visualize_matches--DEPRECATED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize matches\n",
    "\n",
    "This should probably be split to its own file, but here for dev purposes\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import plotly.express as px\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pd.set_option('display.max_rows', None)**: Sets the maximum number of rows displayed when a pandas DataFrame or Series is printed to be unlimited. When this option is set to an integer (as in the commented line # **pd.set_option('display.max_rows', 10)**), only the specified number of rows would be displayed. This is useful for controlling the output length, especially when working with large DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Terms List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "term_list_file = 'terms_all.txt'\n",
    "\n",
    "with open(term_list_file, 'r') as f:\n",
    "    terms = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Finding Aid Data\n",
    "\n",
    "When running in a separate notebook, you will need to load the data.\n",
    "In this case, the \"matched terms\" results from the `match_terms.ipynb` notebook will suffice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "eads_df = pd.read_csv('matched_results_Bentley.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# match term\n",
    "\n",
    "def match_terms(row, terms, columns):\n",
    "    results = []\n",
    "    for term in terms:\n",
    "        for col in columns:\n",
    "            if not isinstance(row[col], float):\n",
    "                # split the column into paragraphs\n",
    "                # wonky try/except to work through integers, if not converted to strings\n",
    "                try:\n",
    "                    paragraphs = row[col].split('\\n')\n",
    "                except:\n",
    "                    paragraphs = str(row[col]).split('\\n')\n",
    "                # loop through each paragraph\n",
    "                for paragraph in paragraphs:\n",
    "                    # check if the term is in the current paragraph\n",
    "                    if re.search(r'\\b' + re.escape(term) + r'\\b', paragraph, re.IGNORECASE):\n",
    "                        results.append({\n",
    "                            'ead_id': row['ead_id'],\n",
    "#                            'source_filename': row['source_filename'],\n",
    "                            'resource_id': row['resource_id'],\n",
    "                            'titleproper': row.get('titleproper', None), \n",
    "                            'Term': term, \n",
    "                            'Matched_Times': len(re.findall(r'\\b' + re.escape(term) + r'\\b', paragraph, re.IGNORECASE)),\n",
    "                            'Matched_From': col, \n",
    "                            'Matched_Paragraph': paragraph\n",
    "                        })\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_and_visualize(df, name):\n",
    "    # Match results\n",
    "    results_df = pd.DataFrame([result for index, row in df.iterrows() for result in match_terms(row, terms, df.columns)])\n",
    "    \n",
    "    # Show matched results\n",
    "    print('Matched results for', name)\n",
    "    display(results_df) # control dislay\n",
    "\n",
    "    # Export to CSV\n",
    "    results_df.to_csv('matched_results_' + name + '.csv', index=True)\n",
    "    return results_df  # Return the DataFrame for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_term_frequency(df, df_name):\n",
    "    term_frequency = df.groupby('Term')['Matched_Times'].sum().reset_index()\n",
    "    term_frequency.rename(columns={'Matched_Times': 'Total_Frequency'}, inplace=True)\n",
    "    term_frequency['DataFrame'] = df_name\n",
    "\n",
    "    # Sort in descending order\n",
    "    term_frequency.sort_values(by='Total_Frequency', ascending=False, inplace=True)\n",
    "    \n",
    "    # Show frequency table\n",
    "    print('Term frequency for', df_name)\n",
    "    display(term_frequency)\n",
    "\n",
    "    return term_frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization values\n",
    "\n",
    "Use the below to set global values, such as the names of repositories\n",
    "under analysis, or fonts and colors for charts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set names and colors here\n",
    "repo_list = ['Bentley']\n",
    "#repo_list = ['Bentley','Clements','SCRC']\n",
    "global_font_info = {'font_family':'Georgia'}\n",
    "colors = {'Bentley': '#CFC096', 'Clements': '#A5A508', 'SCRC': '#FFCB05'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, tally up term matches!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched results for Bentley\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>ead_id</th>\n",
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       "      <th>titleproper</th>\n",
       "      <th>Term</th>\n",
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       "      <th>0</th>\n",
       "      <td>umich-bhl-0336</td>\n",
       "      <td>1051</td>\n",
       "      <td>Grant Kohn Goodman papers, 1943-1995</td>\n",
