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sprint-econtai/archive/analysis.ipynb
Félix Dorn 43076bcbb1 old
2025-07-15 00:41:05 +02:00

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{
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{
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" border-left-color: var(--fill-color);\n",
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" 80% {\n",
" border-color: transparent;\n",
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"\n",
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" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
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" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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" (() => {\n",
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" quickchartButtonEl.style.display =\n",
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"text/plain": [
" onetsoc_code task_id \\\n",
"0 11-1011.00 8823 \n",
"1 11-1011.00 8824 \n",
"2 11-1011.00 8827 \n",
"3 11-1011.00 8826 \n",
"4 11-1011.00 8834 \n",
"... ... ... \n",
"21693 53-7081.00 7172 \n",
"21694 53-7081.00 7178 \n",
"21695 53-7081.00 7179 \n",
"21696 53-7081.00 7183 \n",
"21697 53-7121.00 12796 \n",
"\n",
" task \\\n",
"0 Direct or coordinate an organization's financi... \n",
"1 Confer with board members, organization offici... \n",
"2 Prepare budgets for approval, including those ... \n",
"3 Direct, plan, or implement policies, objective... \n",
"4 Prepare or present reports concerning activiti... \n",
"... ... \n",
"21693 Fill out defective equipment reports. \n",
"21694 Communicate with dispatchers concerning delays... \n",
"21695 Check road or weather conditions to determine ... \n",
"21696 Organize schedules for refuse collection. \n",
"21697 Record operating data such as products and qua... \n",
"\n",
" occupation_title \\\n",
"0 Chief Executives \n",
"1 Chief Executives \n",
"2 Chief Executives \n",
"3 Chief Executives \n",
"4 Chief Executives \n",
"... ... \n",
"21693 Refuse and Recyclable Material Collectors \n",
"21694 Refuse and Recyclable Material Collectors \n",
"21695 Refuse and Recyclable Material Collectors \n",
"21696 Refuse and Recyclable Material Collectors \n",
"21697 Tank Car, Truck, and Ship Loaders \n",
"\n",
" occupation_description \\\n",
"0 Determine and formulate policies and provide o... \n",
"1 Determine and formulate policies and provide o... \n",
"2 Determine and formulate policies and provide o... \n",
"3 Determine and formulate policies and provide o... \n",
"4 Determine and formulate policies and provide o... \n",
"... ... \n",
"21693 Collect and dump refuse or recyclable material... \n",
"21694 Collect and dump refuse or recyclable material... \n",
"21695 Collect and dump refuse or recyclable material... \n",
"21696 Collect and dump refuse or recyclable material... \n",
"21697 Load and unload chemicals and bulk solids, suc... \n",
"\n",
" frequency_category_1 frequency_category_2 frequency_category_3 \\\n",
"0 5.92 15.98 29.68 \n",
"1 1.42 14.44 27.31 \n",
"2 15.50 38.21 32.73 \n",
"3 3.03 17.33 20.30 \n",
"4 1.98 14.06 42.60 \n",
"... ... ... ... \n",
"21693 0.00 1.75 9.69 \n",
"21694 0.00 1.04 5.92 \n",
"21695 0.00 8.98 4.23 \n",
"21696 11.57 25.97 14.88 \n",
"21697 0.00 2.49 2.07 \n",
"\n",
" frequency_category_4 frequency_category_5 ... importance_average \\\n",
"0 21.18 19.71 ... 4.52 \n",
"1 25.52 26.88 ... 4.32 \n",
