所以我為熊貓中特定時間戳之間的值構建了權重。我希望能夠將這些權重分配給另一個資料框，該資料框的日期時間物件的值可以小到小時。我本質上想按它們在另一個資料幀中的時間幀來加權另一個幀的 2 列（每小時資料）。
weight_df
這是帶有每天權重的資料框
weight_df =
|Datetime |w1 |w2
|-----------------------|-----------|-----------
0 |2021-01-03 05:00:00 |0.961538 |0.038462
1 |2021-01-04 05:00:00 |0.923077 |0.076923
2 |2021-01-05 05:00:00 |0.884615 |0.115385
價格_df
這是我想要將權重映射到的銷售資料。
price_df =
Price_1 Quantity_1 Price_2 Quantity_2
Datetime
2021-01-03 18:00:00 2.630859 3127.0 2.607422 507.0
2021-01-03 19:00:00 2.634766 601.0 2.609375 218.0
2021-01-03 20:00:00 2.628906 1162.0 2.607422 369.0
2021-01-03 21:00:00 2.623047 605.0 2.601562 69.0
2021-01-03 22:00:00 2.628906 306.0 2.605469 50.0
2021-01-03 23:00:00 2.632812 496.0 2.609375 35.0
2021-01-04 00:00:00 2.634766 458.0 2.611328 59.0
2021-01-04 01:00:00 2.638672 673.0 2.613281 128.0
2021-01-04 02:00:00 2.626953 1903.0 2.603516 316.0
2021-01-04 03:00:00 2.619141 1500.0 2.597656 190.0
2021-01-04 04:00:00 2.615234 1075.0 2.593750 231.0
2021-01-04 05:00:00 2.619141 597.0 2.597656 123.0
...
2021-01-05 04:00:00 2.695312 1401.0 2.660156 289.0
2021-01-05 05:00:00 2.689453 1021.0 2.658203 211.0
# Where Datetime is the index of this dataframe.
我希望將 w1 和 w2 列從 weight_df 映射/合并到 price_df 中屬于 weight_df 日期的任何行。
前任。輸出：
Price_1 Quantity_1 Price_2 Quantity_2 w1 w2
Datetime
2021-01-03 18:00:00 2.630859 3127.0 2.607422 507.0 0.961538 0.038462
...
2021-01-04 04:00:00 2.615234 1075.0 2.593750 231.0 0.961538 0.038462
2021-01-04 05:00:00 2.619141 597.0 2.597656 123.0 0.923077 0.076923
...
2021-01-05 04:00:00 2.695312 1401.0 2.660156 289.0 0.923077 0.076923
2021-01-05 05:00:00 2.689453 1021.0 2.658203 211.0 0.884615 0.115385
到目前為止，我已經嘗試使用 .merge 并在當天制作 left_on 、 hour 和 right_on ，但這只是行不通。
uj5u.com熱心網友回復：
使用merge_asof：
df = pd.merge_asof(price_df.reset_index(),weight_df, on='Datetime').set_index('Datetime')
或者：
df = pd.merge_asof(price_df, weight_df, on='Datetime').set_index('Datetime')
或者：
df = pd.merge_asof(price_df, weight_df.set_index('Datetime'),
left_index=True, right_index=True)
print (df)
Price_1 Quantity_1 Price_2 Quantity_2 w1 \
Datetime
2021-01-03 18:00:00 2.630859 3127.0 2.607422 507.0 0.961538
2021-01-03 19:00:00 2.634766 601.0 2.609375 218.0 0.961538
2021-01-03 20:00:00 2.628906 1162.0 2.607422 369.0 0.961538
2021-01-03 21:00:00 2.623047 605.0 2.601562 69.0 0.961538
2021-01-03 22:00:00 2.628906 306.0 2.605469 50.0 0.961538
2021-01-03 23:00:00 2.632812 496.0 2.609375 35.0 0.961538
2021-01-04 00:00:00 2.634766 458.0 2.611328 59.0 0.961538
2021-01-04 01:00:00 2.638672 673.0 2.613281 128.0 0.961538
2021-01-04 02:00:00 2.626953 1903.0 2.603516 316.0 0.961538
2021-01-04 03:00:00 2.619141 1500.0 2.597656 190.0 0.961538
2021-01-04 04:00:00 2.615234 1075.0 2.593750 231.0 0.961538
2021-01-04 05:00:00 2.619141 597.0 2.597656 123.0 0.923077
2021-01-05 04:00:00 2.695312 1401.0 2.660156 289.0 0.923077
2021-01-05 05:00:00 2.689453 1021.0 2.658203 211.0 0.884615
w2
Datetime
2021-01-03 18:00:00 0.038462
2021-01-03 19:00:00 0.038462
2021-01-03 20:00:00 0.038462
2021-01-03 21:00:00 0.038462
2021-01-03 22:00:00 0.038462
2021-01-03 23:00:00 0.038462
2021-01-04 00:00:00 0.038462
2021-01-04 01:00:00 0.038462
2021-01-04 02:00:00 0.038462
2021-01-04 03:00:00 0.038462
2021-01-04 04:00:00 0.038462
2021-01-04 05:00:00 0.076923
2021-01-05 04:00:00 0.076923
2021-01-05 05:00:00 0.115385
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