{
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     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>How many years of software engineering experience do you have?</th>\n",
       "      <th>How many years of data science experience do you have?</th>\n",
       "      <th>Write reusable scripts for data cleaning and merging</th>\n",
       "      <th>Make datasets available on shared infrastructure</th>\n",
       "      <th>Use versioning for data, model, configurations and training scripts</th>\n",
       "      <th>Continuously monitor the behaviour of deployed models</th>\n",
       "      <th>Log production predictions with the model’s version and input data</th>\n",
       "      <th>Store models in a single format for ease of use</th>\n",
       "      <th>Equip with web interface, package image, provide REST API</th>\n",
       "      <th>Provide simple API for serving batch and real-time requests</th>\n",
       "      <th>Integration with existing data infrastructure</th>\n",
       "      <th>Querying, visualising and understanding metrics and event logging</th>\n",
       "      <th>Allow experimentation with the inference code</th>\n",
       "      <th>Keep the model’s API and documentation together</th>\n",
       "      <th>Parallelise feature extraction</th>\n",
       "      <th>Cache predictions</th>\n",
       "      <th>Async support for top-down chaining models</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly disagree</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Not applicable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Not applicable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Neither agree nor disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Agree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   How many years of software engineering experience do you have?  \\\n",
       "0                                                  3                \n",
       "1                                                  6                \n",
       "2                                                  1                \n",
       "3                                                  3                \n",
       "4                                                  1                \n",
       "5                                                  6                \n",
       "6                                                  2                \n",
       "7                                                  1                \n",
       "8                                                  0                \n",
       "9                                                  7                \n",
       "\n",
       "   How many years of data science experience do you have?  \\\n",
       "0                                                  1        \n",
       "1                                                  2        \n",
       "2                                                  5        \n",
       "3                                                  3        \n",
       "4                                                  7        \n",
       "5                                                  0        \n",
       "6                                                  2        \n",
       "7                                                  2        \n",
       "8                                                  1        \n",
       "9                                                  1        \n",
       "\n",
       "  Write reusable scripts for data cleaning and merging  \\\n",
       "0                                     Strongly agree     \n",
       "1                         Neither agree nor disagree     \n",
       "2                                  Strongly disagree     \n",
       "3                                              Agree     \n",
       "4                         Neither agree nor disagree     \n",
       "5                                     Strongly agree     \n",
       "6                                           Disagree     \n",
       "7                                           Disagree     \n",
       "8                                  Strongly disagree     \n",
       "9                                     Strongly agree     \n",
       "\n",
       "  Make datasets available on shared infrastructure  \\\n",
       "0                                            Agree   \n",
       "1                                            Agree   \n",
       "2                                         Disagree   \n",
       "3                                            Agree   \n",
       "4                                         Disagree   \n",
       "5                                   Strongly agree   \n",
       "6                       Neither agree nor disagree   \n",
       "7                       Neither agree nor disagree   \n",
       "8                                         Disagree   \n",
       "9                                   Strongly agree   \n",
       "\n",
       "  Use versioning for data, model, configurations and training scripts  \\\n",
       "0                                     Strongly agree                    \n",
       "1                         Neither agree nor disagree                    \n",
       "2                                           Disagree                    \n",
       "3                                           Disagree                    \n",
       "4                         Neither agree nor disagree                    \n",
       "5                                              Agree                    \n",
       "6                                              Agree                    \n",
       "7                                           Disagree                    \n",
       "8                                  Strongly disagree                    \n",
       "9                                              Agree                    \n",
       "\n",
       "  Continuously monitor the behaviour of deployed models  \\\n",
       "0                         Neither agree nor disagree      \n",
       "1                                              Agree      \n",
       "2                                  Strongly disagree      \n",
       "3                         Neither agree nor disagree      \n",
       "4                                           Disagree      \n",
