ch17_llms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ch. 17 LLMs and the Practice of Data Science\n",
    "\n",
    "* This notebook has the rankings I used to measure exposure (of data science tasks and of skills developed in chapter of the book)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.colors as colors\n",
    "import cycler\n",
    "\n",
    "# set plotting parameters from the beginning\n",
    "font = {'family' : 'monospace',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 14}\n",
    "axes = {'titlesize' : 22,\n",
    "        'labelsize' : 20,\n",
    "       'prop_cycle' : cycler.cycler(color='k')}\n",
    "lines = {'color' : 'k'}\n",
    "figure = {'figsize' : (10,4),\n",
    "         'autolayout' : True,}\n",
    "matplotlib.rc('font', **font)\n",
    "matplotlib.rc('axes', **axes)\n",
    "matplotlib.rc('figure', **figure)\n",
    "matplotlib.rc('lines', **lines)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a plot of skills from the OpenAI paper\n",
    "cols = ['Basic Skill','alpha','beta','gamma' ]\n",
    "data = [['Active Listening',0.128,0.214,0.449 ],\n",
    "['Mathematics',-0.127,0.161,0.787 ],\n",
    "['Reading Comprehension',0.153,0.470,-0.346 ],\n",
    "['Science',-0.114,-0.230,-0.346 ],\n",
    "['Speaking',-0.028,0.133,0.294 ],\n",
    "['Writing',0.368,0.467,0.566 ],\n",
    "['Active Learning',-0.157,-0.065, 0.028 ],\n",
    "['Critical Thinking',- 0.264 , - 0.196 , - 0.129 ],\n",
    "['Learning Strategies',- 0.072 , - 0.209 , - 0.346 ],\n",
    "['Monitoring',-0.067 ,-0.149 ,-0.232 ],\n",
    "['Programming',0.637 ,0.623 ,0.609]]\n",
    "\n",
    "df = pd.DataFrame(data,columns=cols)\n",
    "df.set_index(df['Basic Skill'], inplace=True)\n",
    "df.drop(columns=['Basic Skill'], inplace=True)\n",
    "\n",
    "df_sorted = df.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84c153d110>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a plot of skills from the OpenAI paper\n",
    "fig, ax = plt.subplots(figsize=(13,8))\n",
    "#df.loc[df_sorted.index].plot(kind='bar', ax = ax, color=['0.1','0.5','0.9'], edgecolor='k')\n",
    "df_sorted.plot(kind='bar', ax = ax, facecolor='0.5', edgecolor='k')\n",
    "#greek_labs = [r'$\\alpha$',r'$\\beta$',r'$\\gamma$']\n",
    "#string_labs = ['E1', 'E1 + 0.5E2', 'E1 + E2']\n",
    "#ax.legend(string_labs, bbox_to_anchor=(1.0, 1.00))\n",
    "xmin, xmax = ax.get_xlim()\n",
    "ax.plot([xmin, xmax],[0,0])\n",
    "ax.set_title('Exposure and Skills', fontsize=23)\n",
    "ytk = ax.set_yticks([])\n",
    "xcoord = -0.015\n",
    "ax.text(xcoord*3, 0.5, 'Correlation', fontsize=22, transform = ax.transAxes, ha='center', va = 'center', \n",
    "       rotation=90)\n",
    "ax.text(xcoord,0.65, '+', fontsize=22, transform = ax.transAxes, ha='center', va = 'center')\n",
    "ax.text(xcoord,0.15, '-', fontsize=26, transform = ax.transAxes, ha='center', va = 'center')\n",
    "ax.set_xlabel('')\n",
    "ax.annotate('Least Exposed', xytext=(0.33, 0.8), xy=(0.1, 0.8), fontsize=22,\n",
    "            xycoords='axes fraction',textcoords='axes fraction', ha='center',va='center',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=5))\n",
    "ax.annotate('More Exposed', xytext=(0.58, 0.8), xy=(0.81, 0.8), fontsize=22, \n",
    "            xycoords='axes fraction',textcoords='axes fraction', ha='center',va='center',\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=5))\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax.plot([4.5,4.5], [ymin, ymax], ls='dotted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science Tasks\n",
    "\n",
    "* Using the tasks from [O*Net Online](https://www.onetonline.org/link/details/15-2051.00)\n",
    "* I will start by evaluating each task along four skills:\n",
    "    * Business (B)\n",
    "    * ML & Stats (ML)\n",
    "    * Programming (P)\n",
    "    * Soft skills (S)\n",
    "* Evaluations are \"low\" (0), \"medium\" (1), \"high\" (3)\n",
    "* I then compute an exposure metric using the formula in the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tasks</th>\n",
       "      <th>Business</th>\n",
       "      <th>Machine Learning + Statistics</th>\n",
       "      <th>Programming</th>\n",
       "      <th>Soft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Analyze, manipulate, or process large sets of ...</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apply sampling techniques to determine groups ...</td>\n",
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       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clean and manipulate raw data using statistica...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compare models using statistical performance m...</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Deliver oral or written presentations of the r...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Design surveys, opinion polls, or other instru...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Identify business problems or management objec...</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Identify relationships and trends or any facto...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Identify solutions to business problems, such ...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propose solutions in engineering, the sciences...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Read scientific articles, conference papers, o...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Recommend data-driven solutions to key stakeho...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Test, validate, and reformulate models to ensu...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Write new functions or applications in program...</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tasks Business  \\\n",
