[빅데이터분석기사 실기] help(), dir() 활용하기

help(), dir() 활용하기

들어가며

• 빅데이터분석기사 실기 시험장에서 특정 함수명이나 함수의 사용 방법을 잊어버렸을 경우, help() 또는 dir() 함수를 이용하여 확인할 수 있다.
• 이에 대한 내용을 정리해본다.

 

방법

0️⃣ 참고 사항

• help() 또는 dir() 함수의 인자에 넣을 대상을 우선 import 해줘야 한다.

			
			
			
		
import sklearn         # (1) 확인하고자 할 대상 불러오기
 
print(help(sklearn))   # (2) 사용 방법 확인
print(dir(sklearn))    # (3) 속성/메서드 목록 확인

 

1️⃣ help() 함수 사용하기

• 객체, 모듈, 함수, 클래스 등에 대한 도움말 문서(docstring)를 확인하고 싶을 경우 help() 함수를 사용한다.
• 어떤 함수나 클래스의 사용 방법을 알고 싶을 때 사용한다.

 

예 : 사이킷런 패키지의 도움말 문서 확인하기

			
			
			
		
import sklearn
 
print(help(sklearn))

			
			
			
		
Help on package sklearn:
 
NAME
    sklearn - Configure global settings and get information about the working environment.
 
PACKAGE CONTENTS
    __check_build (package)
    _build_utils (package)
    _built_with_meson
    _config
    _distributor_init
    _isotonic
    _loss (package)
    _min_dependencies
    base
    calibration
    cluster (package)
    compose (package)
    conftest
    covariance (package)
    cross_decomposition (package)
    datasets (package)
    decomposition (package)
    discriminant_analysis
    dummy
    ensemble (package)
    exceptions
    experimental (package)
    externals (package)
    feature_extraction (package)
    feature_selection (package)
    gaussian_process (package)
    impute (package)
    inspection (package)
    isotonic
    kernel_approximation
    kernel_ridge
    linear_model (package)
    manifold (package)
    metrics (package)
    mixture (package)
    model_selection (package)
    multiclass
    multioutput
    naive_bayes
    neighbors (package)
    neural_network (package)
    pipeline
    preprocessing (package)
    random_projection
    semi_supervised (package)
    svm (package)
    tests (package)
    tree (package)
    utils (package)
 
FUNCTIONS
    clone(estimator, *, safe=True)
        Construct a new unfitted estimator with the same parameters.
 
        Clone does a deep copy of the model in an estimator
        without actually copying attached data. It returns a new estimator
        with the same parameters that has not been fitted on any data.
 
        .. versionchanged:: 1.3
            Delegates to `estimator.__sklearn_clone__` if the method exists.
 
        Parameters
        ----------
        estimator : {list, tuple, set} of estimator instance or a single             estimator instance
            The estimator or group of estimators to be cloned.
        safe : bool, default=True
            If safe is False, clone will fall back to a deep copy on objects
            that are not estimators. Ignored if `estimator.__sklearn_clone__`
            exists.
 
        Returns
        -------
        estimator : object
            The deep copy of the input, an estimator if input is an estimator.
 
        Notes
        -----
        If the estimator's `random_state` parameter is an integer (or if the
        estimator doesn't have a `random_state` parameter), an *exact clone* is
        returned: the clone and the original estimator will give the exact same
        results. Otherwise, *statistical clone* is returned: the clone might
        return different results from the original estimator. More details can be
        found in :ref:`randomness`.
 
        Examples
        --------
        >>> from sklearn.base import clone
        >>> from sklearn.linear_model import LogisticRegression
        >>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]]
        >>> y = [0, 0, 1, 1]
        >>> classifier = LogisticRegression().fit(X, y)
        >>> cloned_classifier = clone(classifier)
        >>> hasattr(classifier, "classes_")
        True
        >>> hasattr(cloned_classifier, "classes_")
        False
        >>> classifier is cloned_classifier
        False
 
    config_context(*, assume_finite=None, working_memory=None, print_changed_only=None, display=None, pairwise_dist_chunk_size=None, enable_cython_pairwise_dist=None, array_api_dispatch=None, transform_output=None, enable_metadata_routing=None, skip_parameter_validation=None)
        Context manager for global scikit-learn configuration.
 
