name string | instruction string | description string | keywords list | schema_version string | config unknown | metadata unknown | files list | location string |
|---|---|---|---|---|---|---|---|---|
smoldataenvs-train/0000_324_324276_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- 2016-FCC-New-Coders-Survey-Data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pi... | What is the most common job role interest among new coders? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_324_324276_qa_3",
"description": "What is the most common job role interest among new coders?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/324/324276.ipynb_qa_3",
"kaggle_dataset_name": "freecodecamp/2016-new-coder-survey-",
"gold_answer": "Web Development",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_324_324276_qa_3 |
smoldataenvs-train/0000_369_369503_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.sqlite
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ne... | What percentage of all matches have a goal difference of zero (i.e., draws)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_369_369503_qa_1",
"description": "What percentage of all matches have a goal difference of zero (i.e., draws)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
}... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/369/369503.ipynb_qa_1",
"kaggle_dataset_name": "hugomathien/soccer",
"gold_answer": "25.4%",
"reward_mode_initial": "flexible",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/__pycache__/grader.cpython-312.pyc",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_369_369503_qa_1 |
smoldataenvs-train/0000_455_455459_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Pokemon.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What is the error rate (as a percentage) for non-legendary Pokémon in the logistic regression model's predictions? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_455_455459_qa_4",
"description": "What is the error rate (as a percentage) for non-legendary Pokémon in the logistic regression model's predictions?",
"authors": [],
"keywords": [
"smoldataenvs",
"da... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/455/455459.ipynb_qa_4",
"kaggle_dataset_name": "abcsds/pokemon",
"gold_answer": "2",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 4,
"difficulty": "hard",
"difficulty_tier": "hard"
} | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_455_455459_qa_4 |
smoldataenvs-train/0000_465_465850_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Iris.csv
- database.sqlite
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install... | Which species exhibits the highest average sepal length according to the aggregated dataset statistics? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_465_465850_qa_5",
"description": "Which species exhibits the highest average sepal length according to the aggregated dataset statistics?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/465/465850.ipynb_qa_5",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "virginica",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_465_465850_qa_5 |
smoldataenvs-train/0000_526_526258_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- AguaH.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | How many entries are categorized into the non-NA group, edge-NA group, and interrupted group based on missing value patterns? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_526_526258_qa_2",
"description": "How many entries are categorized into the non-NA group, edge-NA group, and interrupted group based on missing value patterns?",
"authors": [],
"keywords": [
"smoldataenvs"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/526/526258.ipynb_qa_2",
"kaggle_dataset_name": "marcomolina/water-consumption-in-a-median-size-city",
"gold_answer": "141205, 32568, 4824",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 4,
"difficul... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_526_526258_qa_2 |
smoldataenvs-train/0000_539_539873_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- ca_offenses_by_city.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mo... | Which city has the lowest crime ratio, and what is the value of this ratio? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_539_539873_qa_3",
"description": "Which city has the lowest crime ratio, and what is the value of this ratio?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/539/539873.ipynb_qa_3",
"kaggle_dataset_name": "fbi-us/california-crime",
"gold_answer": "Imperial3, 0.003403",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 3,
"difficulty": "medium",
"difficulty... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_539_539873_qa_3 |
smoldataenvs-train/0000_582_582934_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | Which state has the lowest proportion of shootings involving individuals with signs of mental illness? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_582_582934_qa_4",
"description": "Which state has the lowest proportion of shootings involving individuals with signs of mental illness?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/582/582934.ipynb_qa_4",
"kaggle_dataset_name": "washingtonpost/police-shootings",
"gold_answer": "Kentucky (KY)",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"d... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_582_582934_qa_4 |
smoldataenvs-train/0000_587_587336_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What is the percentage of total gun-related shootings in Washington state attributed to individuals with mental illness compared to those without? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_587_587336_qa_5",
"description": "What is the percentage of total gun-related shootings in Washington state attributed to individuals with mental illness compared to those without?",
"authors": [],
"keywords": [... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/587/587336.ipynb_qa_5",
"kaggle_dataset_name": "washingtonpost/police-shootings",
"gold_answer": "45%",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_587_587336_qa_5 |
smoldataenvs-train/0000_641_641256_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- cities_r2.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | Which state has the highest average effective literacy rate, and what is that rate? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_641_641256_qa_1",
"description": "Which state has the highest average effective literacy rate, and what is that rate?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/641/641256.ipynb_qa_1",
"kaggle_dataset_name": "zed9941/top-500-indian-cities",
"gold_answer": "Mizoram, 98.8",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"difficulty... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_641_641256_qa_1 |
smoldataenvs-train/0000_656_656399_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- xAPI-Edu-Data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | Which pair of numerical features in the dataset shows the strongest positive correlation according to the correlation analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_656_656399_qa_2",
"description": "Which pair of numerical features in the dataset shows the strongest positive correlation according to the correlation analysis?",
"authors": [],
"keywords": [
"smoldataenv... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/656/656399.ipynb_qa_2",
"kaggle_dataset_name": "aljarah/xAPI-Edu-Data",
"gold_answer": "raisedhands, VisITedResources",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"di... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_656_656399_qa_2 |
smoldataenvs-train/0000_767_767688_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | According to the scatter matrix analysis, which three features exhibited the highest linear correlation with each other in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_767_767688_qa_4",
"description": "According to the scatter matrix analysis, which three features exhibited the highest linear correlation with each other in the dataset?",
"authors": [],
"keywords": [
"smo... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/767/767688.ipynb_qa_4",
"kaggle_dataset_name": "uciml/breast-cancer-wisconsin-data",
"gold_answer": "perimeter_mean, area_mean, radius_mean",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"diffic... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_767_767688_qa_4 |
smoldataenvs-train/0000_780_780974_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- arrests.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What was the maximum number of arrests recorded at the Southwest border and in which year? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_780_780974_qa_4",
"description": "What was the maximum number of arrests recorded at the Southwest border and in which year?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/780/780974.ipynb_qa_4",
"kaggle_dataset_name": "cbp/illegal-immigrants",