       "0   Analyze, manipulate, or process large sets of ...     High   \n",
       "1   Apply feature selection algorithms to models p...     High   \n",
       "2   Apply sampling techniques to determine groups ...      Low   \n",
       "3   Clean and manipulate raw data using statistica...   Medium   \n",
       "4   Compare models using statistical performance m...      Low   \n",
       "5   Deliver oral or written presentations of the r...   Medium   \n",
       "6   Design surveys, opinion polls, or other instru...   Medium   \n",
       "7   Identify business problems or management objec...     High   \n",
       "8   Identify relationships and trends or any facto...   Medium   \n",
       "9   Identify solutions to business problems, such ...     High   \n",
       "10  Propose solutions in engineering, the sciences...     High   \n",
       "11  Read scientific articles, conference papers, o...   Medium   \n",
       "12  Recommend data-driven solutions to key stakeho...     High   \n",
       "13  Test, validate, and reformulate models to ensu...   Medium   \n",
       "14  Write new functions or applications in program...      Low   \n",
       "\n",
       "   Machine Learning + Statistics Programming    Soft  \n",
       "0                            Low        High     Low  \n",
       "1                           High      Medium     Low  \n",
       "2                           High      Medium     Low  \n",
       "3                            Low        High     Low  \n",
       "4                           High      Medium     Low  \n",
       "5                            Low         Low  Medium  \n",
       "6                           High         Low     Low  \n",
       "7                            Low         Low  Medium  \n",
       "8                           High      Medium     Low  \n",
       "9                         Medium         Low  Medium  \n",
       "10                        Medium         Low     Low  \n",
       "11                        Medium         Low     Low  \n",
       "12                        Medium         Low    High  \n",
       "13                          High      Medium     Low  \n",
       "14                        Medium        High     Low  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define column names (Danke ChatGPT!)\n",
    "columns = [\"Tasks\", \"Business\", \"Machine Learning + Statistics\", \"Programming\", \"Soft\"]\n",
    "\n",
    "# Define data values\n",
    "data = [\n",
    "    ['Analyze, manipulate, or process large sets of data using statistical software.', 'High', 'Low', 'High', 'Low'],\n",
    "    ['Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.', 'High', 'High', 'Medium', 'Low'],\n",
    "    ['Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.', 'Low', 'High', 'Medium', 'Low'],\n",
    "    ['Clean and manipulate raw data using statistical software.', 'Medium', 'Low', 'High', 'Low'],\n",
    "    ['Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.', 'Low', 'High', 'Medium', 'Low'],\n",
    "    ['Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.', 'Medium', 'Low', 'Low', 'Medium'],\n",
    "    ['Design surveys, opinion polls, or other instruments to collect data.', 'Medium', 'High', 'Low', 'Low'],\n",
    "    ['Identify business problems or management objectives that can be addressed through data analysis.', 'High', 'Low', 'Low', 'Medium'],\n",
    "    ['Identify relationships and trends or any factors that could affect the results of research.', 'Medium', 'High', 'Medium', 'Low'],\n",
    "    ['Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.', 'High' ,'Medium' ,'Low' ,'Medium'],\n",
    "    ['Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.', 'High' ,'Medium' ,'Low' ,'Low'],\n",
    "    ['Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.', 'Medium' ,'Medium' ,'Low' ,'Low'],\n",
    "    ['Recommend data-driven solutions to key stakeholders.', 'High' ,'Medium' ,'Low' ,'High'],\n",
    "    ['Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.',  'Medium', 'High', 'Medium', 'Low'],\n",
    "    ['Write new functions or applications in programming languages to conduct analyses.', 'Low' ,'Medium' ,'High' ,'Low']\n",
    "]\n",
    "\n",
    "# Create DataFrame\n",
    "df_tasks = pd.DataFrame(data=data, columns=columns)\n",
    "\n",
    "# Print DataFrame\n",
    "df_tasks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Tasks</th>\n",
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       "      <td>Analyze, manipulate, or process large sets of ...</td>\n",
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       "      <td>Apply feature selection algorithms to models p...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Design surveys, opinion polls, or other instru...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Identify business problems or management objec...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Identify relationships and trends or any facto...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Identify solutions to business problems, such ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propose solutions in engineering, the sciences...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Read scientific articles, conference papers, o...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Recommend data-driven solutions to key stakeho...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Test, validate, and reformulate models to ensu...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Write new functions or applications in program...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tasks  Business  \\\n",
