今天给一个python程序添加数据排名，需要使用pandas的rank()函数，使用时发现现排名输出跟自己预想的不太一样，随后在网上查了一下使用方法，发现很多网友介绍这个函数，有的写的异常详细，有的超级简略，最后又看了一下pandas上的说明文档，感觉学习函数最好的方法还是看官方说明文档。
下面代码中的参数，使用的都是默认参数，学习一个函数，不系统了解一下各种参数，上来直接用，就有可能出现意想不到的结果。
DataFrame.rank(axis=0, method='average', numeric_only=False, na_option='keep', ascending=True, pct=False)Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the ranks of those values.
老外的排名习惯好象跟中国真不太一样。遇到相同的成绩，我们一般都是作为并列第几名，老外默认是把所有相同成绩占据的位次取平均数，真是挺有意思的。
Parameters:
axis：{0 or ‘index’, 1 or ‘columns’}, default 0
Index to direct ranking. For Series this parameter is unused and defaults to 0.
method：{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’
How to rank the group of records that have the same value (i.e. ties):
average: average rank of the group
min: lowest rank in the group
max: highest rank in the group
first: ranks assigned in order they appear in the array
dense: like ‘min’, but rank always increases by 1 between groups.
numeric_only：bool, default False
For DataFrame objects, rank only numeric columns if set to True.
Changed in version 2.0.0: The default value of numeric_only is now False.
na_option：{‘keep’, ‘top’, ‘bottom’}, default ‘keep’
How to rank NaN values:
keep: assign NaN rank to NaN values
top: assign lowest rank to NaN values
bottom: assign highest rank to NaN values
ascending： bool, default True
Whether or not the elements should be ranked in ascending order.
pct： bool, default False
Whether or not to display the returned rankings in percentile form.
Returns:
same type as caller
Return a Series or DataFrame with data ranks as values.
