博客
关于我
pandas.groupby().rank()用法详解
阅读量:344 次
发布时间:2019-03-04

本文共 2774 字,大约阅读时间需要 9 分钟。

  • pandas.DataFrame.groupby()

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

  • Parameters
  1. by : mapping, function, label, or list of labes

    Used to determine the groups for the groupby.

    If by is a function, it’s called on each value of the object’s index.

    If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups.

    If an ndarray is passed, the values are used as-is determine the groups.

    A label or list of labels may be passed to group by the columns in self.

  2. axis : {0 or ‘index’, 1 or ‘columns’}, default 0

    Split along rows (0) or columns (1).

  3. level : int, level name, or sequence of such, default None

    If the axis is a MultiIndex (hierarchical), group by a particular level or levels.

  4. as_index : bool, default True

    For aggregated output, return object with group labels. Only relevant for DataFrame input. as_index=False is effectively ‘SQL-style’ grouped output.

  5. sort : bool, default True

    Sort group keys.

    Get better performance by turning this off. Note this does not influence the order of observations within each group.

    Groupby preserves the order of rows within each group.

  6. group_keys : bool, default True

    When calling apply, add group keys to index to identify pieces.

  7. squeeze : bool, default True

    Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

  8. observed : bool, default False

    This only applies if any of the groupers are Categoricals.

    If True : only show observed values for categorical groupers.

    If False : show all values for categorical groupers.

  9. dropna : bool, default True

    If True, and if group keys contain NA values, NA values together with row/column will be dropped.

    If False, NA values will also be treated as the key in groups.

  • Returns

Returns a groupby object that contains information about the groups.

  • PANDAS.DATAFRAME.RANK

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True,pct=False)

Computing 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.

method : {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’

How to rank the group of records that have the same value.

  1. average : average rank of the group
  2. min: lowest rank in the group
  3. max: highest rank in the group
  4. first: ranks assigned in order they appear in the array
  5. dense: like ‘min’, but rank always increases by 1 between groups.

method是针对rank排名讲的,指的是原始数据序列中存在相同的数据,这些相同数据返回的rank排名,如果是max就取相同数据所占顺序中最大的,min就是其中最小的。first就是按照他们在原始数据中所出现的顺序给定rank。

  • References

转载地址:http://zdge.baihongyu.com/

你可能感兴趣的文章
MySQL中group by 与 order by 一起使用排序问题
查看>>
mysql中having的用法
查看>>
MySQL中interactive_timeout和wait_timeout的区别
查看>>
mysql中int、bigint、smallint 和 tinyint的区别、char和varchar的区别详细介绍
查看>>
mysql中json_extract的使用方法
查看>>
mysql中json_extract的使用方法
查看>>
mysql中kill掉所有锁表的进程
查看>>
mysql中like % %模糊查询
查看>>
MySql中mvcc学习记录
查看>>
mysql中null和空字符串的区别与问题!
查看>>
MySQL中ON DUPLICATE KEY UPDATE的介绍与使用、批量更新、存在即更新不存在则插入
查看>>
MYSQL中TINYINT的取值范围
查看>>
MySQL中UPDATE语句的神奇技巧,让你操作数据库如虎添翼!
查看>>
Mysql中varchar类型数字排序不对踩坑记录
查看>>
MySQL中一条SQL语句到底是如何执行的呢?
查看>>
MySQL中你必须知道的10件事,1.5万字!
查看>>
MySQL中使用IN()查询到底走不走索引?
查看>>
Mysql中使用存储过程插入decimal和时间数据递增的模拟数据
查看>>
MySql中关于geometry类型的数据_空的时候如何插入处理_需用null_空字符串插入会报错_Cannot get geometry object from dat---MySql工作笔记003
查看>>
mysql中出现Incorrect DECIMAL value: '0' for column '' at row -1错误解决方案
查看>>