"2 5.15 5.25 ... 4.30 \n",
"3 18.10 33.16 ... 4.24 \n",
"4 21.24 13.18 ... 4.17 \n",
"... ... ... ... ... \n",
"21693 3.08 85.29 ... 4.27 \n",
"21694 3.74 69.00 ... 3.96 \n",
"21695 8.60 61.70 ... 3.81 \n",
"21696 0.00 43.02 ... 3.29 \n",
"21697 0.41 45.74 ... 4.26 \n",
"\n",
" relevance_average dwas \\\n",
"0 74.44 ['Direct financial operations.'] \n",
"1 81.71 ['Confer with organizational members to accomp... \n",
"2 93.41 ['Prepare operational budgets.'] \n",
"3 97.79 ['Implement organizational process or policy c... \n",
"4 92.92 ['Prepare financial documents, reports, or bud... \n",
"... ... ... \n",
"21693 91.18 ['Prepare accident or incident reports.'] \n",
"21694 97.50 ['Report vehicle or equipment malfunctions.', ... \n",
"21695 89.52 ['Gather information about work conditions or ... \n",
"21696 42.06 ['Schedule operational activities.'] \n",
"21697 90.86 ['Record operational or production data.'] \n",
"\n",
" remote_status occ_code total_employment hourly_wage_average \\\n",
"0 remote 11-1011 211230.0 124.47 \n",
"1 remote 11-1011 211230.0 124.47 \n",
"2 remote 11-1011 211230.0 124.47 \n",
"3 remote 11-1011 211230.0 124.47 \n",
"4 remote 11-1011 211230.0 124.47 \n",
"... ... ... ... ... \n",
"21693 remote 53-7081 135430.0 22.99 \n",
"21694 remote 53-7081 135430.0 22.99 \n",
"21695 remote 53-7081 135430.0 22.99 \n",
"21696 remote 53-7081 135430.0 22.99 \n",
"21697 remote 53-7121 11400.0 29.1 \n",
"\n",
" annual_wage_average lb_estimate ub_estimate \n",
"0 258900 1 hour 8 hours \n",
"1 258900 30 minutes 2 hours \n",
"2 258900 1 hour 8 hours \n",
"3 258900 1 week 3 weeks \n",
"4 258900 2 hours 1 week \n",
"... ... ... ... \n",
"21693 47810 10 minutes 30 minutes \n",
"21694 47810 10 minutes 30 minutes \n",
"21695 47810 10 minutes 30 minutes \n",
"21696 47810 30 minutes 2 hours \n",
"21697 60530 10 minutes 30 minutes \n",
"\n",
"[21698 rows x 22 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"task_estimated.csv\").dropna(subset=['lb_estimate', 'ub_estimate'])\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "KdJN4i9yf_F2",
"outputId": "eecd3e30-b2cb-4be5-ce42-4fba43adc42b"
},
"outputs": [
{
"data": {
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"type": "dataframe",
"variable_name": "df"
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" <td>124.47</td>\n",
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"\n",
"\n",
" <div id=\"df-efe633d0-d1e5-4053-9017-7be4ff2f8e85\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efe633d0-d1e5-4053-9017-7be4ff2f8e85')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-efe633d0-d1e5-4053-9017-7be4ff2f8e85 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" <div id=\"id_7fd2ac6e-b910-426b-9c55-49f5963f15ce\">\n",
" <style>\n",
" .colab-df-generate {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-generate:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
" title=\"Generate code using this dataframe.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
" </svg>\n",
" </button>\n",
" <script>\n",
" (() => {\n",
" const buttonEl =\n",
" document.querySelector('#id_7fd2ac6e-b910-426b-9c55-49f5963f15ce button.colab-df-generate');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" buttonEl.onclick = () => {\n",
" google.colab.notebook.generateWithVariable('df');\n",
" }\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" onetsoc_code task_id \\\n",
"0 11-1011.00 8823 \n",
"1 11-1011.00 8824 \n",
"2 11-1011.00 8827 \n",
"3 11-1011.00 8826 \n",