        Parameters
        ----------
        assume_finite : bool, default=None
            If True, validation for finiteness will be skipped,
            saving time, but leading to potential crashes. If
            False, validation for finiteness will be performed,
            avoiding error. If None, the existing value won't change.
            The default value is False.
 
        working_memory : int, default=None
            If set, scikit-learn will attempt to limit the size of temporary arrays
            to this number of MiB (per job when parallelised), often saving both
            computation time and memory on expensive operations that can be
            performed in chunks. If None, the existing value won't change.
            The default value is 1024.
 
        print_changed_only : bool, default=None
            If True, only the parameters that were set to non-default
            values will be printed when printing an estimator. For example,
            ``print(SVC())`` while True will only print 'SVC()', but would print
            'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
            when False. If None, the existing value won't change.
            The default value is True.
 
            .. versionchanged:: 0.23
               Default changed from False to True.
 
        display : {'text', 'diagram'}, default=None
            If 'diagram', estimators will be displayed as a diagram in a Jupyter
            lab or notebook context. If 'text', estimators will be displayed as
            text. If None, the existing value won't change.
            The default value is 'diagram'.
 
            .. versionadded:: 0.23
 
        pairwise_dist_chunk_size : int, default=None
            The number of row vectors per chunk for the accelerated pairwise-
            distances reduction backend. Default is 256 (suitable for most of
            modern laptops' caches and architectures).
 
            Intended for easier benchmarking and testing of scikit-learn internals.
            End users are not expected to benefit from customizing this configuration
            setting.
 
            .. versionadded:: 1.1
 
        enable_cython_pairwise_dist : bool, default=None
            Use the accelerated pairwise-distances reduction backend when
            possible. Global default: True.
 
            Intended for easier benchmarking and testing of scikit-learn internals.
            End users are not expected to benefit from customizing this configuration
            setting.
 
            .. versionadded:: 1.1
 
        array_api_dispatch : bool, default=None
            Use Array API dispatching when inputs follow the Array API standard.
            Default is False.
 
            See the :ref:`User Guide <array_api>` for more details.
 
            .. versionadded:: 1.2
 
        transform_output : str, default=None
            Configure output of `transform` and `fit_transform`.
 
            See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
            for an example on how to use the API.
 
            - `"default"`: Default output format of a transformer
            - `"pandas"`: DataFrame output
            - `"polars"`: Polars output
            - `None`: Transform configuration is unchanged
 
            .. versionadded:: 1.2
            .. versionadded:: 1.4
                `"polars"` option was added.
 
        enable_metadata_routing : bool, default=None
            Enable metadata routing. By default this feature is disabled.
 
            Refer to :ref:`metadata routing user guide <metadata_routing>` for more
            details.
 
            - `True`: Metadata routing is enabled
            - `False`: Metadata routing is disabled, use the old syntax.
            - `None`: Configuration is unchanged
 
            .. versionadded:: 1.3
 
        skip_parameter_validation : bool, default=None
            If `True`, disable the validation of the hyper-parameters' types and values in
            the fit method of estimators and for arguments passed to public helper
            functions. It can save time in some situations but can lead to low level
            crashes and exceptions with confusing error messages.
 
            Note that for data parameters, such as `X` and `y`, only type validation is
            skipped but validation with `check_array` will continue to run.
 
            .. versionadded:: 1.3
 
        Yields
        ------
        None.
 
        See Also
        --------
        set_config : Set global scikit-learn configuration.
        get_config : Retrieve current values of the global configuration.
 
        Notes
        -----
        All settings, not just those presently modified, will be returned to
        their previous values when the context manager is exited.
 
        Examples
        --------
        >>> import sklearn
        >>> from sklearn.utils.validation import assert_all_finite
        >>> with sklearn.config_context(assume_finite=True):
        ...     assert_all_finite([float('nan')])
        >>> with sklearn.config_context(assume_finite=True):
        ...     with sklearn.config_context(assume_finite=False):
        ...         assert_all_finite([float('nan')])
        Traceback (most recent call last):
        ...
        ValueError: Input contains NaN...
 
    get_config()
        Retrieve current values for configuration set by :func:`set_config`.
 
        Returns
        -------
        config : dict
            Keys are parameter names that can be passed to :func:`set_config`.
 
        See Also
        --------
        config_context : Context manager for global scikit-learn configuration.
        set_config : Set global scikit-learn configuration.
 