       "      <td>Death</td>\n",
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       "      <td>Term</td>\n",
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       "      <th>1</th>\n",
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       "      <td>umich-bhl-0336</td>\n",
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       "      <td>Term</td>\n",
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       "      <th>3</th>\n",
       "      <td>umich-bhl-0336</td>\n",
       "      <td>1051</td>\n",
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       "      <td>Death</td>\n",
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       "      <td>Goodman worked as a professor of Japanese hist...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>umich-bhl-0375</td>\n",
       "      <td>2620</td>\n",
       "      <td>Museum of Anthropological Archaeology (Univers...</td>\n",
       "      <td>Native</td>\n",
       "      <td>1</td>\n",
       "      <td>Term</td>\n",
       "      <td>Native</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>umich-bhl-9940</td>\n",
       "      <td>295</td>\n",
       "      <td>James J. Blanchard Papers, 1982-2002</td>\n",
       "      <td>Remains</td>\n",
       "      <td>2</td>\n",
       "      <td>Matched_Paragraph</td>\n",
       "      <td>The Personnel Division was charged with filli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>umich-bhl-9961</td>\n",
       "      <td>1400</td>\n",
       "      <td>Frank R. Kennedy Papers</td>\n",
       "      <td>Death</td>\n",
       "      <td>1</td>\n",
       "      <td>Term</td>\n",
       "      <td>Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>umich-bhl-9961</td>\n",
       "      <td>1400</td>\n",
       "      <td>Frank R. Kennedy Papers</td>\n",
       "      <td>Death</td>\n",
       "      <td>1</td>\n",
       "      <td>Matched_Paragraph</td>\n",
       "      <td>Death</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>umich-bhl-9961</td>\n",
       "      <td>1400</td>\n",
       "      <td>Frank R. Kennedy Papers</td>\n",
       "      <td>Death</td>\n",
       "      <td>1</td>\n",
       "      <td>Term</td>\n",
       "      <td>Death</td>\n",
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       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>umich-bhl-9961</td>\n",
       "      <td>1400</td>\n",
       "      <td>Frank R. Kennedy Papers</td>\n",
       "      <td>Death</td>\n",
       "      <td>1</td>\n",
       "      <td>Matched_Paragraph</td>\n",
       "      <td>Professor Kennedy's relationship with the Uni...</td>\n",
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       "<p>922 rows × 7 columns</p>\n",
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       "             ead_id  resource_id  \\\n",
       "0    umich-bhl-0336         1051   \n",
       "1    umich-bhl-0336         1051   \n",
       "2    umich-bhl-0336         1051   \n",
       "3    umich-bhl-0336         1051   \n",
       "4    umich-bhl-0375         2620   \n",
       "..              ...          ...   \n",
       "917  umich-bhl-9940          295   \n",
       "918  umich-bhl-9961         1400   \n",
       "919  umich-bhl-9961         1400   \n",
       "920  umich-bhl-9961         1400   \n",
       "921  umich-bhl-9961         1400   \n",
       "\n",
       "                                           titleproper     Term  \\\n",
       "0                 Grant Kohn Goodman papers, 1943-1995    Death   \n",
       "1                 Grant Kohn Goodman papers, 1943-1995    Death   \n",
       "2                 Grant Kohn Goodman papers, 1943-1995    Death   \n",
       "3                 Grant Kohn Goodman papers, 1943-1995    Death   \n",
       "4    Museum of Anthropological Archaeology (Univers...   Native   \n",
       "..                                                 ...      ...   \n",
       "917               James J. Blanchard Papers, 1982-2002  Remains   \n",
       "918                            Frank R. Kennedy Papers    Death   \n",
       "919                            Frank R. Kennedy Papers    Death   \n",
       "920                            Frank R. Kennedy Papers    Death   \n",
       "921                            Frank R. Kennedy Papers    Death   \n",
       "\n",
       "     Matched_Times       Matched_From  \\\n",
       "0                1               Term   \n",
       "1                1  Matched_Paragraph   \n",
       "2                1               Term   \n",
       "3                1  Matched_Paragraph   \n",
       "4                1               Term   \n",
       "..             ...                ...   \n",
       "917              2  Matched_Paragraph   \n",
       "918              1               Term   \n",
       "919              1  Matched_Paragraph   \n",
       "920              1               Term   \n",
       "921              1  Matched_Paragraph   \n",
       "\n",
       "                                     Matched_Paragraph  \n",
       "0                                                Death  \n",
       "1                                                Death  \n",