"4 11-1011.00 8834 \n",
"... ... ... \n",
"21693 53-7081.00 7172 \n",
"21694 53-7081.00 7178 \n",
"21695 53-7081.00 7179 \n",
"21696 53-7081.00 7183 \n",
"21697 53-7121.00 12796 \n",
"\n",
" task \\\n",
"0 Direct or coordinate an organization's financi... \n",
"1 Confer with board members, organization offici... \n",
"2 Prepare budgets for approval, including those ... \n",
"3 Direct, plan, or implement policies, objective... \n",
"4 Prepare or present reports concerning activiti... \n",
"... ... \n",
"21693 Fill out defective equipment reports. \n",
"21694 Communicate with dispatchers concerning delays... \n",
"21695 Check road or weather conditions to determine ... \n",
"21696 Organize schedules for refuse collection. \n",
"21697 Record operating data such as products and qua... \n",
"\n",
" occupation_title \\\n",
"0 Chief Executives \n",
"1 Chief Executives \n",
"2 Chief Executives \n",
"3 Chief Executives \n",
"4 Chief Executives \n",
"... ... \n",
"21693 Refuse and Recyclable Material Collectors \n",
"21694 Refuse and Recyclable Material Collectors \n",
"21695 Refuse and Recyclable Material Collectors \n",
"21696 Refuse and Recyclable Material Collectors \n",
"21697 Tank Car, Truck, and Ship Loaders \n",
"\n",
" occupation_description \\\n",
"0 Determine and formulate policies and provide o... \n",
"1 Determine and formulate policies and provide o... \n",
"2 Determine and formulate policies and provide o... \n",
"3 Determine and formulate policies and provide o... \n",
"4 Determine and formulate policies and provide o... \n",
"... ... \n",
"21693 Collect and dump refuse or recyclable material... \n",
"21694 Collect and dump refuse or recyclable material... \n",
"21695 Collect and dump refuse or recyclable material... \n",
"21696 Collect and dump refuse or recyclable material... \n",
"21697 Load and unload chemicals and bulk solids, suc... \n",
"\n",
" frequency_category_1 frequency_category_2 frequency_category_3 \\\n",
"0 5.92 15.98 29.68 \n",
"1 1.42 14.44 27.31 \n",
"2 15.50 38.21 32.73 \n",
"3 3.03 17.33 20.30 \n",
"4 1.98 14.06 42.60 \n",
"... ... ... ... \n",
"21693 0.00 1.75 9.69 \n",
"21694 0.00 1.04 5.92 \n",
"21695 0.00 8.98 4.23 \n",
"21696 11.57 25.97 14.88 \n",
"21697 0.00 2.49 2.07 \n",
"\n",
" frequency_category_4 frequency_category_5 ... \\\n",
"0 21.18 19.71 ... \n",
"1 25.52 26.88 ... \n",
"2 5.15 5.25 ... \n",
"3 18.10 33.16 ... \n",
"4 21.24 13.18 ... \n",
"... ... ... ... \n",
"21693 3.08 85.29 ... \n",
"21694 3.74 69.00 ... \n",
"21695 8.60 61.70 ... \n",
"21696 0.00 43.02 ... \n",
"21697 0.41 45.74 ... \n",
"\n",
" dwas remote_status \\\n",
"0 ['Direct financial operations.'] remote \n",
"1 ['Confer with organizational members to accomp... remote \n",
"2 ['Prepare operational budgets.'] remote \n",
"3 ['Implement organizational process or policy c... remote \n",
"4 ['Prepare financial documents, reports, or bud... remote \n",
"... ... ... \n",
"21693 ['Prepare accident or incident reports.'] remote \n",
"21694 ['Report vehicle or equipment malfunctions.', ... remote \n",
"21695 ['Gather information about work conditions or ... remote \n",
"21696 ['Schedule operational activities.'] remote \n",
"21697 ['Record operational or production data.'] remote \n",
"\n",
" occ_code total_employment hourly_wage_average annual_wage_average \\\n",
"0 11-1011 211230.0 124.47 258900 \n",
"1 11-1011 211230.0 124.47 258900 \n",
"2 11-1011 211230.0 124.47 258900 \n",
"3 11-1011 211230.0 124.47 258900 \n",
