• 技术文章 >常用工具 >Spyder

    怎么使用spyder的帮助

    爱喝马黛茶的安东尼爱喝马黛茶的安东尼2020-01-16 14:02:11原创9471

    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、想知道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

    Python学习网,有大量免费的Spyder使用教程,欢迎大家学习!

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