Python 3.x 按groupby一天数据将列中的值从任意数字更改为10时如何计数

我有一个数据框，有三列，时间，A和标志

首先在天内将groupby函数应用于组，然后在列标志中检查将多少次更改为10以及保留10的时间

输入：

                  Time  flag
0  2019-02-14 00:00:10     1
1  2019-02-14 00:00:16     3
2  2019-02-14 00:00:21     4
3  2019-02-14 00:00:27    10
4  2019-02-14 00:00:32    10
5  2019-02-15 00:00:37     1
6  2019-02-15 00:00:43     0
7  2019-02-15 00:00:48    10
8  2019-02-15 00:00:54    10
9  2019-02-15 00:00:59    10

输出：

    group_start_time 1   group_end_time   count_change_to_10    minimum_duration_of_each_group_value_remains_10    Maximum_duration_of_each_group_value_remains_10

2019-02-14 00:00:10    2019-02-14 00:00:32       1              2              2              2 

2019-02-15 00:00:37    2019-02-15 00:00:59       1              3               3              3

我相信您需要使用命名聚合：

df['Time'] = pd.to_datetime(df['Time'])

m = df['flag'].eq(10)

g = m.ne(m.shift()).cumsum()[m]
df['count'] = g.map(g.value_counts())


df = df.groupby(df['Time'].dt.date).agg(group_start_time_1=('Time','first'),
                                        group_end_time_1=('Time','last'),
                                        count_change_to_10 =('count','nunique'),
                                        minimum_duration_of_each_group_value_remains_10=('count', 'min'),
                                        Maximum_duration_of_each_group_value_remains_10=('count', 'max'))
print (df)
            group_start_time_1    group_end_time_1  count_change_to_10  \
Time                                                                     
2019-02-14 2019-02-14 00:00:10 2019-02-14 00:00:32                   1   
2019-02-15 2019-02-15 00:00:37 2019-02-15 00:00:59                   1   

            minimum_duration_of_each_group_value_remains_10  \
Time                                                          
2019-02-14                                              2.0   
2019-02-15                                              3.0   

            Maximum_duration_of_each_group_value_remains_10  
Time                                                         
2019-02-14                                              2.0  
2019-02-15                                              3.0  

---

编辑：熊猫的解决方案

计数\u更改\u为\u 10

和

每个\u组的持续时间\u值\u保持\u 10

之间有什么区别？是否可能更改每行不同计数的数据？@jezrael，抱歉，我修改了一个小错误，计数更改为10表示值更改为10的次数，在我的数据中是1次更改

df['Time'] = pd.to_datetime(df['Time'])

m = df['flag'].eq(10)
#consecutive groups only by mask
g = m.ne(m.shift()).cumsum()[m]
#counter only matched values by mask
df['count'] = g.map(g.value_counts())

df = df.groupby(df['Time'].dt.date).agg({'Time':['first','last'],
                                         'count':['nunique','min','max']})
df.columns = df.columns.map('_'.join)

d = {'Time_first':'group_start_time_1',
     'Time_last':'group_end_time_1',
     'count_nunique':'count_change_to_10',
     'count_min':'minimum_duration_of_each_group_value_remains_10',
     'count_max':'Maximum_duration_of_each_group_value_remains_10'}

cols = ['Maximum_duration_of_each_group_value_remains_10',
        'Maximum_duration_of_each_group_value_remains_10']
df = df.rename(columns=d)
df[cols] = df[cols].astype(int)
df = df.reset_index()

print (df)
         Time  group_start_time_1    group_end_time_1  count_change_to_10  \
0  2019-02-14 2019-02-14 00:00:10 2019-02-14 00:00:32                   1   
1  2019-02-15 2019-02-15 00:00:37 2019-02-15 00:00:59                   1   

   minimum_duration_of_each_group_value_remains_10  \
0                                              2.0   
1                                              3.0   

   Maximum_duration_of_each_group_value_remains_10  
0                                                2  
1                                                3