Datasets

sklego.datasets.load_abalone(return_X_y=False, as_frame=False)

Loads the abalone dataset where the goal is to predict the gender of the creature.

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_abalone
X, y = load_abalone(return_X_y=True)

X.shape
# (4177, 8)
y.shape
# (4177,)

load_abalone(as_frame=True).columns
# Index(['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight',
#        'viscera_weight', 'shell_weight', 'rings'],
#       dtype='object')

The dataset was copied from Kaggle and can originally be found in the following sources:

• Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994)

  "The Population Biology of Abalone (Haliotis species) in Tasmania."

  Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)

Source code in sklego/datasets.py

def load_abalone(return_X_y=False, as_frame=False):
    """
    Loads the abalone dataset where the goal is to predict the gender of the creature.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    ---------
    ```py
    from sklego.datasets import load_abalone
    X, y = load_abalone(return_X_y=True)

    X.shape
    # (4177, 8)
    y.shape
    # (4177,)

    load_abalone(as_frame=True).columns
    # Index(['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight',
    #        'viscera_weight', 'shell_weight', 'rings'],
    #       dtype='object')
    ```

    The dataset was copied from Kaggle and can originally be found in the following sources:

    - Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994)

        "The Population Biology of Abalone (_Haliotis_ species) in Tasmania."

        Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)
    """
    filepath = importlib_resources.files("sklego") / "data" / "abalone.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X = df[
        [
            "length",
            "diameter",
            "height",
            "whole_weight",
            "shucked_weight",
            "viscera_weight",
            "shell_weight",
            "rings",
        ]
    ].values
    y = df["sex"].values
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.load_arrests(return_X_y=False, as_frame=False)

Loads the arrests dataset which can serve as a benchmark for fairness.

It is data on the police treatment of individuals arrested in Toronto for simple possession of small quantities of marijuana. The goal is to predict whether or not the arrestee was released with a summons while maintaining a degree of fairness.

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_arrests
X, y = load_arrests(return_X_y=True)

X.shape
# (5226, 7)
y.shape
# (5226,)

load_arrests(as_frame=True).columns
# Index(['released', 'colour', 'year', 'age', 'sex', 'employed', 'citizen',
#   'checks'],
#  dtype='object')

The dataset was copied from the carData R package (dataset documentation) and can originally be found in:

• Personal communication from Michael Friendly, York University.

Source code in sklego/datasets.py

def load_arrests(return_X_y=False, as_frame=False):
    """Loads the arrests dataset which can serve as a benchmark for fairness.

    It is data on the police treatment of individuals arrested in Toronto for simple possession of small quantities of
    marijuana. The goal is to predict whether or not the arrestee was released with a summons while maintaining a
    degree of fairness.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    -------
    ```py
    from sklego.datasets import load_arrests
    X, y = load_arrests(return_X_y=True)

    X.shape
    # (5226, 7)
    y.shape
    # (5226,)

    load_arrests(as_frame=True).columns
    # Index(['released', 'colour', 'year', 'age', 'sex', 'employed', 'citizen',
    #   'checks'],
    #  dtype='object')
    ```

    The dataset was copied from the carData R package
    ([dataset documentation](https://vincentarelbundock.github.io/Rdatasets/doc/carData/Arrests.html))
    and can originally be found in:

    - Personal communication from Michael Friendly, York University.
    """
    filepath = importlib_resources.files("sklego") / "data" / "arrests.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X, y = (
        df[["colour", "year", "age", "sex", "employed", "citizen", "checks"]].values,
        df["released"].values,
    )
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.load_chicken(return_X_y=False, as_frame=False)

Loads the chicken dataset.

The chicken data has 578 rows and 4 columns from an experiment on the effect of diet on early growth of chicks. The body weights of the chicks were measured at birth and every second day thereafter until day 20. They were also measured on day 21. There were four groups on chicks on different protein diets.

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_chicken
X, y = load_chicken(return_X_y=True)

X.shape
# (578, 3)
y.shape
# (578,)

load_chicken(as_frame=True).columns
# Index(['weight', 'time', 'chick', 'diet'], dtype='object')

The datasets can be found in the following sources:

• Crowder, M. and Hand, D. (1990), Analysis of Repeated Measures, Chapman and Hall (example 5.3)
• Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis, Chapman and Hall (table A.2)

Source code in sklego/datasets.py

def load_chicken(return_X_y=False, as_frame=False):
    """Loads the chicken dataset.

