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Python是一種高級(jí)編程語(yǔ)言，具有簡(jiǎn)潔易讀的語(yǔ)法特點(diǎn)，在編寫Python程序時(shí)，我們可能會(huì)遇到一些不熟悉的函數(shù)或方法，這時(shí)我們可以使用Python的幫助文檔來查找相關(guān)信息，本文將詳細(xì)介紹如何使用Python的幫助文檔。

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1、內(nèi)置幫助文檔

Python內(nèi)置了豐富的幫助文檔，可以通過help()函數(shù)或pydoc模塊來查看。

（1）使用help()函數(shù)

help()函數(shù)是Python的內(nèi)置函數(shù)，可以用來查看函數(shù)、模塊和類的幫助信息，使用方法如下：

help(函數(shù)名)

查看len()函數(shù)的幫助信息：

help(len)

輸出結(jié)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模塊

pydoc模塊是Python的一個(gè)標(biāo)準(zhǔn)庫(kù)，可以用來查看函數(shù)、模塊和類的詳細(xì)幫助信息，使用方法如下：

import pydoc
pydoc.help(函數(shù)名)

查看len()函數(shù)的幫助信息：

import pydoc
pydoc.help('len')

輸出結(jié)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、第三方庫(kù)幫助文檔

除了內(nèi)置的幫助文檔外，我們還可以使用第三方庫(kù)的幫助文檔，以numpy庫(kù)為例，介紹如何查看第三方庫(kù)的幫助文檔。

（1）安裝numpy庫(kù)

首先需要安裝numpy庫(kù)，可以使用以下命令進(jìn)行安裝：

pip install numpy

（2）查看幫助文檔

安裝完成后，可以使用以下方法查看numpy庫(kù)的幫助文檔：

import numpy as np
help(np)

輸出結(jié)果：

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

3、在線幫助文檔

除了內(nèi)置和第三方庫(kù)的幫助文檔外，還可以使用在線幫助文檔，以scipy庫(kù)為例，介紹如何查看在線幫助文檔。

（1）訪問在線幫助文檔網(wǎng)站：https://docs.scipy.org/doc/scipy/reference/index.html ↗ 打開該網(wǎng)站后，可以看到各種函數(shù)、類和方法的詳細(xì)幫助信息，可以按照字母順序?yàn)g覽，也可以使用搜索框進(jìn)行搜索。

（2）在瀏覽器中搜索需要查看的函數(shù)、類或方法，點(diǎn)擊對(duì)應(yīng)的鏈接，即可查看詳細(xì)的幫助信息，搜索scipy.optimize.minimize，點(diǎn)擊鏈接后，可以看到如下幫助信息：

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如何解釋其值，The optional argument nan_policy,如果給出并且為真，則當(dāng)遇到NaN值時(shí)優(yōu)化將提前停止；參見優(yōu)化文檔以了解有關(guān)如何解釋其值的詳細(xì)信息，The optional argument check_finite`,如果給出并且為真，則當(dāng)遇到無(wú)窮大的值時(shí)優(yōu)化將提前停止；參見優(yōu)化文檔以了解有關(guān)如何解釋其值的詳細(xì)信息。

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