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为什么叫“ABA_problem”？(Why called “ABA_problem”?)

 今天我知道ABA问题。 
 

 http://en.wikipedia.org/wiki/ABA_problem 
 

 顺便说一下，我突然想知道为什么叫“ABA”问题？ 缩写？ 

today I knew ABA problem.
 

http://en.wikipedia.org/wiki/ABA_problem
 

By the way, suddenly, i just like to know why called "ABA" problem? abbreviation?

原文：https://stackoverflow.com/questions/20836319
更新时间：2023-06-23 14:06

最满意答案

 IIUC：您可以尝试使用.groupby项目编号，然后在dataframe .shift使用.groupby ，如下所示。 
 

 假设原始数据框如下所示： 
 

   DIV_NBR  itm_nbr  WEEK_NO  DISTINCT_ITM_CNT  INVENTORY_IN_HAND
0       18    65874   201511               5.0             2925.0
1       18    65874   201512               5.0             2910.0
2       18    65874   201513               5.0             2961.0
3       19    65875   201511               5.0             2965.0
4       19    65875   201512               5.0             2971.0

 

 然后： 
 

# keep record of last week by grouping by item number and then using shift
df['LAST_WEEK'] = df.groupby('itm_nbr')['INVENTORY_IN_HAND'].shift()

# check if current inventory is greater than last week
df['Flag'] = (df['INVENTORY_IN_HAND'] - df['LAST_WEEK'])>0

# delete additional column
del df['LAST_WEEK']

# change flag int
df['Flag'] = df['Flag'].astype(int)

print(df)

 

 结果： 
 

   DIV_NBR  itm_nbr  WEEK_NO  DISTINCT_ITM_CNT  INVENTORY_IN_HAND  Flag
0       18    65874   201511               5.0             2925.0     0
1       18    65874   201512               5.0             2910.0     0
2       18    65874   201513               5.0             2961.0     1
3       19    65875   201511               5.0             2965.0     0
4       19    65875   201512               5.0             2971.0     1


IIUC: You can try using .groupby item number followed by .shift in dataframe as following. 
 

Suppose the original dataframe is as below:
 

   DIV_NBR  itm_nbr  WEEK_NO  DISTINCT_ITM_CNT  INVENTORY_IN_HAND
0       18    65874   201511               5.0             2925.0
1       18    65874   201512               5.0             2910.0
2       18    65874   201513               5.0             2961.0
3       19    65875   201511               5.0             2965.0
4       19    65875   201512               5.0             2971.0

 

Then:
 

# keep record of last week by grouping by item number and then using shift
df['LAST_WEEK'] = df.groupby('itm_nbr')['INVENTORY_IN_HAND'].shift()

# check if current inventory is greater than last week
df['Flag'] = (df['INVENTORY_IN_HAND'] - df['LAST_WEEK'])>0

# delete additional column
del df['LAST_WEEK']

# change flag int
df['Flag'] = df['Flag'].astype(int)

print(df)

 

Result:
 

   DIV_NBR  itm_nbr  WEEK_NO  DISTINCT_ITM_CNT  INVENTORY_IN_HAND  Flag
0       18    65874   201511               5.0             2925.0     0
1       18    65874   201512               5.0             2910.0     0
2       18    65874   201513               5.0             2961.0     1
3       19    65875   201511               5.0             2965.0     0
4       19    65875   201512               5.0             2971.0     1

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