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Martin Hope
Fresto
Asked: 2024-04-11 19:28:11 +0800 CST
  • 6

我有这个数据框

import pandas as pd
import math
from pandas import Timestamp

Date = [Timestamp('2024-03-16 23:59:42'), Timestamp('2024-03-16 23:59:42'), Timestamp('2024-03-16 23:59:44'), Timestamp('2024-03-16 23:59:44'), Timestamp('2024-03-16 23:59:44'), Timestamp('2024-03-16 23:59:47'), Timestamp('2024-03-16 23:59:48'), Timestamp('2024-03-16 23:59:48'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49'), Timestamp('2024-03-16 23:59:49')]
Price = [0.6729, 0.6728, 0.6728, 0.6728, 0.6728, 0.673, 0.6728, 0.6729, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6728, 0.6729, 0.6728]
Side = [-1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, -1]
Amount = [1579.2963000000002, 7.400799999999999, 6.728, 177.61919999999998, 797.2679999999999, 33650.0, 131.196, 48.448800000000006, 0.6728, 0.6728, 0.6728, 6.728, 0.6728, 1.3456, 0.6728, 0.6728, 0.6728, 0.6728, 0.6729, 0.6728]
buy = [math.nan, math.nan, math.nan, 177.61919999999998, math.nan, 33650.0, math.nan, 48.448800000000006, math.nan, math.nan, math.nan, math.nan, math.nan, math.nan, math.nan, math.nan, math.nan, math.nan, 49.121700000000004, math.nan]

df = pd.DataFrame({
    'Date':Date,
    'Price':Price,
    'Side':Side,
    'Amount':Amount,
    'buy':buy
})

print(df)

我得到了buy专栏使用

df['buy'] = df[df['Side'] == 1].groupby([df['Date'].dt.floor('H'), 'Price'])['Amount'].cumsum()

但我想在buy列中获取 0 而不是 nan 值，如果组中尚未满足此价格或累积总和的先前值

结果buy列需要 - [0,0,0,177.6192,177.6192,33650, 177.6192,48.4488, 177.6192,.....]

我怎样才能实现这个？

python
Martin Hope
Fresto
Asked: 2023-10-08 04:45:30 +0800 CST
  • 9

我有一个DataFrame这样的：

index B
0     1
1     2
2     5
3     6
4     7
5     10

我需要合并差值小于或等于 2 的行，选择值较小的行并设置计数合并

结果应该是这样的：

index B count
0     1   2
1     5   3
2     10  1

如何使用 pandas 解决这个问题？

python
Martin Hope
Fresto
Asked: 2023-08-27 03:39:42 +0800 CST
  • 6

我有df这样的

        Open      High       Low     Close     Volume                Date
3     25940.99  25972.27  25934.36  25938.65  176.08278 2023-08-23 02:20:00
4     25938.65  25938.65  25903.73  25921.67   67.24124 2023-08-23 02:25:00
5     25921.66  25963.83  25904.68  25951.51   83.37560 2023-08-23 02:30:00
6     25951.51  26011.07  25950.00  25998.00  119.22832 2023-08-23 02:35:00
7     25997.99  26050.50  25997.99  26015.14  242.94235 2023-08-23 02:40:00

然后我使用重新采样df到 60 分钟 -df_resample

                         Open      High       Low     Close      Volume
Date
2023-08-23 02:00:00  25940.99  26070.04  25903.73  26056.00  1055.06782
2023-08-23 03:00:00  26055.99  26187.99  26030.56  26040.38  2447.77226
2023-08-23 04:00:00  26040.38  26081.64  25994.12  26049.41   728.81260
2023-08-23 05:00:00  26049.41  26091.75  26033.00  26044.42   795.45411
2023-08-23 06:00:00  26044.41  26078.01  25990.15  26006.32   764.41941

如何插入新列df_resample['High'] - df_resample['Low']fromdf_resample到df？基于该Date值的小时数

python