    The chicken data has 578 rows and 4 columns from an experiment on the effect of diet on early growth of chicks.
    The body weights of the chicks were measured at birth and every second day thereafter until day 20.
    They were also measured on day 21. There were four groups on chicks on different protein diets.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    -------
    ```py
    from sklego.datasets import load_chicken
    X, y = load_chicken(return_X_y=True)

    X.shape
    # (578, 3)
    y.shape
    # (578,)

    load_chicken(as_frame=True).columns
    # Index(['weight', 'time', 'chick', 'diet'], dtype='object')
    ```

    The datasets can be found in the following sources:

    - Crowder, M. and Hand, D. (1990), Analysis of Repeated Measures, Chapman and Hall (example 5.3)
    - Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis, Chapman and Hall (table A.2)
    """
    filepath = importlib_resources.files("sklego") / "data" / "chickweight.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X, y = df[["time", "diet", "chick"]].values, df["weight"].values
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.load_heroes(return_X_y=False, as_frame=False)

A dataset from the video game Heroes of the storm.

The goal of the dataset is to predict the attack type. Note that the pandas dataset returns more information. This is because we wanted to keep the X simple in the return_X_y case.

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_heroes
X, y = load_heroes(return_X_y=True)

X.shape
# (84, 2)
y.shape
# (84,)

load_heroes(as_frame=True).columns
# Index(['name', 'attack_type', 'role', 'health', 'attack', 'attack_spd'], dtype='object')

Source code in sklego/datasets.py

def load_heroes(return_X_y=False, as_frame=False):
    """A dataset from the video game [Heroes of the storm](https://heroesofthestorm.blizzard.com/en-us/).

    The goal of the dataset is to predict the attack type. Note that the pandas dataset returns more information.
    This is because we wanted to keep the X simple in the return_X_y case.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    --------
    ```py
    from sklego.datasets import load_heroes
    X, y = load_heroes(return_X_y=True)

    X.shape
    # (84, 2)
    y.shape
    # (84,)

    load_heroes(as_frame=True).columns
    # Index(['name', 'attack_type', 'role', 'health', 'attack', 'attack_spd'], dtype='object')
    ```
    """
    filepath = importlib_resources.files("sklego") / "data" / "heroes.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X = df[["health", "attack"]].values
    y = df["attack_type"].values
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.load_hearts(return_X_y=False, as_frame=False)

Loads the Cleveland Heart Diseases dataset.

The goal is to predict the presence of a heart disease (target values 1, 2, 3, and 4). The data originates from research to heart diseases by four institutions and originally contains 76 attributes. Yet, all published experiments refer to using a subset of 13 features and one target. This implementation loads the Cleveland dataset of the research which is the only set used by ML researchers to this date.

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_hearts
X, y = load_hearts(return_X_y=True)

X.shape
# (303, 13)
y.shape
# (303,)

load_hearts(as_frame=True).columns
# Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
#        'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],
#       dtype='object')

The dataset can originally be found in the UC Irvine Machine Learning Repository

The responsible institutions for the entire database are:

1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

The documentation of the dataset can be viewed at: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names

Source code in sklego/datasets.py

def load_hearts(return_X_y=False, as_frame=False):
    """Loads the Cleveland Heart Diseases dataset.

    The goal is to predict the presence of a heart disease (target values 1, 2, 3, and 4).
    The data originates from research to heart diseases by four institutions and originally contains 76 attributes.
    Yet, all published experiments refer to using a subset of 13 features and one target.
    This implementation loads the Cleveland dataset of the research which is the only set used by ML researchers
    to this date.