       "2                                                Death  \n",
       "3    Goodman worked as a professor of Japanese hist...  \n",
       "4                                               Native  \n",
       "..                                                 ...  \n",
       "917   The Personnel Division was charged with filli...  \n",
       "918                                              Death  \n",
       "919                                              Death  \n",
       "920                                              Death  \n",
       "921   Professor Kennedy's relationship with the Uni...  \n",
       "\n",
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   ],
   "source": [
    "# create a manifest to select which result lists you want to visualize\n",
    "match_groups = [(eads_df, 'Bentley')]\n",
    "matched_results = {name: match_and_visualize(df, name) for df, name in match_groups}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize group matches\n",
    "\n",
    "In the use case at U Michigan, the groups represented finding aids\n",
    "created by different repositories on campus. This can be useful for\n",
    "working with subgroups of finding aids, or in comparing usage between\n",
    "different organizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term frequency for Bentley\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
       "      <th>Total_Frequency</th>\n",
       "      <th>DataFrame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Death</td>\n",
       "      <td>250</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Philippine Islands</td>\n",
       "      <td>136</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Native</td>\n",
       "      <td>89</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Igorot</td>\n",
       "      <td>88</td>\n",
       "      <td>Bentley</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dwellings</td>\n",
       "      <td>71</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Slavery</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Plantations</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hanging</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Moros</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Insurrection</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 3 columns</p>\n",
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      ],
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       "                  Term  Total_Frequency DataFrame\n",
       "5                Death              250   Bentley\n",
       "19  Philippine Islands              136   Bentley\n",
       "17              Native               89   Bentley\n",
       "10              Igorot               88   Bentley\n",
       "6            Dwellings               71   Bentley\n",
       "..                 ...              ...       ...\n",
       "24             Slavery                4   Bentley\n",
       "20         Plantations                4   Bentley\n",
       "8              Hanging                4   Bentley\n",
       "16               Moros                4   Bentley\n",
       "15        Insurrection                4   Bentley\n",
       "\n",
       "[30 rows x 3 columns]"
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   "source": [
    "# bar charts for individual groups\n",
    "\n",
    "def visualize_individual_repository_terms_bar(list_of_dataframes):\n",
    "    '''\n",
    "    This function takes a list of strings (list_of_dataframes), which identify the names of dataframes (corresponding to an archival repository),\n",
    "    then generates a bar chart to show how many times a list of requested terms appears in the corresponding finding aid documents.\n",
    "    Requires the calculate_term_frequency function.\n",
    "    Processes data using pandas and generates charts using plotly (px) & plotly express (pio).\n",
    "    '''\n",
    "    for i in range(len(list_of_dataframes)):\n",
    "        # calculate term frequency\n",
    "        term_frequency = calculate_term_frequency(matched_results[repo_list[i]], repo_list[i])\n",
    "        term_frequency.head()\n",
    "\n",
    "        # Visualization in Multiple Charts\n",
    "        fig = px.bar(term_frequency, x='Term', y='Total_Frequency', text='Total_Frequency', \n",
    "                        color='DataFrame', color_discrete_map=colors, text_auto=True,\n",
    "                        labels={'Total_Frequency':'Term Occurence Count','DataFrame':'Repository'})\n",
    "        fig.update_traces(textposition='outside', insidetextanchor='middle',\n",
    "                          textfont=dict(family='Arial', size=8))\n",
    "        fig.update_layout(title_text=f'Term Frequency for { repo_list[i] }', \n",
    "                          xaxis_title_standoff=10, \n",
    "                          showlegend=False, font_family=global_font_info['font_family'])\n",
    "        fig.update_xaxes(tickangle=45, tickfont=dict(family='Arial', color='black', size=10))\n",
    "        fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "        fig.show()\n",
