pandas long to wide multicolumn reshaping

问题

I have a pandas data frame as follows:

 request_id     crash_id           counter  num_acc_x  num_acc_y  num_acc_z
    745109.0    670140638.0        0      0.010      0.000     -0.045
    745109.0    670140638.0        1      0.016     -0.006     -0.034
    745109.0    670140638.0        2      0.016     -0.006     -0.034

my id vars are : "request_id" and "crash_id", the target vars are nu_acc_x, num_acc_y and num_acc_z I would like to create a new DataFrame where target vars are wide reshaped, that is adding max(counter)*3 new vars like num_acc_x_0, num_acc_x_1, ... num_acc_y_0,num_acc_y_1,... num_acc_z_0, num_acc_z_1 possibly without having a pivot as final result (I would like a true DataFrame as in R).

Thanks in advance for the attention

回答1:

I think you need set_index with unstack, last create columns names from MultiIndex by map:

df = df.set_index(['request_id','crash_id','counter']).unstack()
df.columns = df.columns.map(lambda x: '{}_{}'.format(x[0], x[1]))
df = df.reset_index()
print (df)
   request_id     crash_id  num_acc_x_0  num_acc_x_1  num_acc_x_2  \
0    745109.0  670140638.0         0.01        0.016        0.016   

   num_acc_y_0  num_acc_y_1  num_acc_y_2  num_acc_z_0  num_acc_z_1  \
0          0.0       -0.006       -0.006       -0.045       -0.034   

   num_acc_z_2  
0       -0.034  

Another solution with aggreagting duplicates with pivot_table:

df = df.pivot_table(index=['request_id','crash_id'], columns='counter', aggfunc='mean')
df.columns = df.columns.map(lambda x: '{}_{}'.format(x[0], x[1]))
df = df.reset_index()
print (df)
   request_id     crash_id  num_acc_x_0  num_acc_x_1  num_acc_x_2  \
0    745109.0  670140638.0         0.01        0.016        0.016   

   num_acc_y_0  num_acc_y_1  num_acc_y_2  num_acc_z_0  num_acc_z_1  \
0          0.0       -0.006       -0.006       -0.045       -0.034   

   num_acc_z_2  
0       -0.034  

---

df = df.groupby(['request_id','crash_id','counter']).mean().unstack()
df.columns = df.columns.map(lambda x: '{}_{}'.format(x[0], x[1]))
df = df.reset_index()
print (df)
   request_id     crash_id  num_acc_x_0  num_acc_x_1  num_acc_x_2  \
0    745109.0  670140638.0         0.01        0.016        0.016   

   num_acc_y_0  num_acc_y_1  num_acc_y_2  num_acc_z_0  num_acc_z_1  \
0          0.0       -0.006       -0.006       -0.045       -0.034   

   num_acc_z_2  
0       -0.034  

来源：https://stackoverflow.com/questions/43537317/pandas-long-to-wide-multicolumn-reshaping