        Examples
        --------
        >>> import sklearn
        >>> config = sklearn.get_config()
        >>> config.keys()
        dict_keys([...])
 
    set_config(assume_finite=None, working_memory=None, print_changed_only=None, display=None, pairwise_dist_chunk_size=None, enable_cython_pairwise_dist=None, array_api_dispatch=None, transform_output=None, enable_metadata_routing=None, skip_parameter_validation=None)
        Set global scikit-learn configuration.
 
        .. versionadded:: 0.19
 
        Parameters
        ----------
        assume_finite : bool, default=None
            If True, validation for finiteness will be skipped,
            saving time, but leading to potential crashes. If
            False, validation for finiteness will be performed,
            avoiding error.  Global default: False.
 
            .. versionadded:: 0.19
 
        working_memory : int, default=None
            If set, scikit-learn will attempt to limit the size of temporary arrays
            to this number of MiB (per job when parallelised), often saving both
            computation time and memory on expensive operations that can be
            performed in chunks. Global default: 1024.
 
            .. versionadded:: 0.20
 
        print_changed_only : bool, default=None
            If True, only the parameters that were set to non-default
            values will be printed when printing an estimator. For example,
            ``print(SVC())`` while True will only print 'SVC()' while the default
            behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with
            all the non-changed parameters.
 
            .. versionadded:: 0.21
 
        display : {'text', 'diagram'}, default=None
            If 'diagram', estimators will be displayed as a diagram in a Jupyter
            lab or notebook context. If 'text', estimators will be displayed as
            text. Default is 'diagram'.
 
            .. versionadded:: 0.23
 
        pairwise_dist_chunk_size : int, default=None
            The number of row vectors per chunk for the accelerated pairwise-
            distances reduction backend. Default is 256 (suitable for most of
            modern laptops' caches and architectures).
 
            Intended for easier benchmarking and testing of scikit-learn internals.
            End users are not expected to benefit from customizing this configuration
            setting.
 
            .. versionadded:: 1.1
 
        enable_cython_pairwise_dist : bool, default=None
            Use the accelerated pairwise-distances reduction backend when
            possible. Global default: True.
 
            Intended for easier benchmarking and testing of scikit-learn internals.
            End users are not expected to benefit from customizing this configuration
            setting.
 
            .. versionadded:: 1.1
 
        array_api_dispatch : bool, default=None
            Use Array API dispatching when inputs follow the Array API standard.
            Default is False.
 
            See the :ref:`User Guide <array_api>` for more details.
 
            .. versionadded:: 1.2
 
        transform_output : str, default=None
            Configure output of `transform` and `fit_transform`.
 
            See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
            for an example on how to use the API.
 
            - `"default"`: Default output format of a transformer
            - `"pandas"`: DataFrame output
            - `"polars"`: Polars output
            - `None`: Transform configuration is unchanged
 
            .. versionadded:: 1.2
            .. versionadded:: 1.4
                `"polars"` option was added.
 
        enable_metadata_routing : bool, default=None
            Enable metadata routing. By default this feature is disabled.
 
            Refer to :ref:`metadata routing user guide <metadata_routing>` for more
            details.
 
            - `True`: Metadata routing is enabled
            - `False`: Metadata routing is disabled, use the old syntax.
            - `None`: Configuration is unchanged
 
            .. versionadded:: 1.3
 
        skip_parameter_validation : bool, default=None
            If `True`, disable the validation of the hyper-parameters' types and values in
            the fit method of estimators and for arguments passed to public helper
            functions. It can save time in some situations but can lead to low level
            crashes and exceptions with confusing error messages.
 
            Note that for data parameters, such as `X` and `y`, only type validation is
            skipped but validation with `check_array` will continue to run.
 
            .. versionadded:: 1.3
 
        See Also
        --------
        config_context : Context manager for global scikit-learn configuration.
        get_config : Retrieve current values of the global configuration.
 
        Examples
        --------
        >>> from sklearn import set_config
        >>> set_config(display='diagram')  # doctest: +SKIP
 
    show_versions()
        Print useful debugging information"
 
        .. versionadded:: 0.20
 
        Examples
        --------
        >>> from sklearn import show_versions
        >>> show_versions()  # doctest: +SKIP
 
DATA
    __SKLEARN_SETUP__ = False
    __all__ = ['calibration', 'cluster', 'covariance', 'cross_decompositio...
 