    "        pio.write_image(fig, f'term_frequency_byrepo_{ repo_list[i] }.png', width=700)\n",
    "\n",
    "visualize_individual_repository_terms_bar(repo_list)"
   ]
  },
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    "The above shows a basic bar chart, ordered from most frequent\n",
    "to least frequent term. If looking at multiple groupings, the above\n",
    "function will create multiple bar charts. \n",
    "\n",
    "The following sections demonstrate additional visualizations, for a single grouping\n",
    "of finding aid data. For the case of multiple groups (or, in our case repositories)\n",
    "see the additional visualization notebooks.\n",
    "\n",
    "## Visualization: occurence in a horizontal bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term frequency for Bentley\n"
     ]
    },
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       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Insurrection</td>\n",
       "      <td>4</td>\n",
       "      <td>Bentley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 3 columns</p>\n",
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       "                  Term  Total_Frequency DataFrame\n",
       "5                Death              250   Bentley\n",
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       "10              Igorot               88   Bentley\n",
       "6            Dwellings               71   Bentley\n",
       "..                 ...              ...       ...\n",
       "24             Slavery                4   Bentley\n",
       "20         Plantations                4   Bentley\n",
       "8              Hanging                4   Bentley\n",
       "16               Moros                4   Bentley\n",
       "15        Insurrection                4   Bentley\n",
       "\n",
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   "source": [
    "# bar charts for individual repos - horizontal\n",
    "\n",
    "def visualize_individual_repository_terms_bar_horiz(list_of_dataframes):\n",
    "    '''\n",
    "    This function takes a list of strings (list_of_dataframes), which identify the names of dataframes (corresponding to an archival repository),\n",
    "    then generates a horizontal bar chart to show how many times a list of requested terms appears in the corresponding finding aid documents.\n",
    "    Requires the calculate_term_frequency function.\n",
    "    Processes data using pandas and generates charts using plotly (px) & plotly express (pio).\n",
    "    '''\n",
    "    for i in range(len(list_of_dataframes)):\n",
    "        # calculate term frequency\n",
    "        term_frequency = calculate_term_frequency(matched_results[repo_list[i]], repo_list[i]).sort_values('Total_Frequency', ascending=True)\n",
    "        term_frequency.head()\n",
    "\n",
    "        # Visualization in Charts Horizontallly\n",
    "        fig = px.bar(term_frequency, y='Term', x='Total_Frequency', text='Total_Frequency',\n",
    "                        color='DataFrame', color_discrete_map=colors, text_auto=True,\n",
    "                        labels={'Total_Frequency':'Term Occurence Count','DataFrame':'Repository'})\n",
    "        fig.update_traces(textposition='outside', insidetextanchor='middle', \n",
    "                          textfont=dict(family='Arial',size=8))\n",
    "        fig.update_layout(title_text=f'Term Frequency for { repo_list[i] }', \n",
    "                          xaxis_title_standoff=10, height=600, \n",
    "                          showlegend=False, font_family=global_font_info['font_family'])\n",
    "        fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "        fig.show()\n",
    "        pio.write_image(fig, f'term_frequency_byrepo_{ repo_list[i] }_horizbar.png', scale=4)\n",
    "\n",
    "visualize_individual_repository_terms_bar_horiz(repo_list)"
   ]
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    "The horizontal bar chart seems more well-suited to displaying occurences\n",
    "of the terms, since the terms are easier to read in this orientation.\n",
    "\n",
    "## Visualization: Occurence by EAD tag/element\n",
    "\n",
    "This section of the script helps identify the EAD (Encoded Archival Description) elements with the highest occurrences of the specified harmful terms.\n",
    "\n",
    "The function `calculate_element_frequency(df, df_name)` is used to calculate the sum of matched terms for each subsection in a DataFrame. The results are sorted in descending order and returned as a DataFrame with columns `Subsection`, `harmful_terms_frequency`, and `Source`.\n",
    "\n",
    "This function is applied to each DataFrame in matched_results, and the results are concatenated into a single DataFrame, `all_element_frequencies`.\n",
    "\n",
    "Finally, a grouped bar chart is created to visualize the frequency of terms across different subsections and sources.\n",
    "\n",
    "In the chart, the x-axis represents the subsections, the y-axis shows the frequency of harmful terms, and different colors distinguish between sources. The `barmode='group'` setting places the bars side by side for easier comparison between sources."