"gold_answer": "1643679 in 2000",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_780_780974_qa_4 |
smoldataenvs-train/0000_804_804467_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- mushrooms.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | Which model achieved the highest accuracy using KFold cross-validation, and what was the accuracy score? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_804_804467_qa_1",
"description": "Which model achieved the highest accuracy using KFold cross-validation, and what was the accuracy score?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysi... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/804/804467.ipynb_qa_1",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "RandomForest, 1.0",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 4,
"difficulty": "hard",
"difficul... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_804_804467_qa_1 |
smoldataenvs-train/0000_804_804467_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- mushrooms.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the lowest RMSE value observed in the train_test_split results, and which models achieved it? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_804_804467_qa_3",
"description": "What is the lowest RMSE value observed in the train_test_split results, and which models achieved it?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/804/804467.ipynb_qa_3",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "0, DecisionTree, RandomForest, SVM",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 4,
"difficulty": "h... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_804_804467_qa_3 |
smoldataenvs-train/0000_806_806826_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- archive.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | How does the number of years with above-average temperature changes from February to March compare to the number of years with below-average changes in the dataset spanning 1895-2016? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_806_806826_qa_3",
"description": "How does the number of years with above-average temperature changes from February to March compare to the number of years with below-average changes in the dataset spanning 1895-2016?",... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/806/806826.ipynb_qa_3",
"kaggle_dataset_name": "groundhogclub/groundhog-day",
"gold_answer": "62 above, 60 below",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"difficu... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_806_806826_qa_3 |
smoldataenvs-train/0000_849_849952_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- global.csv
- national.csv
- regional.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotl... | Which group of Western European countries exhibited synchronized fluctuations in Christian adherents over the five decades of analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_849_849952_qa_4",
"description": "Which group of Western European countries exhibited synchronized fluctuations in Christian adherents over the five decades of analysis?",
"authors": [],
"keywords": [
"smo... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/849/849952.ipynb_qa_4",
"kaggle_dataset_name": "umichigan/world-religions",
"gold_answer": "Western European countries",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 4,
"difficulty": "hard",... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_849_849952_qa_4 |
smoldataenvs-train/0000_886_886039_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- battles.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | Which defender was defeated the most times in the dataset, and how many times were they defeated? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_886_886039_qa_2",
"description": "Which defender was defeated the most times in the dataset, and how many times were they defeated?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/886/886039.ipynb_qa_2",
"kaggle_dataset_name": "mylesoneill/game-of-thrones",
"gold_answer": "Robb Stark, 13",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_886_886039_qa_2 |
smoldataenvs-train/0000_981_981197_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- diabetes.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | Which four features were identified as the top-performing predictors for diabetes classification using the chi-square ($\chi^2$) feature selection method? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_981_981197_qa_1",
"description": "Which four features were identified as the top-performing predictors for diabetes classification using the chi-square ($\\chi^2$) feature selection method?",
"authors": [],
"key... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/981/981197.ipynb_qa_1",
"kaggle_dataset_name": "uciml/pima-indians-diabetes-database",
"gold_answer": "Glucose, Insulin, BMI, Age",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "me... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_981_981197_qa_1 |
smoldataenvs-train/0000_982_982280_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- menu.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the item with the highest Trans Fat content, and what is its Trans Fat value in grams? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_982_982280_qa_2",
"description": "What is the item with the highest Trans Fat content, and what is its Trans Fat value in grams?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/982/982280.ipynb_qa_2",
"kaggle_dataset_name": "mcdonalds/nutrition-facts",
"gold_answer": "Double Quarter Pounder with Cheese, 2.5",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_982_982280_qa_2 |
smoldataenvs-train/0000_992_992184_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- lab_petct_vox_5.00mm.h5
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mo... | What are the keys present in the loaded PETCT dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0000_992_992184_qa_3",
"description": "What are the keys present in the loaded PETCT dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0000/992/992184.ipynb_qa_3",
"kaggle_dataset_name": "4quant/soft-tissue-sarcoma",
"gold_answer": "ct_data, label_data, pet_data",
"reward_mode_initial": "list",
"package_tier": 3,
"difficulty_level": 1,
"difficulty": "easy",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0000_992_992184_qa_3 |
smoldataenvs-train/0001_042_1042725_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- marathon_results_2016.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | Which two countries have the most top 100 male marathon runners after the USA in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_042_1042725_qa_5",
"description": "Which two countries have the most top 100 male marathon runners after the USA in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/042/1042725.ipynb_qa_5",
"kaggle_dataset_name": "rojour/boston-results",
"gold_answer": "Kenya, Ethiopia",
"reward_mode_initial": "list",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_042_1042725_qa_5 |
smoldataenvs-train/0001_074_1074738_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | Which U.S. state has the highest number of recorded "Murder or Manslaughter" cases, and what is the exact count of such incidents in that state? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_074_1074738_qa_1",
"description": "Which U.S. state has the highest number of recorded \"Murder or Manslaughter\" cases, and what is the exact count of such incidents in that state?",
"authors": [],
"keywords": ... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/074/1074738.ipynb_qa_1",
"kaggle_dataset_name": "murderaccountability/homicide-reports",
"gold_answer": "California, 98994",
"reward_mode_initial": "list",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_074_1074738_qa_1 |
smoldataenvs-train/0001_074_1074738_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | Which relationship category between victims and perpetrators is most prevalent in the dataset, and what percentage of homicide cases fall into this category? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_074_1074738_qa_4",
"description": "Which relationship category between victims and perpetrators is most prevalent in the dataset, and what percentage of homicide cases fall into this category?",
"authors": [],
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/074/1074738.ipynb_qa_4",
"kaggle_dataset_name": "murderaccountability/homicide-reports",
"gold_answer": "Unknown, 42.76%",
"reward_mode_initial": "list",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_074_1074738_qa_4 |
smoldataenvs-train/0001_085_1085629_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- (see /home/user/input)
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What are the top three features according to the feature importance ranking provided by the Extra Trees Classifier? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_085_1085629_qa_2",
"description": "What are the top three features according to the feature importance ranking provided by the Extra Trees Classifier?",
