如何知道python包的特点？ [关闭]

Posted 2023-02-24

【中文标题】如何知道python包的特点？ [关闭]

【英文标题】：How to know the features of python package? [closed]

【发布时间】：2021-03-03 14:35:32

【问题描述】：

我正在为我的项目解决优化问题。要使用 python 语言进行开发，可以使用几个包，例如mealpy、pyswarm 等。 我已经下载了这些包，但无法知道这些包的确切功能。比如mealpy包有好几种算法，那么怎么写一个代价函数，同一个代价函数怎么访问各种算法呢？

【参考方案1】：

一旦你导入了任何模块，你就可以这样做

help(module_name)

您可以获得该模块中所有功能的文档。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。

【参考方案2】：

在 jupyter 中有一个很酷的技巧，一旦你导入了模块，你可以在开头添加一个问号。下面简要介绍一下它的主要功能：

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.

但正如其他用户已经提到的那样，最好在谷歌上搜索为您提供一些示例的文档。