"4 11-1011 211230.0 124.47 258900 \n",
"... ... ... ... ... \n",
"21693 53-7081 135430.0 22.99 47810 \n",
"21694 53-7081 135430.0 22.99 47810 \n",
"21695 53-7081 135430.0 22.99 47810 \n",
"21696 53-7081 135430.0 22.99 47810 \n",
"21697 53-7121 11400.0 29.1 60530 \n",
"\n",
" lb_estimate ub_estimate lb_estimate_in_hours ub_estimate_in_hours \n",
"0 1 hour 8 hours 1.0 8.0 \n",
"1 30 minutes 2 hours 0.5 2.0 \n",
"2 1 hour 8 hours 1.0 8.0 \n",
"3 1 week 3 weeks 168.0 504.0 \n",
"4 2 hours 1 week 2.0 168.0 \n",
"... ... ... ... ... \n",
"21693 10 minutes 30 minutes 0.1 0.5 \n",
"21694 10 minutes 30 minutes 0.1 0.5 \n",
"21695 10 minutes 30 minutes 0.1 0.5 \n",
"21696 30 minutes 2 hours 0.5 2.0 \n",
"21697 10 minutes 30 minutes 0.1 0.5 \n",
"\n",
"[21698 rows x 24 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DURATION_TO_HOUR_ESTIMATE = {\n",
" '10 minutes': 0.1,\n",
" '30 minutes': .5,\n",
" '1 hour': 1,\n",
" '2 hours': 2,\n",
" '4 hours': 4,\n",
" '8 hours': 8,\n",
" '16 hours': 16,\n",
" '3 days': 72,\n",
" '1 week': 168,\n",
" '3 weeks': 504,\n",
" '6 weeks': 1008,\n",
" '3 months': 3 * (365.25 / 12) * 24,\n",
" '6 months': 6 * (365.25 / 12) * 24,\n",
"}\n",
"\n",
"df['lb_estimate_in_hours'] = df['lb_estimate'].map(DURATION_TO_HOUR_ESTIMATE)\n",
"df['ub_estimate_in_hours'] = df['ub_estimate'].map(DURATION_TO_HOUR_ESTIMATE)\n",
"\n",
"# check that there are no NaNs\n",
"assert df['lb_estimate_in_hours'].isna().sum() == 0\n",
"assert df['ub_estimate_in_hours'].isna().sum() == 0\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 476
},
"id": "0QVuW46vkqdx",
"outputId": "e5fe56e2-0eeb-428e-b4b8-3c2bc1785560"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Sort the DataFrame by 'ub_estimate_in_hours'\n",
"df_sorted = df.sort_values('ub_estimate_in_hours')\n",
"\n",
"# Calculate the cumulative percentage\n",
"cumulative_percentage = np.linspace(0, 100, len(df_sorted))\n",
"\n",
"# Create the plot with log scale for x-axis\n",
"plt.plot(df_sorted['ub_estimate_in_hours'], cumulative_percentage)\n",
"plt.xscale('log') # Set x-axis to log scale\n",
"plt.xlabel('Agent coherence time')\n",
"plt.ylabel('% of tasks in the economy')\n",
"plt.title('[title]')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 523
},
"id": "K8gmWglltGbn",
"outputId": "cf97686e-43c7-4a65-ec81-e7cc0437b131"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Assume 'df' is your DataFrame containing 'ub_estimate_in_hours'\n",
"\n",
"# Define the duration labels and their corresponding hour estimates\n",
"DURATION_TO_HOUR_ESTIMATE = {\n",
" '10 minutes': 0.1,\n",
" '30 minutes': .5,\n",
" '1 hour': 1,\n",
" '2 hours': 2,\n",
" '4 hours': 4,\n",
" '8 hours': 8,\n",
" '16 hours': 16,\n",
" '3 days': 72,\n",
" '1 week': 168,\n",
" '3 weeks': 504,\n",
" '6 weeks': 1008,\n",
" '3 months': 3 * (365.25 / 12) * 24,\n",
" '6 months': 6 * (365.25 / 12) * 24,\n",
"}\n",
"\n",
"# Get unique hour estimates from the dictionary\n",
"hour_estimates = list(DURATION_TO_HOUR_ESTIMATE.values())\n",
"\n",
"# Calculate percentages for each hour estimate\n",
"percentages = []\n",
"for estimate in hour_estimates:\n",
" percentage = (df['ub_estimate_in_hours'] <= estimate).mean() * 100\n",
" percentages.append(percentage)\n",
"\n",
"# Create the plot\n",
"plt.plot(hour_estimates, percentages)\n",
"plt.xscale('log') # Set x-axis to log scale\n",
"plt.xlabel('Agent coherence time')\n",
"plt.ylabel('% of tasks in the economy')\n",
