使用spyder查询帮助文档

在使用spyder时有可能要查询某个函数、或者某个模块的具体用法

1、要查看模块的作用说明、简介，可以直接在交互区直接输入

print( 模块名.__doc__)

例如：要查看pandas的介绍

In [1]: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, moving window linear regressions,
date shifting and lagging, etc.

2、想指导某个函数的用法可以使用

help(函数名)

例如要查询pandas 的fillna的使用方法

In [2] :help(x.fillna)
Help on method fillna in module pandas.core.frame:

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) method of pandas.core.frame.DataFrame instance
Fill NA/NaN values using the specified method

Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). (values not
in the dict/Series/DataFrame will not be filled). This value cannot
be a list.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
axis : {0 or 'index', 1 or 'columns'}
inplace : boolean, default False
If True, fill in place. Note: this will modify any
other views on this object, (e.g. a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
downcast : dict, default is None
a dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible)

See Also
--------
reindex, asfreq

Returns
-------
filled : DataFrame

使用help命令进行查找就不用在去网上寻找介绍了，而且结果一目了然