name string | instruction string | description string | keywords list | schema_version string | config unknown | metadata unknown | files list | location string |
|---|---|---|---|---|---|---|---|---|
smoldataenvs-train/0001_638_1638617_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):
- train.csv
- test.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more ... | How many unique ticket numbers are shared between the training dataset and test dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_638_1638617_qa_4",
"description": "How many unique ticket numbers are shared between the training dataset and test dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/638/1638617.ipynb_qa_4",
"kaggle_dataset_name": "shuofxz/titanic-machine-learning-from-disaster",
"gold_answer": "115",
"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_638_1638617_qa_4 |
smoldataenvs-train/0001_645_1645214_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).
... | Which feature in the Iris dataset has the highest standard deviation according to the descriptive statistics? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_645_1645214_qa_2",
"description": "Which feature in the Iris dataset has the highest standard deviation according to the descriptive statistics?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-a... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/645/1645214.ipynb_qa_2",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "PetalLengthCm",
"reward_mode_initial": "exact_short",
"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_645_1645214_qa_2 |
smoldataenvs-train/0001_646_1646297_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... | Which feature in the dataset has the highest number of unique categories, and how many are there? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_646_1646297_qa_2",
"description": "Which feature in the dataset has the highest number of unique categories, and how many are there?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/646/1646297.ipynb_qa_2",
"kaggle_dataset_name": "uciml/mushroom-classification",
"gold_answer": "gill-color, 12",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficul... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_646_1646297_qa_2 |
smoldataenvs-train/0001_656_1656907_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):
- DigiDB_digimonlist.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the average (mean) Level 50 Attack value for all Digimon in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_656_1656907_qa_2",
"description": "What is the average (mean) Level 50 Attack value for all Digimon in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/656/1656907.ipynb_qa_2",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "124.52",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_656_1656907_qa_2 |
smoldataenvs-train/0001_656_1656907_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):
- DigiDB_digimonlist.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the highest Memory value required by any Digimon in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_656_1656907_qa_4",
"description": "What is the highest Memory value required by any Digimon in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/656/1656907.ipynb_qa_4",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "25",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_656_1656907_qa_4 |
smoldataenvs-train/0001_656_1656907_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):
- DigiDB_digimonlist.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the average number of Equip Slots for all Digimon in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_656_1656907_qa_5",
"description": "What is the average number of Equip Slots for all Digimon in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/656/1656907.ipynb_qa_5",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "1.57",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_656_1656907_qa_5 |
smoldataenvs-train/0001_657_1657214_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):
- DigiDB_digimonlist.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the range (maximum minus minimum) of the Level 50 Attack values across all Digimon? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_657_1657214_qa_3",
"description": "What is the range (maximum minus minimum) of the Level 50 Attack values across all Digimon?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"k... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/657/1657214.ipynb_qa_3",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "266",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_657_1657214_qa_3 |
smoldataenvs-train/0001_659_1659062_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):
- museums.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What is the maximum revenue value recorded for history museums after removing duplicates and missing values from the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_659_1659062_qa_3",
"description": "What is the maximum revenue value recorded for history museums after removing duplicates and missing values from the dataset?",
"authors": [],
"keywords": [
"smoldataenvs... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/659/1659062.ipynb_qa_3",
"kaggle_dataset_name": "imls/museum-directory",
"gold_answer": "5.121523e9",
"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_659_1659062_qa_3 |
smoldataenvs-train/0001_659_1659621_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What are the standard deviations of sugar content for cold and hot cereals respectively? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_659_1659621_qa_3",
"description": "What are the standard deviations of sugar content for cold and hot cereals respectively?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/659/1659621.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "4.333, 2.082",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"difficulty_tier": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_659_1659621_qa_3 |
smoldataenvs-train/0001_660_1660748_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):
- anonymous-survey-responses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ins... | After correcting typos and removing unreadable characters, what is the most frequent pet preference in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_660_1660748_qa_2",
"description": "After correcting typos and removing unreadable characters, what is the most frequent pet preference in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/660/1660748.ipynb_qa_2",
"kaggle_dataset_name": "rtatman/5day-data-challenge-signup-survey-responses",
"gold_answer": "Dogs",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_660_1660748_qa_2 |
smoldataenvs-train/0001_660_1660748_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):
- anonymous-survey-responses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ins... | What is the primary stated interest in data science for respondents with "quite a bit of programming experience"? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_660_1660748_qa_4",
"description": "What is the primary stated interest in data science for respondents with \"quite a bit of programming experience\"?",
"authors": [],
"keywords": [
"smoldataenvs",
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/660/1660748.ipynb_qa_4",
"kaggle_dataset_name": "rtatman/5day-data-challenge-signup-survey-responses",
"gold_answer": "I want to get a job where I use data science",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_660_1660748_qa_4 |
smoldataenvs-train/0001_660_1660748_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):
- anonymous-survey-responses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ins... | Which pet preference category shows the highest frequency of respondents with "quite a bit of programming experience"? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_660_1660748_qa_5",
"description": "Which pet preference category shows the highest frequency of respondents with \"quite a bit of programming experience\"?",
"authors": [],
"keywords": [
"smoldataenvs",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/660/1660748.ipynb_qa_5",
"kaggle_dataset_name": "rtatman/5day-data-challenge-signup-survey-responses",
