Python 无法将两个数据帧与pd.concat合并

我有两个数据帧-

d={'satisfaction_pred': {0: 'neutral or dissatisfied', 1: 'neutral or dissatisfied', 2: 'neutral or dissatisfied', 3: 'neutral or dissatisfied', 4: 'satisfied', 5: 'neutral or dissatisfied', 6: 'neutral or dissatisfied', 7: 'neutral or dissatisfied', 8: 'satisfied', 9: 'satisfied', 10: 'satisfied', 11: 'neutral or dissatisfied', 12: 'satisfied', 13: 'neutral or dissatisfied', 14: 'neutral or dissatisfied', 15: 'neutral or dissatisfied', 16: 'neutral or dissatisfied', 17: 'neutral or dissatisfied', 18: 'neutral or dissatisfied', 19: 'satisfied'}}

d2 ={'Flight Distance': {1183: 448, 1038: 289, 9220: 3390, 908: 689, 8495: 3754, 1864: 1126, 76: 1190, 4581: 925, 620: 427, 7207: 1371, 8406: 3984, 8430: 628, 5848: 2254, 6211: 209, 3487: 674, 9141: 1065, 2124: 1476, 47: 1522, 6226: 631, 3281: 2486}, 'Inflight wifi service': {1183: 2, 1038: 3, 9220: 1, 908: 3, 8495: 5, 1864: 1, 76: 2, 4581: 3, 620: 4, 7207: 1, 8406: 5, 8430: 2, 5848: 1, 6211: 2, 3487: 0, 9141: 4, 2124: 2, 47: 1, 6226: 3, 3281: 5}, 'Departure/Arrival time convenient': {1183: 4, 1038: 4, 9220: 1, 908: 2, 8495: 5, 1864: 1, 76: 4, 4581: 1, 620: 5, 7207: 1, 8406: 5, 8430: 0, 5848: 1, 6211: 3, 3487: 4, 9141: 4, 2124: 3, 47: 1, 6226: 1, 3281: 5}, 'Ease of Online booking': {1183: 2, 1038: 3, 9220: 1, 908: 3, 8495: 5, 1864: 1, 76: 4, 4581: 3, 620: 5, 7207: 1, 8406: 5, 8430: 2, 5848: 1, 6211: 2, 3487: 1, 9141: 4, 2124: 2, 47: 1, 6226: 3, 3281: 3}, 'Gate location': {1183: 2, 1038: 3, 9220: 1, 908: 4, 8495: 5, 1864: 4, 76: 4, 4581: 4, 620: 5, 7207: 1, 8406: 5, 8430: 1, 5848: 1, 6211: 3, 3487: 2, 9141: 3, 2124: 3, 47: 1, 6226: 3, 3281: 5}, 'Food and drink': {1183: 5, 1038: 3, 9220: 1, 908: 3, 8495: 5, 1864: 1, 76: 1, 4581: 4, 620: 5, 7207: 4, 8406: 5, 8430: 4, 5848: 4, 6211: 4, 3487: 3, 9141: 5, 2124: 1, 47: 1, 6226: 2, 3281: 4}, 'Online boarding': {1183: 4, 1038: 3, 9220: 1, 908: 3, 8495: 4, 1864: 1, 76: 4, 4581: 3, 620: 4, 7207: 5, 8406: 5, 8430: 2, 5848: 5, 6211: 2, 3487: 4, 9141: 4, 2124: 2, 47: 1, 6226: 3, 3281: 4}, 'Seat comfort': {1183: 5, 1038: 3, 9220: 1, 908: 3, 8495: 4, 1864: 1, 76: 3, 4581: 4, 620: 1, 7207: 4, 8406: 5, 8430: 4, 5848: 5, 6211: 4, 3487: 4, 9141: 5, 2124: 1, 47: 1, 6226: 2, 3281: 4}, 'Inflight entertainment': {1183: 1, 1038: 3, 9220: 1, 908: 3, 8495: 4, 1864: 1, 76: 2, 4581: 4, 620: 4, 7207: 3, 8406: 5, 8430: 4, 5848: 4, 6211: 4, 3487: 1, 9141: 5, 2124: 1, 47: 1, 6226: 2, 3281: 2}, 'On-board service': {1183: 1, 1038: 5, 9220: 1, 908: 4, 8495: 4, 1864: 5, 76: 2, 4581: 4, 620: 4, 7207: 3, 8406: 4, 8430: 3, 5848: 4, 6211: 1, 3487: 1, 9141: 3, 2124: 4, 47: 3, 6226: 3, 3281: 2}, 'Leg room service': {1183: 2, 1038: 4, 9220: 2, 908: 2, 8495: 4, 1864: 5, 76: 2, 4581: 2, 620: 4, 7207: 4, 8406: 3, 8430: 2, 5848: 4, 6211: 5, 3487: 1, 9141: 4, 2124: 2, 47: 5, 6226: 2, 3281: 2}, 'Baggage handling': {1183: 1, 1038: 4, 9220: 4, 908: 4, 8495: 4, 1864: 5, 76: 2, 4581: 3, 620: 4, 7207: 3, 8406: 5, 8430: 1, 5848: 4, 6211: 4, 3487: 1, 9141: 3, 2124: 3, 47: 3, 6226: 3, 3281: 2}, 'Checkin service': {1183: 4, 1038: 4, 9220: 4, 908: 2, 8495: 4, 1864: 3, 76: 1, 4581: 1, 620: 4, 7207: 3, 8406: 5, 8430: 1, 5848: 3, 6211: 3, 3487: 4, 9141: 1, 2124: 2, 47: 1, 6226: 4, 3281: 4}, 'Inflight service': {1183: 1, 1038: 4, 9220: 3, 908: 4, 8495: 4, 1864: 5, 76: 2, 4581: 3, 620: 4, 7207: 3, 8406: 2, 8430: 2, 5848: 4, 6211: 3, 3487: 1, 9141: 4, 2124: 3, 47: 4, 6226: 4, 3281: 2}, 'Cleanliness': {1183: 3, 1038: 3, 9220: 