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I'm trying to merge two series with mismatching indicies into one, and I'm wondering what best practices are.
I tried combine_first but I'm getting an issue where combining a series [0, 24, ...] with a series [1, 25, ...] should give a series with indicies [0, 1, 24, 25, ...] but instead I'm getting [0, 12, 24, 36, ...]
for row in all_rows:
base_col = base_col.combine_first(row)
I just want two series with mutually exclusive indicies to be combined into a single series that contain both indicies in the correct sorted order. Kind of like a zipper.
216
Combine Two Series Using pandas. merge() can be used for all database join operations between DataFrame or named series objects. You have to pass an extra parameter “name” to the series in this case. For instance, pd. merge(S1, S2, right_index=True, left_index=True) .
Merging Dataframes by index of both the dataframes As both the dataframe contains similar IDs on the index. So, to merge the dataframe on indices pass the left_index & right_index arguments as True i.e. Both the dataframes are merged on index using default Inner Join.
You can use pd.concat followed by sort_index:
s1 = pd.Series([1, 2, 3, 4], index=[0, 24, 30, 40])
s2 = pd.Series([5, 6, 7, 8], index=[1, 25, 35, 38])
s = pd.concat([s1, s2]).sort_index()
print(s)
0 1
1 5
24 2
25 6
30 3
35 7
38 8
40 4
dtype: int64
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