{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9742710,"sourceType":"datasetVersion","datasetId":5962826}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false},"papermill":{"default_parameters":{},"duration":15.602049,"end_time":"2024-11-18T14:50:14.623033","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-11-18T14:49:59.020984","version":"2.6.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"id":"c5c0108a-548d-46dd-b2aa-6a9998e334d8","cell_type":"markdown","source":"<a href=\"https://www.kaggle.com/code/ivanputrapratama/emission?scriptVersionId=208186024\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{}},{"id":"c5952653","cell_type":"markdown","source":"# Carbon Emission","metadata":{"papermill":{"duration":0.008855,"end_time":"2024-11-18T14:50:02.328048","exception":false,"start_time":"2024-11-18T14:50:02.319193","status":"completed"},"tags":[]}},{"id":"bd816d92","cell_type":"markdown","source":"This notebook will contain analytical data about carbon emission around the world and I will predict a couple of years later based on historical data","metadata":{"papermill":{"duration":0.008215,"end_time":"2024-11-18T14:50:02.344868","exception":false,"start_time":"2024-11-18T14:50:02.336653","status":"completed"},"tags":[]}},{"id":"19fd536c","cell_type":"code","source":"import pandas as pd\n\nemission_df = pd.read_csv(\"/kaggle/input/carbon-co2-emissions/Carbon_(CO2)_Emissions_by_Country.csv\")","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:02.363271Z","iopub.status.busy":"2024-11-18T14:50:02.362807Z","iopub.status.idle":"2024-11-18T14:50:03.389029Z","shell.execute_reply":"2024-11-18T14:50:03.388036Z"},"papermill":{"duration":1.038498,"end_time":"2024-11-18T14:50:03.391809","exception":false,"start_time":"2024-11-18T14:50:02.353311","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"f05a7b5f","cell_type":"code","source":"emission_df","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:03.410107Z","iopub.status.busy":"2024-11-18T14:50:03.409297Z","iopub.status.idle":"2024-11-18T14:50:03.444823Z","shell.execute_reply":"2024-11-18T14:50:03.443633Z"},"papermill":{"duration":0.04787,"end_time":"2024-11-18T14:50:03.447755","exception":false,"start_time":"2024-11-18T14:50:03.399885","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"f4b2238e","cell_type":"markdown","source":"## Kilotons of CO₂ per Country","metadata":{"papermill":{"duration":0.008139,"end_time":"2024-11-18T14:50:03.464705","exception":false,"start_time":"2024-11-18T14:50:03.456566","status":"completed"},"tags":[]}},{"id":"c89a2a54","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Asia\n\nasia_data = emission_df[emission_df['Region'] == 'Asia']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Asia\n\nasia_avg_co2 = asia_data.groupby('Country')['Kilotons of Co2'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\nasia_avg_co2 = asia_avg_co2.sort_values(by='Kilotons of Co2', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Kilotons of Co2', data=asia_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Average Kilotons of CO₂ per Country in Asia')\n\nplt.xlabel('Country')\n\nplt.ylabel('Average Kilotons of CO₂')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:03.485396Z","iopub.status.busy":"2024-11-18T14:50:03.484291Z","iopub.status.idle":"2024-11-18T14:50:06.383924Z","shell.execute_reply":"2024-11-18T14:50:06.382644Z"},"papermill":{"duration":2.913336,"end_time":"2024-11-18T14:50:06.386529","exception":false,"start_time":"2024-11-18T14:50:03.473193","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"4b2ff542","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Europe\n\neurope_data = emission_df[emission_df['Region'] == 'Europe']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Europe\n\neurope_avg_co2 = europe_data.groupby('Country')['Kilotons of Co2'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\neurope_avg_co2 = europe_avg_co2.sort_values(by='Kilotons of Co2', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Kilotons of Co2', data=europe_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Average Kilotons of CO₂ per Country in Europe')\n\nplt.xlabel('Country')\n\nplt.ylabel('Average Kilotons of CO₂')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)  # To prevent overlapping country