"gold_answer": "Dogs",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_660_1660748_qa_5 |
smoldataenvs-train/0001_661_1661005_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):
- DigiDB_digimonlist.csv
- DigiDB_movelist.csv
- DigiDB_supportlist.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmod... | What is the maximum HP at level 50 among all Digimon in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_661_1661005_qa_1",
"description": "What is the maximum HP at level 50 among all Digimon in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"me... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/661/1661005.ipynb_qa_1",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "2080",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_661_1661005_qa_1 |
smoldataenvs-train/0001_661_1661005_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... | What is the average SP cost of all moves in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_661_1661005_qa_3",
"description": "What is the average SP cost of all moves in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/661/1661005.ipynb_qa_3",
"kaggle_dataset_name": "rtatman/digidb",
"gold_answer": "14.03",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_661_1661005_qa_3 |
smoldataenvs-train/0001_662_1662961_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the average sugar content in hot cereals? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_662_1662961_qa_3",
"description": "What is the average sugar content in hot cereals?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
"sou... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/662/1662961.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "0.6667",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_662_1662961_qa_3 |
smoldataenvs-train/0001_662_1662961_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the median sugar content in cold cereals? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_662_1662961_qa_5",
"description": "What is the median sugar content in cold cereals?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
"sou... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/662/1662961.ipynb_qa_5",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "7.0",
"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_662_1662961_qa_5 |
smoldataenvs-train/0001_664_1664478_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | Which manufacturer has the highest number of cereal products in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_664_1664478_qa_1",
"description": "Which manufacturer has the highest number of cereal products in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/664/1664478.ipynb_qa_1",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "K",
"reward_mode_initial": "exact_short",
"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_664_1664478_qa_1 |
smoldataenvs-train/0001_666_1666101_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the average cereal rating for manufacturer K? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_666_1666101_qa_5",
"description": "What is the average cereal rating for manufacturer K?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/666/1666101.ipynb_qa_5",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "44.04",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "mediu... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_666_1666101_qa_5 |
smoldataenvs-train/0001_667_1667317_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):
- camera_dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if... | What is the maximum zoom range (difference between maximum Zoom tele and minimum Zoom wide) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_667_1667317_qa_1",
"description": "What is the maximum zoom range (difference between maximum Zoom tele and minimum Zoom wide) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-ana... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/667/1667317.ipynb_qa_1",
"kaggle_dataset_name": "crawford/1000-cameras-dataset",
"gold_answer": "518.0",
"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_667_1667317_qa_1 |
smoldataenvs-train/0001_667_1667317_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):
- camera_dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if... | What is the minimum Zoom wide (W) value recorded in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_667_1667317_qa_4",
"description": "What is the minimum Zoom wide (W) value recorded in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metada... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/667/1667317.ipynb_qa_4",
"kaggle_dataset_name": "crawford/1000-cameras-dataset",
"gold_answer": "0.0",
"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_667_1667317_qa_4 |
smoldataenvs-train/0001_667_1667317_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):
- camera_dataset.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if... | What is the maximum Storage included capacity recorded for any camera in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_667_1667317_qa_5",
"description": "What is the maximum Storage included capacity recorded for any camera in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/667/1667317.ipynb_qa_5",
"kaggle_dataset_name": "crawford/1000-cameras-dataset",
"gold_answer": "450",
"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_667_1667317_qa_5 |
smoldataenvs-train/0001_668_1668567_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | How many unique cereal manufacturers are represented in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_668_1668567_qa_3",
"description": "How many unique cereal manufacturers are represented in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"me... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/668/1668567.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "7",
"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_668_1668567_qa_3 |
smoldataenvs-train/0001_672_1672543_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the critical value of the chi-square distribution at 95% confidence level with 12 degrees of freedom used to determine statistical significance in the analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_672_1672543_qa_1",
"description": "What is the critical value of the chi-square distribution at 95% confidence level with 12 degrees of freedom used to determine statistical significance in the analysis?",
"authors"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/672/1672543.ipynb_qa_1",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "21.026",
"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_672_1672543_qa_1 |
smoldataenvs-train/0001_672_1672543_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What are the degrees of freedom for the chi-square test of independence between cereal manufacturers (mfr) and shelf positions in this dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_672_1672543_qa_3",
"description": "What are the degrees of freedom for the chi-square test of independence between cereal manufacturers (mfr) and shelf positions in this dataset?",
"authors": [],
"keywords": [
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/672/1672543.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "12",
"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_672_1672543_qa_3 |
smoldataenvs-train/0001_673_1673862_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):
- CryptocoinsHistoricalPrices.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip in... | What is the maximum Delta value observed for any cryptocurrency in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_673_1673862_qa_4",
"description": "What is the maximum Delta value observed for any cryptocurrency in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/673/1673862.ipynb_qa_4",
"kaggle_dataset_name": "valeriovaccaro/cryptocoinshistoricalprices",
"gold_answer": "34513.333333",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_673_1673862_qa_4 |
smoldataenvs-train/0001_674_1674081_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):
- carInsurance_train.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install mor... | What is the maximum value in the 'Balance' field before outlier removal in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_674_1674081_qa_1",