       "5                         Neither agree nor disagree      \n",
       "6                                     Strongly agree      \n",
       "7                                              Agree      \n",
       "8                                           Disagree      \n",
       "9                                     Strongly agree      \n",
       "\n",
       "  Log production predictions with the model’s version and input data  \\\n",
       "0                                     Strongly agree                   \n",
       "1                                              Agree                   \n",
       "2                                           Disagree                   \n",
       "3                                              Agree                   \n",
       "4                                  Strongly disagree                   \n",
       "5                                              Agree                   \n",
       "6                         Neither agree nor disagree                   \n",
       "7                                           Disagree                   \n",
       "8                                  Strongly disagree                   \n",
       "9                                              Agree                   \n",
       "\n",
       "  Store models in a single format for ease of use  \\\n",
       "0                                  Strongly agree   \n",
       "1                      Neither agree nor disagree   \n",
       "2                               Strongly disagree   \n",
       "3                                  Not applicable   \n",
       "4                                  Not applicable   \n",
       "5                                  Strongly agree   \n",
       "6                                        Disagree   \n",
       "7                               Strongly disagree   \n",
       "8                                        Disagree   \n",
       "9                                           Agree   \n",
       "\n",
       "  Equip with web interface, package image, provide REST API  \\\n",
       "0                                     Strongly agree          \n",
       "1                                           Disagree          \n",
       "2                                  Strongly disagree          \n",
       "3                                     Not applicable          \n",
       "4                                     Not applicable          \n",
       "5                                              Agree          \n",
       "6                                     Strongly agree          \n",
       "7                                     Strongly agree          \n",
       "8                                              Agree          \n",
       "9                                     Strongly agree          \n",
       "\n",
       "  Provide simple API for serving batch and real-time requests  \\\n",
       "0                                     Strongly agree            \n",
       "1                                  Strongly disagree            \n",
       "2                         Neither agree nor disagree            \n",
       "3                         Neither agree nor disagree            \n",
       "4                                           Disagree            \n",
       "5                                     Strongly agree            \n",
       "6                                           Disagree            \n",
       "7                                     Strongly agree            \n",
       "8                                  Strongly disagree            \n",
       "9                                  Strongly disagree            \n",
       "\n",
       "  Integration with existing data infrastructure  \\\n",
       "0                                Strongly agree   \n",
       "1                                      Disagree   \n",
       "2                             Strongly disagree   \n",
       "3                                         Agree   \n",
       "4                             Strongly disagree   \n",
       "5                                         Agree   \n",
       "6                                      Disagree   \n",
       "7                             Strongly disagree   \n",
       "8                                      Disagree   \n",
       "9                                Strongly agree   \n",
       "\n",
       "  Querying, visualising and understanding metrics and event logging  \\\n",
       "0                                     Strongly agree                  \n",
       "1                                  Strongly disagree                  \n",
       "2                                           Disagree                  \n",
       "3                                           Disagree                  \n",
       "4                                           Disagree                  \n",
       "5                                     Strongly agree                  \n",
       "6                                              Agree                  \n",
       "7                                           Disagree                  \n",
       "8                                  Strongly disagree                  \n",
       "9                                     Not applicable                  \n",
       "\n",
       "  Allow experimentation with the inference code  \\\n",
       "0                                Strongly agree   \n",
       "1                                      Disagree   \n",
       "2                    Neither agree nor disagree   \n",
       "3                                         Agree   \n",
       "4                                      Disagree   \n",
       "5                                      Disagree   \n",
       "6                    Neither agree nor disagree   \n",
       "7                                         Agree   \n",
       "8                                      Disagree   \n",
       "9                                         Agree   \n",
       "\n",
       "  Keep the model’s API and documentation together  \\\n",
       "0                                  Strongly agree   \n",
       "1                               Strongly disagree   \n",
       "2                                           Agree   \n",
       "3                                  Strongly agree   \n",