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    --------
    ```py
    from sklego.datasets import load_hearts
    X, y = load_hearts(return_X_y=True)

    X.shape
    # (303, 13)
    y.shape
    # (303,)

    load_hearts(as_frame=True).columns
    # Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
    #        'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],
    #       dtype='object')
    ```

    The dataset can originally be found in the
    [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Heart+Disease)

    The responsible institutions for the entire database are:

    1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
    2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
    3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
    4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

    The documentation of the dataset can be viewed at:
    https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names
    """
    filepath = importlib_resources.files("sklego") / "data" / "hearts.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X = df[
        [
            "age",
            "sex",
            "cp",
            "trestbps",
            "chol",
            "fbs",
            "restecg",
            "thalach",
            "exang",
            "oldpeak",
            "slope",
            "ca",
            "thal",
        ]
    ].values
    y = df["target"].values
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.load_penguins(return_X_y=False, as_frame=False)

Loads the penguins dataset, which is a lovely alternative for the iris dataset. We've added this dataset for educational use.

Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network. The goal of the dataset is to predict which species of penguin a penguin belongs to.

This data originally appeared as a R package and R users can find this data in the palmerpenguins package. You can also go to the repository for some lovely images that explain the dataset.

To cite this dataset in publications use:

Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism
and Environmental Variability within a Community of Antarctic
Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081.
https://doi.org/10.1371/journal.pone.0090081

Parameters:

Name	Type	Description	Default
return_X_y	bool	If True, returns (data, target) instead of a dict object.	False
as_frame	bool	If True, returns data as a pandas DataFrame instead of X, y matrices.	False

Examples:

from sklego.datasets import load_penguins
X, y = load_penguins(return_X_y=True)

X.shape
# (344, 6)
y.shape
# (344,)

load_penguins(as_frame=True).columns
# Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm',
#    'flipper_length_mm', 'body_mass_g', 'sex'],
#   dtype='object')

Notes

Anyone interested in publishing the data should contact Dr. Kristen Gorman about analysis and working together on any final products.

From Gorman et al. (2014)

Data reported here are publicly available within the PAL-LTER data system (datasets 219, 220, and 221).

Individuals interested in using these data are therefore expected to follow the US LTER Network's Data Access Policy, Requirements and Use Agreement

Please cite data using the following

Adélie penguins:

• Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Adélie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative.

  (Accessed 2020-06-08).

Gentoo penguins:

• Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Gentoo penguin (Pygoscelis papua) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative.

  (Accessed 2020-06-08).

Chinstrap penguins:

• Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Chinstrap penguin (Pygoscelis antarcticus) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 6. Environmental Data Initiative.

  (Accessed 2020-06-08).

Source code in sklego/datasets.py

def load_penguins(return_X_y=False, as_frame=False):
    """Loads the penguins dataset, which is a lovely alternative for the iris dataset.
    We've added this dataset for educational use.

    Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of
    the Long Term Ecological Research Network. The goal of the dataset is to predict which species of penguin a penguin
    belongs to.

    This data originally appeared as a R package and R users can find this data in the
    [palmerpenguins package](https://github.com/allisonhorst/palmerpenguins).
    You can also go to the repository for some lovely images that explain the dataset.

    To cite this dataset in publications use:

        Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism
        and Environmental Variability within a Community of Antarctic
        Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081.
        https://doi.org/10.1371/journal.pone.0090081

    Parameters
    ----------
    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a dict object.
    as_frame : bool, default=False
        If True, returns `data` as a pandas DataFrame  instead of X, y matrices.

    Examples
    -------
    ```py
    from sklego.datasets import load_penguins
    X, y = load_penguins(return_X_y=True)

    X.shape
    # (344, 6)
    y.shape
    # (344,)

    load_penguins(as_frame=True).columns
    # Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm',
    #    'flipper_length_mm', 'body_mass_g', 'sex'],
    #   dtype='object')
    ```

    Notes
    -----
    Anyone interested in publishing the data should contact
    [`Dr. Kristen Gorman`](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php)
    about analysis and working together on any final products.

    !!! quote "From Gorman et al. (2014)"

        Data reported here are publicly available within the
        [PAL-LTER data system (datasets 219, 220, and 221)](http://oceaninformatics.ucsd.edu/datazoo/data/pallter/datasets).

