如何在大熊猫中合并或合并具有不同列数的三个表？

Question

我的麻烦始于JSON文件，其中包含某些“设备”信息以及用于不同设备的某些参数。

我能够将每个设备json捕获为每个设备的单行DataFrame。他们将有40-60列，包括普通列。

示例数据如下：

可复制的代码：

df1 = pd.DataFrame({'id': {0: 1122},
 'c1': {0: 'uid'},
 'c2': {0: 'iopw'},
 'c3': {0: 'uywy'},
 'c4': {0: '7uyw'},
 'c5': {0: 'iwoq'},
 'c6': {0: 'owoe'}}
)

df2 = pd.DataFrame({'id': {0: 9910},
 'c1': {0: 'mnjjj'},
 'c3': {0: 'mhji'},
 'c6': {0: 'mb '},
 'c8': {0: 'bly'},
 'c14': {0: 'bnhg'},
 'c15': {0: 'kkkl'},
 'c20': {0: 'llug'},
 'c25': {0: '87jo'}})


df3 = pd.DataFrame({'id': {0: 2020},
 'c4': {0: 'kvkh'},
 'c5': {0: 'kjhjkh'},
 'c10': {0: 'cvcvc'},
 'c15': {0: 'ququ'}})

我已经尝试过合并，但是下面我尝试过的代码中的问题是它正在创建重复的列。

dfs = [df1, df2, df3]
from functools import reduce
df_final = reduce(lambda left,right: pd.merge(left,right,on='id',how="outer"), dfs)

如何避免重复，或者是否有其他更干净的方式来合并或合并表，以便避免重复的列？

---

预期输出如下所示。它应具有3行，并具有正确的列数

{'id': {0: 1122, 1: 9910, 2: 2020},
 'c1': {0: 'uid', 1: 'mnjj', 2: nan},
 'c2': {0: 'iopw', 1: nan, 2: nan},
 'c3': {0: 'uywy', 1: nan, 2: nan},
 'c4': {0: '7uyw', 1: nan, 2: 'kvkh'},
 'c5': {0: 'iwoq', 1: nan, 2: 'kjhjkh'},
 'c6': {0: 'owoe', 1: 'mb', 2: nan},
 'c7': {0: nan, 1: nan, 2: nan},
 'c8': {0: nan, 1: 'bly', 2: nan},
 'c9': {0: nan, 1: nan, 2: nan},
 'c10': {0: nan, 1: nan, 2: 'cvcvc'},
 'c11': {0: nan, 1: nan, 2: nan},
 'c12': {0: nan, 1: nan, 2: nan},
 'c13': {0: nan, 1: nan, 2: nan},
 'c14': {0: nan, 1: 'bnhg', 2: nan},
 'c15': {0: nan, 1: 'kkkl', 2: 'ququ'},
 'c16': {0: nan, 1: nan, 2: nan},
 'c17': {0: nan, 1: nan, 2: nan},
 'c18': {0: nan, 1: nan, 2: nan},
 'c19': {0: nan, 1: nan, 2: nan},
 'c20': {0: nan, 1: 'llug', 2: nan},
 'c21': {0: nan, 1: nan, 2: nan},
 'c22': {0: nan, 1: nan, 2: nan},
 'c23': {0: nan, 1: nan, 2: nan},
 'c24': {0: nan, 1: nan, 2: nan},
 'c25': {0: nan, 1: '87jo', 2: nan}}

Answer 1

将concat和concat一起使用id创建的索引：