       "4                      Neither agree nor disagree   \n",
       "5                                  Strongly agree   \n",
       "6                      Neither agree nor disagree   \n",
       "7                               Strongly disagree   \n",
       "8                               Strongly disagree   \n",
       "9                                           Agree   \n",
       "\n",
       "  Parallelise feature extraction           Cache predictions  \\\n",
       "0                 Strongly agree  Neither agree nor disagree   \n",
       "1                       Disagree              Strongly agree   \n",
       "2              Strongly disagree           Strongly disagree   \n",
       "3                 Not applicable  Neither agree nor disagree   \n",
       "4              Strongly disagree              Strongly agree   \n",
       "5                          Agree              Strongly agree   \n",
       "6                 Not applicable                    Disagree   \n",
       "7                 Not applicable           Strongly disagree   \n",
       "8                       Disagree                    Disagree   \n",
       "9                 Strongly agree              Strongly agree   \n",
       "\n",
       "  Async support for top-down chaining models  \n",
       "0                             Strongly agree  \n",
       "1                          Strongly disagree  \n",
       "2                                   Disagree  \n",
       "3                                      Agree  \n",
       "4                             Not applicable  \n",
       "5                             Not applicable  \n",
       "6                             Strongly agree  \n",
       "7                          Strongly disagree  \n",
       "8                          Strongly disagree  \n",
       "9                                      Agree  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "best_practices = pd.read_csv(\"Best practices assessment.csv\")\n",
    "best_practices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.5, 2.0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = {\n",
    "    \"Strongly agree\": 4 / 4,\n",
    "    \"Agree\": 3 / 4,\n",
    "    \"Neither agree nor disagree\": 2 / 4,\n",
    "    \"Disagree\": 1 / 4,\n",
    "    \"Strongly disagree\": 0 / 4,\n",
    "}\n",
    "\n",
    "best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()]\n",
    "\n",
    "best_practice_score_ds = []\n",
    "best_practice_score_se = []\n",
    "\n",
    "for d in best_practices_dicts:\n",
    "    ds_experience = int(d[\"How many years of data science experience do you have?\"])\n",
    "    del d[\"How many years of data science experience do you have?\"]\n",
    "\n",
    "    se_experience = int(\n",
    "        d[\"How many years of software engineering experience do you have?\"]\n",
    "    )\n",
    "    del d[\"How many years of software engineering experience do you have?\"]\n",
    "\n",
    "    scores = [m[v] for v in d.values() if v != \"Not applicable\"]\n",
    "    score = sum(scores) / len(scores)\n",
    "    best_practice_score_ds.append((ds_experience, score))\n",
    "    best_practice_score_se.append((se_experience, score))\n",
    "\n",
    "best_practices[\n",
    "    \"How many years of software engineering experience do you have?\"\n",
    "].median(), best_practices[\n",
    "    \"How many years of data science experience do you have?\"\n",
    "].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5440527060340572, 0.10399880919437814)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "best_practice_score_ds = sorted(best_practice_score_ds)\n",
    "plt.scatter(\n",
    "    [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n",
    ")\n",
    "stats.pearsonr(\n",
    "    [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5157738095238095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.6722543326178704, 0.03321124881554773)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "best_practice_score_se = sorted(best_practice_score_se)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
    "plt.rcParams[\"font.size\"] = 24\n",
    "plt.rcParams[\"figure.figsize\"] = (14, 10)\n",
    "\n",
    "X = [x for x, y in best_practice_score_se]\n",
    "Y = [y for x, y in best_practice_score_se]\n",
    "\n",
    "print(sum(Y) / len(Y))\n",
    "sc = plt.scatter(X, Y, c=\"black\", s=[x * 50 for x, y in best_practice_score_ds])\n",
    "plt.ylabel(\"Ratio of implemented deployment best practices\")\n",
    "plt.xlabel(\"Years of professional software engineering experience\")\n",
    "\n",
    "\n",
    "def best_fit(X, Y):\n",
    "    xbar = sum(X) / len(X)\n",
    "    ybar = sum(Y) / len(Y)\n",
    "    n = len(X)\n",
    "\n",
    "    numer = sum([xi * yi for xi, yi in zip(X, Y)]) - n * xbar * ybar\n",
    "    denum = sum([xi**2 for xi in X]) - n * xbar**2\n",
    "\n",
    "    b = numer / denum\n",
    "    a = ybar - b * xbar\n",
    "    return a, b\n",
    "\n",
    "\n",
    "a, b = best_fit(X, Y)\n",
    "yfit = [a + b * xi for xi in X]\n",
    "plt.plot(X, yfit, \"b\", label=\"Best fit\")\n",
    "plt.legend(\n",
    "    sc.legend_elements(\"sizes\", num=5)[0],\n",
    "    [\n",
    "        re.sub(r\"[^\\d]*(\\d+)[^\\d]*\", lambda v: f\"{int(v.group(1)) // 40} years (DS)\", t)\n",
    "        for t in sc.legend_elements(\"sizes\", num=5)[1]\n",
    "    ],\n",
    ")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"best-practices\", dpi=150)\n",
    "\n",
    "stats.pearsonr(\n",
    "    [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>I believe the use of GreatAI improves the quality of AI deployments.</th>\n",
       "      <th>I believe the use of GreatAI would increase my productivity.</th>\n",
       "      <th>I believe the use of GreatAI can lead to robust and trustworthy deployments.</th>\n",
       "      <th>Overall, I found GreatAI useful when working with AI.</th>\n",
       "      <th>I found the GreatAI easy to learn.</th>\n",
       "      <th>I found it is easy to employ GreatAI in practice.</th>\n",
       "      <th>I found it is easy to integrate GreatAI into an existing project.</th>\n",
       "      <th>Overall, I found GreatAI easy to use.</th>\n",
       "      <th>Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.</th>\n",
