pandas.DataFrame.groupby DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) Group series using mapper (dict or key function, apply given function to group, return result as series) or by
import pandas as pd import numpy as np 分割-apply-聚合 大数据的MapReduce The most general-purpose GroupBy method is apply, which is the subject of the rest of this section. As illustrated in Figure 10-2, apply splits the object being manipulated into pieces,
Groupby Count # Party’s Frequency of donations nyc.groupby(’Party’)[’contb receipt amt’].count() The command returns a series where the index is the name of a Party and the value is the count of that Party. Note that the series is ordered by the name
如果Pandas只是能把一些数据变成 dataframe 这样优美的格式,那么Pandas绝不会成为叱咤风云的数据分析中心组件.因为在数据分析过程中,描述数据是通过一些列的统计指标实现的,分析结果也需要由具体的分组行为,对各组横向纵向对比. GroupBy 就是这样的一个有力武器.事实上,SQL语言在Pandas出现的几十年前就成为了高级数据分析人员的标准工具,很大一部分原因正是因为它有标准的SELECT xx FROM xx WHERE condition GROUP BY xx HAVING
import numpy as np import pandas as pd 聚合函数 Aggregations refer to any data transformation that produces scalar values from arrays(输入是数组, 输出是标量值). The preceding examples have used several of them, including mean, count, min, and sum You may wonder wha