       "0   Analyze, manipulate, or process large sets of ...         3   \n",
       "1   Apply feature selection algorithms to models p...         3   \n",
       "2   Apply sampling techniques to determine groups ...         0   \n",
       "3   Clean and manipulate raw data using statistica...         1   \n",
       "4   Compare models using statistical performance m...         0   \n",
       "5   Deliver oral or written presentations of the r...         1   \n",
       "6   Design surveys, opinion polls, or other instru...         1   \n",
       "7   Identify business problems or management objec...         3   \n",
       "8   Identify relationships and trends or any facto...         1   \n",
       "9   Identify solutions to business problems, such ...         3   \n",
       "10  Propose solutions in engineering, the sciences...         3   \n",
       "11  Read scientific articles, conference papers, o...         1   \n",
       "12  Recommend data-driven solutions to key stakeho...         3   \n",
       "13  Test, validate, and reformulate models to ensu...         1   \n",
       "14  Write new functions or applications in program...         0   \n",
       "\n",
       "    Machine Learning + Statistics  Programming  Soft  \n",
       "0                               0            3     0  \n",
       "1                               3            1     0  \n",
       "2                               3            1     0  \n",
       "3                               0            3     0  \n",
       "4                               3            1     0  \n",
       "5                               0            0     1  \n",
       "6                               3            0     0  \n",
       "7                               0            0     1  \n",
       "8                               3            1     0  \n",
       "9                               1            0     1  \n",
       "10                              1            0     0  \n",
       "11                              1            0     0  \n",
       "12                              1            0     3  \n",
       "13                              3            1     0  \n",
       "14                              1            3     0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical Scale\n",
    "dict_change = {'High':3, 'Low':0, 'Medium':1}\n",
    "df_tasks1 = df_tasks.copy()\n",
    "for c, col in enumerate(df_tasks.columns):\n",
    "    df_tasks1[col].replace(dict_change, inplace=True)\n",
    "df_tasks1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's generate a score of how much this will be done by LLM\n",
    "\n",
    "Idea:\n",
    "* In principle, with a large and specialized enough corpus, business, ML and Programming can be learned by an LLM.  Positive score, but I will give less to business, because many things have to be learned \"on the job\"\n",
    "* But there's an interaction between Business and ML and Programming: I'm calling this Analytical Skills, and I assume this won't be easily achieved by LLM.  \n",
    "* Soft skills also have a negative effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = {'B':0.2, 'ML':0.8, 'P':1, 'S':-0.2, 'BxML':-0.2, 'BxP':-0.2}\n",
    "func_analytics = lambda x: (x['Business']*weights['B'] + \n",
    "                    x['Machine Learning + Statistics']*weights['ML'] + \n",
    "                    x['Programming']*weights['P'] + \n",
    "                    x['Soft']*weights['S'] + \n",
    "                    x['Business']*x['Machine Learning + Statistics']*weights['BxML'] + \n",
    "                    x['Business']*x['Programming']*weights['BxP'])\n",
    "# if there's no interaction\n",
    "func_nointer = lambda x: (x['Business']*weights['B'] + \n",
    "                    x['Machine Learning + Statistics']*weights['ML'] + \n",
    "                    x['Programming']*weights['P'])\n",
    "# convert to three categories\n",
    "# this function was created by ChatGPT!\n",
    "def num_to_cat(df, col, thres1, thres2):\n",
    "    # Use pd.cut() to bin the numerical column into three categories\n",
    "    df[col + \"_cat\"] = pd.cut(df[col], bins=[-float(\"inf\"), thres1, thres2, float(\"inf\")], \n",
    "                              labels=[\"Low\", \"Medium\", \"High\"])\n",
    "    # Return the DataFrame with the new categorical column\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 2.24\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Tasks</th>\n",
       "      <th>Business</th>\n",
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       "      <th>5</th>\n",
       "      <td>Deliver oral or written presentations of the r...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Low</td>\n",
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       "      <th>12</th>\n",
       "      <td>Recommend data-driven solutions to key stakeho...</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>Low</td>\n",
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       "      <th>7</th>\n",
       "      <td>Identify business problems or management objec...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Identify solutions to business problems, such ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propose solutions in engineering, the sciences...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Read scientific articles, conference papers, o...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apply feature selection algorithms to models p...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Analyze, manipulate, or process large sets of ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Design surveys, opinion polls, or other instru...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clean and manipulate raw data using statistica...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Identify relationships and trends or any facto...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Test, validate, and reformulate models to ensu...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apply sampling techniques to determine groups ...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compare models using statistical performance m...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Write new functions or applications in program...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tasks  Business  \\\n",