       "      <th>Overall, I intend to use the GreatAI in my personal or professional projects.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   I believe the use of GreatAI improves the quality of AI deployments.  \\\n",
       "0                                                  7                      \n",
       "1                                                  7                      \n",
       "2                                                  4                      \n",
       "3                                                  6                      \n",
       "4                                                  7                      \n",
       "5                                                  6                      \n",
       "6                                                  6                      \n",
       "7                                                  7                      \n",
       "8                                                  4                      \n",
       "9                                                  7                      \n",
       "\n",
       "   I believe the use of GreatAI would increase my productivity.  \\\n",
       "0                                                  7              \n",
       "1                                                  7              \n",
       "2                                                  4              \n",
       "3                                                  7              \n",
       "4                                                  7              \n",
       "5                                                  6              \n",
       "6                                                  6              \n",
       "7                                                  6              \n",
       "8                                                  5              \n",
       "9                                                  7              \n",
       "\n",
       "   I believe the use of GreatAI can lead to robust and trustworthy deployments.  \\\n",
       "0                                                  7                              \n",
       "1                                                  6                              \n",
       "2                                                  5                              \n",
       "3                                                  6                              \n",
       "4                                                  6                              \n",
       "5                                                  6                              \n",
       "6                                                  6                              \n",
       "7                                                  6                              \n",
       "8                                                  5                              \n",
       "9                                                  7                              \n",
       "\n",
       "   Overall, I found GreatAI useful when working with AI.  \\\n",
       "0                                                  7       \n",
       "1                                                  7       \n",
       "2                                                  6       \n",
       "3                                                  7       \n",
       "4                                                  7       \n",
       "5                                                  6       \n",
       "6                                                  4       \n",
       "7                                                  6       \n",
       "8                                                  5       \n",
       "9                                                  7       \n",
       "\n",
       "   I found the GreatAI easy to learn.  \\\n",
       "0                                   7   \n",
       "1                                   5   \n",
       "2                                   6   \n",
       "3                                   7   \n",
       "4                                   4   \n",
       "5                                   6   \n",
       "6                                   5   \n",
       "7                                   4   \n",
       "8                                   7   \n",
       "9                                   5   \n",
       "\n",
       "   I found it is easy to employ GreatAI in practice.  \\\n",
       "0                                                  7   \n",
       "1                                                  6   \n",
       "2                                                  5   \n",
       "3                                                  6   \n",
       "4                                                  5   \n",
       "5                                                  6   \n",
       "6                                                  7   \n",
       "7                                                  5   \n",
       "8                                                  2   \n",
       "9                                                  6   \n",
       "\n",
       "   I found it is easy to integrate GreatAI into an existing project.  \\\n",
       "0                                                  7                   \n",
       "1                                                  7                   \n",
       "2                                                  4                   \n",
       "3                                                  7                   \n",
       "4                                                  5                   \n",
       "5                                                  6                   \n",
       "6                                                  6                   \n",
       "7                                                  3                   \n",
       "8                                                  3                   \n",
       "9                                                  5                   \n",
       "\n",
       "   Overall, I found GreatAI easy to use.  \\\n",
       "0                                      7   \n",
       "1                                      6   \n",
       "2                                      4   \n",
       "3                                      6   \n",