1, 908: 3, 8495: 4, 1864: 1, 76: 4, 4581: 4, 620: 1, 7207: 5, 8406: 5, 8430: 4, 5848: 3, 6211: 4, 3487: 5, 9141: 5, 2124: 1, 47: 1, 6226: 2, 3281: 4}, 'Departure Delay in Minutes': {1183: 0, 1038: 0, 9220: 0, 908: 0, 8495: 12, 1864: 13, 76: 0, 4581: 0, 620: 38, 7207: 13, 8406: 8, 8430: 8, 5848: 67, 6211: 0, 3487: 0, 9141: 31, 2124: 0, 47: 0, 6226: 0, 3281: 0}, 'Arrival Delay in Minutes': {1183: 0.0, 1038: 0.0, 9220: 0.0, 908: 0.0, 8495: 9.0, 1864: 5.0, 76: 6.0, 4581: 19.0, 620: 23.0, 7207: 23.0, 8406: 8.0, 8430: 0.0, 5848: 58.0, 6211: 25.0, 3487: 0.0, 9141: 33.0, 2124: 0.0, 47: 0.0, 6226: 0.0, 3281: 0.0}, 'id': {1183: 103464, 1038: 105204, 9220: 103033, 908: 93708, 8495: 2014, 1864: 67573, 76: 85018, 4581: 2368, 620: 24065, 7207: 103310, 8406: 53173, 8430: 30563, 5848: 115099, 6211: 50884, 3487: 23344, 9141: 35850, 2124: 30989, 47: 81983, 6226: 65370, 3281: 62750}, 'Age': {1183: 53, 1038: 46, 9220: 32, 908: 27, 8495: 51, 1864: 36, 76: 52, 4581: 12, 620: 50, 7207: 35, 8406: 25, 8430: 51, 5848: 51, 6211: 22, 3487: 61, 9141: 27, 2124: 39, 47: 13, 6226: 34, 3281: 42}, 'Gender_Female': {1183: 1, 1038: 0, 9220: 0, 908: 0, 8495: 1, 1864: 0, 76: 0, 4581: 0, 620: 1, 7207: 0, 8406: 1, 8430: 0, 5848: 1, 6211: 0, 3487: 1, 9141: 1, 2124: 1, 47: 1, 6226: 1, 3281: 0}, 'Gender_Male': {1183: 0, 1038: 1, 9220: 1, 908: 1, 8495: 0, 1864: 1, 76: 1, 4581: 1, 620: 0, 7207: 1, 8406: 0, 8430: 1, 5848: 0, 6211: 1, 3487: 0, 9141: 0, 2124: 0, 47: 0, 6226: 0, 3281: 1}, 'Customer Type_Loyal Customer': {1183: 1, 1038: 1, 9220: 1, 908: 1, 8495: 1, 1864: 0, 76: 1, 4581: 1, 620: 1, 7207: 1, 8406: 1, 8430: 0, 5848: 1, 6211: 1, 3487: 1, 9141: 0, 2124: 1, 47: 1, 6226: 0, 3281: 1}, 'Customer Type_disloyal Customer': {1183: 0, 1038: 0, 9220: 0, 908: 0, 8495: 0, 1864: 1, 76: 0, 4581: 0, 620: 0, 7207: 0, 8406: 0, 8430: 1, 5848: 0, 6211: 0, 3487: 0, 9141: 1, 2124: 0, 47: 0, 6226: 1, 3281: 0}, 'Type of Travel_Business travel': {1183: 0, 1038: 0, 9220: 1, 908: 0, 8495: 1, 1864: 1, 76: 1, 4581: 0, 620: 1, 7207: 1, 8406: 1, 8430: 1, 5848: 1, 6211: 0, 3487: 0, 9141: 1, 2124: 0, 47: 1, 6226: 1, 3281: 1}, 'Type of Travel_Personal Travel': {1183: 1, 1038: 1, 9220: 0, 908: 1, 8495: 0, 1864: 0, 76: 0, 4581: 1, 620: 0, 7207: 0, 8406: 0, 8430: 0, 5848: 0, 6211: 1, 3487: 1, 9141: 0, 2124: 1, 47: 0, 6226: 0, 3281: 0}, 'Class_Business': {1183: 0, 1038: 0, 9220: 1, 908: 0, 8495: 1, 1864: 1, 76: 1, 4581: 0, 620: 0, 7207: 1, 8406: 1, 8430: 0, 5848: 1, 6211: 0, 3487: 0, 9141: 0, 2124: 0, 47: 1, 6226: 0, 3281: 1}, 'Class_Eco': {1183: 1, 1038: 0, 9220: 0, 908: 1, 8495: 0, 1864: 0, 76: 0, 4581: 1, 620: 0, 7207: 0, 8406: 0, 8430: 1, 5848: 0, 6211: 1, 3487: 1, 9141: 1, 2124: 0, 47: 0, 6226: 1, 3281: 0}, 'Class_Eco Plus': {1183: 0, 1038: 1, 9220: 0, 908: 0, 8495: 0, 1864: 0, 76: 0, 4581: 0, 620: 1, 7207: 0, 8406: 0, 8430: 0, 5848: 0, 6211: 0, 3487: 0, 9141: 0, 2124: 1, 47: 0, 6226: 0, 3281: 0}, 'satisfaction': {1183: 'neutral or dissatisfied', 1038: 'neutral or dissatisfied', 9220: 'neutral or dissatisfied', 908: 'neutral or dissatisfied', 8495: 'satisfied', 1864: 'neutral or dissatisfied', 76: 'neutral or dissatisfied', 4581: 'neutral or dissatisfied', 620: 'satisfied', 7207: 'satisfied', 8406: 'satisfied', 8430: 'neutral or dissatisfied', 5848: 'satisfied', 6211: 'neutral or dissatisfied', 3487: 'satisfied', 9141: 'neutral or dissatisfied', 2124: 'neutral or dissatisfied', 47: 'neutral or dissatisfied', 6226: 'neutral or dissatisfied', 3281: 'satisfied'}}