"description": "What is the maximum value in the 'Balance' field before outlier removal in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/674/1674081.ipynb_qa_1",
"kaggle_dataset_name": "kondla/carinsurance",
"gold_answer": "98417",
"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_674_1674081_qa_1 |
smoldataenvs-train/0001_683_1683631_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):
- World_Champs_Men's_All-Around.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ... | Which athlete has the highest median total score across all apparatuses? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_683_1683631_qa_1",
"description": "Which athlete has the highest median total score across all apparatuses?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/683/1683631.ipynb_qa_1",
"kaggle_dataset_name": "cjdaffern/gymnastic-champs-mens-all-round",
"gold_answer": "Xiao Ruoteng",
"reward_mode_initial": "exact_short",
"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_683_1683631_qa_1 |
smoldataenvs-train/0001_683_1683832_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):
- poe_stats.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if need... | Which character class is most commonly used in the Harbinger division according to the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_683_1683832_qa_2",
"description": "Which character class is most commonly used in the Harbinger division according to the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/683/1683832.ipynb_qa_2",
"kaggle_dataset_name": "gagazet/path-of-exile-league-statistic",
"gold_answer": "Pathfinder",
"reward_mode_initial": "exact_short",
"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_683_1683832_qa_2 |
smoldataenvs-train/0001_690_1690621_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):
- salaries-by-college-type.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip insta... | Which college type has the highest median salary growth from starting to mid-career (Mid_50th percentile) based on the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_690_1690621_qa_2",
"description": "Which college type has the highest median salary growth from starting to mid-career (Mid_50th percentile) based on the dataset?",
"authors": [],
"keywords": [
"smoldataen... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/690/1690621.ipynb_qa_2",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "Ivy League",
"reward_mode_initial": "exact_short",
"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_690_1690621_qa_2 |
smoldataenvs-train/0001_690_1690622_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):
- degrees-that-pay-back.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | What is the median mid-career salary for the undergraduate major with the highest starting salary? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_690_1690622_qa_2",
"description": "What is the median mid-career salary for the undergraduate major with the highest starting salary?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/690/1690622.ipynb_qa_2",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "91700",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_690_1690622_qa_2 |
smoldataenvs-train/0001_690_1690622_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):
- degrees-that-pay-back.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | What is the average percentage change from starting to mid-career salary across all majors in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_690_1690622_qa_4",
"description": "What is the average percentage change from starting to mid-career salary across all majors in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-anal... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/690/1690622.ipynb_qa_4",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "69.27",
"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_690_1690622_qa_4 |
smoldataenvs-train/0001_690_1690622_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
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | What is the most common range for the percentage change from starting to mid-career salary based on the histogram visualization? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_690_1690622_qa_5",
"description": "What is the most common range for the percentage change from starting to mid-career salary based on the histogram visualization?",
"authors": [],
"keywords": [
"smoldatae... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/690/1690622.ipynb_qa_5",
"kaggle_dataset_name": "wsj/college-salaries",
"gold_answer": "60% to 70%",
"reward_mode_initial": "exact_short",
"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_690_1690622_qa_5 |
smoldataenvs-train/0001_690_1690859_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):
- all_mias_scans.h5
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ... | What is the baseline accuracy achieved by the most frequent class in the test set? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_690_1690859_qa_5",
"description": "What is the baseline accuracy achieved by the most frequent class in the test set?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/690/1690859.ipynb_qa_5",
"kaggle_dataset_name": "kmader/mias-mammography",
"gold_answer": "62.42",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 3,
"difficulty": "medium",
"difficulty_tier": "m... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_690_1690859_qa_5 |
smoldataenvs-train/0001_692_1692435_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):
- Iris.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Which species has the smallest average sepal width in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_692_1692435_qa_3",
"description": "Which species has the smallest average sepal width in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"meta... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/692/1692435.ipynb_qa_3",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "Iris-versicolor",
"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_692_1692435_qa_3 |
smoldataenvs-train/0001_692_1692435_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):
- Iris.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the highest positive correlation between any two measured features in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_692_1692435_qa_4",
"description": "What is the highest positive correlation between any two measured features in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"ka... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/692/1692435.ipynb_qa_4",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "0.962757",
"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_692_1692435_qa_4 |
smoldataenvs-train/0001_692_1692435_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
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Which species exhibits the largest average sepal length in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_692_1692435_qa_5",
"description": "Which species exhibits the largest average sepal length in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/692/1692435.ipynb_qa_5",
"kaggle_dataset_name": "uciml/iris",
"gold_answer": "Iris-virginica",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"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_692_1692435_qa_5 |
smoldataenvs-train/0001_699_1699079_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):
- movie_IMDB.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if nee... | What is the average IMDb score for movies in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_699_1699079_qa_1",
"description": "What is the average IMDb score for movies in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/699/1699079.ipynb_qa_1",
"kaggle_dataset_name": "ankiijindae/imdb-movie-data",
"gold_answer": "6.44",
"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_699_1699079_qa_1 |
smoldataenvs-train/0001_725_1725548_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):
- English.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | How many verses (Ayah) have non-missing values in the dataset after processing? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_725_1725548_qa_1",
"description": "How many verses (Ayah) have non-missing values in the dataset after processing?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/725/1725548.ipynb_qa_1",
