如何了解python包的特性？

Question

我正在为我的项目研究优化问题。要用python语言开发，可以使用几个软件包，如mealpy、PySarm等。 我已经下载了这些软件包，但无法知道这些软件包的确切功能。例如，mealpy包有几个算法，那么如何编写代价函数以及如何访问相同代价函数的各种算法呢

Answer 1

在jupyter中，有一个很酷的技巧，一旦你导入了模块，你就可以在开头添加一个问号。这将简要介绍其主要功能：

import pandas as pd

? pd 

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.

Main Features
      -
Here are just a few of the things that pandas does well:

  - Easy handling of missing data in floating point as well as non-floating
    point data.
  - Size mutability: columns can be inserted and deleted from DataFrame and
    higher dimensional objects
  - Automatic and explicit data alignment: objects can be explicitly aligned
    to a set of labels, or the user can simply ignore the labels and let
    `Series`, `DataFrame`, etc. automatically align the data for you in
    computations.
  - Powerful, flexible group by functionality to perform split-apply-combine
    operations on data sets, for both aggregating and transforming data.
  - Make it easy to convert ragged, differently-indexed data in other Python
    and NumPy data structures into DataFrame objects.
  - Intelligent label-based slicing, fancy indexing, and subsetting of large
    data sets.
  - Intuitive merging and joining data sets.
  - Flexible reshaping and pivoting of data sets.
  - Hierarchical labeling of axes (possible to have multiple labels per tick).
  - Robust IO tools for loading data from flat files (CSV and delimited),
    Excel files, databases, and saving/loading data from the ultrafast HDF5
    format.
  - Time series-specific functionality: date range generation and frequency
    conversion, moving window statistics, moving window linear regressions,
    date shifting and lagging, etc.

但是，正如其他用户已经提到的那样，最好在谷歌上搜索文档，其中提供了一些示例

Answer 2

导入任何模块后，即可执行此操作

help(module_name)

您可以获得该模块中所有函数的文档。你也可以这样做

dir(module_name)

这将简单地列出模块中定义的所有函数和变量的名称