在 for 循环中附加 Dataframe 不起作用

Question

我快疯了。不知道为什么效果不好

我的循环如下：


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])

在第一版中我得到：

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

但是当我显示以下数据框值时：

v_df.loc[0]

我获得：

                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]

看来这只是连接最后一个值。

PD：查看费用列以查看数据帧之间的差异。

非常感谢！

Answer 1

您正在使用 v_df.loc[0]，所以它只显示键为 0 的 df。我想如果您打印 v_df，它会显示所有这些。

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