"kaggle_dataset_name": "zusmani/the-holy-quran",
"gold_answer": "4871",
"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_725_1725548_qa_1 |
smoldataenvs-train/0001_725_1725695_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):
- KaggleV2-May-2016.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more... | What is the percentage of no-shows for patients with diabetes? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_725_1725695_qa_5",
"description": "What is the percentage of no-shows for patients with diabetes?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/725/1725695.ipynb_qa_5",
"kaggle_dataset_name": "joniarroba/noshowappointments",
"gold_answer": "18.0",
"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_725_1725695_qa_5 |
smoldataenvs-train/0001_733_1733851_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | How many columns in the dataset are explicitly identified as containing compensation-related information? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_733_1733851_qa_3",
"description": "How many columns in the dataset are explicitly identified as containing compensation-related information?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analy... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/733/1733851.ipynb_qa_3",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "2",
"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_733_1733851_qa_3 |
smoldataenvs-train/0001_733_1733851_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | How many columns in the dataset are explicitly of numerical data type (float64)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_733_1733851_qa_5",
"description": "How many columns in the dataset are explicitly of numerical data type (float64)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/733/1733851.ipynb_qa_5",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "13",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_733_1733851_qa_5 |
smoldataenvs-train/0001_735_1735855_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):
- en.yusufali.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ne... | What is the most frequently occurring word in the dataset after removing common stopwords? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_735_1735855_qa_1",
"description": "What is the most frequently occurring word in the dataset after removing common stopwords?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"ka... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/735/1735855.ipynb_qa_1",
"kaggle_dataset_name": "zusmani/the-holy-quran",
"gold_answer": "allah",
"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_735_1735855_qa_1 |
smoldataenvs-train/0001_735_1735855_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 word from the list ['paradise', 'hellfire', 'heaven', 'hell'] occurs most frequently in the text? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_735_1735855_qa_3",
"description": "Which word from the list ['paradise', 'hellfire', 'heaven', 'hell'] occurs most frequently in the text?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysi... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/735/1735855.ipynb_qa_3",
"kaggle_dataset_name": "zusmani/the-holy-quran",
"gold_answer": "hell",
"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_735_1735855_qa_3 |
smoldataenvs-train/0001_735_1735855_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):
- en.yusufali.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ne... | Between the words 'good' and 'evil', which has a higher count in the text? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_735_1735855_qa_5",
"description": "Between the words 'good' and 'evil', which has a higher count in the text?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/735/1735855.ipynb_qa_5",
"kaggle_dataset_name": "zusmani/the-holy-quran",
"gold_answer": "good",
"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_735_1735855_qa_5 |
smoldataenvs-train/0001_736_1736876_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):
- multipleChoiceResponses.csv
- conversionRates.csv
- schema.csv
- freeformResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, sciki... | How many numeric columns are present in the dataset before additional processing? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_736_1736876_qa_4",
"description": "How many numeric columns are present in the dataset before additional processing?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/736/1736876.ipynb_qa_4",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "13",
"reward_mode_initial": "numeric",
"package_tier": 3,
"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_736_1736876_qa_4 |
smoldataenvs-train/0001_737_1737901_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):
- multipleChoiceResponses.csv
- freeformResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sql... | What is the median age of respondents in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_737_1737901_qa_1",
"description": "What is the median age of respondents in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/737/1737901.ipynb_qa_1",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "30",
"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_737_1737901_qa_1 |
smoldataenvs-train/0001_737_1737901_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):
- multipleChoiceResponses.csv
- freeformResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sql... | What is the most frequently recommended programming language among respondents? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_737_1737901_qa_2",
"description": "What is the most frequently recommended programming language among respondents?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/737/1737901.ipynb_qa_2",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "Python",
"reward_mode_initial": "exact_short",
"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_737_1737901_qa_2 |
smoldataenvs-train/0001_739_1739101_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | What is the median percentage of time spent on model building according to the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_739_1739101_qa_3",
"description": "What is the median percentage of time spent on model building according to the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/739/1739101.ipynb_qa_3",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "20.00",
"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_739_1739101_qa_3 |
smoldataenvs-train/0001_739_1739101_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | What is the average percentage of time spent on finding insights according to the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_739_1739101_qa_5",
"description": "What is the average percentage of time spent on finding insights according to the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"ka... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/739/1739101.ipynb_qa_5",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "13.09",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "e... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_739_1739101_qa_5 |
smoldataenvs-train/0001_741_1741634_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):
- multipleChoiceResponses.csv
- conversionRates.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlit... | After excluding compensation outliers (values below $1 or above $2,000,000), what is the maximum compensation value retained in the dataset for analysis? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_741_1741634_qa_2",
"description": "After excluding compensation outliers (values below $1 or above $2,000,000), what is the maximum compensation value retained in the dataset for analysis?",
"authors": [],
"keyw... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/741/1741634.ipynb_qa_2",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "2000000",
"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_741_1741634_qa_2 |
smoldataenvs-train/0001_741_1741873_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):
- movies_metadata.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more i... | Which country has produced the highest number of movies according to the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_741_1741873_qa_4",
