Appending Dataframe in for loop is not working

I’m getting crazy with that. I don’t know why is not working well.

My loop is the following:

for f in tqdm.tqdm(frames[::10]):
   
   col.set_state_from_frame(f)
   
   vertices = generate_dataframe(col)
   print(f)
   print(vertices)
   v_df.append(vertices)
   
v_df = pd.concat(v_df,keys=frames[::10])

In the first print I obtain:

0
                x            y  coordination  charge   dx   dy
0      -28.384776   -56.769553             3      -1  1.0 -2.0
1     1107.006276  1021.851947             3      -1 -1.0 -2.0
2     1050.236724  1050.236724             2       0 -1.0  1.0
3      965.082395   709.619408             3      -3  0.0  1.0
4      823.158513  1021.851947             3       3 -1.0  0.0
...           ...          ...           ...     ...  ...  ...
1232  1107.006276   709.619408             2       0 -1.0  1.0
1233   652.849855   -85.154329             2       0  1.0  1.0
1234   -28.384776   936.697618             2      -2  1.0  1.0
1235   -28.384776   823.158513             2       2 -1.0 -1.0
1236   -28.384776   766.388960             2       0 -1.0 -1.0

[1237 rows x 6 columns]
10
                x            y  coordination  charge   dx   dy
0      -28.384776   -56.769553             3      -1  1.0 -2.0
1     1107.006276  1021.851947             3      -1 -1.0 -2.0
2     1050.236724  1050.236724             2       0 -1.0  1.0
3      965.082395   709.619408             3      -3  0.0  1.0
4      823.158513  1021.851947             3       3 -1.0  0.0
...           ...          ...           ...     ...  ...  ...
1232  1107.006276   709.619408             2       0 -1.0  1.0
1233   652.849855   -85.154329             2       0  1.0  1.0
1234   -28.384776   936.697618             2      -2  1.0  1.0
1235   -28.384776   823.158513             2       2 -1.0 -1.0
1236   -28.384776   766.388960             2       0 -1.0 -1.0

[1237 rows x 6 columns]
20
                x            y  coordination  charge   dx   dy
0      -28.384776   -56.769553             3      -1  1.0 -2.0
1     1107.006276  1021.851947             3      -1 -1.0 -2.0
2     1050.236724  1050.236724             2       0 -1.0  1.0
3      965.082395   709.619408             3      -3  0.0  1.0
4      823.158513  1021.851947             3       3 -1.0  0.0
...           ...          ...           ...     ...  ...  ...
1232  1107.006276   709.619408             2       0 -1.0  1.0
1233   652.849855   -85.154329             2       0  1.0  1.0
1234   -28.384776   936.697618             2       0 -1.0  1.0
1235   -28.384776   823.158513             2       2 -1.0 -1.0
1236   -28.384776   766.388960             2       0 -1.0 -1.0

[1237 rows x 6 columns]
30
                x            y  coordination  charge   dx   dy
0      -28.384776   -56.769553             3      -1  1.0 -2.0
1     1107.006276  1021.851947             3      -1 -1.0 -2.0
2     1050.236724  1050.236724             2       0 -1.0  1.0
3      965.082395   709.619408             3      -1  2.0  1.0
4      823.158513  1021.851947             3       3 -1.0  0.0
...           ...          ...           ...     ...  ...  ...
1232  1107.006276   709.619408             2       0 -1.0  1.0
1233   652.849855   -85.154329             2       0  1.0  1.0
1234   -28.384776   936.697618             2       0 -1.0  1.0
1235   -28.384776   823.158513             2       0 -1.0  1.0
1236   -28.384776   766.388960             2       0 -1.0 -1.0

But when I show the following dataframe value:

v_df.loc[0]

I obtain:

                x            y  coordination  charge   dx   dy
0      -28.384776   -56.769553             3      -1  1.0 -2.0
1     1107.006276  1021.851947             3      -1 -1.0 -2.0
2     1050.236724  1050.236724             2       0 -1.0  1.0
3      965.082395   709.619408             3      -1  2.0  1.0
4      823.158513  1021.851947             3       3 -1.0  0.0
...           ...          ...           ...     ...  ...  ...
1232  1107.006276   709.619408             2       0 -1.0  1.0
1233   652.849855   -85.154329             2       0  1.0  1.0
1234   -28.384776   936.697618             2       0 -1.0  1.0
1235   -28.384776   823.158513             2       0 -1.0  1.0
1236   -28.384776   766.388960             2       0 -1.0 -1.0

[1237 rows x 6 columns]

So it seems that is only concatenating the last value.

PD: See charge column in order to see differences between dataframes.

Thank you so much!

Answer

You are using v_df.loc[0] so it only shows the df with key 0. I think if you print v_df it will show all of them.