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-floatingpoint data- Size mutability: columns can be inserted and deleted from DataFrame andhigher dimensional objects- Automatic and explicit data alignment: objects can  be explicitly alignedto a set of labels, or the user can simply ignore the labels and let`Series`, `DataFrame`, etc. automatically align the data for you incomputations- Powerful, flexible group by functionality to perform split-apply-combineoperations on data sets, for both aggregating and transforming data- Make it easy to convert ragged, differently-indexed data in other Pythonand NumPy data structures into DataFrame objects- Intelligent label-based slicing, fancy indexing, and subsetting of largedata 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 HDF5format- Time series-specific functionality: date range generation and frequencyconversion, 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 instanceFill NA/NaN values using the specified methodParameters----------value : scalar, dict, Series, or DataFrameValue to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use foreach index (for a Series) or column (for a DataFrame). (values notin the dict/Series/DataFrame will not be filled). This value cannotbe a list.method : {'backfill', 'bfill', 'pad', 'ffill', None}, default NoneMethod to use for filling holes in reindexed Seriespad / ffill: propagate last valid observation forward to next validbackfill / bfill: use NEXT valid observation to fill gapaxis : {0 or 'index', 1 or 'columns'}inplace : boolean, default FalseIf True, fill in place. Note: this will modify anyother views on this object, (e.g. a no-copy slice for a column in aDataFrame).limit : int, default NoneIf method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there isa gap with more than this number of consecutive NaNs, it will onlybe partially filled. If method is not specified, this is themaximum number of entries along the entire axis where NaNs will befilled. Must be greater than 0 if not None.downcast : dict, default is Nonea dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriateequal type (e.g. float64 to int64 if possible)See Also--------reindex, asfreqReturns-------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

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