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copied!<p>I'm a bit curious why you'd like it in the format. There's probably a much better way to store your data. But here goes.</p> <pre><code>In [137]: import numpy as np In [138]: import pandas as pd In [139]: df_reshape_test = pd.DataFrame( {'location' : ['A', 'A', 'A', 'B', 'B ', 'B'], 'dist_to_A' : [0, 0, 0, 50, 50, 50], 'dist_to_B' : [50, 50, 50, 0, 0, 0], 'location_var': [10, 10, 10, 14, 14, 14], 'ind_var': [3, 8, 10, 1, 3, 4]}) In [140]: print(df_reshape_test) dist_to_A dist_to_B ind_var location location_var 0 0 50 3 A 10 1 0 50 8 A 10 2 0 50 10 A 10 3 50 0 1 B 14 4 50 0 3 B 14 5 50 0 4 B 14 In [141]: # Get the new axis separately: In [142]: idx = pd.Index(df_reshape_test.index.tolist() * 2) In [143]: df2 = df_reshape_test[['ind_var', 'location', 'location_var']].reindex(idx) In [144]: print(df2) ind_var location location_var 0 3 A 10 1 8 A 10 2 10 A 10 3 1 B 14 4 3 B 14 5 4 B 14 0 3 A 10 1 8 A 10 2 10 A 10 3 1 B 14 4 3 B 14 5 4 B 14 In [145]: # Swap the location for the second half In [146]: # replace any 6 with len(df) / 2 + 1 if you have more rows.d In [147]: df2['choice'] = [1] * 6 + [0] * 6 # may need to play with this. In [148]: df2.iloc[6:].location.replace({'A': 'B', 'B': 'A'}, inplace=True) In [149]: df2 = df2.sort() In [150]: df2['dist_S'] = np.abs((df2.choice - 1) * 50) In [151]: print(df2) ind_var location location_var choice dist_S 0 3 A 10 1 0 0 3 B 10 0 50 1 8 A 10 1 0 1 8 B 10 0 50 2 10 A 10 1 0 2 10 B 10 0 50 3 1 B 14 1 0 3 1 A 14 0 50 4 3 B 14 1 0 4 3 A 14 0 50 5 4 B 14 1 0 5 4 A 14 0 50 </code></pre> <p>It's not going to generalize well, but there are probably alternative (better) ways to get around the uglier parts like generating the choice col.</p>

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