我很难掌握像 merge_asof() 这样的 Pandas 特殊合并函数。
我有两个数据帧：coords - 来自 EV gps 的 ping，以及 info - 其他 EV 属性，例如导航目的地和电池电量。我的目标是合并它们，使输出数据帧行号等于两个数据帧行数的总和。例如：
coords.shape
(10, 3)
coords
ts lat lng
2021-01-02 16:08:24.067971 58.3019 -134.4197
2021-01-06 12:54:18.535681 58.3021 -134.4195
2021-01-08 22:15:35.036423 58.3025 -134.4195
2021-01-16 01:10:39.610540 58.3029 -134.4193
2021-01-27 12:28:45.202376 58.3030 -134.4197
2021-01-30 05:32:09.404525 58.3031 -134.4190
2021-02-08 10:39:19.686159 58.3033 -134.4187
2021-02-15 01:30:16.733921 58.3039 -134.4187
2021-02-16 12:49:55.366025 58.3040 -134.4185
2021-02-19 23:57:57.369978 58.3041 -134.4181
info.shape
(3, 3)
info
ts nav_to battery
2021-01-26 12:47:52.972586 Juneau 90
2021-02-14 23:23:18.186058 Anchorage 50
2021-02-19 07:26:35.357977 Fairbanks 30
info 和 coord 应该合并，这样时间戳 ts 是连续的，并且 info 行应该与 {{1} 中的行匹配使用“之前”最近的时间戳。最后，coords、nav_to、battery 和 lat 应该向前填充。以上示例的输出将是：
lng我尝试过 output
ts lat lng nav_to battery
2021-01-02 16:08:24.067971 58.3019 -134.4197 None NaN
2021-01-06 12:54:18.535681 58.3021 -134.4195 None NaN
2021-01-08 22:15:35.036423 58.3025 -134.4195 None NaN
2021-01-16 01:10:39.610540 58.3029 -134.4193 None NaN
2021-01-26 12:47:52.972586 58.3029 -134.4193 Juneau 90.0
2021-01-27 12:28:45.202376 58.3030 -134.4197 Juneau 90.0
2021-01-30 05:32:09.404525 58.3031 -134.4190 Juneau 90.0
2021-02-08 10:39:19.686159 58.3033 -134.4187 Juneau 90.0
2021-02-14 23:23:18.186058 58.3033 -134.4187 Anchorage 50.0
2021-02-15 01:30:16.733921 58.3039 -134.4187 Anchorage 50.0
2021-02-16 12:49:55.366025 58.3040 -134.4185 Anchorage 50.0
2021-02-19 07:26:35.357977 58.3040 -134.4185 Fairbanks 30.0
2021-02-19 23:57:57.369978 58.3041 -134.4181 Fairbanks 30.0
但这不会产生正确的结果，它向后填充并且只保留来自 pd.merge_asof(coords, info, on="ts", direction="forward") 的记录。在 coords 中产生所需结果的正确命令是什么？
答案 0 :(得分：2)
尝试使用默认的 direction='backward'，然后使用第二个数据框 concat
(pd.concat([pd.merge_asof(df1, df2, on='ts'), df2])
.sort_values('ts')
)
输出：
ts lat lng nav_to battery
0 2021-01-02 16:08:24.067971 58.3019 -134.4197 NaN NaN
1 2021-01-06 12:54:18.535681 58.3021 -134.4195 NaN NaN
2 2021-01-08 22:15:35.036423 58.3025 -134.4195 NaN NaN
3 2021-01-16 01:10:39.610540 58.3029 -134.4193 NaN NaN
0 2021-01-26 12:47:52.972586 NaN NaN Juneau 90.0
4 2021-01-27 12:28:45.202376 58.3030 -134.4197 Juneau 90.0
5 2021-01-30 05:32:09.404525 58.3031 -134.4190 Juneau 90.0
6 2021-02-08 10:39:19.686159 58.3033 -134.4187 Juneau 90.0
1 2021-02-14 23:23:18.186058 NaN NaN Anchorage 50.0
7 2021-02-15 01:30:16.733921 58.3039 -134.4187 Anchorage 50.0
8 2021-02-16 12:49:55.366025 58.3040 -134.4185 Anchorage 50.0
2 2021-02-19 07:26:35.357977 NaN NaN Fairbanks 30.0
9 2021-02-19 23:57:57.369978 58.3041 -134.4181 Fairbanks 30.0
然后您可以选择bfill lat 和lng 列。或者你可以只merge_asof两次：
(pd.concat([pd.merge_asof(df1, df2, on='ts'),
pd.merge_asof(df2, df1, on='ts')
])
.sort_values('ts')
)
输出：
ts lat lng nav_to battery
0 2021-01-02 16:08:24.067971 58.3019 -134.4197 NaN NaN
1 2021-01-06 12:54:18.535681 58.3021 -134.4195 NaN NaN
2 2021-01-08 22:15:35.036423 58.3025 -134.4195 NaN NaN
3 2021-01-16 01:10:39.610540 58.3029 -134.4193 NaN NaN
0 2021-01-26 12:47:52.972586 58.3029 -134.4193 Juneau 90.0
4 2021-01-27 12:28:45.202376 58.3030 -134.4197 Juneau 90.0
5 2021-01-30 05:32:09.404525 58.3031 -134.4190 Juneau 90.0
6 2021-02-08 10:39:19.686159 58.3033 -134.4187 Juneau 90.0
1 2021-02-14 23:23:18.186058 58.3033 -134.4187 Anchorage 50.0
7 2021-02-15 01:30:16.733921 58.3039 -134.4187 Anchorage 50.0
8 2021-02-16 12:49:55.366025 58.3040 -134.4185 Anchorage 50.0
2 2021-02-19 07:26:35.357977 58.3040 -134.4185 Fairbanks 30.0
9 2021-02-19 23:57:57.369978 58.3041 -134.4181 Fairbanks 30.0