当我这么做的时候-

df =pd.DataFrame.from_dict(d)
df2 =pd.DataFrame.from_dict(d2)

df3=pd.concat([df2,df],axis=1,ignore_index=True)

这是印刷品-

    0   1   2   3   4   5   6   7   8   9   ... 20  21  22  23  24  25  26  27  28  29
0   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN neutral or dissatisfied
1   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN neutral or dissatisfied
2   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN neutral or dissatisfied
3   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN neutral or dissatisfied
4   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN satisfied

---

我想通过在第一个数据帧的右侧添加第二个数据帧来合并这两个数据帧。

这可能是由于索引不同

你可以用这个。首先重新编制df的索引

df3 = pd.concat([df.reindex(df2.index), df2], axis=1, sort=False)

---

我认为这是因为不同的索引问题

我尝试了这个方法并成功：

df_d = pd.DataFrame(d).reset_index(drop=True)
df_d2 = pd.DataFrame(d2).reset_index(drop=True)

df_result = pd.concat([df_d, df_d2], axis=1)

结果总目:

0   neutral or dissatisfied 448 2   4   2   2   5   4   5   1   ... 1   0   1   0   0   1   0   1   0   neutral or dissatisfied
1   neutral or dissatisfied 289 3   4   3   3   3   3   3   3   ... 0   1   1   0   0   1   0   0   1   neutral or dissatisfied
2   neutral or dissatisfied 3390    1   1   1   1   1   1   1   1   ... 0   1   1   0   1   0   1   0   0   neutral or dissatisfied
3   neutral or dissatisfied 689 3   2   3   4   3   3   3   3   ... 0   1   1   0   0   1   0   1   0   neutral or dissatisfied
4   satisfied   3754    5   5   5   5   5   4   4   4   ... 1   0   1   0   1   0   1   0   0   satisfied

---

df2在这方面正处于劣势。我也附上了数据。你可以检查一下，我刚得到一个NaN列。在这里，F2是NaN。你试过我的代码了吗？我在

df_result

上的结果中没有nan，这是由索引未匹配引起的。只是添加了结果概述。您能告诉我发生这种情况的原因吗？索引问题？熊猫索引未匹配。

d

和

d2

上的索引不同，因此我们需要

.reset_Index（）

它。