names\n\nplt.tight_layout()\n\nplt.show()\n","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:06.408703Z","iopub.status.busy":"2024-11-18T14:50:06.407562Z","iopub.status.idle":"2024-11-18T14:50:07.204071Z","shell.execute_reply":"2024-11-18T14:50:07.201239Z"},"papermill":{"duration":0.810737,"end_time":"2024-11-18T14:50:07.207213","exception":false,"start_time":"2024-11-18T14:50:06.396476","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"47b30453","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Africa\n\nafrica_data = emission_df[emission_df['Region'] == 'Africa']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Africa\n\nafrica_avg_co2 = africa_data.groupby('Country')['Kilotons of Co2'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\nafrica_avg_co2 = africa_avg_co2.sort_values(by='Kilotons of Co2', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Kilotons of Co2', data=africa_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Average Kilotons of CO₂ per Country in Africa')\n\nplt.xlabel('Country')\n\nplt.ylabel('Average Kilotons of CO₂')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)  # To prevent overlapping country names\n\nplt.tight_layout()\n\nplt.show()\n","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:07.231573Z","iopub.status.busy":"2024-11-18T14:50:07.231095Z","iopub.status.idle":"2024-11-18T14:50:08.060427Z","shell.execute_reply":"2024-11-18T14:50:08.059132Z"},"papermill":{"duration":0.845256,"end_time":"2024-11-18T14:50:08.063823","exception":false,"start_time":"2024-11-18T14:50:07.218567","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"b563d34a","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Amricas\n\namreicas_data = emission_df[emission_df['Region'] == 'Americas']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Amricas\n\namreicas_avg_co2 = amreicas_data.groupby('Country')['Kilotons of Co2'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\namreicas_avg_co2 = amreicas_avg_co2.sort_values(by='Kilotons of Co2', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Kilotons of Co2', data=amreicas_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Average Kilotons of CO₂ per Country in Americas')\n\nplt.xlabel('Country')\n\nplt.ylabel('Average Kilotons of CO₂')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)  # To prevent overlapping country names\n\nplt.tight_layout()\n\nplt.show()\n","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:08.092614Z","iopub.status.busy":"2024-11-18T14:50:08.092121Z","iopub.status.idle":"2024-11-18T14:50:08.95818Z","shell.execute_reply":"2024-11-18T14:50:08.956701Z"},"papermill":{"duration":0.883263,"end_time":"2024-11-18T14:50:08.960946","exception":false,"start_time":"2024-11-18T14:50:08.077683","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"8aad5595","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Oceania\n\noceania_data = emission_df[emission_df['Region'] == 'Oceania']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Oceania\n\noceania_avg_co2 = oceania_data.groupby('Country')['Kilotons of Co2'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\noceania_avg_co2 = oceania_avg_co2.sort_values(by='Kilotons of Co2', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Kilotons of Co2', data=oceania_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Average Kilotons of CO₂ per Country in Oceania')\n\nplt.xlabel('Country')\n\nplt.ylabel('Average Kilotons of CO₂')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)  # To prevent overlapping country names\n\nplt.tight_layout()\n\nplt.show()\n","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:08.991359Z","iopub.status.busy":"2024-11-18T14:50:08.990323Z","iopub.status.idle":"2024-11-18T14:50:09.493933Z","shell.execute_reply":"2024-11-18T14:50:09.49254Z"},"papermill":{"duration":0.522411,"end_time":"2024-11-18T14:50:09.497363","exception":false,"start_time":"2024-11-18T14:50:08.974952","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"c5f24e6b","cell_type":"markdown","source":"From the data we know that Average Kilotons of C02 per country is related with area size, the bigger the area the bigger the C02 too, as we can see China and US are the most country with C02 because both country are the most largest country in the world with largest population, so it does not surprise use.","metadata":{"papermill":{"duration":0.015261,"end_time":"2024-11-18T14:50:09.528298","exception":false,"start_time":"2024-11-18T14:50:09.513037","status":"completed"},"tags":[]}},{"id":"99cc311d","cell_type":"markdown","source":"## Metric Tons per Capita","metadata":{"papermill":{"duration":0.015087,"end_time":"2024-11-18T14:50:09.55861","exception":false,"start_time":"2024-11-18T14:50:09.543523","status":"completed"},"tags":[]}},{"id":"c2c2bdcf","cell_type":"markdown","source":"Metric