsWc8ypU6c0fvx4lZeXKzMz07tNJAAvz/P3hcNhpaWlKSnJ/j8ld/r0ae3atSvmHElSUVHRJc9RMBiMmz9ixAjt3LlTZ86caXFOS+fdMq/O8/edOnVKZ86c0c0339w6C08wXp7nuXPn6pZbbtFTTz3V+gtPUASOcaFQSN27d48b7969u0Kh0CWPkaSMjIyY8YyMjJhjpk6dqoKCAo0aNaoVV5yYvDzP33X8+HG9/PLLmjRp0v+44sTQ0NCgc+fOXdU5CoVCzc4/e/asGhoaWpxzqde0zqvz/H0zZszQrbfequHDh7fOwhOMV+f5ww8/VGVlpSoqKrxZeIIicBLUnDlz5PP5Wnzs3LlTkuTz+eKOd841O/5d33/+u8esW7dOmzZt0uLFi1tnQx1Ue5/n74pEInrggQc0YMAAzZ49+3/YVeK50nPU0vzvj1/ta14LvDjPFy1cuFArVqzQmjVrlJKS0gqrTVyteZ4bGxv1+OOPq6KiQunp6a2/2ARm/xq3Uc8884zGjRvX4pzevXvr448/1pdffhn33FdffRX3t4KLLv66KRQKqUePHtHxY8eORY/ZtGmTPvvsM910000xx44ZM0aFhYXavHnzVeym42rv83xRY2OjiouLdeONN2rt2rVKTk6+2q0kpPT0dHXq1Cnub7fNnaOLMjMzm52flJSkbt26tTjnUq9pnVfn+aJXXnlF8+bN0/vvv6+77rqrdRefQLw4z/v27dPBgwf10EMPRZ8/f/68JCkpKUkHDhzQ7bff3so7SRDtdO8P2sjFm1+3b98eHdu2bdsV3fy6YMGC6FhTU1PMza/19fWupqYm5iHJ/eEPf3Cff/65t5vqgLw6z845Fw6H3dChQ92wYcPcN998490mOqghQ4a43/zmNzFj/fv3b/GmzP79+8eMlZaWxt1kPHLkyJg5xcXF1/xNxq19np1zbuHChS4tLc0Fg8HWXXCCau3z/O2338b9v3jUqFHupz/9qaupqXFNTU3ebCQBEDjXgOLiYnfXXXe5YDDogsGgu/POO+Pevty3b1+3Zs2a6Nfz5893gUDArVmzxtXU1Ljx48df8m3iF+kafheVc96c50gk4vLy8tydd97pPv30U1dfXx99nD17tk33114uvq22srLS7d+/302ZMsXdcMMN7uDBg84552bMmOFKSkqi8y++rXbq1Klu//79rrKyMu5ttR9++KHr1KmTmz9/vqutrXXz58/nbeIenOcFCxa4zp07u3fffTfmz25jY2Ob76+j8OI8fx/vorqAwLkGHD9+3D322GMuNTXVpaamuscee8ydOHEiZo4k96c//Sn69fnz593s2bNdZmam8/v97p577nE1NTUtfp9rPXC8OM8ffPCBk9Tso66urm021gEsWbLE9erVy3Xu3NkNGjTIbdmyJfrcE0884YYNGxYzf/Pmze7uu+92nTt3dr1793bLli2Le8133nnH9e3b1yUnJ7t+/fq51atXe72NDq+1z3OvXr2a/bM7e/bsNthNx+XFn+fvInAu8Dn3/+5WAgAAMIJ3UQEAAHMIHAAAYA6BAwAAzCFwAACAOQQOAAAwh8ABAADmEDgAAMAcAgcAAJhD4AAAAHMIHAAAYA6BAwAAzCFwAACAOf8Ht4uZEzvoVekAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_police[(df_police['intent'] == 'homicide') & (df['race'] == 1)]\n",
    "\n",
    "plt.hist(x, density=True, bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "366a3cd8-614a-4aa5-a28f-2a71c6cf30a3",
   "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>police</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66549</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68150</th>\n",
       "      <td>1</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28462</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36764</th>\n",
       "      <td>1</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45149</th>\n",
       "      <td>1</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33820</th>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819</th>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33818</th>\n",
       "      <td>0</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33817</th>\n",
       "      <td>0</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100797</th>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100798 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        police      race\n",
       "66549        1     White\n",
       "68150        1     Black\n",
       "28462        1     White\n",
       "36764        1  Hispanic\n",
       "45149        1     Black\n",
       "...        ...       ...\n",
       "33820        0     White\n",
       "33819        0     White\n",
       "33818        0     White\n",
       "33817        0  Hispanic\n",
       "100797       0     Black\n",
       "\n",
       "[100798 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_police.sort_values('police', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f8bd4ac-3106-41cf-8cf4-8de2c7fe9c73",
   "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>police</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>1</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100701</th>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100714</th>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100719</th>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100782</th>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100788</th>\n",
       "      <td>0</td>\n",
       "      <td>Asian/Pacific Islander</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100798 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        police                    race\n",
       "61           1                   White\n",
       "83           1                   White\n",
       "1004         1                   White\n",
       "1067         1                   White\n",
       "1111         1                   White\n",
       "...        ...                     ...\n",
       "100701       0  Asian/Pacific Islander\n",
       "100714       0  Asian/Pacific Islander\n",
       "100719       0  Asian/Pacific Islander\n",
       "100782       0  Asian/Pacific Islander\n",
       "100788       0  Asian/Pacific Islander\n",
       "\n",
       "[100798 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_police.sort_values( by= ['police','race'], ascending=False)[['police','race']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6994c679-544f-4051-910d-2e2bc96b7c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age'>"
      ]
     },
     "execution_count": 70,
     "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": [
    "df.plot(x='age', y='year')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f172454f-95ad-498b-b356-66f8704f53f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='race', ylabel='age'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "plt.xticks(rotation=90)\n",
    "sns.boxplot(x='race', y='age', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35e29414-6df8-4997-accd-366426875293",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5e72148b-0daf-44f0-9aa2-06bfba3a22e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': '0'}, xlabel='race'>,\n",
       "       <Axes: title={'center': 'Accidental'}, xlabel='race'>,\n",
       "       <Axes: title={'center': 'Homicide'}, xlabel='race'>,\n",
