我有三个pd.DataFrames：
df1 = pd.DataFrame({'var1': {0: 2210, 1: 2210, 2: 2210, 3: 2210, 4: 2210, 5: 2210, 6: 2210, 7: 2210, 8: 2210, 9: 2210, 10: 2210, 11: 2210, 12: 2210, 13: 2210, 14: 2210, 15: 2210, 16: 2210, 17: 2210, 18: 2210, 19: 2210, 20: 2210, 21: 2210}, 'var2': {0: 1, 1: 2, 2: 1, 3: 2, 4: 1, 5: 2, 6: 1, 7: 2, 8: 1, 9: 2, 10: 1, 11: 2, 12: 1, 13: 2, 14: 1, 15: 2, 16: 1, 17: 2, 18: 1, 19: 2, 20: 1, 21: 2}, 'var3': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0}, 'var4': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0}, 'var5': {0: '121160', 1: '20066', 2: ' 58621', 3: ' 201084', 4: ' 100180', 5: ' 74230', 6: ' 27789', 7: ' 66975', 8: ' 57410', 9: ' 49413', 10: ' 57112', 11: ' 19188', 12: ' 61366', 13: ' 27341', 14: ' 59859', 15: ' 173954', 16: ' 205651', 17: ' 54861', 18: ' 165809', 19: ' 60252', 20: ' 182156', 21: ' 82403'}})
df2 = pd.DataFrame({'var1': {349176: 2210, 349225: 2210, 349913: 2210, 350247: 2210, 350342: 2210, 350518: 2210}, 'var2': {349176: 2, 349225: 1, 349913: 1, 350247: 2, 350342: 1, 350518: 2}, 'var5': {349176: 58786.0, 349225: 37572.0, 349913: 103955.0, 350247: 19197.0, 350342: 14664.0, 350518: 75773.0}, 'var3': {349176: 19, 349225: 22, 349913: 56, 350247: 75, 350342: 80, 350518: 95}, 'var4': {349176: 8, 349225: 52, 349913: 42, 350247: 0, 350342: 50, 350518: 17}})
df3 = pd.DataFrame({'var1': {349175: 2210, 349224: 2210, 349912: 2210, 350246: 2210, 350341: 2210, 350517: 2210, 350521: 2210}, 'var2': {349175: 2, 349224: 1, 349912: 1, 350246: 2, 350341: 1, 350517: 2, 350521: 1}, 'var5': {349175: 19188.0, 349224: 205651.0, 349912: 59859.0, 350246: 27341.0, 350341: 165809.0, 350517: 19197.0, 350521: 61366.0}, 'var6': {349175: 19, 349224: 22, 349912: 56, 350246: 75, 350341: 80, 350517: 95, 350521: 95}, 'var7': {349175: 8, 349224: 52, 349912: 42, 350246: 0, 350341: 50, 350517: 17, 350521: 40}})
我需要将df1和df2堆叠在一起，然后通过左连接与基于多个变量的df3连接它们：var1, var2, var5 .
所以我写道：
pd.concat([df1, df2], axis = 0, sort = False).merge(df3, how = 'left', on = ['var1', 'var2', 'var5'])
但它找不到所有匹配的行。将类型更改为外连接，我们可以观察到它们的类型是例如具有相同值的 var1, var2 和 var3 的两行 - 第 11 行和第 28 行，但它们尚未连接：
pd.concat([df1, df2], axis = 0, sort = False).merge(df3, how = 'outer', on = ['var1', 'var2', 'var5'])
我正在努力寻找这种行为的原因。我想也许连接列中的数据类型不同，但没有 - 它们是相同的。我对熊猫比较陌生，所以也许我在这里错过了一些明显的东西？这种(意外(行为的原因是什么？
df1 = pd.DataFrame({'var1': {0: 2210, 1: 2210, 2: 2210, 3: 2210, 4: 2210, 5: 2210, 6: 2210, 7: 2210, 8: 2210, 9: 2210, 10: 2210, 11: 2210, 12: 2210, 13: 2210, 14: 2210, 15: 2210, 16: 2210, 17: 2210, 18: 2210, 19: 2210, 20: 2210, 21: 2210}, 'var2': {0: 1, 1: 2, 2: 1, 3: 2, 4: 1, 5: 2, 6: 1, 7: 2, 8: 1, 9: 2, 10: 1, 11: 2, 12: 1, 13: 2, 14: 1, 15: 2, 16: 1, 17: 2, 18: 1, 19: 2, 20: 1, 21: 2}, 'var3': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0}, 'var4': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0}, 'var5': {0: '121160', 1: '20066', 2: ' 58621', 3: ' 201084', 4: ' 100180', 5: ' 74230', 6: ' 27789', 7: ' 66975', 8: ' 57410', 9: ' 49413', 10: ' 57112', 11: ' 19188', 12: ' 61366', 13: ' 27341', 14: ' 59859', 15: ' 173954', 16: ' 205651', 17: ' 54861', 18: ' 165809', 19: ' 60252', 20: ' 182156', 21: ' 82403'}})