Tons per Capita is a metric to shows average carbon emission on each person in some areas","metadata":{"papermill":{"duration":0.014717,"end_time":"2024-11-18T14:50:09.588594","exception":false,"start_time":"2024-11-18T14:50:09.573877","status":"completed"},"tags":[]}},{"id":"73c7240b","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Asia\n\nasia_data = emission_df[emission_df['Region'] == 'Asia']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Asia\n\nasia_avg_co2 = asia_data.groupby('Country')['Metric Tons Per Capita'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\nasia_avg_co2 = asia_avg_co2.sort_values(by='Metric Tons Per Capita', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Metric Tons Per Capita', data=asia_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Averages Metric Tons Per Capita in Asia')\n\nplt.xlabel('Country')\n\nplt.ylabel('Averages Metric Tons Per Capita')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:09.622262Z","iopub.status.busy":"2024-11-18T14:50:09.621818Z","iopub.status.idle":"2024-11-18T14:50:10.47519Z","shell.execute_reply":"2024-11-18T14:50:10.474007Z"},"papermill":{"duration":0.87417,"end_time":"2024-11-18T14:50:10.477739","exception":false,"start_time":"2024-11-18T14:50:09.603569","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"98c8cd2c","cell_type":"markdown","source":"Qatar have the most Metric tons Per Capita in Asia, maybe it is because they have a fossil fuel and industrial that makes emission high, and Qatar has small population so the averages is so high.","metadata":{"papermill":{"duration":0.01599,"end_time":"2024-11-18T14:50:10.510137","exception":false,"start_time":"2024-11-18T14:50:10.494147","status":"completed"},"tags":[]}},{"id":"c7e14bf4","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Africa\n\nafrica_data = emission_df[emission_df['Region'] == 'Africa']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Africa\n\nafrica_avg_co2 = africa_data.groupby('Country')['Metric Tons Per Capita'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\nafrica_avg_co2 = africa_avg_co2.sort_values(by='Metric Tons Per Capita', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Metric Tons Per Capita', data=africa_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Averages Metric Tons Per Capita in Africa')\n\nplt.xlabel('Country')\n\nplt.ylabel('Averages Metric Tons Per Capita')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:10.545049Z","iopub.status.busy":"2024-11-18T14:50:10.544186Z","iopub.status.idle":"2024-11-18T14:50:11.472161Z","shell.execute_reply":"2024-11-18T14:50:11.470825Z"},"papermill":{"duration":0.948267,"end_time":"2024-11-18T14:50:11.474593","exception":false,"start_time":"2024-11-18T14:50:10.526326","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"0ff19070","cell_type":"markdown","source":"Libya have highest Averages Metric Tons Per Capita it mainly because electricity production and the oil sector, and natural gas that released into the atmosphere. Another factor that can affect the metric is Libya's population with only 6,8 million people.","metadata":{"papermill":{"duration":0.017815,"end_time":"2024-11-18T14:50:11.510797","exception":false,"start_time":"2024-11-18T14:50:11.492982","status":"completed"},"tags":[]}},{"id":"c3e546df","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Europe\n\neurope_data = emission_df[emission_df['Region'] == 'Europe']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Europe\n\neurope_avg_co2 = europe_data.groupby('Country')['Metric Tons Per Capita'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\neurope_avg_co2 = europe_avg_co2.sort_values(by='Metric Tons Per Capita', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Metric Tons Per Capita', data=europe_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Averages Metric Tons Per Capita in Europe')\n\nplt.xlabel('Country')\n\nplt.ylabel('Averages Metric Tons Per Capita')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:11.549075Z","iopub.status.busy":"2024-11-18T14:50:11.548208Z","iopub.status.idle":"2024-11-18T14:50:12.30253Z","shell.execute_reply":"2024-11-18T14:50:12.301233Z"},"papermill":{"duration":0.776652,"end_time":"2024-11-18T14:50:12.305287","exception":false,"start_time":"2024-11-18T14:50:11.528635","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"a6ae4888","cell_type":"markdown","source":"Luxembourg has high demand for trucks and commuter, and their main fuel is from fossil and with only around 668 thousand people, so Luxembourg has