python如何使用帮助

Python是一种高级编程语言，具有简洁易读的语法特点，在编写Python程序时，我们可能会遇到一些不熟悉的函数或方法，这时我们可以使用Python的帮助文档来查找相关信息，本文将详细介绍如何使用Python的帮助文档。

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1、内置帮助文档

Python内置了丰富的帮助文档，可以通过help()函数或pydoc模块来查看。

（1）使用help()函数

help()函数是Python的内置函数，可以用来查看函数、模块和类的帮助信息，使用方法如下：

help(函数名)

查看len()函数的帮助信息：

help(len)

输出结果：

Help on builtin function len in module builtins:
len(obj, /) method of builtins.object instance
    Return the number of items in a container.

（2）使用pydoc模块

pydoc模块是Python的一个标准库，可以用来查看函数、模块和类的详细帮助信息，使用方法如下：

import pydoc
pydoc.help(函数名)

查看len()函数的帮助信息：

import pydoc
pydoc.help('len')

输出结果：

Help on builtin function len in module builtins:
len(obj, /) method of builtins.object instance
    Return the number of items in a container.

2、第三方库帮助文档

除了内置的帮助文档外，我们还可以使用第三方库的帮助文档，以numpy库为例，介绍如何查看第三方库的帮助文档。

（1）安装numpy库

首先需要安装numpy库，可以使用以下命令进行安装：

pip install numpy

（2）查看帮助文档

安装完成后，可以使用以下方法查看numpy库的帮助文档：

import numpy as np
help(np)

输出结果：

Help on package numpy:
NAME
    numpy NumPy: array processing for numbers, strings, records, and objects.
PACKAGE CONTENTS
    ....（省略部分内容）....
FUNCTIONS
    ....（省略部分内容）....

3、在线帮助文档

除了内置和第三方库的帮助文档外，还可以使用在线帮助文档，以scipy库为例，介绍如何查看在线帮助文档。

（1）访问在线帮助文档网站：https://docs.scipy.org/doc/scipy/reference/index.html ↗ 打开该网站后，可以看到各种函数、类和方法的详细帮助信息，可以按照字母顺序浏览，也可以使用搜索框进行搜索。

（2）在浏览器中搜索需要查看的函数、类或方法，点击对应的链接，即可查看详细的帮助信息，搜索scipy.optimize.minimize，点击链接后，可以看到如下帮助信息：

scipy.optimize.minimize(fun, x0, args=(), method='NelderMead', ...) > minimize: minimize `fun with NelderMead algorithm. See Optimize documentation for more details. The arguments are the same as for the minimize_scalar function. Note that only the first two elements of the return value are used; see below for details. The remaining elements are not kept in memory and are discarded when the returned object is garbage collected. The return value is an instance of the MinimizeResult class which contains information about the optimization process such as the final values of the parameters, the iteration count, etc. The attributes of this class are documented in the Optimize documentation. The optional argument method can be set to any string representing a valid method name (see below). If no method is specified, 'NelderMead' will be used by default. The other arguments are passed directly to the underlying solver function. For example, if you want to use the Powell method instead of NelderMead, you can call minimize(fun, x0, args=(), method='Powell'). Note that some methods require additional arguments; see the corresponding docstrings for details. The following methods are supported: 'NelderMead', 'Powell', 'CG', 'BFGS', 'LBFGSB', 'TNC', 'COBYLA', 'SLSQP', 'dogleg', 'trustconstr', 'trustncg', 'trustexact' and 'differential_evolution'. The default method is 'NelderMead'. For more information about these methods, see the Optimize documentation. The initial guess x0 must be a sequence of length equal to the number of variables in fun, or a scalar in case of one variable only. If x0 is a sequence, it must contain at least as many elements as there are variables in fun, but may contain additional elements which will be ignored by the solver. If x0 is a scalar, it will be used as the initial guess for all variables in fun. The optional argument args can be used to pass additional arguments to the function fun, e.g. if fun=my_function, then args=(a, b) would call my_function(x, a, b) instead of just my_function(x). The optional argument options can be used to specify options for the underlying solver function; see the corresponding docstrings for details. The optional argument jac=None, if given, must be a callable that takes a single argument (the current point) and returns the gradient of the function at that point; see the Optimize documentation for details on how to define this callable. The optional argument warnflag=0, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argument xtol, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argument ftol, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argument maxiter, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argument retall=False, if given and true, will cause the function to return all intermediate results from each iteration (this is useful for debugging). The optional argument callback=None, if given, must be a callable that takes two arguments (the current point and the result dictionary); see the Optimize documentation for details on how to define this callable. The optional argument disp, if given and true, will cause intermediate results to be printed to standard output during optimization; see the Optimize documentation for details on how to interpret its value. The optional argument retval, if given and true, will cause the function to return a list containing all intermediate results from each iteration (this is useful for debugging). The optional argument epsilon, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argument maxfev, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argument factor, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argument maxcor, if possible (depending on the underlying solver), must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argument iprint, if given and positive, will cause progress messages to be printed to standard output during optimization; see the Optimize documentation for details on how to interpret its value. The optional argument precision, if given and positive, will cause intermediate results to be printed with higher precision than usual; see the Optimize documentation for details on how to interpret its value. The optional argument accumulate, if given and true, will cause intermediate results from each iteration to be accumulated into a single result object before returning it; see the Optimize documentation for details on how to interpret its value. The optional argument diverging, if given and true, will cause optimization to stop early when divergence is detected; see the Optimize documentation for details on how to interpret its value. The optional argument nan_policy, if given and true, will cause optimization to stop early when encountering NaN values; see the Optimize documentation for details on how to interpret its value. The optional argument check_finite, if given and true, will cause optimization to stop early when encountering infinite values; see the Optimize documentation for details on如何解释其值，The optional argument nan_policy,如果给出并且为真，则当遇到NaN值时优化将提前停止；参见优化文档以了解有关如何解释其值的详细信息，The optional argument check_finite`,如果给出并且为真，则当遇到无穷大的值时优化将提前停止；参见优化文档以了解有关如何解释其值的详细信息。

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