"authors": [],
"keywords": [
"smoldataenvs",
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/085/1085629.ipynb_qa_2",
"kaggle_dataset_name": "uciml/adult-census-income",
"gold_answer": "fnlwgt, age, hours.per.week",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 4,
"difficulty": "hard",
"d... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_085_1085629_qa_2 |
smoldataenvs-train/0001_085_1085629_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- adult.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | What is the accuracy of the KNN model when using only the top two features from the Feature Importance ranking (fnlwgt and age) with the optimal K value? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_085_1085629_qa_4",
"description": "What is the accuracy of the KNN model when using only the top two features from the Feature Importance ranking (fnlwgt and age) with the optimal K value?",
"authors": [],
"keyw... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/085/1085629.ipynb_qa_4",
"kaggle_dataset_name": "uciml/adult-census-income",
"gold_answer": "0.762",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 4,
"difficulty": "hard",
"difficulty_tier": "h... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_085_1085629_qa_4 |
smoldataenvs-train/0001_090_1090499_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- xAPI-Edu-Data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | Which two nationalities have the highest representation in the dataset based on the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_090_1090499_qa_1",
"description": "Which two nationalities have the highest representation in the dataset based on the analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/090/1090499.ipynb_qa_1",
"kaggle_dataset_name": "aljarah/xAPI-Edu-Data",
"gold_answer": "Kuwait, Jordan",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_090_1090499_qa_1 |
smoldataenvs-train/0001_133_1133625_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- up_res.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | In which phase did BJP+ achieve the highest percentage of total votes, and what was that percentage? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_133_1133625_qa_4",
"description": "In which phase did BJP+ achieve the highest percentage of total votes, and what was that percentage?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/133/1133625.ipynb_qa_4",
"kaggle_dataset_name": "ankit2106/uttar-pradesh-assembly-elections-2017",
"gold_answer": "Phase 1, 45.48",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "me... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_133_1133625_qa_4 |
smoldataenvs-train/0001_137_1137361_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- dataset_TSMC2014_NYC.csv
- dataset_TSMC2014_TKY.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sql... | What is the total number of check-ins recorded in the New York City dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_137_1137361_qa_2",
"description": "What is the total number of check-ins recorded in the New York City dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/137/1137361.ipynb_qa_2",
"kaggle_dataset_name": "chetanism/foursquare-nyc-and-tokyo-checkin-dataset",
"gold_answer": "227428",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_137_1137361_qa_2 |
smoldataenvs-train/0001_137_1137537_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- dataset_TSMC2014_NYC.csv
- dataset_TSMC2014_TKY.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sql... | What are the geographic coordinates of the closest check-in point to the convex hull centroid in New York City? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_137_1137537_qa_2",
"description": "What are the geographic coordinates of the closest check-in point to the convex hull centroid in New York City?",
"authors": [],
"keywords": [
"smoldataenvs",
"data... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/137/1137537.ipynb_qa_2",
"kaggle_dataset_name": "chetanism/foursquare-nyc-and-tokyo-checkin-dataset",
"gold_answer": "40.77607305, -73.98191214",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 4,
"di... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_137_1137537_qa_2 |
smoldataenvs-train/0001_155_1155051_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What is the average age for customers who defaulted compared to those who did not? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_155_1155051_qa_5",
"description": "What is the average age for customers who defaulted compared to those who did not?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/155/1155051.ipynb_qa_5",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "Defaulters: 35.73, Non-defaulters: 35.42",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level":... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_155_1155051_qa_5 |
smoldataenvs-train/0001_155_1155264_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- (see /home/user/input)
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the most common instance type in the south zone identified through the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_155_1155264_qa_5",
"description": "What is the most common instance type in the south zone identified through the analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/155/1155264.ipynb_qa_5",
"kaggle_dataset_name": "noqcks/aws-spot-pricing-market",
"gold_answer": "m4.large",
"reward_mode_initial": "exact_short",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"diffic... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_155_1155264_qa_5 |
smoldataenvs-train/0001_160_1160639_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- banknifty.csv
- nifty50.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | What is the average opening price of Nifty 50 across all recorded dates in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_160_1160639_qa_1",
"description": "What is the average opening price of Nifty 50 across all recorded dates in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/160/1160639.ipynb_qa_1",
"kaggle_dataset_name": "ramamet4/nse-stocks-database",
"gold_answer": "7374.52",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_160_1160639_qa_1 |
smoldataenvs-train/0001_170_1170198_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- bird.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | After imputing missing values with the column mean, how many missing values remain in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_170_1170198_qa_3",
"description": "After imputing missing values with the column mean, how many missing values remain in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/170/1170198.ipynb_qa_3",
"kaggle_dataset_name": "zhangjuefei/birds-bones-and-living-habits",
"gold_answer": "0",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"diffic... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_170_1170198_qa_3 |
smoldataenvs-train/0001_170_1170198_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- bird.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the frequency of the most populated bin in the 'huml' histogram, and what is the bin range? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_170_1170198_qa_4",
"description": "What is the frequency of the most populated bin in the 'huml' histogram, and what is the bin range?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/170/1170198.ipynb_qa_4",
"kaggle_dataset_name": "zhangjuefei/birds-bones-and-living-habits",
"gold_answer": "234, 9.85 to 50.865",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "med... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_170_1170198_qa_4 |
smoldataenvs-train/0001_173_1173665_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Churn_Modelling.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | After normalization, what is the mean value of the 'Balance' feature? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_173_1173665_qa_3",
"description": "After normalization, what is the mean value of the 'Balance' feature?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"m... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/173/1173665.ipynb_qa_3",
"kaggle_dataset_name": "filippoo/deep-learning-az-ann",
"gold_answer": "0.3048",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_ti... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_173_1173665_qa_3 |
smoldataenvs-train/0001_173_1173665_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Churn_Modelling.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | Which feature, 'Balance' or 'EstimatedSalary', has a higher standard deviation after normalization? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_173_1173665_qa_5",
"description": "Which feature, 'Balance' or 'EstimatedSalary', has a higher standard deviation after normalization?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/173/1173665.ipynb_qa_5",
"kaggle_dataset_name": "filippoo/deep-learning-az-ann",