        Individuals interested in using these data are therefore expected to follow the [US LTER Network's Data Access
        Policy, Requirements and Use Agreement](https://lternet.edu/data-access-policy/)

    Please cite data using the following
    ------------------------------------
    **Adélie penguins:**

      - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of
        foraging among adult male and female Adélie penguins (*Pygoscelis adeliae*) nesting along the Palmer Archipelago
        near Palmer Station, 2007-2009 ver 5. Environmental Data
        Initiative](https://doi.org/10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f).

        (Accessed 2020-06-08).

    **Gentoo penguins:**

      - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of
        foraging among adult male and female Gentoo penguin (*Pygoscelis papua*) nesting along the Palmer Archipelago
        near Palmer Station, 2007-2009 ver 5. Environmental Data
        Initiative](https://doi.org/10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689).

        (Accessed 2020-06-08).

    **Chinstrap penguins:**

      - [Palmer Station Antarctica LTER and K. Gorman, 2020. Structural size measurements and isotopic signatures of
        foraging among adult male and female Chinstrap penguin (*Pygoscelis antarcticus*) nesting along the Palmer
        Archipelago near Palmer Station, 2007-2009 ver 6.
        Environmental Data Initiative](https://doi.org/10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e).

        (Accessed 2020-06-08).
    """
    filepath = importlib_resources.files("sklego") / "data" / "penguins.zip"
    df = pd.read_csv(filepath)
    if as_frame:
        return df
    X, y = (
        df[
            [
                "island",
                "bill_length_mm",
                "bill_depth_mm",
                "flipper_length_mm",
                "body_mass_g",
                "sex",
            ]
        ].values,
        df["species"].values,
    )
    if return_X_y:
        return X, y
    return {"data": X, "target": y}

sklego.datasets.make_simpleseries(n_samples=365 * 5, trend=0.001, season_trend=0.001, noise=0.5, as_frame=False, seed=None, stack_noise=False, start_date=None)

Generate a very simple timeseries dataset to play with.

The generator returns a daily time-series with a yearly seasonality, trend, and noise.

Parameters:

Name	Type	Description	Default
n_samples	int	The number of days to simulate the timeseries for.	365 * 5
trend	float	The long term trend in the dataset.	0.001
season_trend	float	The long term trend in the seasonality.	0.001
noise	float	The noise that is applied to the dataset.	0.5
as_frame	bool	Whether to return a pandas dataframe instead of a numpy array.	False
seed	int | None	The seed value for the randomness.	None
stack_noise	bool	Set the noise to be stacked by a cumulative sum.	False
start_date	str | None	Also add a start date (only works if as_frame=True).	None

Returns:

Type	Description
ndarray | DataFrame	Timeseries dataset with specified characteristics.

Examples:

from sklego.datasets import make_simpleseries

make_simpleseries(seed=42)
# array([-0.34078806, -0.61828731, -0.18458236, ..., -0.27547402,
#        -0.38237413,  0.13489355])

make_simpleseries(as_frame=True, start_date="2018-01-01", seed=42).head(3)
'''
         yt       date
0 -0.340788 2018-01-01
1 -0.618287 2018-01-02
2 -0.184582 2018-01-03
'''

Source code in sklego/datasets.py

def make_simpleseries(
    n_samples=365 * 5,
    trend=0.001,
    season_trend=0.001,
    noise=0.5,
    as_frame=False,
    seed=None,
    stack_noise=False,
    start_date=None,
):
    """Generate a very simple timeseries dataset to play with.

    The generator returns a daily time-series with a yearly seasonality, trend, and noise.

    Parameters
    ----------
    n_samples : int, default=365 * 5
        The number of days to simulate the timeseries for.
    trend : float, default=0.001
        The long term trend in the dataset.
    season_trend : float, default=0.001
        The long term trend in the seasonality.
    noise : float, default=0.5
        The noise that is applied to the dataset.
    as_frame : bool, default=False
        Whether to return a pandas dataframe instead of a numpy array.
    seed : int | None, default=None
        The seed value for the randomness.
    stack_noise : bool, default=False
        Set the noise to be stacked by a cumulative sum.
    start_date : str | None, default=None
        Also add a start date (only works if `as_frame`=True).

    Returns
    -------
    np.ndarray | pd.DataFrame
        Timeseries dataset with specified characteristics.