"plt.title('Over 80% of the tasks in the economy can be automated with a time coherence of 8 hours')\n",
"plt.grid(True)\n",
"\n",
"# Set x-axis ticks and labels\n",
"plt.xticks(hour_estimates, DURATION_TO_HOUR_ESTIMATE.keys(), rotation=45, ha='right')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ty1lqOydwqDE"
},
"outputs": [],
"source": [
"METR = [\n",
" [2022, 0],\n",
" [2024, 0.1],\n",
" [2026, 4],\n",
" [2028, 68]\n",
"]\n",
"\n",
"# These are the values for the X axis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "wAEzzt34zam7",
"outputId": "bc649834-85fc-4be0-b4c7-b6966abaa1e7"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Your existing data and DataFrame 'df' should be loaded here\n",
"\n",
"METR = [\n",
" [2022, 0],\n",
" [2024, 0.1],\n",
" [2026, 4],\n",
" [2028, 68],\n",
" [2030, 1224] # Added 2030 data point\n",
"]\n",
"\n",
"x_values = [point[0] for point in METR]\n",
"thresholds = [point[1] for point in METR]\n",
"\n",
"# Calculate percentages for each threshold\n",
"percentages = []\n",
"for threshold in thresholds:\n",
" percentage = (df['ub_estimate_in_hours'] <= threshold).mean() * 100\n",
" percentages.append(percentage)\n",
"\n",
"# Calculate y-values (100% - percentage, capped at 0)\n",
"y_values = [max(0, 100 - percentage) for percentage in percentages]\n",
"\n",
"# Create the plot\n",
"plt.plot(x_values, y_values, marker='o') # Added markers for data points\n",
"plt.xlabel('Year')\n",
"plt.ylabel('% of tasks NOT automatable') # Updated y-axis label\n",
"plt.title('Tasks NOT automatable over time based on agent coherence time') # Updated title\n",
"plt.grid(True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 913
},
"id": "3OefjK-BAk7g",
"outputId": "2e5628c4-7e3b-4f69-ce24-e0ebbe551299"
},
"outputs": [
{
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "hardest_task_df"
},
"text/html": [
"\n",
" <div id=\"df-3189d25d-a8f5-4587-b31f-21c8609f614d\" class=\"colab-df-container\">\n",
" <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>onetsoc_code</th>\n",
" <th>task_id</th>\n",
" <th>task</th>\n",
" <th>occupation_title</th>\n",
" <th>occupation_description</th>\n",
" <th>frequency_category_1</th>\n",
" <th>frequency_category_2</th>\n",
" <th>frequency_category_3</th>\n",
" <th>frequency_category_4</th>\n",
" <th>frequency_category_5</th>\n",
" <th>...</th>\n",
" <th>dwas</th>\n",
" <th>remote_status</th>\n",
" <th>occ_code</th>\n",
" <th>total_employment</th>\n",
" <th>hourly_wage_average</th>\n",
" <th>annual_wage_average</th>\n",
" <th>lb_estimate</th>\n",
" <th>ub_estimate</th>\n",
" <th>lb_estimate_in_hours</th>\n",
" <th>ub_estimate_in_hours</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1146</th>\n",
" <td>13-2011.00</td>\n",
" <td>21521</td>\n",
" <td>Develop, implement, modify, and document recor...</td>\n",
" <td>Accountants and Auditors</td>\n",
" <td>Examine, analyze, and interpret accounting rec...</td>\n",
" <td>11.23</td>\n",
" <td>35.87</td>\n",
" <td>23.35</td>\n",
" <td>10.40</td>\n",
" <td>14.22</td>\n",
" <td>...</td>\n",
" <td>['Develop business or financial information sy...</td>\n",
" <td>remote</td>\n",
" <td>13-2011</td>\n",
" <td>1435770.0</td>\n",
" <td>43.65</td>\n",
" <td>90780</td>\n",
" <td>1 week</td>\n",
" <td>3 weeks</td>\n",
" <td>168.0</td>\n",
" <td>504.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19821</th>\n",
" <td>27-2011.00</td>\n",
" <td>7654</td>\n",
" <td>Write original or adapted material for dramas,...</td>\n",
