在 Python 代码中，我们经常会使用一些内置函数或第三方库中的函数，但有时候我们需要更深入了解这些函数的具体实现或使用方法。例如，在下面的 Python 代码中，我们使用了 array 函数来创建一个数组：
from pylab import *
x = array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
y = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
此时，我们可能会有以下几个问题：
- array 函数属于哪个库？
- array 函数的具体使用方法和参数是什么？
- 如何将外部文件中的数据导入到 x 和 y 数组中？
2、解决方案
2.1 确定函数所属的库
要确定 array 函数所属的库，我们可以使用以下方法：
- 查看函数的文档。例如，我们可以使用 help(array) 命令来查看 array 函数的文档说明：
help(array)
这将输出类似以下的内容：
Help on function array in module numpy:
array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
Create an array.
Parameters
----------
object : array_like
An array, any object exposing the array interface, an object whose
__array__ method returns an array, or any (nested) sequence.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence. This argument can only be used to 'upcast' the array. For
downcasting, use the .astype() method.
copy : bool, optional
If True, then the object is copied. Otherwise, a reference to the
object is returned.
order : {'C', 'F', 'A'}, optional
Whether to interpret the object as a row-major (C-style) or
column-major (Fortran-style) array. 'C' means C-order (row major)
and 'F' means F-order (column major). 'A' means 'any', and will
interpret the object as C- or F-order, depending on whether a Fortran
compiler is available or not. The default is 'K', which means that if
a Fortran compiler is available, 'F' order is used, otherwise 'C'
order is used.
subok : bool, optional
If True, then sub-classes will be passed to __array__() instead of
raising an exception. Defaults to False.
ndmin : int, optional
Specifies the minimum number of dimensions. One is added to the
result if necessary.
Returns
-------
out : ndarray
Array interpreted as a ndim-dimensional array with the given dtype.
Raises
------
TypeError
if object is not a suitable array-like object.
ValueError
if object is an invalid shape.
See Also
--------
asarray: Convert input to an array, but pass ndarray subclasses through.
asanyarray: Convert input to an array, but never pass ndarray subclasses
through.
ndarray.astype: Copy of the array, cast to a specified type.
empty, empty_like: Construct an empty array with a given shape and type, but
uninitialized memory. To avoid copying, use numpy.zeros() instead.
full, full_like: Construct a new array with the same shape and type as
a given array, filled with specified value.
ones, ones_like: Construct an array filled with ones.
zeros, zeros_like: Construct an array filled with zeros.
Examples
--------
Create a simple rank 1 array:
>>> np.array([1, 2, 3])
array([1, 2, 3])
Create a 2D array:
>>> np.array([[1, 2, 3], [3, 4, 5]])
array([[1, 2, 3],
[3, 4, 5]])
Create a 3D array:
>>> np.array([[[1, 2, 3], [3, 4, 5]], [[6, 7, 8], [8, 9, 10]]])
array([[[1, 2, 3],
[3, 4, 5]],
[[6, 7, 8],
[8, 9, 10]]])
Create an array from nested sequences:
>>> np.array([1, 2, (3, 4), [5, 6]], dtype=int)
array([1, 2, 3, 4, 5, 6], dtype=int32)
从输出中我们可以看到，array 函数属于 numpy 库。
2.2 探索函数的具体使用方法和参数
要探索 array 函数的具体使用方法和参数，我们可以使用以下方法：
- 查看函数的文档。如前所述，我们可以使用 help(array) 命令来查看 array 函数的文档说明。
- 阅读函数的源代码。我们可以使用 inspect 模块来查看 array 函数的源代码：
import inspect
print(inspect.getsource(array))
这将输出如下内容：
def array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0):
"""Create an array.
Parameters
----------
object : array_like
An array, any object exposing the array interface, an object whose
__array__ method returns an array, or any (nested) sequence.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence. This argument can only be used to 'upcast' the array. For
downcasting, use the .astype() method.
copy : bool, optional
If True, then the object is copied. Otherwise, a reference to the
object is returned.
order : {'C', 'F', 'A'}, optional
Whether to interpret the object as a row-major (C-style) or
column-major (Fortran-style) array. 'C' means C-order (row major)
and 'F' means F-order (column major). 'A' means 'any', and will
interpret the object as C- or F-order, depending on whether a Fortran
compiler is available or not. The default is 'K', which means that if
a Fortran compiler is available, 'F' order is used, otherwise 'C'
order is used.
subok : bool, optional
If True, then sub-classes will be passed to __array__() instead of
raising an exception. Defaults to False.
ndmin : int, optional
Specifies the minimum number of dimensions. One is added to the
result if necessary.
Returns
-------
out : ndarray
Array interpreted as a ndim-dimensional array with the given dtype.
Raises
------
TypeError
if object is not a suitable array-like object.
ValueError
if object is an invalid shape.
See Also
--------
asarray: Convert input to an array, but pass ndarray subclasses through.
asanyarray: Convert input to an array, but never pass ndarray subclasses
through.
ndarray.astype: Copy of the array, cast to a specified type.
empty, empty_like: Construct an empty array with a given shape and type, but
uninitialized memory. To avoid copying, use numpy.zeros() instead.
full, full_like: Construct a new array with the same shape and type as
a given array, filled with specified value.
ones, ones_like: Construct an array filled with ones.
zeros, zeros_like: Construct an array filled with zeros.
Examples
--------
Create a simple rank 1 array:
>>> np.array([1, 2, 3])
array([1, 2, 3])
Create a 2D array:
>>> np.array([[1, 2, 3], [3, 4, 5]])
array([[1, 2, 3],
[3, 4, 5]])
Create a 3D array:
>>> np.array([[[1, 2, 3], [3, 4,
