pandas 中所有先前行的有条件运行计数 [英] Conditional Running Count in Pandas for All Previous Rows Only

问题描述

假设我具有以下DataFrame:

Suppose I have the following DataFrame:

df = pd.DataFrame({'Event': ['A', 'B', 'A', 'A', 'B', 'C', 'B', 'B', 'A', 'C'], 
                    'Date': ['2019-01-01', '2019-02-01', '2019-03-01', '2019-03-01', '2019-02-15', 
                             '2019-03-15', '2019-04-05', '2019-04-05', '2019-04-15', '2019-06-10'],
                    'Sale':[100,200,150,200,150,100,300,250,500,400]})
df['Date'] = pd.to_datetime(df['Date'])
df

Event         Date
    A   2019-01-01
    B   2019-02-01
    A   2019-03-01
    A   2019-03-01
    B   2019-02-15
    C   2019-03-15
    B   2019-04-05
    B   2019-04-05
    A   2019-04-15
    C   2019-06-10

我想获得以下结果:

Event         Date  Previous_Event_Count
    A   2019-01-01                     0
    B   2019-02-01                     0
    A   2019-03-01                     1
    A   2019-03-01                     1
    B   2019-02-15                     1
    C   2019-03-15                     0
    B   2019-04-05                     2
    B   2019-04-05                     2
    A   2019-04-15                     3
    C   2019-06-10                     1

其中，df['Previous_Event_Count']是事件(df['Event'])在其相邻日期(df['Date'])之前发生的事件(行)的编号.例如，

where df['Previous_Event_Count'] is the number of an event (rows) when the event (df['Event']) takes place before its adjacent date (df['Date']). For instance,

• 2019年1月1日之前发生的事件A的数量为0，
• 2019年1月1日之前发生的事件A的数量为1，并且
• 事件A发生在2019-04-15之前的数目是3.

我可以使用此行获得所需的结果:

I am able to obtain the desired result using this line:

df['Previous_Event_Count'] = [df.loc[(df.loc[i, 'Event'] == df['Event']) & (df.loc[i, 'Date'] > df['Date']), 
                                     'Date'].count() for i in range(len(df))]

虽然速度很慢，但是效果很好.我相信有更好的方法可以做到这一点.我已经尝试过这一行:

Although, it is slow but it works fine. I believe there is a better way to do that. I have tried this line:

df['Previous_Event_Count'] = df.query('Date < Date').groupby(['Event', 'Date']).cumcount()

但是会产生NaNs.

推荐答案

groupby +

groupby + rank

Dates can be treated as numeric. Use'min' to get your counting logic.

df['PEC'] = (df.groupby('Event').Date.rank(method='min')-1).astype(int)

  Event       Date  PEC
0     A 2019-01-01    0
1     B 2019-02-01    0
2     A 2019-03-01    1
3     A 2019-03-01    1
4     B 2019-02-15    1
5     C 2019-03-15    0
6     B 2019-04-05    2
7     B 2019-04-05    2
8     A 2019-04-15    3
9     C 2019-06-10    1

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