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print( 模块名.__doc__)

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.

help(函数名)

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