"gold_answer": "EstimatedSalary",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_173_1173665_qa_5 |
smoldataenvs-train/0001_175_1175291_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What is the maximum earthquake magnitude recorded in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_175_1175291_qa_4",
"description": "What is the maximum earthquake magnitude recorded in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metad... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/175/1175291.ipynb_qa_4",
"kaggle_dataset_name": "usgs/earthquake-database",
"gold_answer": "9.1",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "easy... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_175_1175291_qa_4 |
smoldataenvs-train/0001_181_1181828_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- glass.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | Which model demonstrated the highest training accuracy but the lowest test accuracy in the comparison analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_181_1181828_qa_4",
"description": "Which model demonstrated the highest training accuracy but the lowest test accuracy in the comparison analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/181/1181828.ipynb_qa_4",
"kaggle_dataset_name": "uciml/glass",
"gold_answer": "Decision Tree",
"reward_mode_initial": "exact_short",
"package_tier": 2,
"difficulty_level": 4,
"difficulty": "hard",
"difficulty_tier": "har... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_181_1181828_qa_4 |
smoldataenvs-train/0001_182_1182948_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- SolarPrediction.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What is the minimum recorded solar radiation value in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_182_1182948_qa_1",
"description": "What is the minimum recorded solar radiation value in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"meta... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/182/1182948.ipynb_qa_1",
"kaggle_dataset_name": "dronio/SolarEnergy",
"gold_answer": "1.11",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "easy"
} | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_182_1182948_qa_1 |
smoldataenvs-train/0001_188_1188925_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What is the mean age of individuals who defaulted on their credit card payments compared to those who did not? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_188_1188925_qa_1",
"description": "What is the mean age of individuals who defaulted on their credit card payments compared to those who did not?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/188/1188925.ipynb_qa_1",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "Default: 35.73, Non-Default: 35.42",
"reward_mode_initial": "list",
"package_tier": 2,
"difficulty_level": 2,
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_188_1188925_qa_1 |
smoldataenvs-train/0001_188_1188925_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What percentage of the dataset consists of credit card defaults? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_188_1188925_qa_3",
"description": "What percentage of the dataset consists of credit card defaults?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metada... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/188/1188925.ipynb_qa_3",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "22",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 1,
"difficulty": "easy",
"diff... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_188_1188925_qa_3 |
smoldataenvs-train/0001_189_1189227_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What percentage of the dataset represents credit card defaults? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_189_1189227_qa_1",
"description": "What percentage of the dataset represents credit card defaults?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadat... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/189/1189227.ipynb_qa_1",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "22",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 2,
"difficulty": "medium",
"di... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_189_1189227_qa_1 |
smoldataenvs-train/0001_189_1189227_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What is the mean age of credit card holders who defaulted? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_189_1189227_qa_2",
"description": "What is the mean age of credit card holders who defaulted?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/189/1189227.ipynb_qa_2",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "35.73",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_189_1189227_qa_2 |
smoldataenvs-train/0001_189_1189227_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- UCI_Credit_Card.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | How many samples were allocated to the training set? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_189_1189227_qa_5",
"description": "How many samples were allocated to the training set?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/189/1189227.ipynb_qa_5",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "24000",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_189_1189227_qa_5 |
smoldataenvs-train/0001_191_1191057_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- WA_Fn-UseC_-HR-Employee-Attrition.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (... | Which original features were removed from the dataset because they contained only a single unique value across all observations? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_191_1191057_qa_4",
"description": "Which original features were removed from the dataset because they contained only a single unique value across all observations?",
"authors": [],
"keywords": [
"smoldatae... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/191/1191057.ipynb_qa_4",
"kaggle_dataset_name": "pavansubhasht/ibm-hr-analytics-attrition-dataset",
"gold_answer": "EmployeeCount, Over18, StandardHours",
"reward_mode_initial": "list",
"package_tier": 2,
"difficulty_level":... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_191_1191057_qa_4 |
smoldataenvs-train/0001_193_1193343_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- food_coded.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if nee... | Which cuisine type is mentioned most frequently in the "fav_cuisine" column of the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_193_1193343_qa_3",
"description": "Which cuisine type is mentioned most frequently in the \"fav_cuisine\" column of the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/193/1193343.ipynb_qa_3",
"kaggle_dataset_name": "borapajo/food-choices",
"gold_answer": "italian",
"reward_mode_initial": "exact_short",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_193_1193343_qa_3 |
smoldataenvs-train/0001_196_1196803_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- directory.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the most common ownership type among all Starbucks stores in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_196_1196803_qa_3",
"description": "What is the most common ownership type among all Starbucks stores in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/196/1196803.ipynb_qa_3",
"kaggle_dataset_name": "starbucks/store-locations",
"gold_answer": "Company Owned",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficul... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_196_1196803_qa_3 |
smoldataenvs-train/0001_197_1197721_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- bgg_db_2017_04.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if... | Which two variables exhibit the strongest positive correlation with the number of games owned in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_197_1197721_qa_2",
"description": "Which two variables exhibit the strongest positive correlation with the number of games owned in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-a... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/197/1197721.ipynb_qa_2",
"kaggle_dataset_name": "mrpantherson/board-game-data",
"gold_answer": "geek_rating, num_votes",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"d... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_197_1197721_qa_2 |
smoldataenvs-train/0001_202_1202888_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Pokemon.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | Which generation has the highest probability of producing a legendary Pokémon in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_202_1202888_qa_1",
"description": "Which generation has the highest probability of producing a legendary Pokémon in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/202/1202888.ipynb_qa_1",
"kaggle_dataset_name": "abcsds/pokemon",
"gold_answer": "Generation 3",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_202_1202888_qa_1 |