       "5   Deliver oral or written presentations of the r...         1   \n",
       "12  Recommend data-driven solutions to key stakeho...         3   \n",
       "7   Identify business problems or management objec...         3   \n",
       "9   Identify solutions to business problems, such ...         3   \n",
       "10  Propose solutions in engineering, the sciences...         3   \n",
       "11  Read scientific articles, conference papers, o...         1   \n",
       "1   Apply feature selection algorithms to models p...         3   \n",
       "0   Analyze, manipulate, or process large sets of ...         3   \n",
       "6   Design surveys, opinion polls, or other instru...         1   \n",
       "3   Clean and manipulate raw data using statistica...         1   \n",
       "8   Identify relationships and trends or any facto...         1   \n",
       "13  Test, validate, and reformulate models to ensu...         1   \n",
       "2   Apply sampling techniques to determine groups ...         0   \n",
       "4   Compare models using statistical performance m...         0   \n",
       "14  Write new functions or applications in program...         0   \n",
       "\n",
       "    Machine Learning + Statistics  Programming  Soft  score_analytics  \\\n",
       "5                               0            0     1              0.0   \n",
       "12                              1            0     3              0.2   \n",
       "7                               0            0     1              0.4   \n",
       "9                               1            0     1              0.6   \n",
       "10                              1            0     0              0.8   \n",
       "11                              1            0     0              0.8   \n",
       "1                               3            1     0              1.6   \n",
       "0                               0            3     0              1.8   \n",
       "6                               3            0     0              2.0   \n",
       "3                               0            3     0              2.6   \n",
       "8                               3            1     0              2.8   \n",
       "13                              3            1     0              2.8   \n",
       "2                               3            1     0              3.4   \n",
       "4                               3            1     0              3.4   \n",
       "14                              1            3     0              3.8   \n",
       "\n",
       "    score_nointer score_analytics_cat  \n",
       "5             0.2                 Low  \n",
       "12            1.4                 Low  \n",
       "7             0.6                 Low  \n",
       "9             1.4                 Low  \n",
       "10            1.4                 Low  \n",
       "11            1.0                 Low  \n",
       "1             4.0              Medium  \n",
       "0             3.6              Medium  \n",
       "6             2.6              Medium  \n",
       "3             3.2                High  \n",
       "8             3.6                High  \n",
       "13            3.6                High  \n",
       "2             3.4                High  \n",
       "4             3.4                High  \n",
       "14            3.8                High  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tasks1['score_analytics'] = df_tasks1.apply(func_analytics, axis=1)\n",
    "df_tasks1['score_nointer'] = df_tasks1.apply(func_nointer, axis=1)\n",
    "df_tasks1.sort_values(by='score_analytics', inplace=True)\n",
    "# create a categorical variable\n",
    "thres1, thres2 = df_tasks1.score_analytics.quantile([0.3, 0.6])\n",
    "print(thres1, thres2)\n",
    "df_tasks2 = num_to_cat(df=df_tasks1, col='score_analytics', thres1 = thres1, thres2 =thres2)\n",
    "df_tasks1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/pylabtools.py:132: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df_tasks1.score_analytics.plot(kind='bar', ax=ax, facecolor='0.5', edgecolor='k')\n",
    "ytk = ax.set_xticklabels(df_tasks1['Tasks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's convert to Asciidoc: this function was created with ChatGPT!\n",
    "# Define a function to convert a DataFrame to an Asciidoc table\n",
    "def df_to_asciidoc(df):\n",
    "    # Get the column names and the data values from the DataFrame\n",
    "    columns = df.columns.tolist()\n",
    "    data = df.values.tolist()\n",
    "\n",
    "    # Initialize an empty string to store the Asciidoc table syntax\n",
    "    asciidoc_table = \"\"\n",
    "\n",
    "    # Add the [cols=\"...\"] line to specify the relative widths of the columns\n",
    "    # You can adjust the widths according to your preference\n",
    "    asciidoc_table += \"[cols=\\\"\"\n",
    "    for col in columns:\n",
    "        asciidoc_table += \"1,\"\n",
    "    asciidoc_table = asciidoc_table[:-1] + \"\\\"]\\n\"\n",
    "\n",
    "    # Add the |=== line to start the table\n",
    "    asciidoc_table += \"|===\\n\"\n",