" <td>Actors</td>\n",
" <td>Play parts in stage, television, radio, video,...</td>\n",
" <td>46.42</td>\n",
" <td>16.84</td>\n",
" <td>22.00</td>\n",
" <td>1.51</td>\n",
" <td>3.67</td>\n",
" <td>...</td>\n",
" <td>['Write material for artistic or entertainment...</td>\n",
" <td>remote</td>\n",
" <td>27-2011</td>\n",
" <td>62560.0</td>\n",
" <td>41.01</td>\n",
" <td>*</td>\n",
" <td>1 hour</td>\n",
" <td>3 days</td>\n",
" <td>1.0</td>\n",
" <td>72.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1776</th>\n",
" <td>15-2011.00</td>\n",
" <td>3500</td>\n",
" <td>Ascertain premium rates required and cash rese...</td>\n",
" <td>Actuaries</td>\n",
" <td>Analyze statistical data, such as mortality, a...</td>\n",
" <td>3.57</td>\n",
" <td>10.71</td>\n",
" <td>21.43</td>\n",
" <td>17.86</td>\n",
" <td>25.00</td>\n",
" <td>...</td>\n",
" <td>['Manage financial activities of the organizat...</td>\n",
" <td>remote</td>\n",
" <td>15-2011</td>\n",
" <td>25470.0</td>\n",
" <td>63.7</td>\n",
" <td>132500</td>\n",
" <td>3 days</td>\n",
" <td>1 week</td>\n",
" <td>72.0</td>\n",
" <td>168.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20295</th>\n",
" <td>29-1291.00</td>\n",
" <td>17361</td>\n",
" <td>Adhere to local, state, and federal laws, regu...</td>\n",
" <td>Acupuncturists</td>\n",
" <td>Diagnose, treat, and prevent disorders by stim...</td>\n",
" <td>0.39</td>\n",
" <td>0.39</td>\n",
" <td>0.00</td>\n",
" <td>0.57</td>\n",
" <td>31.66</td>\n",
" <td>...</td>\n",
" <td>['Follow protocols or regulations for healthca...</td>\n",
" <td>remote</td>\n",
" <td>29-1291</td>\n",
" <td>9370.0</td>\n",
" <td>40.51</td>\n",
" <td>84260</td>\n",
" <td>30 minutes</td>\n",
" <td>2 hours</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20172</th>\n",
" <td>29-1141.01</td>\n",
" <td>18322</td>\n",
" <td>Participate in the development of practice pro...</td>\n",
" <td>Acute Care Nurses</td>\n",
" <td>Provide advanced nursing care for patients wit...</td>\n",
" <td>25.00</td>\n",
" <td>37.50</td>\n",
" <td>20.83</td>\n",
" <td>8.33</td>\n",
" <td>0.00</td>\n",
" <td>...</td>\n",
" <td>['Establish nursing policies or standards.']</td>\n",
" <td>remote</td>\n",
" <td>29-1141</td>\n",
" <td>3175390.0</td>\n",
" <td>45.42</td>\n",
" <td>94480</td>\n",
" <td>1 week</td>\n",
" <td>3 weeks</td>\n",
" <td>168.0</td>\n",
" <td>504.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2337</th>\n",
" <td>17-2199.10</td>\n",
" <td>16549</td>\n",
" <td>Recommend process or infrastructure changes to...</td>\n",
" <td>Wind Energy Engineers</td>\n",
" <td>Design underground or overhead wind farm colle...</td>\n",
" <td>2.35</td>\n",
" <td>34.16</td>\n",
" <td>29.24</td>\n",
" <td>17.09</td>\n",
" <td>13.77</td>\n",
" <td>...</td>\n",
" <td>['Recommend technical design or process change...</td>\n",
" <td>remote</td>\n",
" <td>17-2199</td>\n",
" <td>150990.0</td>\n",
" <td>56.9</td>\n",
" <td>118350</td>\n",
" <td>4 hours</td>\n",
" <td>16 hours</td>\n",
" <td>4.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>636</th>\n",
" <td>11-9199.09</td>\n",
" <td>15820</td>\n",
" <td>Develop processes or procedures for wind opera...</td>\n",
" <td>Wind Energy Operations Managers</td>\n",
" <td>Manage wind field operations, including person...</td>\n",
" <td>18.51</td>\n",
" <td>29.41</td>\n",
" <td>26.40</td>\n",
" <td>8.92</td>\n",
" <td>12.09</td>\n",
" <td>...</td>\n",
" <td>['Develop operating strategies, plans, or proc...</td>\n",
" <td>remote</td>\n",