smoldataenvs-train/0001_221_1221016_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Players.csv
- Seasons_Stats.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip in... | Who is the tallest player in NBA history based on the dataset, and what is their height in centimeters? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_221_1221016_qa_1",
"description": "Who is the tallest player in NBA history based on the dataset, and what is their height in centimeters?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysi... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/221/1221016.ipynb_qa_1",
"kaggle_dataset_name": "drgilermo/nba-players-stats",
"gold_answer": "Manute Bol, 231.0",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_221_1221016_qa_1 |
smoldataenvs-train/0001_231_1231918_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- menu.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What percentage of McDonald's menu items contain zero sugar based on the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_231_1231918_qa_1",
"description": "What percentage of McDonald's menu items contain zero sugar based on the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/231/1231918.ipynb_qa_1",
"kaggle_dataset_name": "mcdonalds/nutrition-facts",
"gold_answer": "9.61",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_231_1231918_qa_1 |
smoldataenvs-train/0001_231_1231918_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- menu.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | How many menu items in the dataset have zero sugar content? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_231_1231918_qa_2",
"description": "How many menu items in the dataset have zero sugar content?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": ... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/231/1231918.ipynb_qa_2",
"kaggle_dataset_name": "mcdonalds/nutrition-facts",
"gold_answer": "25",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "easy... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_231_1231918_qa_2 |
smoldataenvs-train/0001_233_1233959_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- mushrooms.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the most common cap shape in the dataset based on the feature frequency analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_233_1233959_qa_2",
"description": "What is the most common cap shape in the dataset based on the feature frequency analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/233/1233959.ipynb_qa_2",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "convex",
"reward_mode_initial": "exact_short",
"package_tier": 2,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_233_1233959_qa_2 |
smoldataenvs-train/0001_233_1233959_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- mushrooms.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the most common cap color in the dataset based on the feature frequency analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_233_1233959_qa_5",
"description": "What is the most common cap color in the dataset based on the feature frequency analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/233/1233959.ipynb_qa_5",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "brown",
"reward_mode_initial": "exact_short",
"package_tier": 2,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_t... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_233_1233959_qa_5 |
smoldataenvs-train/0001_234_1234901_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- degrees-that-pay-back.csv
- salaries-by-college-type.csv
- salaries-by-region.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-lea... | Which undergraduate major has the highest mid-career median salary, and what is that value? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_234_1234901_qa_3",
"description": "Which undergraduate major has the highest mid-career median salary, and what is that value?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"k... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/234/1234901.ipynb_qa_3",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "Chemical Engineering, 107000",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"dif... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_234_1234901_qa_3 |
smoldataenvs-train/0001_234_1234901_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- degrees-that-pay-back.csv
- salaries-by-college-type.csv
- salaries-by-region.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-lea... | Which undergraduate major has the highest starting median salary, and what is that value? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_234_1234901_qa_5",
"description": "Which undergraduate major has the highest starting median salary, and what is that value?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/234/1234901.ipynb_qa_5",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "Physician Assistant, 74300",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"diffi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_234_1234901_qa_5 |
smoldataenvs-train/0001_238_1238370_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Netflix Shows.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | How many unique Netflix shows are present in the dataset, considering duplicate titles? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_238_1238370_qa_1",
"description": "How many unique Netflix shows are present in the dataset, considering duplicate titles?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/238/1238370.ipynb_qa_1",
"kaggle_dataset_name": "chasewillden/netflix-shows",
"gold_answer": "496",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_238_1238370_qa_1 |
smoldataenvs-train/0001_239_1239559_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- chopstick-effectiveness.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | How much does the average food pinching efficiency decrease from the optimal length (240mm) to the next length tested (270mm)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_239_1239559_qa_4",
"description": "How much does the average food pinching efficiency decrease from the optimal length (240mm) to the next length tested (270mm)?",
"authors": [],
"keywords": [
"smoldataenv... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/239/1239559.ipynb_qa_4",
"kaggle_dataset_name": "priya2908/chopsticks-1992",
"gold_answer": "1.999",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_239_1239559_qa_4 |
smoldataenvs-train/0001_240_1240535_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Computers.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the mean price of computers in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_240_1240535_qa_1",
"description": "What is the mean price of computers in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
"s... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/240/1240535.ipynb_qa_1",
"kaggle_dataset_name": "kingburrito666/basic-computer-data-set",
"gold_answer": "2219.58",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",
"diffi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_240_1240535_qa_1 |
smoldataenvs-train/0001_243_1243037_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- diabetes.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What percentage of individuals in the dataset have diabetes (Outcome=1)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_243_1243037_qa_1",
"description": "What percentage of individuals in the dataset have diabetes (Outcome=1)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/243/1243037.ipynb_qa_1",
"kaggle_dataset_name": "uciml/pima-indians-diabetes-database",
"gold_answer": "34.90",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_243_1243037_qa_1 |
smoldataenvs-train/0001_243_1243037_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- diabetes.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | Which feature exhibits the strongest positive correlation with the Outcome variable (diabetes status)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_243_1243037_qa_2",
"description": "Which feature exhibits the strongest positive correlation with the Outcome variable (diabetes status)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/243/1243037.ipynb_qa_2",
"kaggle_dataset_name": "uciml/pima-indians-diabetes-database",
"gold_answer": "Glucose",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"d... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_243_1243037_qa_2 |
smoldataenvs-train/0001_244_1244861_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- 2015.csv
- 2016.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | What was the average family score in 2015 compared to 2016? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_244_1244861_qa_4",
"description": "What was the average family score in 2015 compared to 2016?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": ... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/244/1244861.ipynb_qa_4",
"kaggle_dataset_name": "unsdsn/world-happiness",
