有一种方法可以根据其他两列的条件来移动数据框列吗？像这样的东西:

df["cumulated_closed_value"] = df.groupby("user").['close_cumsum'].shiftWhile(df['close_time']>df['open_time])

我已经找到了一种方法来做到这一点，但效率很低:

1)加载数据并创建要移动的列

df=pd.read_csv('data.csv')
df.sort_values(['user','close_time'],inplace=True)
df['close_cumsum']=df.groupby('user')['value'].cumsum()
df.sort_values(['user','open_time'],inplace=True)
print(df)

输出:

   user  open_time close_time  value  close_cumsum
0     1 2017-01-01 2017-03-01      5            18
1     1 2017-01-02 2017-02-01      6             6
2     1 2017-02-03 2017-02-05      7            13
3     1 2017-02-07 2017-04-01      3            21
4     1 2017-09-07 2017-09-11      1            22
5     2 2018-01-01 2018-02-01     15            15
6     2 2018-03-01 2018-04-01      3            18

2) 使用自连接和一些过滤器移动列

自连接(这是内存效率低下的)df2=pd.merge(df[['user','open_time']],df[['user','close_time','close_cumsum']] , on='用户')

过滤 'close_time' <>

df2=df2[df2['close_time']<df2['open_time']]
idx = df2.groupby(['user','open_time'])['close_time'].transform(max) == df2['close_time']
df2=df2[idx]

3)与原始数据集合并:

df3=pd.merge(df[['user','open_time','close_time','value']],df2[['user','open_time','close_cumsum']],how='left')
print(df3)

输出:

   user  open_time close_time  value  close_cumsum
0     1 2017-01-01 2017-03-01      5           NaN
1     1 2017-01-02 2017-02-01      6           NaN
2     1 2017-02-03 2017-02-05      7           6.0
3     1 2017-02-07 2017-04-01      3          13.0
4     1 2017-09-07 2017-09-11      1          21.0
5     2 2018-01-01 2018-02-01     15           NaN
6     2 2018-03-01 2018-04-01      3          15.0

有没有更多的 pandas 方法来获得相同的结果？

编辑:我添加了一根数据线以使案例更加清晰。 我的目标是得到在新交易开启时间之前关闭的所有交易的总和

最佳答案

我在这里使用一个新的para记录条件df2['close_time']<df2['open_time']

df['New']=((df.open_time-df.close_time.shift()).dt.days>0).shift(-1)
s=df.groupby('user').apply(lambda x : (x['value']*x['New']).cumsum().shift()).reset_index(level=0,drop=True)
s.loc[~(df.New.shift()==True)]=np.nan

df['Cumsum']=s




df

Out[1043]: 
   user  open_time close_time  value    New Cumsum
0     1 2017-01-01 2017-03-01      5  False    NaN
1     1 2017-01-02 2017-02-01      6   True    NaN
2     1 2017-02-03 2017-02-05      7   True      6
3     1 2017-02-07 2017-04-01      3  False     13
4     2 2017-01-01 2017-02-01     15   True    NaN
5     2 2017-03-01 2017-04-01      3    NaN     15

更新:因为 op 更新了问题(来自 Gabriel A 的数据)

df['New']=df.user.map(df.groupby('user').close_time.apply(lambda x: np.array(x)))
df['New1']=df.user.map(df.groupby('user').value.apply(lambda x: np.array(x)))
df['New2']=[[x>m for m in y] for x,y in zip(df['open_time'],df['New'])  ]
df['Yourtarget']=list(map(sum,df['New2']*df['New1'].values))
df.drop(['New','New1','New2'],1)


Out[1376]: 
   user  open_time close_time  value  Yourtarget
0     1 2016-12-30 2016-12-31      1           0
1     1 2017-01-01 2017-03-01      5           1
2     1 2017-01-02 2017-02-01      6           1
3     1 2017-02-03 2017-02-05      7           7
4     1 2017-02-07 2017-04-01      3          14
5     1 2017-09-07 2017-09-11      1          22
6     2 2018-01-01 2018-02-01     15           0
7     2 2018-03-01 2018-04-01      3          15

关于python - Pandas :有条件的转变，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48646684/