I&#39;m surely missing something simple here. Trying to merge two dataframes in pandas that have mostly the same column names, but the right dataframe has some columns that the left doesn&#39;t have, and vice versa. 

    &gt;df_may

      id  quantity  attr_1  attr_2
    0  1        20       0       1
    1  2        23       1       1
    2  3        19       1       1
    3  4        19       0       0

    &gt;df_jun

      id  quantity  attr_1  attr_3
    0  5         8       1       0
    1  6        13       0       1
    2  7        20       1       1
    3  8        25       1       1

I&#39;ve tried joining with an outer join:

    mayjundf = pd.DataFrame.merge(df_may, df_jun, how=&quot;outer&quot;)

But that yields:

    Left data columns not unique: Index([....

I&#39;ve also specified a single column to join on (`on = &quot;id&quot;`, e.g.), but that duplicates all columns except `id` like `attr_1_x`, `attr_1_y`, which is not ideal. I&#39;ve also passed the entire list of columns (there are many) to `on`:

    mayjundf = pd.DataFrame.merge(df_may, df_jun, how=&quot;outer&quot;, on=list(df_may.columns.values))

Which yields:

    ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

What am I missing? I&#39;d like to get a df with all rows appended, and `attr_1`, `attr_2`, `attr_3` populated where possible, NaN where they don&#39;t show up. This seems like a pretty typical workflow for data munging, but I&#39;m stuck.

Thanks in advance.


All Data Munging Solutions on Gang of Coders

Total of 1 Data Munging Solutions

Pandas merge two dataframes with different columns