    "\n",
    "    # Add the header row with | and . symbols\n",
    "    for col in columns:\n",
    "        asciidoc_table += \"|\" + \".\" + col + \"\\n\"\n",
    "\n",
    "    # Add the data rows with | symbols\n",
    "    for row in data:\n",
    "        for value in row:\n",
    "            asciidoc_table += \"|\" + str(value) + \"\\n\"\n",
    "\n",
    "    # Add the |=== line to end the table\n",
    "    asciidoc_table += \"|===\\n\"\n",
    "\n",
    "    # Return the Asciidoc table syntax as a string\n",
    "    return asciidoc_table\n"
   ]
  },
  {
   "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>Tasks</th>\n",
       "      <th>score_analytics_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Deliver oral or written presentations of the r...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Recommend data-driven solutions to key stakeho...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Identify business problems or management objec...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Identify solutions to business problems, such ...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Propose solutions in engineering, the sciences...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Read scientific articles, conference papers, o...</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apply feature selection algorithms to models p...</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Analyze, manipulate, or process large sets of ...</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Design surveys, opinion polls, or other instru...</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clean and manipulate raw data using statistica...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Identify relationships and trends or any facto...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Test, validate, and reformulate models to ensu...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apply sampling techniques to determine groups ...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compare models using statistical performance m...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Write new functions or applications in program...</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tasks score_analytics_cat\n",
       "5   Deliver oral or written presentations of the r...                 Low\n",
       "12  Recommend data-driven solutions to key stakeho...                 Low\n",
       "7   Identify business problems or management objec...                 Low\n",
       "9   Identify solutions to business problems, such ...                 Low\n",
       "10  Propose solutions in engineering, the sciences...                 Low\n",
       "11  Read scientific articles, conference papers, o...                 Low\n",
       "1   Apply feature selection algorithms to models p...              Medium\n",
       "0   Analyze, manipulate, or process large sets of ...              Medium\n",
       "6   Design surveys, opinion polls, or other instru...              Medium\n",
       "3   Clean and manipulate raw data using statistica...                High\n",
       "8   Identify relationships and trends or any facto...                High\n",
       "13  Test, validate, and reformulate models to ensu...                High\n",
       "2   Apply sampling techniques to determine groups ...                High\n",
       "4   Compare models using statistical performance m...                High\n",
       "14  Write new functions or applications in program...                High"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tasks1[['Tasks','score_analytics_cat']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[cols=\"1,1\"]\n",
      "|===\n",
      "|.Tasks\n",
      "|.score_analytics_cat\n",
      "|Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.\n",
      "|Low\n",
      "|Recommend data-driven solutions to key stakeholders.\n",
      "|Low\n",
      "|Identify business problems or management objectives that can be addressed through data analysis.\n",
      "|Low\n",
      "|Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.\n",
      "|Low\n",
      "|Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.\n",
      "|Low\n",
      "|Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.\n",
      "|Low\n",
      "|Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.\n",
      "|Medium\n",
      "|Analyze, manipulate, or process large sets of data using statistical software.\n",
      "|Medium\n",
      "|Design surveys, opinion polls, or other instruments to collect data.\n",
      "|Medium\n",
      "|Clean and manipulate raw data using statistical software.\n",
      "|High\n",
      "|Identify relationships and trends or any factors that could affect the results of research.\n",
      "|High\n",
      "|Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.\n",
      "|High\n",
      "|Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.\n",
      "|High\n",
      "|Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.\n",
      "|High\n",
      "|Write new functions or applications in programming languages to conduct analyses.\n",
      "|High\n",
      "|===\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_to_asciidoc(df_tasks1[['Tasks','score_analytics_cat']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This book and LLMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        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>Num</th>\n",
       "      <th>Chapter</th>\n",
       "      <th>Main Lesson</th>\n",
       "      <th>Business</th>\n",
       "      <th>ML &amp; Stats</th>\n",
       "      <th>Programming</th>\n",
       "      <th>Soft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>So What</td>\n",