"gold_answer": "0.9910, 0.7936",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_244_1244861_qa_4 |
smoldataenvs-train/0001_247_1247152_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- beers.csv
- breweries.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | Which U.S. state has the highest number of breweries based on the dataset analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_247_1247152_qa_1",
"description": "Which U.S. state has the highest number of breweries based on the dataset analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/247/1247152.ipynb_qa_1",
"kaggle_dataset_name": "nickhould/craft-cans",
"gold_answer": "Colorado",
"reward_mode_initial": "exact_short",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_247_1247152_qa_1 |
smoldataenvs-train/0001_250_1250826_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- survey_results_public.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | What is the average salary of users who use both R and Python compared to those who use neither language? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_250_1250826_qa_3",
"description": "What is the average salary of users who use both R and Python compared to those who use neither language?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analy... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/250/1250826.ipynb_qa_3",
"kaggle_dataset_name": "stackoverflow/so-survey-2017",
"gold_answer": "63584.37, 54166.61",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"diffi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_250_1250826_qa_3 |
smoldataenvs-train/0001_257_1257061_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- kc_house_data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | What is the skewness of the original SalePrice distribution before any transformation? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257061_qa_1",
"description": "What is the skewness of the original SalePrice distribution before any transformation?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257061.ipynb_qa_1",
"kaggle_dataset_name": "harlfoxem/housesalesprediction",
"gold_answer": "4.024069",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_t... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257061_qa_1 |
smoldataenvs-train/0001_257_1257061_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- kc_house_data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | Which feature has the highest absolute correlation with SalePrice, and what is the magnitude of that correlation? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257061_qa_2",
"description": "Which feature has the highest absolute correlation with SalePrice, and what is the magnitude of that correlation?",
"authors": [],
"keywords": [
"smoldataenvs",
"da... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257061.ipynb_qa_2",
"kaggle_dataset_name": "harlfoxem/housesalesprediction",
"gold_answer": "sqft_living with 0.7",
"reward_mode_initial": "flexible",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257061_qa_2 |
smoldataenvs-train/0001_257_1257061_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- kc_house_data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | What transformation was applied to the SalePrice and sqft_living features to achieve a more normal distribution? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257061_qa_3",
"description": "What transformation was applied to the SalePrice and sqft_living features to achieve a more normal distribution?",
"authors": [],
"keywords": [
"smoldataenvs",
"dat... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257061.ipynb_qa_3",
"kaggle_dataset_name": "harlfoxem/housesalesprediction",
"gold_answer": "log transformation",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257061_qa_3 |
smoldataenvs-train/0001_257_1257756_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- FMEL_Dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | What is the average percentage of matches won by the home team across all seasons in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257756_qa_1",
"description": "What is the average percentage of matches won by the home team across all seasons in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257756.ipynb_qa_1",
"kaggle_dataset_name": "ricardomoya/football-matches-of-spanish-league",
"gold_answer": "51.16",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257756_qa_1 |
smoldataenvs-train/0001_257_1257756_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- FMEL_Dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | Which team has the highest number of home wins in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257756_qa_2",
"description": "Which team has the highest number of home wins in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257756.ipynb_qa_2",
"kaggle_dataset_name": "ricardomoya/football-matches-of-spanish-league",
"gold_answer": "Real Madrid",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257756_qa_2 |
smoldataenvs-train/0001_257_1257756_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- FMEL_Dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | What is the average total number of goals scored per match in the dataset (local + visitor goals)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_257_1257756_qa_3",
"description": "What is the average total number of goals scored per match in the dataset (local + visitor goals)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/257/1257756.ipynb_qa_3",
"kaggle_dataset_name": "ricardomoya/football-matches-of-spanish-league",
"gold_answer": "2.45",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_257_1257756_qa_3 |
smoldataenvs-train/0001_261_1261978_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- (see /home/user/input)
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | Which chopstick length has the lowest mean food pinching efficiency based on the dataset analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_261_1261978_qa_5",
"description": "Which chopstick length has the lowest mean food pinching efficiency based on the dataset analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/261/1261978.ipynb_qa_5",
"kaggle_dataset_name": "priya2908/chopsticks-1992",
"gold_answer": "330",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_261_1261978_qa_5 |
smoldataenvs-train/0001_262_1262014_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | How many of the female-on-female homicides had the weapon listed as unknown? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_262_1262014_qa_4",
"description": "How many of the female-on-female homicides had the weapon listed as unknown?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/262/1262014.ipynb_qa_4",
"kaggle_dataset_name": "murderaccountability/homicide-reports",
"gold_answer": "1507",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficu... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_262_1262014_qa_4 |
smoldataenvs-train/0001_272_1272652_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- genome.fa
- genes-augustus.csv
- genes-genscan.csv
- genes-ensembl.csv
- genes-refseq.csv
- genes-xeno-refseq.csv
Installed: pandas, numpy, matplo... | What is the total length of the first six chromosomes listed in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_272_1272652_qa_1",
"description": "What is the total length of the first six chromosomes listed in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/272/1272652.ipynb_qa_1",
"kaggle_dataset_name": "mylesoneill/drosophila-melanogaster-genome",
"gold_answer": "110357861",
"reward_mode_initial": "numeric",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_272_1272652_qa_1 |
smoldataenvs-train/0001_273_1273208_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- creditcard.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if nee... | What percentage of the original dataset consists of fraudulent transactions (Class 1)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_273_1273208_qa_1",
"description": "What percentage of the original dataset consists of fraudulent transactions (Class 1)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/273/1273208.ipynb_qa_1",
"kaggle_dataset_name": "mlg-ulb/creditcardfraud",
"gold_answer": "0.1727485630620034",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_273_1273208_qa_1 |
smoldataenvs-train/0001_273_1273208_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- (see /home/user/input)
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | After undersampling, how many total rows are present in the balanced dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_273_1273208_qa_2",
"description": "After undersampling, how many total rows are present in the balanced dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/273/1273208.ipynb_qa_2",
"kaggle_dataset_name": "mlg-ulb/creditcardfraud",
"gold_answer": "984",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "med... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_273_1273208_qa_2 |