df2 = pd.DataFrame({'var1': {349176: 2210, 349225: 2210, 349913: 2210, 350247: 2210, 350342: 2210, 350518: 2210}, 'var2': {349176: 2, 349225: 1, 349913: 1, 350247: 2, 350342: 1, 350518: 2}, 'var5': {349176: 58786.0, 349225: 37572.0, 349913: 103955.0, 350247: 19197.0, 350342: 14664.0, 350518: 75773.0}, 'var3': {349176: 19, 349225: 22, 349913: 56, 350247: 75, 350342: 80, 350518: 95}, 'var4': {349176: 8, 349225: 52, 349913: 42, 350247: 0, 350342: 50, 350518: 17}})
df3 = pd.DataFrame({'var1': {349175: 2210, 349224: 2210, 349912: 2210, 350246: 2210, 350341: 2210, 350517: 2210, 350521: 2210}, 'var2': {349175: 2, 349224: 1, 349912: 1, 350246: 2, 350341: 1, 350517: 2, 350521: 1}, 'var5': {349175: 19188.0, 349224: 205651.0, 349912: 59859.0, 350246: 27341.0, 350341: 165809.0, 350517: 19197.0, 350521: 61366.0}, 'var6': {349175: 19, 349224: 22, 349912: 56, 350246: 75, 350341: 80, 350517: 95, 350521: 95}, 'var7': {349175: 8, 349224: 52, 349912: 42, 350246: 0, 350341: 50, 350517: 17, 350521: 40}})
pd.concat([df1, df2], axis = 0).dtypes
结果在
var1 int64
var2 int64
var3 int64
var4 int64
var5 object
dtype: object
正如你在连接后看到的，var5是一个对象。 如果在此时合并，您将不会得到任何结果，因为 df3 中的 var5 是浮点数。
以下是我的建议：
df1['var5'] = df1['var5'].astype(float)
df2['var5'] = df2['var5'].astype(float)
df3['var5'] = df3['var5'].astype(float)
pd.concat([df1, df2], axis = 0).merge(df3, how = 'left', on = ['var1', 'var2', 'var5'])
这将生成以下数据帧：
var1 var2 var3 var4 var5 var6 var7
0 2210 1 0 0 121160.0 NaN NaN
1 2210 2 0 0 20066.0 NaN NaN
2 2210 1 0 0 58621.0 NaN NaN
3 2210 2 0 0 201084.0 NaN NaN
4 2210 1 0 0 100180.0 NaN NaN
5 2210 2 0 0 74230.0 NaN NaN
6 2210 1 0 0 27789.0 NaN NaN
7 2210 2 0 0 66975.0 NaN NaN
8 2210 1 0 0 57410.0 NaN NaN
9 2210 2 0 0 49413.0 NaN NaN
10 2210 1 0 0 57112.0 NaN NaN
11 2210 2 0 0 19188.0 19.0 8.0
12 2210 1 0 0 61366.0 95.0 40.0
13 2210 2 0 0 27341.0 75.0 0.0
14 2210 1 0 0 59859.0 56.0 42.0
15 2210 2 0 0 173954.0 NaN NaN
16 2210 1 0 0 205651.0 22.0 52.0
17 2210 2 0 0 54861.0 NaN NaN
18 2210 1 0 0 165809.0 80.0 50.0
19 2210 2 0 0 60252.0 NaN NaN
20 2210 1 0 0 182156.0 NaN NaN
21 2210 2 0 0 82403.0 NaN NaN
22 2210 2 19 8 58786.0 NaN NaN
23 2210 1 22 52 37572.0 NaN NaN
24 2210 1 56 42 103955.0 NaN NaN
25 2210 2 75 0 19197.0 95.0 17.0
26 2210 1 80 50 14664.0 NaN NaN
27 2210 2 95 17 75773.0 NaN NaN
当我在计算机上运行您的代码，然后使用df#.dtypes获取类型时，df1中var5列的 dtype 是 object ，而在 df2 和 df3 中它是float64 。concat 运行良好(在 concat 之后，dtype 是 object (，但是当我尝试运行合并(外部或左侧(时，我得到了一个 ValueError：
ValueError: You are trying to merge on object and float64 columns. If you wish to proceed you should use pd.concat
我建议再次仔细检查类型(我知道您已经检查过了(。如果它们在您的计算机上确实相同，我不确定发生了什么。