Pivot 特定行成列

Question

I have a pandas dataframe with the following structure

Layer   distance    angle        X_UTM_0        Y_UTM_0
layer0  0           65.57315016  627792.796  4785636.433
layer0  66.77813701 44.90815387  627852.132  4785666.409
layer1  0           236.3706271  627854.177  4785669.487
layer1  71.46111277 244.9836507  627791.986  4785636.986
layer2  0           60.78662778  627791.393  4785637.658
layer2  70.2562004  57.98453037  627853.635  4785669.768

What I want to do is pivot the repeated layer0...n to columns to obtain something like

Layer   distance    angle       X_UTM_0     Y_UTM_0     X_UTM_1     Y_UTM_1
layer0  0       65.57315016 627792.796  4785636.433 627852.132  4785666.409     
layer1  0       236.3706271 627854.177  4785669.487 627791.986  4785636.986 
layer2  0       60.78662778 627791.393  4785637.658 627853.635  4785669.768

where i have both coordinates of each layer in the same row. I was thinking in using pivot_table, but I'm not sure how to approach this. Any hint would be very appreciated.

EDIT: To clarify, I do not need distance and angle columns, I can drop them. The idea is to convert to columns each field of repeated Layer row.

Answer 1

Assuming that the splitting condition is always in the 'distance' column, the following actions can be taken. If not, this response is invalid.

df1 = df[df['distance'] == 0.0]
df2 = df[~(df['distance'] == 0.0)]
df3 = df1.merge(df2[['Layer','X_UTM_0','Y_UTM_0']], on='Layer', how='outer')
df3.columns = ['Layer', 'distance', 'angle', 'X_UTM_0', 'Y_UTM_0', 'X_UTM_1', 'Y_UTM_1']

df3
    Layer   distance    angle   X_UTM_0 Y_UTM_0 X_UTM_1 Y_UTM_1
0   layer0  0.0 65.573150   627792.796  4785636.433 627852.132  4785666.409
1   layer1  0.0 236.370627  627854.177  4785669.487 627791.986  4785636.986
2   layer2  0.0 60.786628   627791.393  4785637.658 627853.635  4785669.768