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   "source": [
    "def calculate_element_frequency(df, df_name):\n",
    "    element_counts = df.groupby('Matched_From')['Matched_Times'].sum()\n",
    "    element_counts_sorted = element_counts.sort_values(ascending=False)\n",
    "    df_element_counts = pd.DataFrame(list(element_counts_sorted.items()), columns=['Subsection', 'harmful_terms_frequency'])\n",
    "    df_element_counts['Source'] = df_name  # Indicate the source dataframe\n",
    "    return df_element_counts\n",
    "\n",
    "# Use the function for each dataframes\n",
    "element_frequencies_list = [calculate_element_frequency(df, name) for name, df in matched_results.items()]\n",
    "\n",
    "# Concatenate all element frequencies\n",
    "all_element_frequencies = pd.concat(element_frequencies_list)\n",
    "\n",
    "# Show the DataFrame\n",
    "print(\"Element frequencies across all file pools:\")\n",
    "display(all_element_frequencies)\n",
    "\n",
    "# Set colors\n",
    "colors = {'Bentley': '#CFC096', 'Clements': '#A5A508', 'SCRC': '#FFCB05'}\n",
    "\n",
    "# Visualization\n",
    "fig = px.bar(all_element_frequencies, x='Subsection', y='harmful_terms_frequency', color='Source', \n",
    "             text='harmful_terms_frequency', barmode='group',\n",
    "             labels={'Subsection':'EAD Tag','Source':'Repository'},\n",
    "             color_discrete_map=colors)\n",
    "fig.update_traces(textposition='outside', textfont=dict(family='Arial',size=8))\n",
    "fig.update_layout(title_text=\"Term Occurence Frequency by EAD Tag\", yaxis_title=\"Term Frequency\", height=600,\n",
    "                  font_family=global_font_info['font_family'])\n",
    "fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "fig.show()\n",
    "pio.write_image(fig, 'element_frequency_across_dfs.png', scale=5)\n",
    "\n",
    "# Visualization Variation: Stacked bars\n",
    "fig = px.bar(all_element_frequencies, x='Subsection', y='harmful_terms_frequency', color='Source', \n",
    "             text='harmful_terms_frequency', barmode='stack',\n",
    "             labels={'Subsection':'EAD Tag','Source':'Repository'},\n",
    "             color_discrete_map=colors)\n",
    "fig.update_traces(textposition='inside', insidetextanchor='middle', textfont=dict(family='Arial',size=8))\n",
    "fig.update_layout(title_text=\"Term Occurence Frequency by EAD Tag (Stacked Totals)\", yaxis_title=\"Term Frequency\", height=600,\n",
    "                  font_family=global_font_info['font_family'])\n",
    "fig.update_yaxes(tickfont=dict(family='Arial', size=10))\n",
    "fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=10))\n",
    "fig.show()\n",
    "pio.write_image(fig, 'element_frequency_across_dfs_stacked.png', scale=5)"
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