smoldataenvs-train/0001_275_1275171_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- world-cities.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | How many Indian states have more than 100 cities listed in the dataset based on the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_275_1275171_qa_3",
"description": "How many Indian states have more than 100 cities listed in the dataset based on the analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/275/1275171.ipynb_qa_3",
"kaggle_dataset_name": "okfn/world-cities",
"gold_answer": "10",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium"
} | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_275_1275171_qa_3 |
smoldataenvs-train/0001_275_1275171_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- world-cities.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | What is the total number of cities listed for India in the dataset according to the filtered data? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_275_1275171_qa_4",
"description": "What is the total number of cities listed for India in the dataset according to the filtered data?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/275/1275171.ipynb_qa_4",
"kaggle_dataset_name": "okfn/world-cities",
"gold_answer": "2443",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium"
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_275_1275171_qa_4 |
smoldataenvs-train/0001_277_1277058_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- us_companies.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | How many unique countries are represented in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_277_1277058_qa_1",
"description": "How many unique countries are represented in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/277/1277058.ipynb_qa_1",
"kaggle_dataset_name": "govlab/open-data-500-companies",
"gold_answer": "1",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_277_1277058_qa_1 |
smoldataenvs-train/0001_277_1277058_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- us_companies.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | Which year had the highest number of companies founded, and how many companies were founded that year? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_277_1277058_qa_2",
"description": "Which year had the highest number of companies founded, and how many companies were founded that year?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/277/1277058.ipynb_qa_2",
"kaggle_dataset_name": "govlab/open-data-500-companies",
"gold_answer": "2011, 51",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_277_1277058_qa_2 |
smoldataenvs-train/0001_277_1277058_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- us_companies.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if n... | Which year had the second-highest number of companies founded, and how many companies were founded that year? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_277_1277058_qa_5",
"description": "Which year had the second-highest number of companies founded, and how many companies were founded that year?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-a... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/277/1277058.ipynb_qa_5",
"kaggle_dataset_name": "govlab/open-data-500-companies",
"gold_answer": "2010 and 50",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"dif... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_277_1277058_qa_5 |
smoldataenvs-train/0001_282_1282413_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- database.sqlite
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ne... | What percentage of matches in the English Premier League ended in draws according to the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_282_1282413_qa_5",
"description": "What percentage of matches in the English Premier League ended in draws according to the analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/282/1282413.ipynb_qa_5",
"kaggle_dataset_name": "hugomathien/soccer",
"gold_answer": "25.76",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_282_1282413_qa_5 |
smoldataenvs-train/0001_289_1289812_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Netflix Shows.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | How many Netflix shows in the dataset contain missing values in at least one column? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_289_1289812_qa_4",
"description": "How many Netflix shows in the dataset contain missing values in at least one column?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/289/1289812.ipynb_qa_4",
"kaggle_dataset_name": "chasewillden/netflix-shows",
"gold_answer": "426",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_289_1289812_qa_4 |
smoldataenvs-train/0001_293_1293142_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- kc_house_data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | What is the correlation coefficient between the sqft_living feature and the log-transformed price variable in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_293_1293142_qa_5",
"description": "What is the correlation coefficient between the sqft_living feature and the log-transformed price variable in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/293/1293142.ipynb_qa_5",
"kaggle_dataset_name": "harlfoxem/housesalesprediction",
"gold_answer": "0.70",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tie... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_293_1293142_qa_5 |
smoldataenvs-train/0001_312_1312239_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- 2015.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Which region has the highest average Happiness Score when grouping by geographic regions? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_312_1312239_qa_2",
"description": "Which region has the highest average Happiness Score when grouping by geographic regions?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/312/1312239.ipynb_qa_2",
"kaggle_dataset_name": "unsdsn/world-happiness",
"gold_answer": "Australia and New Zealand",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_312_1312239_qa_2 |
smoldataenvs-train/0001_315_1315610_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Iris.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | After MinMax scaling, what is the range (maximum value minus minimum value) of the SepalWidthCm feature? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_315_1315610_qa_2",
"description": "After MinMax scaling, what is the range (maximum value minus minimum value) of the SepalWidthCm feature?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analys... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/315/1315610.ipynb_qa_2",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "1.0",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium"
} | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_315_1315610_qa_2 |
smoldataenvs-train/0001_323_1323152_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- IMDB-Movie-Data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | Which movie generated the highest revenue in the dataset, and what was the exact revenue amount? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_323_1323152_qa_1",
"description": "Which movie generated the highest revenue in the dataset, and what was the exact revenue amount?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/323/1323152.ipynb_qa_1",
"kaggle_dataset_name": "PromptCloudHQ/imdb-data",
"gold_answer": "Star Wars: Episode VII - The Force Awakens, 936.63",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 1,
"diff... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_323_1323152_qa_1 |
smoldataenvs-train/0001_325_1325110_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- kc_house_data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | How many distinct latitude-based groups were created based on the arbitrary thresholds defined in the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_325_1325110_qa_5",
"description": "How many distinct latitude-based groups were created based on the arbitrary thresholds defined in the analysis?",
"authors": [],
"keywords": [
"smoldataenvs",
"data... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/325/1325110.ipynb_qa_5",
"kaggle_dataset_name": "harlfoxem/housesalesprediction",
"gold_answer": "4",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier":... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_325_1325110_qa_5 |
smoldataenvs-train/0001_330_1330281_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- shot_logs.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | What is the base shot success rate across all shots in the dataset, regardless of contextual factors? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_330_1330281_qa_3",
"description": "What is the base shot success rate across all shots in the dataset, regardless of contextual factors?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/330/1330281.ipynb_qa_3",
