我有以下 Dataframe ：

# Creating Data_One_df
data_one = {
    "Date": ["12/1/2022"] * 7,
    "Time": ["6:30", "6:35", "6:40", "6:45", "6:50", "6:55", "7:00"],
    "Bars_left": [6, 5, 4, 3, 2, 1, 0],
    "6:30": [""] * 7,
    "6:35": [""] * 7,
    "6:40": [""] * 7,
    "6:45": [""] * 7,
    "6:50": [""] * 7,
    "6:55": [""] * 7,
    "7:00": [""] * 7,
}
Data_One_df = pd.DataFrame(data_one)

# Creating Data_Two_df
data_two = {
    "Time": ["6:30", "6:35", "6:40", "6:45", "6:50", "6:55", "7:00"],
    "Fcst": [1, 2, 1, 1, 2, 1, 1],
}
Data_Two_df = pd.DataFrame(data_two)

我想填充Data_One_df（输入表）的列，方法是将"Time"列的行值与Data_Two_df（查找表）中的"Time"列匹配，并将"Fcst"的累计和的偏移量（向下1行）返回到"Input table"的偏移量（向右1列）。
例如：对于Data_One_df中的行6：30，我想在Data_Two_df（'lookup table'）中找到对应的行（即6：30），然后偏移（即6：35）。接下来，计算从6：35开始的累计和，即8。将结果放入'input table'的列（6：35）中。然后，我想在所有列中重复此值。
以下是预期结果：

data_three = {
    "Date": ["12/1/2022"] * 7,
    "Time": ["6:30", "6:35", "6:40", "6:45", "6:50", "6:55", "7:00"],
    "Bars_left": [6, 5, 4, 3, 2, 1, 0],
    "6:30": ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN"],
    "6:35": [8, "NaN", "NaN", "NaN", "NaN", "NaN", "NaN"],
    "6:40": [8, 6, "NaN", "NaN", "NaN", "NaN", "NaN"],
    "6:45": [8, 6, 5, "NaN", "NaN", "NaN", "NaN"],
    "6:50": [8, 6, 5, 4, "NaN", "NaN", "NaN"],
    "6:55": [8, 6, 5, 4, 2, "NaN", "NaN"],
    "7:00": [8, 6, 5, 4, 2, 1, "NaN"],
}
Desired_df = pd.DataFrame(data_three)

我尝试了许多方法，但都无济于事。最新的代码如下：

# Get a list of time columns
time_cols = Data_One_df.columns[3:]

# Loop through the time columns and update the values
# in Data_One_df based on Data_Two_df
for i, col in enumerate(time_cols):
    # Get the corresponding forecast value from Data_Two_df
    fcst = Data_Two_df["Fcst"][i]
    # Update the values in Data_One_df based on the forecast
    Data_One_df[col] = [np.nan] * fcst + list(Data_One_df[col][fcst:])