Python Pandas 列绑定（cbind）两个数据框
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Pandas column bind (cbind) two data frames
提问by breezymri
I've got a dataframe df_awith id information:
我有一个df_a带有 id 信息的数据框：
unique_id lacet_number
15 5570613 TLA-0138365
24 5025490 EMP-0138757
36 4354431 DXN-0025343
and another dataframe df_b, with the same number of rows that I know correspond to the rows in df_a:
和另一个数据框df_b，与我知道的行数相同，对应于以下行df_a：
latitude longitude
0 -93.193560 31.217029
1 -93.948082 35.360874
2 -103.131508 37.787609
What I want to do is simply cbind the two and get:
我想要做的就是简单地将两者绑定并得到：
unique_id lacet_number latitude longitude
0 5570613 TLA-0138365 -93.193560 31.217029
1 5025490 EMP-0138757 -93.948082 35.360874
2 4354431 DXN-0025343 -103.131508 37.787609
What I have tried:
我尝试过的：
df_c = pd.concat([df_a, df_b], axis=1)
which gives me an outer join.
这给了我一个外部连接。
unique_id lacet_number latitude longitude
0 NaN NaN -93.193560 31.217029
1 NaN NaN -93.948082 35.360874
2 NaN NaN -103.131508 37.787609
15 5570613 TLA-0138365 NaN NaN
24 5025490 EMP-0138757 NaN NaN
36 4354431 DXN-0025343 NaN NaN
The problem is that the indices for the two dataframes do not match. I read the documentation for pandas.concat, and saw that there is an option "ignore_index". But that only applies to the concatenation axis, in my case the columns and it certainly is not the right choice for me. So my question is: is there a simple way to achieve this?
问题是两个数据帧的索引不匹配。我阅读了 pandas.concat 的文档，看到有一个选项“ignore_index”。但这仅适用于串联轴，在我的情况下是列，这对我来说肯定不是正确的选择。所以我的问题是：有没有一种简单的方法可以实现这一目标？
采纳答案by EdChum
If you're sure the index row values are the same then to avoid the index alignment order then just call reset_index(), this will reset your index values back to start from 0:
如果您确定索引行值相同，那么为了避免索引对齐顺序，只需调用reset_index()，这会将您的索引值重置为从0：
df_c = pd.concat([df_a.reset_index(drop=True), df_b], axis=1)
