矢量化或在大型数据帧上使用多重处理[重复]

我有一个奇怪的数据框架,其中有高达第30天的理论存款量,所以有30个三元组的列。我想把所有的数据收集到一列中,在那里我有所有的日期和存款,无论它们发生在该玩家的第1天、第2天还是第X天。我尝试了以下代码,它给出了正确的输出,但接管了>在大型数据集上运行120分钟:

import pandas as pd

# Sample DataFrame with Day 1 and Day 2 data
data = {
    'Day_date_1': ['2024-01-01', '2024-01-02'],
    'Day_date_1_week_year': ['2024-W01', '2024-W01'],
    'Day1_deposit': [100, 200],
    'Day_date_2': ['2024-01-02', '2024-01-03'],
    'Day_date_2_week_year': ['2024-W01', '2024-W01'],
    'Day2_deposit': [150, 250],
    'PLAYER_ID': [1, 2],
}


date_columns = [f'Day_date_{i}' for i in range(1, 3)]  # Includes Day 1 and Day 2
deposit_columns = [f'Day{i}_deposit' for i in range(1, 3)]  # Includes Day 1 and Day 2
deposit_week_columns = [f'Day_date_{i}_week_year' for i in range(1, 3)]  # Includes Day 1 and Day 2

# Empty DataFrame to store results
result_df = pd.DataFrame()

# Processing the DataFrame
for date_col, week_col, value_col in zip(date_columns, deposit_week_columns, deposit_columns):
    temp_df = df[[date_col, week_col, value_col, "PLAYER_ID"]]
    temp_df.columns = ["deposit_date", 'deposit_week', "deposit_amount", "PLAYER_ID"]
    result_df = result_df.append(temp_df, ignore_index=True)

result_df['deposit_date'] = pd.to_datetime(result_df['deposit_date'])
result_deposit = result_df.groupby(['PLAYER_ID', 'deposit_date', 'deposit_week'])['deposit_amount'].mean().reset_index().rename(columns={'deposit_amount': 'mean_deposit_amount'})

# Output the result
print(result_deposit)

有没有办法将循环矢量化或通过多处理加快处理速度?

我想要的输出如下:

PLAYER_ID   deposit_date    deposit_week    mean_deposit_amount
1              2024-01-01     2024-W01            100.0
1              2024-01-02     2024-W01            150.0
2              2024-01-02     2024-W01            200.0
2              2024-01-03     2024-W01            250.0