为了满足您的要求，我建议稍微修改您现有的代码。您需要确保一旦发现价格高于当前行的行，就停止考虑以前的行。这可以通过在切片之前按'Price'列以降序对DataFrame进行排序来实现，然后一旦遇到更高的价格就中断循环。
以下是Python中的完整答案：

import pandas as pd

df = {'ID': ['A', 'A', 'A', 'A','A','A','B', 'B', 'B', 'B','B','B'],
        'Date': ['01-05-2023','02-05-2023', '03-05-2023', '04-05-2023', '06-05-2023','07-05-2023','01-05-2023','02-05-2023', '03-05-2023', '04-05-2023', '06-05-2023','07-05-2023'],
      'MT': [2, 5, 10, 15, 20, 30,2, 5, 10, 15, 20, 30],
        'Price': [300, 100.5, 200, 150.35, 250, 90,300, 100.5, 200, 150.35, 250, 90,]}

df = pd.DataFrame(df)

matched_prices = []
matched_dates = []

for i, row in df.iterrows():
    dfa = df.iloc[:i, :] # slice with rows above current
    dfa = dfa[dfa.ID == row.ID] # filter by ID
    dfa = dfa.sort_values(by='Price', ascending=False) # sort by price descending
    temp_prices = []
    temp_dates = []
    for j, rowa in dfa.iterrows():
        if rowa.MT < row.MT and rowa.Price < row.Price:
            temp_prices.append(rowa.Price)
            temp_dates.append(rowa.Date)
        elif rowa.Price > row.Price:
            break
    matched_prices.append(','.join(map(str, temp_prices)))
    matched_dates.append(','.join(map(str, temp_dates)))

df['matched_Price'] = matched_prices
df['matched_dates'] = matched_dates

print(df)

这段代码的工作方式是遍历dataframe中的每一行，过滤当前行之上且具有相同ID的行。对于这些行中的每一行，如果“MT”值小于当前行并且“Price”值小于当前行，则这些值将添加到临时列表中。如果找到大于当前行的“Price”值，则循环中断，并且临时列表被追加到匹配的列表。在处理完所有行之后，将匹配的列表添加到 Dataframe 中。

我通常更喜欢在.apply()函数中执行这些操作，而不是在循环中执行。使用循环并没有错，但我发现它会使pandas world中的事情更难处理。它通常也比使用apply或其他更好的矢量化解决方案慢。
这是我的想法：

import pandas as pd

df = {'ID': ['A', 'A', 'A', 'A','A','A','B', 'B', 'B', 'B','B','B'],
        'Date': ['01-05-2023','02-05-2023', '03-05-2023', '04-05-2023', '06-05-2023','07-05-2023','01-05-2023','02-05-2023', '03-05-2023', '04-05-2023', '06-05-2023','07-05-2023'],
      'MT': [2, 5, 10, 15, 20, 30,2, 5, 10, 15, 20, 30],
        'Price': [300, 100.5, 200, 150.35, 250, 90,300, 100.5, 200, 150.35, 250, 90,]}

df = pd.DataFrame(df)

# Store in a copy so we can manipulate it and maintain the source
mod_df = df.copy()

# Shift gives us a series "pushed" along one entry. Allows comparison with the previous entry
mod_df["shift"] = df["Price"].shift()
# diff is where we will evaluate the dataframe. Where this diff is positive within
# a product ID is the rows you want to focus on.
mod_df["diff"] = mod_df["Price"] - mod_df["shift"]

# Function for doing the heavy lifting
def pull_values_w_price_less_than(row: pd.Series, column: str):
    # The case that the price decreased or stayed the same
    if row["diff"] <= 0:
        return pd.NA
    # Price increased (where we want to eval)
    else:
        # Entries relevant to this row
        relevant_df = mod_df[
            # Product ID is the same
            (mod_df["ID"] == row["ID"])
            # Price is less than this one
            & (mod_df["Price"] < row["Price"])
            # And it happened before this one
            & (mod_df["Date"] < row["Date"])
        ]

        # The matched values (be it Price or Date) determined by the passed arg
        # column placed in a list
        matched_values = relevant_df[column].to_list()

        # If nothing was matched (this happens at ID boundaries where the price
        # went down so we are in this region, but we don't want to return an
        # empty list)
        if len(matched_values) == 0:
            return pd.NA
        # Return the values to put in the row
        return matched_values

# Do this for the prices
mod_df["Matched_Price"] = mod_df.apply(
    pull_values_w_price_less_than, args=("Price",), axis=1
)
# And the dates
mod_df["Date_Values"] = mod_df.apply(
    pull_values_w_price_less_than, args=("Date",), axis=1
)

# Remove our helper columns
output = mod_df.drop(columns=["shift", "diff"])

# View the df (optional ofc)
output

我还将值放入列表而不是逗号分隔的字符串中，但您可以使用此函数轻松转换这些列：

def list_to_comma_string(value_list) -> str:
    # If it was already null we cant change that
    if value_list is pd.NA:
        return pd.NA
    
    # list comprehension to change all the values to strings in the list
    joiner = [str(x) for x in value_list]

    # The most efficient way to combine strings in python
    return ','.join(joiner)

# Convert both columns
output['Matched_Price'] = output['Matched_Price'].apply(list_to_comma_string)
output['Date_Values'] = output['Date_Values'].apply(list_to_comma_string)

# Again view the df
output

希望这有帮助！