" <td>11-9199</td>\n",
" <td>589750.0</td>\n",
" <td>70.35</td>\n",
" <td>146320</td>\n",
" <td>1 week</td>\n",
" <td>3 weeks</td>\n",
" <td>168.0</td>\n",
" <td>504.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21311</th>\n",
" <td>43-9022.00</td>\n",
" <td>806</td>\n",
" <td>Work with technical material, preparing statis...</td>\n",
" <td>Word Processors and Typists</td>\n",
" <td>Use word processor, computer, or typewriter to...</td>\n",
" <td>10.05</td>\n",
" <td>1.81</td>\n",
" <td>21.73</td>\n",
" <td>40.06</td>\n",
" <td>19.34</td>\n",
" <td>...</td>\n",
" <td>['Compile data or documentation.', 'Prepare re...</td>\n",
" <td>remote</td>\n",
" <td>43-9022</td>\n",
" <td>37200.0</td>\n",
" <td>22.68</td>\n",
" <td>47170</td>\n",
" <td>2 hours</td>\n",
" <td>8 hours</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19984</th>\n",
" <td>27-3043.00</td>\n",
" <td>22658</td>\n",
" <td>Conduct research and interviews to determine w...</td>\n",
" <td>Writers and Authors</td>\n",
" <td>Originate and prepare written material, such a...</td>\n",
" <td>21.45</td>\n",
" <td>17.33</td>\n",
" <td>21.72</td>\n",
" <td>32.12</td>\n",
" <td>7.37</td>\n",
" <td>...</td>\n",
" <td>['Conduct market research.']</td>\n",
" <td>remote</td>\n",
" <td>27-3043</td>\n",
" <td>49450.0</td>\n",
" <td>42.11</td>\n",
" <td>87590</td>\n",
" <td>2 hours</td>\n",
" <td>16 hours</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2472</th>\n",
" <td>19-1023.00</td>\n",
" <td>23957</td>\n",
" <td>Develop, or make recommendations on, managemen...</td>\n",
" <td>Zoologists and Wildlife Biologists</td>\n",
" <td>Study the origins, behavior, diseases, genetic...</td>\n",
" <td>1.21</td>\n",
" <td>26.32</td>\n",
" <td>25.58</td>\n",
" <td>17.95</td>\n",
" <td>18.58</td>\n",
" <td>...</td>\n",
" <td>['Advise others about environmental management...</td>\n",
" <td>remote</td>\n",
" <td>19-1023</td>\n",
" <td>17100.0</td>\n",
" <td>36.41</td>\n",
" <td>75740</td>\n",
" <td>4 hours</td>\n",
" <td>16 hours</td>\n",
" <td>4.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>697 rows × 24 columns</p>\n",
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],
"text/plain": [
" onetsoc_code task_id \\\n",
"1146 13-2011.00 21521 \n",
"19821 27-2011.00 7654 \n",
"1776 15-2011.00 3500 \n",
"20295 29-1291.00 17361 \n",
"20172 29-1141.01 18322 \n",
"... ... ... \n",
"2337 17-2199.10 16549 \n",
"636 11-9199.09 15820 \n",
"21311 43-9022.00 806 \n",
"19984 27-3043.00 22658 \n",
"2472 19-1023.00 23957 \n",
"\n",
" task \\\n",
"1146 Develop, implement, modify, and document recor... \n",
"19821 Write original or adapted material for dramas,... \n",
"1776 Ascertain premium rates required and cash rese... \n",
"20295 Adhere to local, state, and federal laws, regu... \n",
"20172 Participate in the development of practice pro... \n",
"... ... \n",
"2337 Recommend process or infrastructure changes to... \n",
"636 Develop processes or procedures for wind opera... \n",
"21311 Work with technical material, preparing statis... \n",
"19984 Conduct research and interviews to determine w... \n",
"2472 Develop, or make recommendations on, managemen... \n",
"\n",
" occupation_title \\\n",
"1146 Accountants and Auditors \n",
"19821 Actors \n",
"1776 Actuaries \n",
"20295 Acupuncturists \n",
"20172 Acute Care Nurses \n",
"... ... \n",
"2337 Wind Energy Engineers \n",
"636 Wind Energy Operations Managers \n",
"21311 Word Processors and Typists \n",
"19984 Writers and Authors \n",
