help(该对象)
eg:

import numpy as np
print(help(np.random.normal))

Help on built-in function normal:

normal(...) method of numpy.random.mtrand.RandomState instance
    normal(loc=0.0, scale=1.0, size=None)
    
    Draw random samples from a normal (Gaussian) distribution.
    
    The probability density function of the normal distribution, first
    derived by De Moivre and 200 years later by both Gauss and Laplace
    independently [2]_, is often called the bell curve because of
    its characteristic shape (see the example below).
    
    The normal distributions occurs often in nature.  For example, it
    describes the commonly occurring distribution of samples influenced
    by a large number of tiny, random disturbances, each with its own
    unique distribution [2]_.
    
    .. note::
        New code should use the ``normal`` method of a ``default_rng()``
        instance instead; see `random-quick-start`.
    
    Parameters
    ----------
    loc : float or array_like of floats
        Mean ("centre") of the distribution.
    scale : float or array_like of floats
        Standard deviation (spread or "width") of the distribution. Must be
        non-negative.
    size : int or tuple of ints, optional
        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
        ``m * n * k`` samples are drawn.  If size is ``None`` (default),
        a single value is returned if ``loc`` and ``scale`` are both scalars.
        Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
    
    Returns
    -------
    out : ndarray or scalar
        Drawn samples from the parameterized normal distribution.
    
    See Also
    --------
    scipy.stats.norm : probability density function, distribution or
        cumulative density function, etc.
    Generator.normal: which should be used for new code.
    
    Notes
    -----
    The probability density for the Gaussian distribution is
    
    .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
                     e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
    
    where :math:`\mu` is the mean and :math:`\sigma` the standard
    deviation. The square of the standard deviation, :math:`\sigma^2`,
    is called the variance.
    
    The function has its peak at the mean, and its "spread" increases with
    the standard deviation (the function reaches 0.607 times its maximum at
    :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
    normal is more likely to return samples lying close to the mean, rather
    than those far away.
    
    References
    ----------
    .. [1] Wikipedia, "Normal distribution",
           https://en.wikipedia.org/wiki/Normal_distribution
    .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
           Random Variables and Random Signal Principles", 4th ed., 2001,
           pp. 51, 51, 125.
    
    Examples
    --------
    Draw samples from the distribution:
    
    >>> mu, sigma = 0, 0.1 # mean and standard deviation
    >>> s = np.random.normal(mu, sigma, 1000)
    
    Verify the mean and the variance:
    
    >>> abs(mu - np.mean(s))
    0.0  # may vary
    
    >>> abs(sigma - np.std(s, ddof=1))
    0.1  # may vary
    
    Display the histogram of the samples, along with
    the probability density function:
    
    >>> import matplotlib.pyplot as plt
    >>> count, bins, ignored = plt.hist(s, 30, density=True)
    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
    ...          linewidth=2, color='r')
    >>> plt.show()
    
    Two-by-four array of samples from N(3, 6.25):
    
    >>> np.random.normal(3, 2.5, size=(2, 4))
    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

None

Process finished with exit code 0