"description": "Which country has produced the highest number of movies according to the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/741/1741873.ipynb_qa_4",
"kaggle_dataset_name": "rounakbanik/the-movies-dataset",
"gold_answer": "United States",
"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_741_1741873_qa_4 |
smoldataenvs-train/0001_746_1746593_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):
- 911.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the most common reason for 911 calls according to the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_746_1746593_qa_2",
"description": "What is the most common reason for 911 calls according to the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/746/1746593.ipynb_qa_2",
"kaggle_dataset_name": "mchirico/montcoalert",
"gold_answer": "EMS",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "me... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_746_1746593_qa_2 |
smoldataenvs-train/0001_750_1750005_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | What percentage of the CompensationAmount data is missing before filtering for USD currency in the original dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_750_1750005_qa_1",
"description": "What percentage of the CompensationAmount data is missing before filtering for USD currency in the original dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/750/1750005.ipynb_qa_1",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "68.75",
"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_750_1750005_qa_1 |
smoldataenvs-train/0001_754_1754320_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):
- bikes.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | How many distinct operational bike stations are present in the dataset after removing closed stations? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_754_1754320_qa_2",
"description": "How many distinct operational bike stations are present in the dataset after removing closed stations?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/754/1754320.ipynb_qa_2",
"kaggle_dataset_name": "alvarolopez/tusbic",
"gold_answer": "17",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_754_1754320_qa_2 |
smoldataenvs-train/0001_755_1755036_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):
- train.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | Which feature has the strongest negative correlation with survival (Survived) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_755_1755036_qa_1",
"description": "Which feature has the strongest negative correlation with survival (Survived) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/755/1755036.ipynb_qa_1",
"kaggle_dataset_name": "hussienelsawy/titanic-training-data",
"gold_answer": "Pclass",
"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_755_1755036_qa_1 |
smoldataenvs-train/0001_755_1755036_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):
- train.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | What is the correlation coefficient between passenger class (Pclass) and survival (Survived)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_755_1755036_qa_2",
"description": "What is the correlation coefficient between passenger class (Pclass) and survival (Survived)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/755/1755036.ipynb_qa_2",
"kaggle_dataset_name": "hussienelsawy/titanic-training-data",
"gold_answer": "-0.338481",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"diffic... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_755_1755036_qa_2 |
smoldataenvs-train/0001_761_1761668_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_set_ALL_AML_independent.csv
- data_set_ALL_AML_train.csv
- actual.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, sta... | How many columns containing the term "call" were removed from the training dataset during preprocessing? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_761_1761668_qa_2",
"description": "How many columns containing the term \"call\" were removed from the training dataset during preprocessing?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-anal... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/761/1761668.ipynb_qa_2",
"kaggle_dataset_name": "crawford/gene-expression",
"gold_answer": "38",
"reward_mode_initial": "numeric",
"package_tier": 1,
"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_761_1761668_qa_2 |
smoldataenvs-train/0001_761_1761668_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... | What is the median value of the gene expression for the gene 'X64594_at' in the training dataset's sample subset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_761_1761668_qa_3",
"description": "What is the median value of the gene expression for the gene 'X64594_at' in the training dataset's sample subset?",
"authors": [],
"keywords": [
"smoldataenvs",
"da... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/761/1761668.ipynb_qa_3",
"kaggle_dataset_name": "crawford/gene-expression",
"gold_answer": "-134.0",
"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_761_1761668_qa_3 |
smoldataenvs-train/0001_781_1781985_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
- matches.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install ... | Which player has the highest total number of match wins, and what is that number? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_781_1781985_qa_1",
"description": "Which player has the highest total number of match wins, and what is that number?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/781/1781985.ipynb_qa_1",
"kaggle_dataset_name": "joaoevangelista/wta-matches-and-rankings",
"gold_answer": "Serena Williams, 708",
"reward_mode_initial": "list",
"package_tier": 0,
"difficulty_level": 3,
"difficulty": "med... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_781_1781985_qa_1 |
smoldataenvs-train/0001_784_1784765_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 highest weekly wage reported for female workers across all specific jobs in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_784_1784765_qa_5",
"description": "What is the highest weekly wage reported for female workers across all specific jobs in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/784/1784765.ipynb_qa_5",
"kaggle_dataset_name": "jonavery/incomes-by-career-and-gender",
"gold_answer": "1836.0",
"reward_mode_initial": "numeric",
"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_784_1784765_qa_5 |
smoldataenvs-train/0001_789_1789575_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):
- tmdb_5000_movies.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more ... | Which movie in the dataset has the highest profit (calculated as revenue minus budget)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_789_1789575_qa_1",
"description": "Which movie in the dataset has the highest profit (calculated as revenue minus budget)?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/789/1789575.ipynb_qa_1",
"kaggle_dataset_name": "tmdb/tmdb-movie-metadata",
"gold_answer": "Avatar",
"reward_mode_initial": "exact_short",
"package_tier": 0,
"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_789_1789575_qa_1 |
smoldataenvs-train/0001_789_1789575_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):
- tmdb_5000_movies.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more ... | What is the maximum budget value recorded for any movie in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_789_1789575_qa_2",
"description": "What is the maximum budget value recorded for any movie in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/789/1789575.ipynb_qa_2",
"kaggle_dataset_name": "tmdb/tmdb-movie-metadata",
"gold_answer": "380000000",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_789_1789575_qa_2 |
smoldataenvs-train/0001_825_1825877_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):
- haberman.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What is the standard deviation of the year of operation for all patients in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_825_1825877_qa_3",
"description": "What is the standard deviation of the year of operation for all patients in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/825/1825877.ipynb_qa_3",
"kaggle_dataset_name": "gilsousa/habermans-survival-data-set",
