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I am trying to merge two dataframe that are multi-index, while preserving the highest level index. The problem is merging on axis=1 results in the below two columns. Merging/joining on axis=0 drops any value in the 0_y column that has the same sub-index as an entry in )_x. An example below is (226,0), where the value 1510123295301 gets dropped if I merge/join on axis=0.

Is there any way to merge two multi-index dataframes into a single column, preserving the primary index (e.g. 226), but expanding to include non-duplicates in the right-hand column (e.g. 226(0-6))?

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  • Are you looking for df2.combine_first(df)? Commented Sep 25, 2017 at 18:28
  • I tried that, but it drops anything in the second dataframe with the same index as the first. For example, the (226,0) 1510123295301 is dropped. Commented Sep 25, 2017 at 18:37
  • Then would you do df.combine_first(df2)? You could just reverse the dataframes. Commented Sep 25, 2017 at 18:39
  • Then it drops 014113734066. If an entry in both columns has both a primary and secondary index in common, one of the entries will get dropped. I need the subindex to expand to merge entries from both columns. Commented Sep 25, 2017 at 18:45
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