{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>category</th>\n",
       "      <th>angle</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>high angle</td>\n",
       "      <td>2</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>13</td>\n",
       "      <td>59.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>low angle</td>\n",
       "      <td>7</td>\n",
       "      <td>31.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>high angle</td>\n",
       "      <td>5</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>8</td>\n",
       "      <td>44.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>low angle</td>\n",
       "      <td>5</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>casual</td>\n",
       "      <td>high angle</td>\n",
       "      <td>6</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casual</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>casual</td>\n",
       "      <td>low angle</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category       angle  count        pct\n",
       "0  white-collar  high angle      2   9.090909\n",
       "1  white-collar   eye-level     13  59.090909\n",
       "2  white-collar   low angle      7  31.818182\n",
       "3   blue-collar  high angle      5  27.777778\n",
       "4   blue-collar   eye-level      8  44.444444\n",
       "5   blue-collar   low angle      5  27.777778\n",
       "6        casual  high angle      6  60.000000\n",
       "7        casual   eye-level      3  30.000000\n",
       "8        casual   low angle      1  10.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'angle': [\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "    ],\n",
    "    'count': [\n",
    "        2,\n",
    "        13,\n",
    "        7,\n",
    "        5,\n",
    "        8,\n",
    "        5,\n",
    "        6,\n",
    "        3,\n",
    "        1,\n",
    "    ],\n",
    "    'pct': [\n",
    "            2/22*100,\n",
    "            13/22*100,\n",
    "            7/22*100,\n",
    "            5/18*100,\n",
    "            8/18*100,\n",
    "            5/18*100,\n",
    "            6/10*100,\n",
    "            3/10*100,\n",
    "            1/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.625x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"angle\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/angle.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>category</th>\n",
       "      <th>contact</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>14</td>\n",
       "      <td>63.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>8</td>\n",
       "      <td>36.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>18</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>casual</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>casual</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>9</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category           contact  count         pct\n",
       "0  white-collar  demand structure     14   63.636364\n",
       "1  white-collar   offer structure      8   36.363636\n",
       "2   blue-collar  demand structure      0    0.000000\n",
       "3   blue-collar   offer structure     18  100.000000\n",
       "4        casual  demand structure      1   10.000000\n",
       "5        casual   offer structure      9   90.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'contact': [\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "    ],\n",
    "    'count': [\n",
    "        14,\n",
    "        8,\n",
    "        0,\n",
    "        18,\n",
    "        1,\n",
    "        9,\n",
    "    ],\n",
    "    'pct': [\n",
    "            14/22*100,\n",
    "            8/22*100,\n",
    "            0/18*100,\n",
    "            18/18*100,\n",
    "            1/10*100,\n",
    "            9/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 773.75x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"contact\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/contact.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Point-of-view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>category</th>\n",
       "      <th>poit_of_view</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>frontal</td>\n",
       "      <td>16</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>oblique</td>\n",
       "      <td>2</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>frontal</td>\n",
       "      <td>4</td>\n",
       "      <td>22.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>oblique</td>\n",
       "      <td>14</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>casual</td>\n",
       "      <td>frontal</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>casual</td>\n",
       "      <td>oblique</td>\n",
       "      <td>7</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category poit_of_view  count        pct\n",
       "0  white-collar      frontal     16  72.727273\n",
       "1  white-collar      oblique      2   9.090909\n",
       "2   blue-collar      frontal      4  22.222222\n",
       "3   blue-collar      oblique     14  77.777778\n",
       "4        casual      frontal      3  30.000000\n",
       "5        casual      oblique      7  70.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'poit_of_view': [\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "    ],\n",
    "    'count': [\n",
    "        16,\n",
    "        2,\n",
    "        4,\n",
    "        14,\n",
    "        3,\n",
    "        7,\n",
    "    ],\n",
    "    'pct': [\n",
    "            16/22*100,\n",
    "            2/22*100,\n",
    "            4/18*100,\n",
    "            14/18*100,\n",
    "            3/10*100,\n",
    "            7/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 704.847x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"poit_of_view\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/point-of-view.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>category</th>\n",
       "      <th>distance</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>long shot</td>\n",
       "      <td>6</td>\n",
       "      <td>27.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>16</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>close-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>long shot</td>\n",