"gold_answer": "3.249405",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",
"diffic... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_825_1825877_qa_3 |
smoldataenvs-train/0001_825_1825877_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):
- haberman.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What is the 75th percentile value for the age of patients in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_825_1825877_qa_5",
"description": "What is the 75th percentile value for the age of patients in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/825/1825877.ipynb_qa_5",
"kaggle_dataset_name": "gilsousa/habermans-survival-data-set",
"gold_answer": "60.75",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_825_1825877_qa_5 |
smoldataenvs-train/0001_840_1840182_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):
- winemag-data-130k-v2.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install m... | Which wine taster has assigned the highest average rating to wines they reviewed? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_840_1840182_qa_2",
"description": "Which wine taster has assigned the highest average rating to wines they reviewed?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/840/1840182.ipynb_qa_2",
"kaggle_dataset_name": "zynicide/wine-reviews",
"gold_answer": "Anne Krebiehl MW",
"reward_mode_initial": "exact_short",
"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_840_1840182_qa_2 |
smoldataenvs-train/0001_840_1840182_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):
- winemag-data-130k-v2.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install m... | Which country is represented in the most expensive wines (highest price) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_840_1840182_qa_3",
"description": "Which country is represented in the most expensive wines (highest price) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/840/1840182.ipynb_qa_3",
"kaggle_dataset_name": "zynicide/wine-reviews",
"gold_answer": "France",
"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_840_1840182_qa_3 |
smoldataenvs-train/0001_840_1840182_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):
- winemag-data-130k-v2.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install m... | What is the wine variety with the largest number of reviews in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_840_1840182_qa_4",
"description": "What is the wine variety with the largest number of reviews in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
}... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/840/1840182.ipynb_qa_4",
"kaggle_dataset_name": "zynicide/wine-reviews",
"gold_answer": "Pinot Noir",
"reward_mode_initial": "exact_short",
"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_840_1840182_qa_4 |
smoldataenvs-train/0001_850_1850034_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):
- indian_liver_patient.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install m... | What is the mean value of the 'Total_Bilirubin' feature for the entire dataset after preprocessing but before scaling? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_850_1850034_qa_2",
"description": "What is the mean value of the 'Total_Bilirubin' feature for the entire dataset after preprocessing but before scaling?",
"authors": [],
"keywords": [
"smoldataenvs",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/850/1850034.ipynb_qa_2",
"kaggle_dataset_name": "uciml/indian-liver-patient-records",
"gold_answer": "3.298799",
"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_850_1850034_qa_2 |
smoldataenvs-train/0001_850_1850034_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):
- indian_liver_patient.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install m... | What is the 75th percentile (third quartile) of the 'Albumin' feature in the dataset after preprocessing but before scaling? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_850_1850034_qa_5",
"description": "What is the 75th percentile (third quartile) of the 'Albumin' feature in the dataset after preprocessing but before scaling?",
"authors": [],
"keywords": [
"smoldataenvs"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/850/1850034.ipynb_qa_5",
"kaggle_dataset_name": "uciml/indian-liver-patient-records",
"gold_answer": "3.8",
"reward_mode_initial": "numeric",
"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/0001_850_1850034_qa_5 |
smoldataenvs-train/0001_860_1860651_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):
- chipotle.tsv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | How many distinct types of items are available in the Chipotle menu based on the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_860_1860651_qa_4",
"description": "How many distinct types of items are available in the Chipotle menu based on the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/860/1860651.ipynb_qa_4",
"kaggle_dataset_name": "andyxie/chipotle",
"gold_answer": "50",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_860_1860651_qa_4 |
smoldataenvs-train/0001_864_1864781_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):
- nyc-east-river-bicycle-counts.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ... | What is the standard deviation of the total number of cyclists per day? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_864_1864781_qa_3",
"description": "What is the standard deviation of the total number of cyclists per day?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/864/1864781.ipynb_qa_3",
"kaggle_dataset_name": "new-york-city/nyc-east-river-bicycle-crossings",
"gold_answer": "5569.17",
"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_864_1864781_qa_3 |
smoldataenvs-train/0001_864_1864781_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):
- nyc-east-river-bicycle-counts.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip ... | What is the average total number of cyclists per day? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_864_1864781_qa_4",
"description": "What is the average total number of cyclists per day?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/864/1864781.ipynb_qa_4",
"kaggle_dataset_name": "new-york-city/nyc-east-river-bicycle-crossings",
"gold_answer": "14534.5",
"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_864_1864781_qa_4 |
smoldataenvs-train/0001_866_1866353_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | What is the age group with the highest number of participants in the survey dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_866_1866353_qa_1",
"description": "What is the age group with the highest number of participants in the survey dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/866/1866353.ipynb_qa_1",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "25.0",
"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_866_1866353_qa_1 |
smoldataenvs-train/0001_866_1866353_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):
- multipleChoiceResponses.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip instal... | How many participants in the dataset are aged 25 years? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_866_1866353_qa_2",
"description": "How many participants in the dataset are aged 25 years?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/866/1866353.ipynb_qa_2",
"kaggle_dataset_name": "kaggle/kaggle-survey-2017",
"gold_answer": "969",
"reward_mode_initial": "numeric",
"package_tier": 1,
"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_866_1866353_qa_2 |
smoldataenvs-train/0001_869_1869308_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the median value of the carbohydrate content (carbo) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_869_1869308_qa_4",
"description": "What is the median value of the carbohydrate content (carbo) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/869/1869308.ipynb_qa_4",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "14.0",
"reward_mode_initial": "numeric",
"package_tier": 3,
"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_869_1869308_qa_4 |
smoldataenvs-train/0001_869_1869526_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):
- deepspeech-labels.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more... | How many times was the true label "bed" mispredicted as "bad"? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_869_1869526_qa_5",