    Examples
    --------
    ```py
    from sklego.datasets import make_simpleseries

    make_simpleseries(seed=42)
    # array([-0.34078806, -0.61828731, -0.18458236, ..., -0.27547402,
    #        -0.38237413,  0.13489355])

    make_simpleseries(as_frame=True, start_date="2018-01-01", seed=42).head(3)
    '''
             yt       date
    0 -0.340788 2018-01-01
    1 -0.618287 2018-01-02
    2 -0.184582 2018-01-03
    '''
    ```
    """
    if seed:
        np.random.seed(seed)
    time = np.arange(0, n_samples)
    noise = np.random.normal(0, noise, n_samples)
    if stack_noise:
        noise = noise.cumsum()
    r1, r2 = np.random.normal(0, 1, 2)
    seasonality = r1 * np.sin(time / 365 * 2 * np.pi) + r2 * np.cos(time / 365 * 4 * np.pi + 1)
    result = seasonality + season_trend * seasonality * time + trend * time + noise
    if as_frame:
        if start_date:
            stamps = pd.date_range(start_date, periods=n_samples)
            return pd.DataFrame({"yt": result, "date": stamps})
        return pd.DataFrame({"yt": result})
    return result

sklego.datasets.fetch_creditcard(*, cache=True, data_home=None, as_frame=False, return_X_y=False)

Load the creditcard dataset. Download it if necessary.

Note that internally this is using fetch_openml from scikit-learn, which is experimental.

==============   ==============
Samples total            284807
Dimensionality               29
Features                   real
Target                 int 0, 1
==============   ==============

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

Please cite:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi.
Calibrating Probability with Undersampling for Unbalanced Classification.
In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Parameters:

Name	Type	Description	Default
cache	bool	Whether to cache downloaded datasets using joblib.	True
data_home	str | None	Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.	None
as_frame	bool	If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a frame attribute with the target and the data. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as describe above.	False
return_X_y	bool	If True, returns (data.data, data.target) instead of a Bunch object.	False.

Returns:

Type	Description
Dictionary - like	With the following attributes: data ndarray, shape (284807, 29) if as_frame is True, data is a pandas object. target ndarray, shape (284807, ) if as_frame is True, target is a pandas object. feature_names Array of ordered feature names used in the dataset. DESCR Description of the creditcard dataset. Best to use print.

Notes

This dataset consists of 284807 samples and 29 features.

Source code in sklego/datasets.py

def fetch_creditcard(*, cache=True, data_home=None, as_frame=False, return_X_y=False):
    """Load the creditcard dataset. Download it if necessary.

    Note that internally this is using
    [`fetch_openml`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html) from
    scikit-learn, which is experimental.

        ==============   ==============
        Samples total            284807
        Dimensionality               29
        Features                   real
        Target                 int 0, 1
        ==============   ==============

    The datasets contains transactions made by credit cards in September 2013 by european cardholders.
    This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.
    The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

    Please cite:

        Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi.
        Calibrating Probability with Undersampling for Unbalanced Classification.
        In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

    Parameters
    ----------
    cache : bool, default=True
        Whether to cache downloaded datasets using joblib.
    data_home : str | None, default=None
        Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in
        '~/scikit_learn_data' subfolders.
    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or
        categorical). The target is a pandas DataFrame or Series depending on the number of target_columns.
        The Bunch will contain a `frame` attribute with the target and the data. If `return_X_y` is True, then
        `(data, target)` will be pandas DataFrames or Series as describe above.
    return_X_y : bool, default=False.
        If True, returns `(data.data, data.target)` instead of a Bunch object.

    Returns
    -------
    Dictionary-like
        With the following attributes:

         * data
            ndarray, shape (284807, 29) if ``as_frame`` is True, ``data`` is a pandas object.
         * target
            ndarray, shape (284807, ) if ``as_frame`` is True, ``target`` is a pandas object.
         * feature_names
            Array of ordered feature names used in the dataset.
         * DESCR
            Description of the creditcard dataset. Best to use print.

    Notes
    -----
    This dataset consists of 284807 samples and 29 features.
    """
    return fetch_openml(
        data_id=1597,
        data_home=data_home,
        cache=cache,
        as_frame=as_frame,
        return_X_y=return_X_y,
    )