       "      <td>15</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>3</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>close-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>casual</td>\n",
       "      <td>long shot</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casual</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>5</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>casual</td>\n",
       "      <td>close-up</td>\n",
       "      <td>2</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category     distance  count        pct\n",
       "0  white-collar    long shot      6  27.272727\n",
       "1  white-collar  medium shot     16  72.727273\n",
       "2  white-collar     close-up      0   0.000000\n",
       "3   blue-collar    long shot     15  83.333333\n",
       "4   blue-collar  medium shot      3  16.666667\n",
       "5   blue-collar     close-up      0   0.000000\n",
       "6        casual    long shot      3  30.000000\n",
       "7        casual  medium shot      5  50.000000\n",
       "8        casual     close-up      2  20.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'distance': [\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "    ],\n",
    "    'count': [\n",
    "        6,\n",
    "        16,\n",
    "        0,\n",
    "        15,\n",
    "        3,\n",
    "        0,\n",
    "        3,\n",
    "        5,\n",
    "        2,\n",
    "    ],\n",
    "    'pct': [\n",
    "            6/22*100,\n",
    "            16/22*100,\n",
    "            0/22*100,\n",
    "            15/18*100,\n",
    "            3/18*100,\n",
    "            0/18*100,\n",
    "            3/10*100,\n",
    "            5/10*100,\n",
    "            2/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 742.25x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"distance\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/distance.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie Charts about Subtypes of Processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>non-transactional\\naction</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unidirectional\\ntransactional\\naction</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bidirectional\\ntransactional\\naction</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>non-transactional\\nreaction</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unidirectional\\ntransactional\\nreaction</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bidirectional\\ntransactional\\nreaction</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>conversion</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speech</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  category  count\n",
       "0                non-transactional\\naction     26\n",
       "1    unidirectional\\ntransactional\\naction     23\n",
       "2     bidirectional\\ntransactional\\naction      2\n",
       "3              non-transactional\\nreaction      6\n",
       "4  unidirectional\\ntransactional\\nreaction      5\n",
       "5   bidirectional\\ntransactional\\nreaction      2\n",
       "6                               conversion      2\n",
       "7                                   speech      2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'non-transactional\\naction',\n",
    "        'unidirectional\\ntransactional\\naction',\n",
    "        'bidirectional\\ntransactional\\naction',\n",
    "        'non-transactional\\nreaction',\n",
    "        'unidirectional\\ntransactional\\nreaction',\n",
    "        'bidirectional\\ntransactional\\nreaction',\n",
    "        'conversion',\n",
    "        'speech',\n",
    "    ],\n",
    "    'count': [\n",
    "        26,\n",
    "        23,\n",
    "        2,\n",
    "        6,\n",
    "        5,\n",
    "        2,\n",
    "        2,\n",
    "        2,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues')[0:len(df)]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(df['count'], labels=df['category'], colors = colors, autopct='%.0f%%', startangle=40)\n",
    "plt.savefig('plots/narrative_processes.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>classification\\novert\\ntaxonomy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>analytical\\nexhaustive</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>analytical\\ndisarranged</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>analytical\\ntemporal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>analytical\\ndistributed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>analytical\\ntopological</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>analytical\\nexploded</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>analytical\\ninclusive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>symbolic\\nsuggestive</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>symbolic\\nattributive</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          category  count\n",
       "0  classification\\novert\\ntaxonomy      4\n",
       "1           analytical\\nexhaustive     18\n",
       "2          analytical\\ndisarranged      6\n",
       "3             analytical\\ntemporal      2\n",
       "4          analytical\\ndistributed      1\n",
       "5          analytical\\ntopological      2\n",
       "6             analytical\\nexploded      4\n",
       "7            analytical\\ninclusive      1\n",
       "8             symbolic\\nsuggestive     13\n",
       "9            symbolic\\nattributive      5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'classification\\novert\\ntaxonomy',\n",
    "        'analytical\\nexhaustive',\n",
    "        'analytical\\ndisarranged',\n",
    "        'analytical\\ntemporal',\n",
    "        'analytical\\ndistributed',\n",
    "        'analytical\\ntopological',\n",
    "        'analytical\\nexploded',\n",
    "        'analytical\\ninclusive',\n",
    "        'symbolic\\nsuggestive',\n",
    "        'symbolic\\nattributive',\n",
    "    ],\n",
    "    'count': [\n",
    "        4,\n",
    "        18,\n",
    "        6,\n",
    "        2,\n",
    "        1,\n",
    "        2,\n",
    "        4,\n",
    "        1,\n",
    "        13,\n",
    "        5,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues')[0:len(df)]\n",
    "\n",
    "#create pie chart\n",
    "plt.pie(df['count'], labels=df['category'], colors = colors, autopct='%.0f%%', startangle=-15)\n",
    "plt.savefig('plots/conceptual_processes.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}