"description": "How many times was the true label \"bed\" mispredicted as \"bad\"?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"meta... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/869/1869526.ipynb_qa_5",
"kaggle_dataset_name": "holzner/tf-speechrec-deepspeech-train",
"gold_answer": "92",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficult... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_869_1869526_qa_5 |
smoldataenvs-train/0001_869_1869604_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):
- creditcard.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if nee... | What is the average transaction amount across all records in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_869_1869604_qa_2",
"description": "What is the average transaction amount across all records in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/869/1869604.ipynb_qa_2",
"kaggle_dataset_name": "mlg-ulb/creditcardfraud",
"gold_answer": "88.35",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "eas... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_869_1869604_qa_2 |
smoldataenvs-train/0001_871_1871102_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):
- output3.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | How many unique classes are present in the categorical labels after conversion to categorical format? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_871_1871102_qa_2",
"description": "How many unique classes are present in the categorical labels after conversion to categorical format?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/871/1871102.ipynb_qa_2",
"kaggle_dataset_name": "richardbj/2chan10dbnoise",
"gold_answer": "3",
"reward_mode_initial": "numeric",
"package_tier": 2,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_tier": "medi... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_871_1871102_qa_2 |
smoldataenvs-train/0001_875_1875604_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | Which cereal has the highest health rating score in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_875_1875604_qa_1",
"description": "Which cereal has the highest health rating score in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metada... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/875/1875604.ipynb_qa_1",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "All-Bran with Extra Fiber",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"di... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_875_1875604_qa_1 |
smoldataenvs-train/0001_876_1876377_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... | Which gender category has the highest default payment probability, and what is its value? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_876_1876377_qa_2",
"description": "Which gender category has the highest default payment probability, and what is its value?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kag... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/876/1876377.ipynb_qa_2",
"kaggle_dataset_name": "uciml/default-of-credit-card-clients-dataset",
"gold_answer": "Male, 24.17%",
"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_876_1876377_qa_2 |
smoldataenvs-train/0001_878_1878667_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):
- database.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if neede... | What is the most common flight phase associated with bird strike incidents in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_878_1878667_qa_2",
"description": "What is the most common flight phase associated with bird strike incidents in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"ka... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/878/1878667.ipynb_qa_2",
"kaggle_dataset_name": "faa/wildlife-strikes",
"gold_answer": "Approach",
"reward_mode_initial": "exact_short",
"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_878_1878667_qa_2 |
smoldataenvs-train/0001_878_1878667_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 most frequently reported visibility condition during bird strike incidents? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_878_1878667_qa_5",
"description": "What is the most frequently reported visibility condition during bird strike incidents?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/878/1878667.ipynb_qa_5",
"kaggle_dataset_name": "faa/wildlife-strikes",
"gold_answer": "Day",
"reward_mode_initial": "exact_short",
"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_878_1878667_qa_5 |
smoldataenvs-train/0001_878_1878746_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the highest rating given to any cereal in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_878_1878746_qa_1",
"description": "What is the highest rating given to any cereal in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/878/1878746.ipynb_qa_1",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "93.704912",
"reward_mode_initial": "numeric",
"package_tier": 0,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "eas... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_878_1878746_qa_1 |
smoldataenvs-train/0001_878_1878746_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the standard deviation of sugar content across all cereals in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_878_1878746_qa_2",
"description": "What is the standard deviation of sugar content across all cereals in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/878/1878746.ipynb_qa_2",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "4.444885",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_878_1878746_qa_2 |
smoldataenvs-train/0001_878_1878746_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the maximum sodium content found in any cereal in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_878_1878746_qa_4",
"description": "What is the maximum sodium content found in any cereal in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/878/1878746.ipynb_qa_4",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "320",
"reward_mode_initial": "numeric",
"package_tier": 0,
"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_878_1878746_qa_4 |
smoldataenvs-train/0001_882_1882893_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):
- Suicides in India 2001-2012.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip in... | What is the leading known cause of suicide in the dataset when excluding "Causes Not known"? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_882_1882893_qa_3",
"description": "What is the leading known cause of suicide in the dataset when excluding \"Causes Not known\"?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/882/1882893.ipynb_qa_3",
"kaggle_dataset_name": "rajanand/suicides-in-india",
"gold_answer": "Family problems",
"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_882_1882893_qa_3 |
smoldataenvs-train/0001_885_1885477_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the average vitamin content in cereals rated 50 or below? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_885_1885477_qa_5",
"description": "What is the average vitamin content in cereals rated 50 or below?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metad... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/885/1885477.ipynb_qa_5",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "32.589285714285715",
"reward_mode_initial": "numeric",
"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/0001_885_1885477_qa_5 |
smoldataenvs-train/0001_888_1888068_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What are the mean sodium contents (in mg) for hot and cold cereals respectively? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_888_1888068_qa_2",
"description": "What are the mean sodium contents (in mg) for hot and cold cereals respectively?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/888/1888068.ipynb_qa_2",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "26.67, 165.07",
"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_888_1888068_qa_2 |
smoldataenvs-train/0001_892_1892691_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the maximum sodium content observed in hot cereals compared to cold cereals? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_892_1892691_qa_2",
