如何使用 pandas 按月和年对行进行分组和计数? [英] How to group and count rows by month and year using Pandas?

问题描述

我有一个包含个人数据的数据集，例如姓名，身高，体重和出生日期.我将用特定月份和年份中出生的人数来构建图表.我正在使用python熊猫来完成此操作，我的策略是尝试按年份和月份分组并使用计数进行添加.但是我得到的最接近的是按年或按月，而不是按两者来统计人数.

I have a dataset with personal data such as name, height, weight and date of birth. I would build a graph with the number of people born in a particular month and year. I'm using python pandas to accomplish this and my strategy was to try to group by year and month and add using count. But the closest I got is to get the count of people by year or by month but not by both.

df['birthdate'].groupby(df.birthdate.dt.year).agg('count')

stackoverflow中的其他问题指向一个名为TimeGrouper的石斑鱼，但在熊猫文档中搜索没有发现任何问题.有什么主意吗?

Other questions in stackoverflow point to a Grouper called TimeGrouper but searching in pandas documentation found nothing. Any idea?

推荐答案

要对多个条件进行分组，请传递列或条件的列表:

To group on multiple criteria, pass a list of the columns or criteria:

df['birthdate'].groupby([df.birthdate.dt.year, df.birthdate.dt.month]).agg('count')

示例:

In [165]:
df = pd.DataFrame({'birthdate':pd.date_range(start=dt.datetime(2015,12,20),end=dt.datetime(2016,3,1))})
df.groupby([df['birthdate'].dt.year, df['birthdate'].dt.month]).agg({'count'})

Out[165]:
                    birthdate
                        count
birthdate birthdate          
2015      12               12
2016      1                31
          2                29
          3                 1

更新

从版本 0.23.0 开始，由于多重索引级别名称必须唯一的限制，现在您需要rename级别才能起作用:

As of version 0.23.0 the above code no longer works due to the restriction that multi-index level names must be unique, you now need to rename the levels in order for this to work:

In[107]:
df.groupby([df['birthdate'].dt.year.rename('year'), df['birthdate'].dt.month.rename('month')]).agg({'count'})

Out[107]: 
           birthdate
               count
year month          
2015 12           12
2016 1            31
     2            29
     3             1

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