Python 数据帧 : Seperate rows based on custom condition?

Question

我的数据框包含三列 name、content 和 day

df

        content          day           name
    0     first_day      01-01-2017      marcus
    1     present        10-01-2017      marcus
    2     first_day      01-02-2017      marcus
    3     first_day      01-03-2017      marcus
    4     absent         05-03-2017      marcus
    5     present        20-03-2017      marcus
    6     first_day      01-04-2017      bruno
    7     present        11-04-2017      bruno
    8     first_day      01-05-2017      bruno
    9     absent         02-05-2017      bruno
    10    first_day      01-06-2017      bruno
    11    absent         02-06-2017      bruno
    12    payment        09-06-2017      bruno

我试图找出 month wise 的用户，其行有 first_day、absent 和 present 连续.

示例输出:

        content          day           name         absent_after_present
    0     first_day      01-01-2017      marcus         False
    1     first_day      01-02-2017      marcus         False
    2     first_day      01-03-2017      marcus         True
    3     first_day      01-04-2017      bruno          False
    4     first_day      01-05-2017      bruno          False
    5     first_day      01-06-2017      bruno          True

例如:marcus first_day、缺席 和 present 从 01-03-2017 连续、05-03-2017 和20-03-2017 同一个月。所以 marcus 状态应该是 True

Answer 1

也许您可以尝试提取每月的内容，然后按名称和月份分组，如下所示。

import pandas as pd

data = pd.DataFrame({'content' : ['first_day','present', 'first_day', 'first_day', 'absent', 
'present', 'first_day', 'present', 'first_day', 'absent', 'first_day', 'absent', 'present'],
'day' : ['2017-01-01', '2017-01-10', '2017-02-01', '2017-03-01', '2017-03-05', '2017-03-20',
'2017-04-01', '2017-04-11', '2017-05-01', '2017-05-02', '2017-06-01', '2017-06-02', '2017-06-09'],
'name' : ['marcus', 'marcus', 'marcus', 'marcus', 'marcus', 'marcus', 'bruno', 'bruno', 'bruno',
'bruno', 'bruno', 'bruno', 'bruno']})

data['day'] = pd.to_datetime(data['day'])

data['month'] = data.day.dt.month

data_new = pd.DataFrame(data.groupby(['name', 'month'])['content'].unique()).join(pd.DataFrame(data.groupby(['name', 'month'])['day'].unique()), on=['name', 'month'])

data_new['absent_after_present'] = data_new['content'].apply(lambda x : True if len(x) == 3 and len(set(x)) == 3 else False)
data_new['day'] = data_new['day'].apply(lambda x : x[0])
data_new['content'] = data_new['content'].apply(lambda x : x[0])

data_new = data_new.droplevel(1)



data_new


name    content        day  absent_after_present

bruno   first_day   2017-04-01  False
bruno   first_day   2017-05-01  False
bruno   first_day   2017-06-01  True
marcus  first_day   2017-01-01  False
marcus  first_day   2017-02-01  False
marcus  first_day   2017-03-01  True