       "      <td>How to measure the impact of your team</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Metrics Design</td>\n",
       "      <td>Find better metrics to action</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Growth Decompositions</td>\n",
       "      <td>Understand what's happening with the business</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2x2 Designs</td>\n",
       "      <td>Simplify to understand</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Business Cases</td>\n",
       "      <td>How to measure the impact of specific projects</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num                Chapter                                     Main Lesson  \\\n",
       "0    1                So What          How to measure the impact of your team   \n",
       "1    2         Metrics Design                   Find better metrics to action   \n",
       "2    3  Growth Decompositions   Understand what's happening with the business   \n",
       "3    4            2x2 Designs                          Simplify to understand   \n",
       "4    5         Business Cases  How to measure the impact of specific projects   \n",
       "\n",
       "  Business ML & Stats Programming    Soft  \n",
       "0   Medium        Low         Low  Medium  \n",
       "1     High        Low         Low     Low  \n",
       "2     High        Low         Low     Low  \n",
       "3     High        Low         Low     Low  \n",
       "4     High        Low         Low     Low  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usign ChatGPT to create the DF!\n",
    "# Create a list of lists that contains the data for each row of the DataFrame\n",
    "data = [\n",
    "    [1, 'So What', 'How to measure the impact of your team', 'Medium', 'Low', 'Low', 'Medium'],\n",
    "    [2, 'Metrics Design', 'Find better metrics to action', 'High', 'Low', 'Low', 'Low'],\n",
    "    [3, 'Growth Decompositions', \"Understand what's happening with the business\", 'High', 'Low', 'Low', 'Low'],\n",
    "    [4, '2x2 Designs', 'Simplify to understand', 'High', 'Low', 'Low', 'Low'],\n",
    "    [5, 'Business Cases', 'How to measure the impact of specific projects', 'High', 'Low', 'Low', 'Low'],\n",
    "    [6, 'Lift', \"Technique to see differences across groups\", 'Medium', 'Low', 'Low', 'Low'],\n",
    "    [7, 'Narratives', \"How to build narratives before and after creating a project\", 'High', 'Low', 'Low', 'High'],\n",
    "    [8, 'Datavis', \"How to as present results visually\", 'High', 'Low', 'Medium', 'Low'],\n",
    "    [9, 'Simulation & Boot', \"Tools to deepen your understanding of ML algorithms\", 'Low', 'High', 'Medium', 'Low'],\n",
    "    [10, 'Linear Regression', \"Strengthen your intuition of how ML algorithms work\", 'Low', 'Medium', 'Medium', 'Low'],\n",
    "    [11, 'Data Leakage', \"Identify and correct data leakage\", 'Low', 'High', 'Medium', 'Low'],\n",
    "    [12, \"Productionizing Models\", \"Minimal framework to deploy in production\", \"Low\", \"Medium\", \"High\", \"Low\"],\n",
    "    [13, \"Storytelling ML\", \"Storytelling to create features and to interpret results\", \"Medium\", \"High\", \"Medium\", \"Low\"],\n",
    "    [14, \"Predictions to Decisions\", \"Making decisions from ML\", \"Medium\", \"High\", \"Medium\", \"Low\"],\n",
    "    [15, \"Incrementality\", \"Understand the basics of causality\", \"High\", \"High\", \"Medium\", \"Low\"],\n",
    "    [16, \"A/B Tests\", \"Design of experiments\", \"High\", \"High\", \"Medium\", \"Low\"]\n",
    "]\n",
    "\n",
    "# Create a list of strings that contains the column names of the DataFrame\n",
    "columns = ['Num','Chapter','Main Lesson','Business','ML & Stats','Programming','Soft']\n",
    "\n",
    "# Use the pd.DataFrame() function to create a DataFrame object from the list of lists and the list of strings\n",
    "df = pd.DataFrame(data=data, columns=columns)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "      <td>2</td>\n",
       "      <td>Metrics Design</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2x2 Designs</td>\n",
       "      <td>Simplify to understand</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>How to measure the impact of specific projects</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num                Chapter                                     Main Lesson  \\\n",
       "0    1                So What          How to measure the impact of your team   \n",
       "1    2         Metrics Design                   Find better metrics to action   \n",
       "2    3  Growth Decompositions   Understand what's happening with the business   \n",
       "3    4            2x2 Designs                          Simplify to understand   \n",
       "4    5         Business Cases  How to measure the impact of specific projects   \n",
       "\n",
       "   Business  Machine Learning + Statistics  Programming  Soft  \n",
       "0         1                              0            0     1  \n",
       "1         3                              0            0     0  \n",
       "2         3                              0            0     0  \n",
       "3         3                              0            0     0  \n",
       "4         3                              0            0     0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_book = df.copy()\n",
    "cols = ['Business', 'ML & Stats', 'Programming', 'Soft']\n",
    "for c, col in enumerate(cols):\n",
    "    df_book[col].replace(dict_change, inplace=True)\n",
    "df_book.rename(columns={'ML & Stats':'Machine Learning + Statistics'}, inplace=True)\n",
    "cols = ['Business', 'Machine Learning + Statistics', 'Programming', 'Soft']\n",
