pandas 如果groupby后满足条件，则为先前值的累积和

我有不平衡的面板。基本上，有人会在完全减少流动性之前增加流动性。我有组（NF_TOKEN_ID）。我想先groupby，每当ACTION是DECREASE_LIQUIDITY时，我想用所有已提取的资金替换零值。该量对应于先前添加的量。因此，如果ACTION的值是INCREASE_LIQUIDITY，则必须对以前的值进行cumsum。cumsum只适用于之前添加的资金，而不是追溯。
我现在的DF

BLOCK_TIMESTAMP NF_TOKEN_ID ACTION  LIQUIDITY   AMOUNT0_ADJUSTED    AMOUNT1_ADJUSTED
0   2023-01-19 11:43:23+00:00   417467.0    INCREASE_LIQUIDITY  2.0002500037479372e+16  0.0  999999.999999
1   2023-01-21 10:08:35+00:00   417467.0    DECREASE_LIQUIDITY  2.0002500037479372e+16  0.0 0.0
2   2023-01-23 17:43:23+00:00   417467.0    INCREASE_LIQUIDITY  1.9999500037496876e+16  1000000.0   0.0
3   2023-01-28 21:42:47+00:00   417467.0    DECREASE_LIQUIDITY  1.9999500037496876e+16  0.0 0.0
4   2023-01-31 09:20:11+00:00   417467.0    INCREASE_LIQUIDITY  2.001358136187257e+16   0.0 1000553.996968
5   2023-02-05 14:19:11+00:00   417467.0    DECREASE_LIQUIDITY  2.001358136187257e+16   0.0 0.0
6   2023-02-06 16:00:59+00:00   417467.0    INCREASE_LIQUIDITY  3.9510177985927736e+16  900000.0    1075372.476351
7   2023-02-11 16:21:47+00:00   417467.0    DECREASE_LIQUIDITY  3.9510177985927736e+16  0.0 0.0
8   2023-02-11 18:17:47+00:00   417467.0    INCREASE_LIQUIDITY  3.999900007499375e+16   2000000.0   0.0
9   2023-02-13 08:42:47+00:00   417467.0    DECREASE_LIQUIDITY  3.999900007499375e+16   0.0 0.0
10  2023-02-16 23:39:11+00:00   417467.0    INCREASE_LIQUIDITY  6.000384593243181e+16   3000267.297679  0.0
11  2023-02-18 13:02:47+00:00   417467.0    INCREASE_LIQUIDITY  2.000210525110979e+16   1000130.263937  0.0
64  2023-01-19 11:52:47+00:00   417520.0    INCREASE_LIQUIDITY  1.5233876511464717e+21  2360900.644245  17981.537918728
65  2023-01-19 11:52:47+00:00   417520.0    DECREASE_LIQUIDITY  1.5233876511464717e+21  0.0 0.0
66  2023-01-19 11:52:59+00:00   417521.0    INCREASE_LIQUIDITY  1e+19   0.05981737761   0.05981737761
81  2023-01-19 11:54:35+00:00   417537.0    INCREASE_LIQUIDITY  17130998133876.0    49.99712    0.02400335355
82  2023-01-23 07:29:23+00:00   417537.0    INCREASE_LIQUIDITY  28028281686564.0    121.373999  0.01412890286
83  2023-01-23 17:34:35+00:00   417537.0    INCREASE_LIQUIDITY  9508091561328.0 39.513265   0.00581565507
84  2023-01-25 00:55:47+00:00   417537.0    DECREASE_LIQUIDITY  54667371381768.0    0.0 0.0