"kaggle_dataset_name": "dansbecker/nba-shot-logs",
"gold_answer": "45.2139",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_330_1330281_qa_3 |
smoldataenvs-train/0001_330_1330281_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- shot_logs.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | How does the 1st period’s shot success rate compare to the 4th period’s shot success rate during regulation? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_330_1330281_qa_4",
"description": "How does the 1st period’s shot success rate compare to the 4th period’s shot success rate during regulation?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-an... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/330/1330281.ipynb_qa_4",
"kaggle_dataset_name": "dansbecker/nba-shot-logs",
"gold_answer": "1st period 46.0528%, 4th period 44.0099%",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_330_1330281_qa_4 |
smoldataenvs-train/0001_341_1341821_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Pokemon.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What is the most common dual-type Pokémon combination across all generations, and what is its total count? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_341_1341821_qa_1",
"description": "What is the most common dual-type Pokémon combination across all generations, and what is its total count?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-anal... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/341/1341821.ipynb_qa_1",
"kaggle_dataset_name": "abcsds/pokemon",
"gold_answer": "Normal/Flying, 24",
"reward_mode_initial": "list",
"package_tier": 3,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_341_1341821_qa_1 |
smoldataenvs-train/0001_349_1349978_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- mushrooms.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | How many categorical features were originally present in the mushroom dataset before numerical encoding? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_349_1349978_qa_4",
"description": "How many categorical features were originally present in the mushroom dataset before numerical encoding?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analys... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/349/1349978.ipynb_qa_4",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "23",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_349_1349978_qa_4 |
smoldataenvs-train/0001_351_1351211_qa_2 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the most common degree of endangerment among languages in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_351_1351211_qa_2",
"description": "What is the most common degree of endangerment among languages in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/351/1351211.ipynb_qa_2",
"kaggle_dataset_name": "the-guardian/extinct-languages",
"gold_answer": "Definitely endangered",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_351_1351211_qa_2 |
smoldataenvs-train/0001_352_1352372_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- celebrity_deaths_4.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | Which year had the highest number of celebrity deaths based on the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_352_1352372_qa_1",
"description": "Which year had the highest number of celebrity deaths based on the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
}... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/352/1352372.ipynb_qa_1",
"kaggle_dataset_name": "hugodarwood/celebrity-deaths",
"gold_answer": "2016",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": ... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_352_1352372_qa_1 |
smoldataenvs-train/0001_352_1352372_qa_5 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- celebrity_deaths_4.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | How many celebrities in the dataset died as a result of accidents? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_352_1352372_qa_5",
"description": "How many celebrities in the dataset died as a result of accidents?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"meta... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/352/1352372.ipynb_qa_5",
"kaggle_dataset_name": "hugodarwood/celebrity-deaths",
"gold_answer": "141",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier":... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_352_1352372_qa_5 |
smoldataenvs-train/0001_353_1353632_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What is the difference between the number of "run" samples collected on the left wrist versus "walk" samples on the same wrist? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_353_1353632_qa_3",
"description": "What is the difference between the number of \"run\" samples collected on the left wrist versus \"walk\" samples on the same wrist?",
"authors": [],
"keywords": [
"smolda... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/353/1353632.ipynb_qa_3",
"kaggle_dataset_name": "vmalyi/run-or-walk",
"gold_answer": "5086",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medium"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_353_1353632_qa_3 |
smoldataenvs-train/0001_353_1353930_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- data.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Which language in the dataset has the highest number of speakers, and what is its speaker count? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_353_1353930_qa_1",
"description": "Which language in the dataset has the highest number of speakers, and what is its speaker count?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/353/1353930.ipynb_qa_1",
"kaggle_dataset_name": "the-guardian/extinct-languages",
"gold_answer": "South Italian, 7500000",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_353_1353930_qa_1 |
smoldataenvs-train/0001_354_1354131_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- Iris.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Is the distribution of species in the Iris dataset balanced across all classes? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_354_1354131_qa_1",
"description": "Is the distribution of species in the Iris dataset balanced across all classes?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/354/1354131.ipynb_qa_1",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "yes",
"reward_mode_initial": "exact_bool",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "easy"
} | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_354_1354131_qa_1 |
smoldataenvs-train/0001_361_1361614_qa_1 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- GlobalLandTemperaturesByMajorCity.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (... | Which Indian city has the highest temperature difference between its maximum and minimum recorded temperatures in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_361_1361614_qa_1",
"description": "Which Indian city has the highest temperature difference between its maximum and minimum recorded temperatures in the dataset?",
"authors": [],
"keywords": [
"smoldataenv... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/361/1361614.ipynb_qa_1",
"kaggle_dataset_name": "berkeleyearth/climate-change-earth-surface-temperature-data",
"gold_answer": "New Delhi",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 3,
"di... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_361_1361614_qa_1 |
smoldataenvs-train/0001_364_1364936_qa_3 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- (see /home/user/input)
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | Which movie has the lowest total count of entries in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_364_1364936_qa_3",
"description": "Which movie has the lowest total count of entries in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metad... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/364/1364936.ipynb_qa_3",
"kaggle_dataset_name": "fivethirtyeight/cuss-words-and-deaths-in-quentin-tarantino-films",
"gold_answer": "Kill Bill: Vol. 2",
"reward_mode_initial": "exact_short",
"package_tier": 0,
"difficulty_lev... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_364_1364936_qa_3 |
smoldataenvs-train/0001_364_1364936_qa_4 | You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
Files (in /home/user/input, no subfolders):
- tarantino.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | How many movies in the dataset were released after the year 2004? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_364_1364936_qa_4",
"description": "How many movies in the dataset were released after the year 2004?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metad... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/364/1364936.ipynb_qa_4",
"kaggle_dataset_name": "fivethirtyeight/cuss-words-and-deaths-in-quentin-tarantino-films",
"gold_answer": "2",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_364_1364936_qa_4 |
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