我正在處理一些金融報價資料。給定兩個這樣的示例資料框：
left_df =
Time Bid Price Ask Price
2022-01-02 00:00:01.323597 100 101
2022-01-02 00:00:01.828502 100 101
2022-01-02 00:00:01.845020 100 101
2022-01-02 00:00:03.123567 100 101
right_df =
Time Bid Price Ask Price
2022-01-02 00:00:01.110223 500 501
2022-01-02 00:00:01.800000 500 501
2022-01-02 00:00:03.100000 500 501
如果我從左到右“合并”，我希望合并的資料框看起來像這樣：
Time_left Time_right Bid Price_left Ask Price_left Bid Price_right Ask Price_right
2022-01-02 00:00:01.323597 2022-01-02 00:00:01.110223 100 101 500 501
2022-01-02 00:00:01 828502 2022-01-02 00:00:01.800000 100 101 500 501
2022-01-02 00:00:01.845020 2022-01-02 00:00:01.800000 100 101 500 501
2022-01-02 00:00:03.123567 2022-01-02 00:00:03.100000 100 101 500 501
即對于每一個time_left x，獲取最近的time_right y到x，y可以等于x。
而如果我想從右到左“合并”，則生成的資料框應如下所示：
Time_right Time_left Bid Price_right Ask Price_right Bid Price_left Ask Price_left
2022-01-02 00:00:01.800000 2022-01-02 00:00:01.323597 500 501 100 101
2022-01-02 00:00:03.100000 2022-01-02 00:00:01.845020 500 501 100 101
在可能有數千萬行的資料集上執行此操作的最有效方法是什么？
uj5u.com熱心網友回復：
試試這個
# convert to datetime
left_df['Time'] = pd.to_datetime(left_df['Time'])
right_df['Time'] = pd.to_datetime(right_df['Time'])
# insert time_right column
right_df.insert(1, 'Time_right', right_df['Time'])
# merge_asof
df = pd.merge_asof(left_df, right_df, on='Time', suffixes=('_left','_right'))
print(df)
Time Bid_Price_left Ask_Price_left Time_right Bid_Price_right Ask_Price_right
0 2022-01-02 00:00:01.323597 100 101 2022-01-02 00:00:01.110223 500 501
1 2022-01-02 00:00:01.828502 100 101 2022-01-02 00:00:01.800000 500 501
2 2022-01-02 00:00:01.845020 100 101 2022-01-02 00:00:01.800000 500 501
3 2022-01-02 00:00:03.123567 100 101 2022-01-02 00:00:03.100000 500 501
來源
轉載請註明出處，本文鏈接：https://www.uj5u.com/net/479574.html
上一篇：如何正確地將年月轉換為年季