1167 lines
60 KiB
Text
1167 lines
60 KiB
Text
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"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>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",
|
||
" </tr>\n",
|
||
" <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": [],
|
||
"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))"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0.6722543326178704, 0.03321124881554773)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAATHUlEQVR4nO3df4xd+X3W8feTWa86pGmN2KHEYye2kGuwulEdRo7QojZqstjbVl53KyE7CiKo1CDVISWtwaZoCYvQhhoF+seqwmy2pNDG3W5dy1DDUHU3gqKmeBxvYuztLMa09YyBnaYZQmDo2ubDH3O93J2dH3fWd+bOOX6/pNHe8z1f3/PI8j6+/p5z7klVIUlqvncMOoAkqT8sdElqCQtdklrCQpeklrDQJaklHhjUgR966KHavn37oA4vSY108eLF36uqkcX2DazQt2/fzsTExKAOL0mNlOR3ltrnkosktYSFLkktYaFLUktY6JLUEha6JLXEwK5yke7F2UvTnByf5ObsHFs2D3Ns3y4O7hkddCxpoCx0Nc7ZS9OcOHOZuVt3AJienePEmcsAlrruay65qHFOjk++UeZ3zd26w8nxyQElkjYGC12Nc3N2blXj0v2ip0JPsj/JZJJrSY4vsv+9SX4tyVeSfCHJ1v5HleZt2Ty8qnHpfrFioScZAp4BHgN2A4eT7F4w7R8AP1tV7wOeAp7ud1DprmP7djG8aehNY8Obhji2b9eAEkkbQy+f0PcC16rqelW9DpwGHl8wZzfwYuf1S4vsl/rm4J5Rnn7iYUY3DxNgdPMwTz/xsCdEdd/r5SqXUeBG1/YU8IEFc74MPAH8FPADwLuS/JGq+mpfUkoLHNwzaoFLC/TrpOiPA9+d5BLw3cA0cGfhpCRHkkwkmZiZmenToSVJ0FuhTwPbura3dsbeUFU3q+qJqtoD/ERnbHbhG1XVqaoaq6qxkZFFv85XkvQ29VLoF4CdSXYkeRA4BJzrnpDkoSR33+sE8Fx/Y0qSVrJioVfVbeAoMA68AjxfVVeSPJXkQGfaB4HJJK8C3wb8vTXKK0laQqpqIAceGxsrn1gkSauT5GJVjS22zztFJaklLHRJagkLXZJawkKXpJaw0CWpJSx0SWoJC12SWsJCl6SWsNAlqSUsdElqCQtdklrCQpeklujliUWStCGdvTTNyfFJbs7OsWXzMMf27drQT7Ja67wWuqRGOntpmhNnLjN3a/7haNOzc5w4cxlgQ5b6euR1yUVSI50cn3yjHO+au3WHk+OTA0q0vPXIa6FLaqSbs3OrGh+09chroUtqpC2bh1c1PmjrkddCl9RIx/btYnjT0JvGhjcNcWzfrgElWt565O2p0JPsTzKZ5FqS44vsf0+Sl5JcSvKVJN/bt4SStIiDe0Z5+omHGd08TIDRzcM8/cTDG/KEKKxP3hWfKZpkCHgVeBSYAi4Ah6vqatecU8ClqvrpJLuB81W1fbn39ZmikrR69/pM0b3Ataq6XlWvA6eBxxfMKeBbOq+/Fbj5dsNKkt6eXgp9FLjRtT3VGev2KeCjSaaA88DHF3ujJEeSTCSZmJmZeRtxJUlL6ddJ0cPAP62qrcD3Av8syVveu6pOVdVYVY2NjIz06dCSJOit0KeBbV3bWztj3X4IeB6gqn4D+CbgoX4ElCT1ppdCvwDsTLIjyYPAIeDcgjm/C3wIIMmfZL7QXVORpHW0YqFX1W3gKDAOvAI8X1VXkjyV5EBn2o8BP5zky8DngY/VSpfPSJL6qqcv56qq88yf7Owee7Lr9VXgkf5GkySthneKSlJLWOiS1BIWuiS1hIUuSS1hoUtSS1joktQSFroktYSFLkktYaFLUktY6JLUEha6JLWEhS5JLWGhS1JLWOiS1BIWuiS1hIUuSS1hoUtSS/RU6En2J5lMci3J8UX2/8MkL3d+Xk0y2/ekkqRlrfgIuiRDwDPAo8AUcCHJuc5j5wCoqr/WNf/jwJ41yCpJWkYvn9D3Ateq6npVvQ6cBh5fZv5h5h8ULUlaR70U+ihwo2t7qjP2FkneC+wAXlxi/5EkE0kmZmZmVptVkrSMfp8UPQS8UFV3FttZVaeqaqyqxkZGRvp8aEm6v/VS6NPAtq7trZ2xxRzC5RZJGoheCv0CsDPJjiQPMl/a5xZOSvIngD8M/EZ/I0qSerFioVfVbeAoMA68AjxfVVeSPJXkQNfUQ8Dpqqq1iSpJWs6Kly0CVNV54PyCsScXbH+qf7EkSavlnaKS1BIWuiS1hIUuSS1hoUtSS1joktQSFroktYSFLkktYaFLUktY6JLUEha6JLWEhS5JLdHTd7lIG83ZS9OcHJ/k5uwcWzYPc2zfLg7uWfS5K9J9w0JX45y9NM2JM5eZuzX/HJXp2TlOnLkMYKnrvuaSixrn5PjkG2V+19ytO5wcnxxQImljsNDVODdn51Y1Lt0vLHQ1zpbNw6sal+4XFroa59i+XQxvGnrT2PCmIY7t2zWgRNLG4ElRNc7dE59e5SK9WU+FnmQ/8FPAEPBsVX16kTl/DvgUUMCXq+ojfcwpvcnBPaMWuLTAioWeZAh4BngUmAIuJDlXVVe75uwETgCPVNXXkvzRtQosSVpcL2voe4FrVXW9ql4HTgOPL5jzw8AzVfU1gKp6rb8xJUkr6aXQR4EbXdtTnbFu3w58e5J/n+SLnSWat0hyJMlEkomZmZm3l1iStKh+XeXyALAT+CBwGPgnSTYvnFRVp6pqrKrGRkZG+nRoSRL0VujTwLau7a2dsW5TwLmqulVV/wV4lfmClyStk14K/QKwM8mOJA8Ch4BzC+acZf7TOUkeYn4J5nr/YkqSVrJioVfVbeAoMA68AjxfVVeSPJXkQGfaOPDVJFeBl4BjVfXVtQotSXqrVNVADjw2NlYTExMDObYkNVWSi1U1ttg+b/2XpJaw0CWpJSx0SWoJv5xLgI90k9rAQpePdJNawiUX+Ug3qSUsdPlIN6klLHT5SDepJSx0+Ug3qSU8KSof6Sa1hIUuwEe6SW3gkosktYSFLkktYaFLUktY6JLUEha6JLWEhS5JLdFToSfZn2QyybUkxxfZ/7EkM0le7vz8pf5HlSQtZ8Xr0JMMAc8AjwJTwIUk56rq6oKpv1BVR9cgoySpB718Qt8LXKuq61X1OnAaeHxtY0mSVquXQh8FbnRtT3XGFvrBJF9J8kKSbYu9UZIjSSaSTMzMzLyNuJKkpfTrpOi/ALZX1fuAXwU+t9ikqjpVVWNVNTYyMtKnQ0uSoLdCnwa6P3Fv7Yy9oaq+WlV/0Nl8FvhT/YknSepVL4V+AdiZZEeSB4FDwLnuCUne3bV5AHilfxElSb1Y8SqXqrqd5CgwDgwBz1XVlSRPARNVdQ74q0kOALeB3wc+toaZJUmLSFUN5MBjY2M1MTExkGNLUlMluVhVY4vt805RSWoJC12SWsJCl6SWsNAlqSUsdElqCQtdklrCQpeklrDQJaklLHRJagkLXZJawkKXpJaw0CWpJSx0SWoJC12SWsJCl6SWsNAlqSUsdElqiZ4KPcn+JJNJriU5vsy8H0xSSRZ9moYkae2sWOhJhoBngMeA3cDhJLsXmfcu4BPAb/Y7pCRpZb18Qt8LXKuq61X1OnAaeHyReX8X+PvA/+ljPklSj3op9FHgRtf2VGfsDUneD2yrql9Z7o2SHEkykWRiZmZm1WElSUu755OiSd4BfAb4sZXmVtWpqhqrqrGRkZF7PbQkqcsDPcyZBrZ1bW/tjN31LuA7gC8kAfhjwLkkB6pqol9BJWmhs5emOTk+yc3ZObZsHubYvl0c3DO68i9sqV4K/QKwM8kO5ov8EPCRuzur6n8AD93dTvIF4Mctc0lr6eylaU6cuczcrTsATM/OceLMZYD7ttRXXHKpqtvAUWAceAV4vqquJHkqyYG1DihJizk5PvlGmd81d+sOJ8cnB5Ro8Hr5hE5VnQfOLxh7com5H7z3WJK0vJuzc6savx94p6ikRtqyeXhV4/cDC11SIx3bt4vhTUNvGhveNMSxfbsGlGjwelpykaSN5u6JT69y+f8s9DXkJVXS2jq4Z9T/p7pY6GvES6okrTfX0NeIl1RJWm8W+hrxkipJ681CXyNeUiVpvVnoa8RLqiStN0+KrhEvqZK03iz0NeQlVZLWk0suktQSFroktYSFLkkt4Rr6GvLWf0nryUJfI976L2m9ueSyRrz1X9J6s9DXiLf+S1pvPRV6kv1JJpNcS3J8kf1/JcnlJC8n+fUku/sftVm89V/Selux0JMMAc8AjwG7gcOLFPbPV9XDVfWdwE8Cn+l30Kbx1n9J662Xk6J7gWtVdR0gyWngceDq3QlV9fWu+e8Eqp8hm8hb/yWtt14KfRS40bU9BXxg4aQkPwJ8EngQ+J7F3ijJEeAIwHve857VZm0cb/2XtJ76dlK0qp6pqj8O/A3gby0x51RVjVXV2MjISL8OLUmit0KfBrZ1bW/tjC3lNHDwHjJJkt6GXgr9ArAzyY4kDwKHgHPdE5Ls7Nr8PuA/9S+iJKkXK66hV9XtJEeBcWAIeK6qriR5CpioqnPA0SQfBm4BXwP+wlqGliS9VU+3/lfVeeD8grEnu15/os+5JEmr5J2iktQSFroktYSFLkktYaFLUktY6JLUEha6JLWEhS5JLWGhS1JLWOiS1BIWuiS1hIUuSS1hoUtSS1joktQSFroktYSFLkktYaFLUktY6JLUEj0VepL9SSaTXEtyfJH9n0xyNclXkvxakvf2P2rznL00zSOffpEdx3+FRz79ImcvLfdsbUm6NysWepIh4BngMWA3cDjJ7gXTLgFjVfU+4AXgJ/sdtGnOXprmxJnLTM/OUcD07Bwnzly21CWtmV4+oe8FrlXV9ap6HTgNPN49oapeqqr/3dn8IrC1vzGb5+T4JHO37rxpbO7WHU6OTw4okaS266XQR4EbXdtTnbGl/BDwrxbbkeRIkokkEzMzM72nbKCbs3OrGpeke9XXk6JJPgqMAScX219Vp6pqrKrGRkZG+nnoDWfL5uFVjUvSveql0KeBbV3bWztjb5Lkw8BPAAeq6g/6E6+5ju3bxfCmoTeNDW8a4ti+XQNKJKntHuhhzgVgZ5IdzBf5IeAj3ROS7AH+MbC/ql7re8oGOrhnflXq5PgkN2fn2LJ5mGP7dr0xLkn9tmKhV9XtJEeBcWAIeK6qriR5CpioqnPML7F8M/CLSQB+t6oOrGHuRji4Z9QCl7RuevmETlWdB84vGHuy6/WH+5xLkrRKPRX6RnH20rRLGJK0hMYU+t0bde5e2333Rh3AUpckGvRdLt6oI0nLa0yhe6OOJC2vMYXujTqStLzGFLo36kjS8hpzUtQbdSRpeY0pdPBGHUlaTmOWXCRJy7PQJaklLHRJagkLXZJawkKXpJZIVQ3mwMkM8Dtv85c/BPxeH+OstSblbVJWaFbeJmWFZuVtUla4t7zvrapFH/k2sEK/F0kmqmps0Dl61aS8TcoKzcrbpKzQrLxNygprl9clF0lqCQtdklqiqYV+atABVqlJeZuUFZqVt0lZoVl5m5QV1ihvI9fQJUlv1dRP6JKkBSx0SWqJxhV6kv1JJpNcS3J80HmWk+S5JK8l+Y+DzrKSJNuSvJTkapIrST4x6ExLSfJNSf5Dki93sv6dQWfqRZKhJJeS/MtBZ1lOkt9OcjnJy0kmBp1nJUk2J3khyW8leSXJnx50psUk2dX5Pb378/UkP9rXYzRpDT3JEPAq8CgwBVwADlfV1YEGW0KS7wK+AfxsVX3HoPMsJ8m7gXdX1ZeSvAu4CBzciL+3SQK8s6q+kWQT8OvAJ6rqiwOOtqwknwTGgG+pqu8fdJ6lJPltYKyqGnGjTpLPAf+uqp5N8iDwh6pqdsCxltXpsmngA1X1dm+wfIumfULfC1yrqutV9TpwGnh8wJmWVFX/Fvj9QefoRVX916r6Uuf1/wReATbkl8/XvG90Njd1fjb0J5MkW4HvA54ddJY2SfKtwHcBnwWoqtc3epl3fAj4z/0sc2heoY8CN7q2p9igpdNkSbYDe4DfHHCUJXWWL14GXgN+tao2bNaOfwT8deD/DjhHLwr4N0kuJjky6DAr2AHMAD/TWc56Nsk7Bx2qB4eAz/f7TZtW6FpjSb4Z+CXgR6vq64POs5SqulNV3wlsBfYm2bBLWkm+H3itqi4OOkuP/kxVvR94DPiRztLhRvUA8H7gp6tqD/C/gI1+bu1B4ADwi/1+76YV+jSwrWt7a2dMfdBZj/4l4Oeq6syg8/Si88/rl4D9A46ynEeAA5216dPA9yT554ONtLSqmu789zXgl5lf6tyopoCprn+hvcB8wW9kjwFfqqr/3u83blqhXwB2JtnR+VvuEHBuwJlaoXOi8bPAK1X1mUHnWU6SkSSbO6+HmT9J/lsDDbWMqjpRVVurajvzf2ZfrKqPDjjWopK8s3NSnM7SxZ8FNuxVWlX134AbSXZ1hj4EbLgT+QscZg2WW6BhD4muqttJjgLjwBDwXFVdGXCsJSX5PPBB4KEkU8DfrqrPDjbVkh4B/jxwubM2DfA3q+r84CIt6d3A5zpXCrwDeL6qNvSlgA3ybcAvz//9zgPAz1fVvx5spBV9HPi5zoe868BfHHCeJXX+knwU+Mtr8v5NumxRkrS0pi25SJKWYKFLUktY6JLUEha6JLWEhS5JLWGhS1JLWOiS1BL/D/JNMPkIwyOSAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"best_practice_score_se = sorted(best_practice_score_se)\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.scatter(\n",
|
||
" [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]\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": [
|
||
"(0.5515422017785757, 0.09838124227663879)"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"stats.pearsonr(tam[\"peou\"], tam[\"pu\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0.8066270322592023, 0.004809023073123024)"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"stats.pearsonr(tam[\"peou\"], tam[\"itu\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0.7880605510627579, 0.006774486564715021)"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"stats.pearsonr(tam[\"pu\"], tam[\"itu\"])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.10.4 ('.env': venv)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.4"
|
||
},
|
||
"orig_nbformat": 4,
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|