       "4                                      5   \n",
       "5                                      6   \n",
       "6                                      6   \n",
       "7                                      5   \n",
       "8                                      3   \n",
       "9                                      6   \n",
       "\n",
       "   Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.  \\\n",
       "0                                                  7                                                           \n",
       "1                                                  7                                                           \n",
       "2                                                  5                                                           \n",
       "3                                                  7                                                           \n",
       "4                                                  6                                                           \n",
       "5                                                  6                                                           \n",
       "6                                                  7                                                           \n",
       "7                                                  5                                                           \n",
       "8                                                  3                                                           \n",
       "9                                                  7                                                           \n",
       "\n",
       "   Overall, I intend to use the GreatAI in my personal or professional projects.  \n",
       "0                                                  7                              \n",
       "1                                                  7                              \n",
       "2                                                  4                              \n",
       "3                                                  6                              \n",
       "4                                                  6                              \n",
       "5                                                  6                              \n",
       "6                                                  7                              \n",
       "7                                                  6                              \n",
       "8                                                  3                              \n",
       "9                                                  7                              "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tam = pd.read_csv(\"Technology acceptance model questionnaire.csv\")\n",
    "tam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8813771517996869, array([0.688, 0.967]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pingouin\n",
    "\n",
    "pu = [\n",
    "    \"I believe the use of GreatAI improves the quality of AI deployments.\",\n",
    "    \"I believe the use of GreatAI would increase my productivity.\",\n",
    "    \"I believe the use of GreatAI can lead to robust and trustworthy deployments.\",\n",
    "    \"Overall, I found GreatAI useful when working with AI.\",\n",
    "]\n",
    "pingouin.cronbach_alpha(tam[pu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7729220222793487, array([0.403, 0.937]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peou = [\n",
    "    \"I found the GreatAI easy to learn.\",\n",
    "    \"I found it is easy to employ GreatAI in practice.\",\n",
    "    \"I found it is easy to integrate GreatAI into an existing project.\",\n",
    "    \"Overall, I found GreatAI easy to use.\",\n",
    "]\n",
    "pingouin.cronbach_alpha(tam[peou])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9538950715421304, array([0.814, 0.989]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itu = [\n",
    "    \"Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.\",\n",
    "    \"Overall, I intend to use the GreatAI in my personal or professional projects.\",\n",
    "]\n",
    "pingouin.cronbach_alpha(tam[itu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>pu</th>\n",
       "      <th>peou</th>\n",
       "      <th>itu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.75</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.50</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.25</td>\n",
       "      <td>4.25</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>5.50</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pu  peou  itu\n",
       "0  7.00  7.00  7.0\n",
       "1  6.75  6.00  7.0\n",
       "2  4.75  4.75  4.5\n",
       "3  6.50  6.50  6.5\n",
       "4  6.75  4.75  6.0\n",
       "5  6.00  6.00  6.0\n",
       "6  5.50  6.00  7.0\n",
       "7  6.25  4.25  5.5\n",
       "8  4.75  3.75  3.0\n",
       "9  7.00  5.50  7.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tam[\"pu\"] = tam[pu].mean(1)\n",
    "tam[\"peou\"] = tam[peou].mean(1)\n",
    "tam[\"itu\"] = tam[itu].mean(1)\n",
    "tam[[\"pu\", \"peou\", \"itu\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pu      6.125\n",
       "peou    5.450\n",
       "itu     5.950\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tam[[\"pu\", \"peou\", \"itu\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pu      6.375\n",
       "peou    5.750\n",
       "itu     6.250\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tam[[\"pu\", \"peou\", \"itu\"]].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pu      0.859990\n",
       "peou    1.039498\n",
       "itu     1.321825\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tam[[\"pu\", \"peou\", \"itu\"]].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5515422017785757, 0.09838124227663879)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(tam[\"peou\"], tam[\"pu\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8066270322592023, 0.004809023073123024)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(tam[\"peou\"], tam[\"itu\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7880605510627579, 0.006774486564715021)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(tam[\"pu\"], tam[\"itu\"])"
   ]
  }
 ],
 "metadata": {
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