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python如何使用幫助
Python是一種高級編程語言，具有簡潔易讀的語法特點，在編寫Python程序時，我們可能會遇到一些不熟悉的函數(shù)或方法，這時我們可以使用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的一個標(biāo)準(zhǔn)庫，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的幫助文檔，以numpy庫為例，介紹如何查看第三方庫的幫助文檔。
（1）安裝numpy庫
首先需要安裝numpy庫，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)置和第三方庫的幫助文檔外，還可以使用在線幫助文檔，以scipy庫為例，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，可以按照字母順序瀏覽，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，點擊對應(yīng)的鏈接，即可查看詳細(xì)的幫助信息，搜索scipy.optimize.minimize，點擊鏈接后，可以看到如下幫助信息：
scipy.optimize.minimize(fun, x0, args=(), method='NelderMead', ...) > minimize: minimize `funwith NelderMead algorithm. See Optimize documentation for more details. The arguments are the same as for theminimize_scalarfunction. 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 argumentmethodcan 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 callminimize(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 guessx0must be a sequence of length equal to the number of variables infun, or a scalar in case of one variable only. Ifx0is a sequence, it must contain at least as many elements as there are variables infun, but may contain additional elements which will be ignored by the solver. Ifx0is a scalar, it will be used as the initial guess for all variables infun. The optional argumentargscan be used to pass additional arguments to the functionfun, e.g. iffun=my_function, thenargs=(a, b)would callmy_function(x, a, b)instead of justmy_function(x). The optional argumentoptionscan be used to specify options for the underlying solver function; see the corresponding docstrings for details. The optional argumentjac=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 argumentwarnflag=0, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argumentxtol, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argumentftol, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argumentmaxiter, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argumentretall=False, if given and true, will cause the function to return all intermediate results from each iteration (this is useful for debugging). The optional argumentcallback=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 argumentdisp, 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 argumentretval, 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 argumentepsilon, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argumentmaxfev, if given, must be an integer; see the Optimize documentation for details on how to interpret its value. The optional argumentfactor, if given, must be a float; see the Optimize documentation for details on how to interpret its value. The optional argumentmaxcor, 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 argumentiprint, 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 argumentprecision, 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 argumentaccumulate, 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 argumentdiverging, 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 argumentnan_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 argumentcheck_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 argumentnan_policy,如果給出并且為真，則當(dāng)遇到NaN值時優(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 argumentcheck_finite`,如果給出并且為真，則當(dāng)遇到無窮大的值時優(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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