    "df_book.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Narratives</td>\n",
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       "      <td>3</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Lift</td>\n",
       "      <td>Technique to see differences across groups</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Metrics Design</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Growth Decompositions</td>\n",
       "      <td>Understand what's happening with the business</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2x2 Designs</td>\n",
       "      <td>Simplify to understand</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Business Cases</td>\n",
       "      <td>How to measure the impact of specific projects</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Datavis</td>\n",
       "      <td>How to as present results visually</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Incrementality</td>\n",
       "      <td>Understand the basics of causality</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>A/B Tests</td>\n",
       "      <td>Design of experiments</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>Strengthen your intuition of how ML algorithms...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Storytelling ML</td>\n",
       "      <td>Storytelling to create features and to interpr...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Predictions to Decisions</td>\n",
       "      <td>Making decisions from ML</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Simulation &amp; Boot</td>\n",
       "      <td>Tools to deepen your understanding of ML algor...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Data Leakage</td>\n",
       "      <td>Identify and correct data leakage</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Productionizing Models</td>\n",
       "      <td>Minimal framework to deploy in production</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Num                   Chapter  \\\n",
       "0     1                   So What   \n",
       "6     7                Narratives   \n",
       "5     6                      Lift   \n",
       "1     2            Metrics Design   \n",
       "2     3     Growth Decompositions   \n",
       "3     4               2x2 Designs   \n",
       "4     5            Business Cases   \n",
       "7     8                   Datavis   \n",
       "14   15            Incrementality   \n",
       "15   16                 A/B Tests   \n",
       "9    10         Linear Regression   \n",
       "12   13           Storytelling ML   \n",
       "13   14  Predictions to Decisions   \n",
       "8     9         Simulation & Boot   \n",
       "10   11              Data Leakage   \n",
       "11   12    Productionizing Models   \n",
       "\n",
       "                                          Main Lesson  Business  \\\n",
       "0              How to measure the impact of your team         1   \n",
       "6   How to build narratives before and after creat...         3   \n",
       "5          Technique to see differences across groups         1   \n",
       "1                       Find better metrics to action         3   \n",
       "2       Understand what's happening with the business         3   \n",
       "3                              Simplify to understand         3   \n",
       "4      How to measure the impact of specific projects         3   \n",
       "7                  How to as present results visually         3   \n",
       "14                 Understand the basics of causality         3   \n",
       "15                              Design of experiments         3   \n",
       "9   Strengthen your intuition of how ML algorithms...         0   \n",
       "12  Storytelling to create features and to interpr...         1   \n",
       "13                           Making decisions from ML         1   \n",
       "8   Tools to deepen your understanding of ML algor...         0   \n",
       "10                  Identify and correct data leakage         0   \n",
       "11          Minimal framework to deploy in production         0   \n",
       "\n",
       "    Machine Learning + Statistics  Programming  Soft  exposure Exposure  \n",
       "0                               0            0     1       0.0      Low  \n",
       "6                               0            0     3       0.0      Low  \n",
       "5                               0            0     0       0.2      Low  \n",
       "1                               0            0     0       0.6      Low  \n",
       "2                               0            0     0       0.6      Low  \n",
       "3                               0            0     0       0.6      Low  \n",
       "4                               0            0     0       0.6      Low  \n",
       "7                               0            1     0       1.0   Medium  \n",
       "14                              3            1     0       1.6   Medium  \n",
       "15                              3            1     0       1.6   Medium  \n",
       "9                               1            1     0       1.8   Medium  \n",
       "12                              3            1     0       2.8     High  \n",
       "13                              3            1     0       2.8     High  \n",
       "8                               3            1     0       3.4     High  \n",
       "10                              3            1     0       3.4     High  \n",
       "11                              1            3     0       3.8     High  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_book['exposure'] = df_book[cols].apply(func_analytics, axis=1)\n",