       "       <Axes: title={'center': 'Suicide'}, xlabel='race'>,\n",
       "       <Axes: title={'center': 'Undetermined'}, xlabel='race'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "policeD= df_police[[ 'race', 'intent']].dropna()\n",
    "policeD.groupby([ 'race', 'intent']).size().unstack(fill_value=0).plot.bar(subplots=True,figsize=(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2c97261a-54a3-4454-a834-099214a41e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f65c8933590>"
      ]
     },
     "execution_count": 82,
     "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": [
    "ax = df.groupby('race')['intent'].value_counts(normalize=True).unstack(level=1).plot.bar(stacked=True)\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1113af8-9028-40c0-aa93-5b83dcea271b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "34af9ebe-d1bf-4eac-9ef0-c33ac281181a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.00244868, 0.01224338, 0.02301755, 0.02987384, 0.02987384,\n",
       "        0.02546623, 0.0279149 , 0.02595596, 0.01518179, 0.02693543,\n",
       "        0.0181202 , 0.02105861, 0.02301755, 0.02252782, 0.01077417,\n",
       "        0.01224338, 0.00489735, 0.00489735, 0.00342815, 0.00293841,\n",
       "        0.00048974, 0.00097947, 0.00048974, 0.00146921, 0.00097947]),\n",
       " array([15.  , 17.88, 20.76, 23.64, 26.52, 29.4 , 32.28, 35.16, 38.04,\n",
       "        40.92, 43.8 , 46.68, 49.56, 52.44, 55.32, 58.2 , 61.08, 63.96,\n",
       "        66.84, 69.72, 72.6 , 75.48, 78.36, 81.24, 84.12, 87.  ]),\n",
       " <BarContainer object of 25 artists>)"
      ]
     },
     "execution_count": 92,
     "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": [
    "x = df[(df['race'] == 'White') & (df['police'] == 1)]\n",
    "x = x['age']\n",
    "plt.hist(x, density=True, bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "77e188ad-c070-4ac0-83a2-35b5661e6737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100798 entries, 0 to 100797\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   place   100798 non-null  object\n",
      " 1   race    100798 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ef4670f1-9ada-4bca-ada5-c7750030fc1c",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'intent'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'intent'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[104], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m df[(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintent\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhomicide\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m&\u001b[39m (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrace\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m)]\n\u001b[1;32m      2\u001b[0m x \u001b[38;5;241m=\u001b[39m x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(x, density\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'intent'"
     ]
    }
   ],
   "source": [
    "x = df[(df['intent'] == 'homicide') & (df['race'] == 1)]\n",
    "x = x['age']\n",
    "plt.hist(x, density=True, bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2a8d5b34-bc6b-4c41-a96b-fb3cb99611c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='place'>"
      ]
     },
     "execution_count": 96,
     "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": [
    "df = df[['place', 'race']].dropna()\n",
    "df.groupby(['place', 'race']).size().unstack(fill_value=0).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cecc9448-6e95-4e48-bb1f-44bc82a930fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f597d35d-248d-4303-818f-0f2d40f2692a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "03439f74-da9a-475d-b315-952199bcc999",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_list= df['race'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8154e5d3-21c5-4b74-b673-223d14697dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function _draw_all_if_interactive at 0x7f65e3de8fe0> (for post_execute):\n"
     ]
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/pyplot.py:120\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_draw_all_if_interactive\u001b[39m():\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[0;32m--> 120\u001b[0m         draw_all()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:132\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[0;32m--> 132\u001b[0m         manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2082\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m   2081\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 2082\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backends/backend_agg.py:400\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m    397\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m RendererAgg\u001b[38;5;241m.\u001b[39mlock, \\\n\u001b[1;32m    398\u001b[0m      (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m    399\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrenderer)\n\u001b[1;32m    401\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m    403\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m printer(obj)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m printer(obj)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m printer(obj)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m printer(obj)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ConversionError",
     "evalue": "Failed to convert value(s) to axis units: ['police']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1732\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1731\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n",
