Merging List Of DFs With Alternating Columns Output Using Pandas
I have the following codes: import pandas as pd rep1 = pd.DataFrame.from_items([('Probe', ['x', 'y', 'z']), ('Gene', ['foo', 'bar', 'qux']), ('RP1',[1.00,23.22,11.12]),('RP1',['A'
Solution 1:
You could dedupe the column names. Here's a kind of hacky way:
In [11]: list(rep1.columns[0:2]) + [rep1.columns[2] + "_value"] + [rep1.columns[2] + "_letter"]
Out[11]: ['Probe', 'Gene', 'RP1_value', 'RP1_letter']
In [12]: for rep in tmp:
.....: rep.columns = list(rep.columns[0:2]) + [rep.columns[2] + "_value"] + [rep.columns[2] + "_letter"]
In [13]: reduce(pd.merge,tmp)
Out[13]:
Probe Gene RP1_value RP1_letter RP2_value RP2_letter RP3_value RP3_letter
0 x foo 1.00 A 3.33 G 99.99 M
1 y bar 23.22 B 77.22 I 98.29 P
You also need to specify it as an outer merge (to get the NaN rows):
In [21]: reduce(lambda x, y: pd.merge(x, y, how='outer'),tmp)
Out[21]:
Probe Gene RP1_value RP1_letter RP2_value RP2_letter RP3_value RP3_letter
0 x foo 1.00 A 3.33 G 99.99 M
1 y bar 23.22 B 77.22 I 98.29 P
2 z qux 11.12 C 18.12 K NaN NaN
3 k kux NaN NaN NaN NaN 8.10 J
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