    "df_book.sort_values(by='exposure', inplace=True)\n",
    "df_book2 = num_to_cat(df=df_book, col='exposure', thres1 = thres1, thres2 =thres2)\n",
    "df_book2.rename(columns={'exposure_cat':'Exposure'}, inplace=True)\n",
    "df_book2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Num</th>\n",
       "      <th>Chapter</th>\n",
       "      <th>Main Lesson</th>\n",
       "      <th>Business</th>\n",
       "      <th>Machine Learning + Statistics</th>\n",
       "      <th>Programming</th>\n",
       "      <th>Soft</th>\n",
       "      <th>exposure</th>\n",
       "      <th>Exposure</th>\n",
       "      <th>num_chapter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>So What</td>\n",
       "      <td>How to measure the impact of your team</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>1. So What</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Narratives</td>\n",
       "      <td>How to build narratives before and after creat...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>7. Narratives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Lift</td>\n",
       "      <td>Technique to see differences across groups</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Low</td>\n",
       "      <td>6. Lift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Metrics Design</td>\n",
       "      <td>Find better metrics to action</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "      <td>2. Metrics Design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Growth Decompositions</td>\n",
       "      <td>Understand what's happening with the business</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Low</td>\n",
       "      <td>3. Growth Decompositions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num                Chapter  \\\n",
       "0    1                So What   \n",
       "6    7             Narratives   \n",
       "5    6                   Lift   \n",
       "1    2         Metrics Design   \n",
       "2    3  Growth Decompositions   \n",
       "\n",
       "                                         Main Lesson  Business  \\\n",
       "0             How to measure the impact of your team         1   \n",
       "6  How to build narratives before and after creat...         3   \n",
       "5         Technique to see differences across groups         1   \n",
       "1                      Find better metrics to action         3   \n",
       "2      Understand what's happening with the business         3   \n",
       "\n",
       "   Machine Learning + Statistics  Programming  Soft  exposure Exposure  \\\n",
       "0                              0            0     1       0.0      Low   \n",
       "6                              0            0     3       0.0      Low   \n",
       "5                              0            0     0       0.2      Low   \n",
       "1                              0            0     0       0.6      Low   \n",
       "2                              0            0     0       0.6      Low   \n",
       "\n",
       "                num_chapter  \n",
       "0                1. So What  \n",
       "6             7. Narratives  \n",
       "5                   6. Lift  \n",
       "1         2. Metrics Design  \n",
       "2  3. Growth Decompositions  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_book2['num_chapter'] = df_book2['Num'].astype('str') + '. ' + df_book2['Chapter']\n",
    "df_book2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[cols=\"1,1,1\"]\n",
      "|===\n",
      "|.num_chapter\n",
      "|.Main Lesson\n",
      "|.Exposure\n",
      "|1. So What\n",
      "|How to measure the impact of your team\n",
      "|Low\n",
      "|7. Narratives\n",
      "|How to build narratives before and after creating a project\n",
      "|Low\n",
      "|6. Lift\n",
      "|Technique to see differences across groups\n",
      "|Low\n",
      "|2. Metrics Design\n",
      "|Find better metrics to action\n",
      "|Low\n",
      "|3. Growth Decompositions\n",
      "|Understand what's happening with the business\n",
      "|Low\n",
      "|4. 2x2 Designs\n",
      "|Simplify to understand\n",
      "|Low\n",
      "|5. Business Cases\n",
      "|How to measure the impact of specific projects\n",
      "|Low\n",
      "|8. Datavis\n",
      "|How to as present results visually\n",
      "|Medium\n",
      "|15. Incrementality\n",
      "|Understand the basics of causality\n",
      "|Medium\n",
      "|16. A/B Tests\n",
      "|Design of experiments\n",
      "|Medium\n",
      "|10. Linear Regression\n",
      "|Strengthen your intuition of how ML algorithms work\n",
      "|Medium\n",
      "|13. Storytelling ML\n",
      "|Storytelling to create features and to interpret results\n",
      "|High\n",
      "|14. Predictions to Decisions\n",
      "|Making decisions from ML\n",
      "|High\n",
      "|9. Simulation & Boot\n",
      "|Tools to deepen your understanding of ML algorithms\n",
      "|High\n",
      "|11. Data Leakage\n",
      "|Identify and correct data leakage\n",
      "|High\n",
      "|12. Productionizing Models\n",
      "|Minimal framework to deploy in production\n",
      "|High\n",
      "|===\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_book_preasc = df_book2[['num_chapter','Main Lesson','Exposure']]\n",
    "print(df_to_asciidoc(df_book_preasc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now create a plot\n",
    "fig, ax = plt.subplots()\n",
    "df_plt = df_book2.sort_values(by='Num', ascending=False)[['exposure','num_chapter']]\n",
    "df_plt.exposure.plot(ax=ax, kind='barh', edgecolor='k', facecolor='0.5')\n",
    "ytk = ax.set_yticklabels(df_plt.num_chapter)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}