How to concatenate the column by column name in pandas?

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Is there any efficient way to concatenate Pandas column name, and don't use loop.

My current method is very slow.

input :

         F1        F2        F3        F4
0  0.653150 -0.877143 -1.640587 -0.571843
1  0.118184  1.499173  0.637869 -0.410608

feature_map ={"F1":["F1"], "F2": ["F2","F3"] , "F4":["F4"]} 
delta_x = pd.DataFrame(np.random.randn(2,4),index=[0,1],columns=["F1", "F2", "F3", "F4"])

Output :

                 F1                                          F2                     F4
0  [0.6531501163310599]  [-0.8771426082487118, -1.6405865645819901]  [-0.5718426901939191]
1  [0.1181836121394836]    [1.4991725444466424, 0.6378685281925491]  [-0.4106075515826911]


result = pd.DataFrame([list(delta_x.loc[:, i].values) for i in feature_map.values()],index=feature_map.keys(), columns=delta_x.index).T
pandas numpy dictionary python
1 Answers

You could rework your dictionary to form groups and use groupby+agg(list):

groups = {k:v for v,l in feature_map.items() for k in l}
# {'F1': 'F1', 'F2': 'F2', 'F3': 'F2', 'F4': 'F4'}

out = delta_x.T.groupby(delta_x.columns.map(groups)).agg(list).T

output:

                       F1                                          F2                    F4
0   [-1.8341169478884958]    [0.543785421630868, 0.29151404233014466]  [0.6325262957339908]
1  [-0.18774374391279974]  [-0.4323328409917436, -0.8389437070428051]  [1.2530256320658806]
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