VERSION
    1.5.2
 
FILE
    /usr/local/lib/python3.12/site-packages/sklearn/__init__.py

 

예 2 : 사이킷런 패키지의 preprocessing 모듈 도움말 문서 확인하기

			
			
			
		
import sklearn.preprocessing
 
print(help(sklearn.preprocessing))

			
			
			
		
Help on package sklearn.preprocessing in sklearn:
 
NAME
    sklearn.preprocessing - Methods for scaling, centering, normalization, binarization, and more.
 
PACKAGE CONTENTS
    _csr_polynomial_expansion
    _data
    _discretization
    _encoders
    _function_transformer
    _label
    _polynomial
    _target_encoder
    _target_encoder_fast
    tests (package)
 
CLASSES
    sklearn.base.BaseEstimator(sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin, sklearn.utils._metadata_requests._MetadataRequester)
        sklearn.preprocessing._data.Binarizer(sklearn.base.OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.KernelCenterer(sklearn.base.ClassNamePrefixFeaturesOutMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.MaxAbsScaler(sklearn.base.OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.MinMaxScaler(sklearn.base.OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.Normalizer(sklearn.base.OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.PowerTransformer(sklearn.base.OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)
        sklearn.preprocessing._data.QuantileTransformer(sklearn.base.OneToOneFeatureMixin, sklearn.base.Tran
        
        
        ...

 

2️⃣ dir() 함수 사용하기

• 객체, 모듈 등이 가진 속성(Attribute) 및 메서드(Method)를 리스트 형태로 확인하고 싶을 때 dir() 함수를 사용한다.
• 객체의 구조나 사용할 수 있는 메서드를 확인하고자 할 때 사용한다.

 

예 1 : 사이킷런 패키지의 속성 및 메서드 목록 확인하기

			
			
			
		
import sklearn
 
print(dir(sklearn))

			
			
			
		
['_BUILT_WITH_MESON', '__SKLEARN_SETUP__', '__all__', '__builtins__', '__cached__', '__check_build', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_built_with_meson', '_config', '_distributor_init', 'base', 'clone', 'config_context', 'exceptions', 'externals', 'get_config', 'logger', 'logging', 'os', 'random', 'set_config', 'setup_module', 'show_versions', 'sklearn', 'sys', 'utils']

 

예 2: 사이킷런 패키지의 preprocessing 모듈의 속성 및 메서드 목록 확인하기

			
			
			
		
import sklearn.preprocessing
 
print(dir(sklearn.preprocessing))

			
			
			
		
['Binarizer', 'FunctionTransformer', 'KBinsDiscretizer', 'KernelCenterer', 'LabelBinarizer', 'LabelEncoder', 'MaxAbsScaler', 'MinMaxScaler', 'MultiLabelBinarizer', 'Normalizer', 'OneHotEncoder', 'OrdinalEncoder', 'PolynomialFeatures', 'PowerTransformer', 'QuantileTransformer', 'RobustScaler', 'SplineTransformer', 'StandardScaler', 'TargetEncoder', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_csr_polynomial_expansion', '_data', '_discretization', '_encoders', '_function_transformer', '_label', '_polynomial', '_target_encoder', '_target_encoder_fast', 'add_dummy_feature', 'binarize', 'label_binarize', 'maxabs_scale', 'minmax_scale', 'normalize', 'power_transform', 'quantile_transform', 'robust_scale', 'scale']

 

정리

• 사이킷런(sklearn) 패키지의 preprocessing 모듈에 포함되어 있는 MinMaxScaler 함수의 사용 방법을 잊어 버렸을 경우, help(sklearn.preprocessing.MinMaxScaler) 을 이용하여 확인한다.
  • 사용 예시 코드까지 확인할 수 있다.

			
			
			
		
import sklearn.preprocessing.MinMaxScaler
 
print(help(sklearn.preprocessing.MinMaxScaler))

 

• 사이킷런(sklearn) 패키지의 preprocessing 모듈에 포함되어 있는 특정 함수명을 잊어 버렸을 경우, dir(sklearn.preprocessing) 을 이용하여 확인한다.

			
			
			
		
import sklearn.preprocessing
 
print(dir(sklearn.preprocessing))

 

참고 사이트

Built-in Functions