"2472 Zoologists and Wildlife Biologists \n",
"\n",
" occupation_description \\\n",
"1146 Examine, analyze, and interpret accounting rec... \n",
"19821 Play parts in stage, television, radio, video,... \n",
"1776 Analyze statistical data, such as mortality, a... \n",
"20295 Diagnose, treat, and prevent disorders by stim... \n",
"20172 Provide advanced nursing care for patients wit... \n",
"... ... \n",
"2337 Design underground or overhead wind farm colle... \n",
"636 Manage wind field operations, including person... \n",
"21311 Use word processor, computer, or typewriter to... \n",
"19984 Originate and prepare written material, such a... \n",
"2472 Study the origins, behavior, diseases, genetic... \n",
"\n",
" frequency_category_1 frequency_category_2 frequency_category_3 \\\n",
"1146 11.23 35.87 23.35 \n",
"19821 46.42 16.84 22.00 \n",
"1776 3.57 10.71 21.43 \n",
"20295 0.39 0.39 0.00 \n",
"20172 25.00 37.50 20.83 \n",
"... ... ... ... \n",
"2337 2.35 34.16 29.24 \n",
"636 18.51 29.41 26.40 \n",
"21311 10.05 1.81 21.73 \n",
"19984 21.45 17.33 21.72 \n",
"2472 1.21 26.32 25.58 \n",
"\n",
" frequency_category_4 frequency_category_5 ... \\\n",
"1146 10.40 14.22 ... \n",
"19821 1.51 3.67 ... \n",
"1776 17.86 25.00 ... \n",
"20295 0.57 31.66 ... \n",
"20172 8.33 0.00 ... \n",
"... ... ... ... \n",
"2337 17.09 13.77 ... \n",
"636 8.92 12.09 ... \n",
"21311 40.06 19.34 ... \n",
"19984 32.12 7.37 ... \n",
"2472 17.95 18.58 ... \n",
"\n",
" dwas remote_status \\\n",
"1146 ['Develop business or financial information sy... remote \n",
"19821 ['Write material for artistic or entertainment... remote \n",
"1776 ['Manage financial activities of the organizat... remote \n",
"20295 ['Follow protocols or regulations for healthca... remote \n",
"20172 ['Establish nursing policies or standards.'] remote \n",
"... ... ... \n",
"2337 ['Recommend technical design or process change... remote \n",
"636 ['Develop operating strategies, plans, or proc... remote \n",
"21311 ['Compile data or documentation.', 'Prepare re... remote \n",
"19984 ['Conduct market research.'] remote \n",
"2472 ['Advise others about environmental management... remote \n",
"\n",
" occ_code total_employment hourly_wage_average annual_wage_average \\\n",
"1146 13-2011 1435770.0 43.65 90780 \n",
"19821 27-2011 62560.0 41.01 * \n",
"1776 15-2011 25470.0 63.7 132500 \n",
"20295 29-1291 9370.0 40.51 84260 \n",
"20172 29-1141 3175390.0 45.42 94480 \n",
"... ... ... ... ... \n",
"2337 17-2199 150990.0 56.9 118350 \n",
"636 11-9199 589750.0 70.35 146320 \n",
"21311 43-9022 37200.0 22.68 47170 \n",
"19984 27-3043 49450.0 42.11 87590 \n",
"2472 19-1023 17100.0 36.41 75740 \n",
"\n",
" lb_estimate ub_estimate lb_estimate_in_hours ub_estimate_in_hours \n",
"1146 1 week 3 weeks 168.0 504.0 \n",
"19821 1 hour 3 days 1.0 72.0 \n",
"1776 3 days 1 week 72.0 168.0 \n",
"20295 30 minutes 2 hours 0.5 2.0 \n",
"20172 1 week 3 weeks 168.0 504.0 \n",
"... ... ... ... ... \n",
"2337 4 hours 16 hours 4.0 16.0 \n",
"636 1 week 3 weeks 168.0 504.0 \n",
"21311 2 hours 8 hours 2.0 8.0 \n",
"19984 2 hours 16 hours 2.0 16.0 \n",
"2472 4 hours 16 hours 4.0 16.0 \n",
"\n",
"[697 rows x 24 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hardest_task_by_occupation_df = df.loc[df.groupby('occupation_title')['ub_estimate_in_hours'].idxmax()]\n",
"hardest_task_by_occcupation_df"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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