high Averages Metric Tons per Capita","metadata":{"papermill":{"duration":0.018773,"end_time":"2024-11-18T14:50:12.34359","exception":false,"start_time":"2024-11-18T14:50:12.324817","status":"completed"},"tags":[]}},{"id":"86ac5608","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Americas\n\nasia_data = emission_df[emission_df['Region'] == 'Americas']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Americas\n\nasia_avg_co2 = asia_data.groupby('Country')['Metric Tons Per Capita'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\nasia_avg_co2 = asia_avg_co2.sort_values(by='Metric Tons Per Capita', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Metric Tons Per Capita', data=asia_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Averages Metric Tons Per Capita in Americas')\n\nplt.xlabel('Country')\n\nplt.ylabel('Averages Metric Tons Per Capita')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:12.384442Z","iopub.status.busy":"2024-11-18T14:50:12.383569Z","iopub.status.idle":"2024-11-18T14:50:13.105933Z","shell.execute_reply":"2024-11-18T14:50:13.104734Z"},"papermill":{"duration":0.745656,"end_time":"2024-11-18T14:50:13.108445","exception":false,"start_time":"2024-11-18T14:50:12.362789","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"1219d9e0","cell_type":"markdown","source":"If we saw this chart maybe no one will surprised with the result, Americas has large population, and emission is always from their transportation, electricity. Large population with large transportation and electricity make the Averages Metric Tons per Capita huge.","metadata":{"papermill":{"duration":0.021417,"end_time":"2024-11-18T14:50:13.152479","exception":false,"start_time":"2024-11-18T14:50:13.131062","status":"completed"},"tags":[]}},{"id":"ba518c5f","cell_type":"code","source":"import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Filter data for countries in Oceania\n\noceania_data = emission_df[emission_df['Region'] == 'Oceania']\n\n\n\n# Calculate the average Kilotons of CO₂ per country in Oceania\n\noceania_avg_co2 = oceania_data.groupby('Country')['Metric Tons Per Capita'].mean().reset_index()\n\n\n\n# Sort countries by the average Kilotons of CO₂\n\noceania_avg_co2 = oceania_avg_co2.sort_values(by='Metric Tons Per Capita', ascending=False)\n\n\n\n# Create a vertical bar plot using seaborn\n\nplt.figure(figsize=(10, 6))\n\nsns.barplot(x='Country', y='Metric Tons Per Capita', data=oceania_avg_co2, palette='viridis')\n\n\n\n# Add title and labels\n\nplt.title('Averages Metric Tons Per Capita in Oceania')\n\nplt.xlabel('Country')\n\nplt.ylabel('Averages Metric Tons Per Capita')\n\n\n\n# Display the chart\n\nplt.xticks(rotation=90)\n\nplt.tight_layout()\n\nplt.show()","metadata":{"execution":{"iopub.execute_input":"2024-11-18T14:50:13.195147Z","iopub.status.busy":"2024-11-18T14:50:13.194679Z","iopub.status.idle":"2024-11-18T14:50:13.706522Z","shell.execute_reply":"2024-11-18T14:50:13.705293Z"},"papermill":{"duration":0.536382,"end_time":"2024-11-18T14:50:13.709061","exception":false,"start_time":"2024-11-18T14:50:13.172679","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"73781826","cell_type":"markdown","source":"Australia has the biggest averages it is mainly because their fossil fuels export, they also responsible for around 4.5% of global fossil carbon dioxide emissions, Australia is also one of the world’s largest exporters of fossil fuels so it make sense that Australia has a biggest Averages than any other country from Oceania","metadata":{"papermill":{"duration":0.020988,"end_time":"2024-11-18T14:50:13.752538","exception":false,"start_time":"2024-11-18T14:50:13.73155","status":"completed"},"tags":[]}},{"id":"b21bd7ec","cell_type":"markdown","source":"So with the data that we have, maybe we can predict how much their Kilotons of CO₂ per Country or Averages Metric Tons per Capita in future.","metadata":{"papermill":{"duration":0.021042,"end_time":"2024-11-18T14:50:13.794569","exception":false,"start_time":"2024-11-18T14:50:13.773527","status":"completed"},"tags":[]}},{"id":"1a6c79f6","cell_type":"markdown","source":"# Predict Emission","metadata":{"papermill":{"duration":0.02074,"end_time":"2024-11-18T14:50:13.836958","exception":false,"start_time":"2024-11-18T14:50:13.816218","status":"completed"},"tags":[]}},{"id":"b6371221","cell_type":"code","source":"","metadata":{"papermill":{"duration":0.020721,"end_time":"2024-11-18T14:50:13.878667","exception":false,"start_time":"2024-11-18T14:50:13.857946","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}