python - pandas merge dataframes generated inside a loop -


suppose have dataframes (generated inside loop , added list):

column  row data_503    plate 0   1     1   2 1   1   b   2   2 2   1   c   3   2 3   1   d   4   2  column  row data_280    plate 0   1     1   2 1   1   b   2   2 2   1   c   3   2 3   1   d   4   2  column  row data_503    plate 0   1     1   1 1   1   b   2   1 2   1   c   3   1 3   1   d   4   1  column  row data_280    plate 0   1     1   1 1   1   b   2   1 2   1   c   3   1 3   1   d   4   1

i have layout file links maps measurements specific conditions:

column  row cond    plate 0   1     5   1 1   1   b   5   1 2   1   c   5   1 3   1   d   4   1 0   1     5   2 1   1   b   5   2 2   1   c   5   2 3   1   d   4   2

i can combine dataframes like:

for df in df_list:     layout= pd.merge(layout, df, on=['plate', 'row', 'column'], how = 'outer')

however, data_280_x , data_280_y columns obtain data_280 , data_503 columns. changing outer left not change anything.

any ideas how obtain like?:

column  row cond    plate    data_280    data_503 0   1     5   1    1    1 1   1   b   5   1    2    2 2   1   c   5   1    3    3 3   1   d   4   1    4    4 0   1     5   2    1    1 1   1   b   5   2    2    2 2   1   c   5   2    3    3 3   1   d   4   2    4    4

you can combine _x , _y columns since not going have overlapping values (based on layout df), this:

df['data_208'] = df['data_208_x'] + df['data_208_y']

then can drop _x , _y columns.

update example:

df1 = pd.dataframe({"column": [1, 1, 1, 1], "row": ["a", "b", "c", "d"], "plate": [1, 1, 1, 1], "data_503": [4, 5, 6, 7]}) df2 = pd.dataframe({"column": [1, 1, 1, 1], "row": ["a", "b", "c", "d"], "plate": [1, 1, 1, 1], "data_280": [1, 2, 3, 4]}) df3 = pd.dataframe({"column": [1, 1, 1, 1], "row": ["a", "b", "c", "d"], "plate": [2, 2, 2, 2], "data_503": [4, 5, 6, 7]}) df4 = pd.dataframe({"column": [1, 1, 1, 1], "row": ["a", "b", "c", "d"], "plate": [2, 2, 2, 2], "data_280": [1, 2, 3, 4]}) layout = pd.dataframe({"column": [1, 1, 1, 1, 1, 1, 1, 1], "row": ["a", "b", "c", "d", "a", "b", "c", "d"], "cond": [5, 5, 5, 4, 5, 5, 5, 4], "plate": [1, 1, 1, 1, 2, 2, 2, 2]})  out = [] df in [df1, df2, df3, df4]:     _ = pd.merge(layout, df, on=['column', 'row', 'plate'], how='outer').dropna()     out.append(_)  merged = out[0] df in out[1:]:     merged = pd.merge(merged, df, on=['column', 'row', 'plate', 'cond'], how='outer')  merged = merged.fillna(0)  merged['data_280'] = merged['data_280_x'] + merged['data_280_y'] merged['data_503'] = merged['data_503_x'] + merged['data_503_y']  merged = merged.drop(['data_280_x','data_280_y','data_503_x','data_503_y'],1)

gave me:

column  cond  plate row  data_280  data_503 0       1     5      1         1.0       4.0 1       1     5      1   b       2.0       5.0 2       1     5      1   c       3.0       6.0 3       1     4      1   d       4.0       7.0 4       1     5      2         1.0       4.0 5       1     5      2   b       2.0       5.0 6       1     5      2   c       3.0       6.0 7       1     4      2   d       4.0       7.0

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