load_data_timeline_prior_meds

Load and return the MSK-IMPACT prior medications timeline dataset (deidentified).

Returns:

Name	Type	Description
data	Bunch	Dictionary-like object, with the following attributes. data : pandas DataFrame The data matrix. description_columns : list The names of the dataset columns. (Future release) description_dataset : str The full description of the dataset. (Future release) filename : str The path to the location of the data. (Future release)

Examples

from msk_cdm.datasets import connect_to_db
from msk_cdm.datasets.impact import load_data_timeline_prior_meds

# Connect to the database
auth_file = 'path/to/config.txt'
connect_to_db(auth_file=auth_file)

# Load the dataset
df_timeline_prior_meds = load_data_timeline_prior_meds()

# Access the data
df_prior_meds = df_timeline_prior_meds['data']

# Display the first few rows of the data
print(df_prior_meds.head())

Source code in msk_cdm/datasets/impact/datasets_impact.py

def load_data_timeline_prior_meds() -> Bunch:
    """Load and return the MSK-IMPACT prior medications timeline dataset (deidentified).

    Returns:
        data: Dictionary-like object, with the following attributes.

            - **data** : pandas DataFrame
                The data matrix.
            - **description_columns** : list
                The names of the dataset columns. (Future release)
            - **description_dataset** : str
                The full description of the dataset. (Future release)
            - **filename** : str
                The path to the location of the data. (Future release)

    Examples
    --------
    ```python
    from msk_cdm.datasets import connect_to_db
    from msk_cdm.datasets.impact import load_data_timeline_prior_meds

    # Connect to the database
    auth_file = 'path/to/config.txt'
    connect_to_db(auth_file=auth_file)

    # Load the dataset
    df_timeline_prior_meds = load_data_timeline_prior_meds()

    # Access the data
    df_prior_meds = df_timeline_prior_meds['data']

    # Display the first few rows of the data
    print(df_prior_meds.head())
    ```
    """
    df = _loader._load_impact_data_timeline_prior_meds()
    output = Bunch(data=df)
    return output