Início / coding / Perguntas / 76984560
Accepted
Fresto
Asked: 2023-08-27 03:39:42 +0800 CST

Pandas Dataframe adiciona colunas com base no valor de data

  • 772

eu tenho dfassim

        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

Então usei a reamostragem dfpara 60 min -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

Como inserir uma nova coluna df_resample['High'] - df_resample['Low']no ? Com base em que hora o valor édf_resampledfDate

python

1 respostas

  1. Best Answer

    IIUC, você pode criar colunas temporárias e depois mesclar:

    df1["date"] = df1.Date.dt.date
df1["hour"] = df1.Date.dt.hour

df2["date"] = df2.index.date
df2["hour"] = df2.index.hour

df2["Value"] = df2["High"] - df2["Low"]

df1 = df1.merge(df2[["date", "hour", "Value"]], on=["date", "hour"]).drop(
    columns=["date", "hour"]
)
print(df1)

    Impressões:

           Open      High       Low     Close     Volume                Date   Value
0  25940.99  25972.27  25934.36  25938.65  176.08278 2023-08-23 02:20:00  166.31
1  25938.65  25938.65  25903.73  25921.67   67.24124 2023-08-23 02:25:00  166.31
2  25921.66  25963.83  25904.68  25951.51   83.37560 2023-08-23 02:30:00  166.31
3  25951.51  26011.07  25950.00  25998.00  119.22832 2023-08-23 02:35:00  166.31
4  25997.99  26050.50  25997.99  26015.14  242.94235 2023-08-23 02:40:00  166.31
    • 1

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