      "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mConversionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m printer(obj)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3176\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m mimage\u001b[38;5;241m.\u001b[39m_draw_list_compositing_images(\n\u001b[1;32m   3065\u001b[0m     renderer, \u001b[38;5;28mself\u001b[39m, artists, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39msuppressComposite)\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m draw(artist, renderer)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:588\u001b[0m, in \u001b[0;36mPatch.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    587\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_path()\n\u001b[0;32m--> 588\u001b[0m transform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    589\u001b[0m tpath \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mtransform_path_non_affine(path)\n\u001b[1;32m    590\u001b[0m affine \u001b[38;5;241m=\u001b[39m transform\u001b[38;5;241m.\u001b[39mget_affine()\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:264\u001b[0m, in \u001b[0;36mPatch.get_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    263\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_patch_transform() \u001b[38;5;241m+\u001b[39m artist\u001b[38;5;241m.\u001b[39mArtist\u001b[38;5;241m.\u001b[39mget_transform(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:747\u001b[0m, in \u001b[0;36mRectangle.get_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_patch_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    743\u001b[0m     \u001b[38;5;66;03m# Note: This cannot be called until after this has been added to\u001b[39;00m\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;66;03m# an Axes, otherwise unit conversion will fail. This makes it very\u001b[39;00m\n\u001b[1;32m    745\u001b[0m     \u001b[38;5;66;03m# important to call the accessor method and not directly access the\u001b[39;00m\n\u001b[1;32m    746\u001b[0m     \u001b[38;5;66;03m# transformation member variable.\u001b[39;00m\n\u001b[0;32m--> 747\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_bbox()\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrotation_point \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    749\u001b[0m         width, height \u001b[38;5;241m=\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39mx0, bbox\u001b[38;5;241m.\u001b[39my1 \u001b[38;5;241m-\u001b[39m bbox\u001b[38;5;241m.\u001b[39my0\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:878\u001b[0m, in \u001b[0;36mRectangle.get_bbox\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_bbox\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    877\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the `.Bbox`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     x0, y0, x1, y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_units()\n\u001b[1;32m    879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m transforms\u001b[38;5;241m.\u001b[39mBbox\u001b[38;5;241m.\u001b[39mfrom_extents(x0, y0, x1, y1)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/patches.py:737\u001b[0m, in \u001b[0;36mRectangle._convert_units\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Convert bounds of the rectangle.\"\"\"\u001b[39;00m\n\u001b[1;32m    736\u001b[0m x0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0)\n\u001b[0;32m--> 737\u001b[0m y0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0)\n\u001b[1;32m    738\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[1;32m    739\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y0 \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_height)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/artist.py:293\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mconvert_units(y)\n",
      "File \u001b[0;32m/opt/conda/envs/anaconda-panel-2023.05-py310/lib/python3.11/site-packages/matplotlib/axis.py:1734\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1732\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   1733\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1734\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1735\u001b[0m                                  \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['police']"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in id_list:\n",
    "    df.loc[(df['race'] ==i)].hist(y=['police'], figsize=(15,4))\n",
    "    plt.title(i, fontsize=18)\n",
    " \n",
    "    plt.ylabel('police killings', fontsize=12)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36bb1bc2-3eb4-4e37-8c24-8970ea05965d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c48ae43-7b2a-4fbe-8d25-36395450675f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ebab8c35-3536-4ffa-9174-cc4bd4249bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= py.array([3])\n",
    "y = py.array([6])\n",
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f438534a-9a23-4d3a-ac41-909dd22f87d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6197f554-81ee-4eb1-8270-18cd5d7429c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53230774-6691-4bd1-8889-a9f2d4cd4ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "598e0166-53c5-4279-a32e-397283cc61c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11d5c96-1f06-4047-9371-3e7647e24660",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd0b53d3-48eb-4b78-9a83-a5cdcae89537",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a953f7ce-4d19-4076-8755-3f079b2eaca4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c587bf5-3ae1-4a9c-a962-6a750027cf3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca2afe8-9cb4-4717-9977-f14802a57dcb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anaconda-panel-2023.05-py310",
   "language": "python",
   "name": "conda-env-anaconda-panel-2023.05-py310-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}