创建模块

创建一个模块非常简单，有两种方式：

• 创建一个 .py 文件，即可创建一个模块；

• 创建一个文件夹，则该文件夹也是一个模块（包）。

模块中可以无任何内容。但模块是用来组织代码，实现处理各类问题或完成各种功能的，这更便于应用或开发。例如正则模块 re，科学计算库 pandas 等。

创建一个文件夹，如果文件夹中包含一个 __init__.py 模块，则该文件夹是一个常规包；否则是一个命名空间包。包中还可以再创建子包或子模块。

__init__.py 文件中可以无任何内容，但因为当一个常规包被导入时，这个 __init__.py 文件会隐式地被执行，所以通常用来写入一些导入包即可执行的代码，或导入子包，或导入子包中模块的属性等，从而可以直接调用某些属性，例如 pandas 包的文档描述属性 __doc__ 和 DataFrame 数据结构。

import folder # 创建的空文件夹
folder

<module 'folder' (namespace)>

import pandas as pd

print(pd.__doc__)

pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**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, date shifting and lagging.

# 直接调用子包 core 中模块 frame 的属性 DataFrame
pd.DataFrame

pandas.core.frame.DataFrame