所需df

BLOCK_TIMESTAMP NF_TOKEN_ID ACTION  LIQUIDITY   AMOUNT0_ADJUSTED    AMOUNT1_ADJUSTED
0   2023-01-19 11:43:23+00:00   417467.0    INCREASE_LIQUIDITY  2.0002500037479372e+16  0.0  999999.999999
1   2023-01-21 10:08:35+00:00   417467.0    DECREASE_LIQUIDITY  2.0002500037479372e+16  0.0  999999.999999
2   2023-01-23 17:43:23+00:00   417467.0    INCREASE_LIQUIDITY  1.9999500037496876e+16  1000000.0   0.0
3   2023-01-28 21:42:47+00:00   417467.0    DECREASE_LIQUIDITY  1.9999500037496876e+16  1000000.0   0.0
4   2023-01-31 09:20:11+00:00   417467.0    INCREASE_LIQUIDITY  2.001358136187257e+16   0.0 1000553.996968
5   2023-02-05 14:19:11+00:00   417467.0    DECREASE_LIQUIDITY  2.001358136187257e+16   0.0 1000553.996968
6   2023-02-06 16:00:59+00:00   417467.0    INCREASE_LIQUIDITY  3.9510177985927736e+16  900000.0    1075372.476351
7   2023-02-11 16:21:47+00:00   417467.0    DECREASE_LIQUIDITY  3.9510177985927736e+16  900000.0    1075372.476351
8   2023-02-11 18:17:47+00:00   417467.0    INCREASE_LIQUIDITY  3.999900007499375e+16   2000000.0   0.0
9   2023-02-13 08:42:47+00:00   417467.0    DECREASE_LIQUIDITY  3.999900007499375e+16   2000000.0   0.0
10  2023-02-16 23:39:11+00:00   417467.0    INCREASE_LIQUIDITY  6.000384593243181e+16   3000267.297679  0.0
11  2023-02-18 13:02:47+00:00   417467.0    INCREASE_LIQUIDITY  2.000210525110979e+16   1000130.263937  0.0
64  2023-01-19 11:52:47+00:00   417520.0    INCREASE_LIQUIDITY  1.5233876511464717e+21  2360900.644245  17981.537918728
65  2023-01-19 11:52:47+00:00   417520.0    DECREASE_LIQUIDITY  1.5233876511464717e+21  2360900.644245  17981.537918728
66  2023-01-19 11:52:59+00:00   417521.0    INCREASE_LIQUIDITY  1e+19   0.05981737761   0.05981737761
81  2023-01-19 11:54:35+00:00   417537.0    INCREASE_LIQUIDITY  17130998133876.0    49.99712    0.02400335355
82  2023-01-23 07:29:23+00:00   417537.0    INCREASE_LIQUIDITY  28028281686564.0    121.373999  0.01412890286
83  2023-01-23 17:34:35+00:00   417537.0    INCREASE_LIQUIDITY  9508091561328.0     39.513265   0.00581565507
84  2023-01-25 00:55:47+00:00   417537.0    DECREASE_LIQUIDITY  54667371381768.0    210.884384  0.04394791148

IIUC，您可以用途：

cols = ['AMOUNT0_ADJUSTED', 'AMOUNT1_ADJUSTED']
decl = df['ACTION'] == 'DECREASE_LIQUIDITY'

df.loc[decl, cols] = -(df.groupby(['NF_TOKEN_ID', decl.shift(fill_value=0).cumsum()])
                       [cols].cumsum()[decl])

输出：

>>> df[['NF_TOKEN_ID', 'ACTION'] + cols]

    NF_TOKEN_ID              ACTION  AMOUNT0_ADJUSTED  AMOUNT1_ADJUSTED
0      417467.0  INCREASE_LIQUIDITY      0.000000e+00      1.000000e+06
1      417467.0  DECREASE_LIQUIDITY     -0.000000e+00     -1.000000e+06
2      417467.0  INCREASE_LIQUIDITY      1.000000e+06      0.000000e+00
3      417467.0  DECREASE_LIQUIDITY     -1.000000e+06     -0.000000e+00
4      417467.0  INCREASE_LIQUIDITY      0.000000e+00      1.000554e+06
5      417467.0  DECREASE_LIQUIDITY     -0.000000e+00     -1.000554e+06
6      417467.0  INCREASE_LIQUIDITY      9.000000e+05      1.075372e+06
7      417467.0  DECREASE_LIQUIDITY     -9.000000e+05     -1.075372e+06
8      417467.0  INCREASE_LIQUIDITY      2.000000e+06      0.000000e+00
9      417467.0  DECREASE_LIQUIDITY     -2.000000e+06     -0.000000e+00
10     417467.0  INCREASE_LIQUIDITY      3.000267e+06      0.000000e+00
11     417467.0  INCREASE_LIQUIDITY      1.000130e+06      0.000000e+00
64     417520.0  INCREASE_LIQUIDITY      2.360901e+06      1.798154e+04
65     417520.0  DECREASE_LIQUIDITY     -2.360901e+06     -1.798154e+04
66     417521.0  INCREASE_LIQUIDITY      5.981738e-02      5.981738e-02
81     417537.0  INCREASE_LIQUIDITY      4.999712e+01      2.400335e-02
82     417537.0  INCREASE_LIQUIDITY      1.213740e+02      1.412890e-02
83     417537.0  INCREASE_LIQUIDITY      3.951326e+01      5.815655e-03
84     417537.0  DECREASE_LIQUIDITY     -2.108844e+02     -4.394791e-02

编辑

当ACTION为DECREASE_LIQUIDITY时，如何将值设置为负值？

pandas 如果groupby后满足条件，则为先前值的累积和

1条答案

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