"description": "What is the maximum sodium content observed in hot cereals compared to cold cereals?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/892/1892691.ipynb_qa_2",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "hot=80, cold=320",
"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_892_1892691_qa_2 |
smoldataenvs-train/0001_892_1892691_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the mean potassium content for hot cereals versus cold cereals? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_892_1892691_qa_3",
"description": "What is the mean potassium content for hot cereals versus cold cereals?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/892/1892691.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "hot=68.0, cold=97.22",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 2,
"difficulty": "medium",
"difficulty_t... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_892_1892691_qa_3 |
smoldataenvs-train/0001_892_1892776_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):
- tips.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | What is the most frequently occurring bet type in the dataset and how many times does it occur? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_892_1892776_qa_1",
"description": "What is the most frequently occurring bet type in the dataset and how many times does it occur?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/892/1892776.ipynb_qa_1",
"kaggle_dataset_name": "gunner38/horseracing",
"gold_answer": "Win, 30417",
"reward_mode_initial": "list",
"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_892_1892776_qa_1 |
smoldataenvs-train/0001_892_1892776_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):
- tips.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | Which tipster has the highest number of entries in the dataset and how many entries do they have? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_892_1892776_qa_2",
"description": "Which tipster has the highest number of entries in the dataset and how many entries do they have?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/892/1892776.ipynb_qa_2",
"kaggle_dataset_name": "gunner38/horseracing",
"gold_answer": "Tipster X, 4383",
"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_892_1892776_qa_2 |
smoldataenvs-train/0001_892_1892776_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):
- tips.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
... | How many unique tracks are represented in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_892_1892776_qa_4",
"description": "How many unique tracks are represented in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
"metadata": {
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/892/1892776.ipynb_qa_4",
"kaggle_dataset_name": "gunner38/horseracing",
"gold_answer": "116",
"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_892_1892776_qa_4 |
smoldataenvs-train/0001_893_1893246_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | What is the range of the health rating metric (difference between maximum and minimum values) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_893_1893246_qa_3",
"description": "What is the range of the health rating metric (difference between maximum and minimum values) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-a... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/893/1893246.ipynb_qa_3",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "75.662061",
"reward_mode_initial": "numeric",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty_tier": "eas... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_893_1893246_qa_3 |
smoldataenvs-train/0001_894_1894014_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):
- museums.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | What is the mean revenue for zoo-type museums after removing duplicate and null records? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_894_1894014_qa_2",
"description": "What is the mean revenue for zoo-type museums after removing duplicate and null records?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/894/1894014.ipynb_qa_2",
"kaggle_dataset_name": "imls/museum-directory",
"gold_answer": "5483602",
"reward_mode_initial": "numeric",
"package_tier": 1,
"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_894_1894014_qa_2 |
smoldataenvs-train/0001_897_1897174_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):
- museums.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed... | How many museums in the dataset have a revenue value of exactly zero before imputation? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_897_1897174_qa_5",
"description": "How many museums in the dataset have a revenue value of exactly zero before imputation?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggl... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/897/1897174.ipynb_qa_5",
"kaggle_dataset_name": "imls/museum-directory",
"gold_answer": "10783",
"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_897_1897174_qa_5 |
smoldataenvs-train/0001_898_1898542_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):
- train.csv
- test.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more ... | How many entries in the training dataset have missing values for the 'Embarked' feature? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_898_1898542_qa_4",
"description": "How many entries in the training dataset have missing values for the 'Embarked' feature?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kagg... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/898/1898542.ipynb_qa_4",
"kaggle_dataset_name": "robinreni/titanic-dataset",
"gold_answer": "2",
"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_898_1898542_qa_4 |
smoldataenvs-train/0001_902_1902242_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):
- voice.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).... | What is the total number of samples in each class (male and female) in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_902_1902242_qa_4",
"description": "What is the total number of samples in each class (male and female) in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/902/1902242.ipynb_qa_4",
"kaggle_dataset_name": "primaryobjects/voicegender",
"gold_answer": "male: 1584, female: 1584",
"reward_mode_initial": "list",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"dif... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_902_1902242_qa_4 |
smoldataenvs-train/0001_902_1902890_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):
- Basic_Stats.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if ne... | What is the most common experience category among players in the dataset? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_902_1902890_qa_5",
"description": "What is the most common experience category among players in the dataset?",
"authors": [],
"keywords": [
"smoldataenvs",
"data-analysis",
"kaggle"
]
},
... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/902/1902890.ipynb_qa_5",
"kaggle_dataset_name": "kendallgillies/nflstatistics",
"gold_answer": "1 Season",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"difficulty_level": 1,
"difficulty": "easy",
"difficulty... | [
"environment/Dockerfile",
"environment/pull_bucket.py",
"instruction.md",
"task.toml",
"tests/grader.py",
"tests/test.sh"
] | tasks/0001_902_1902890_qa_5 |
smoldataenvs-train/0001_903_1903160_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):
- cereal.csv
Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed)... | Which nutritional attribute in the dataset has the highest coefficient of variation (standard deviation relative to the mean)? | [
"smoldataenvs",
"data-analysis",
"kaggle"
] | 1.2 | {
"schema_version": "1.2",
"artifacts": [],
"task": {
"name": "smoldataenvs-train/0001_903_1903160_qa_2",
"description": "Which nutritional attribute in the dataset has the highest coefficient of variation (standard deviation relative to the mean)?",
"authors": [],
"keywords": [
"smoldataenv... | {
"source_dataset": "jupyter-agent/jupyter-agent-dataset",
"source_row_id": "0001/903/1903160.ipynb_qa_2",
"kaggle_dataset_name": "crawford/80-cereals",
"gold_answer": "fiber",
"reward_mode_initial": "exact_short",
"package_tier": 1,
"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_903_1903160_qa_2 |
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