Python 为什么 Pandas 内连接会给出 ValueError: len(left_on) 必须等于“right”索引中的级别数？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/28228781/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Why does Pandas inner join give ValueError: len(left_on) must equal the number of levels in the index of "right"?
提问by Ian Joyce
I'm trying to inner join DataFrame A to DataFrame B and am running into an error.
我正在尝试将 DataFrame A 内部连接到 DataFrame B 并且遇到错误。
Here's my join statement:
这是我的加入声明：
merged = DataFrameA.join(DataFrameB, on=['Code','Date'])
And here's the error:
这是错误：
ValueError: len(left_on) must equal the number of levels in the index of "right"
I'm not sure the column order matters (they aren't truly "ordered" are they?), but just in case, the DataFrames are organized like this:
我不确定列顺序是否重要（它们不是真正“有序”的吗？），但以防万一，DataFrame 的组织方式如下：
DataFrameA: Code, Date, ColA, ColB, ColC, ..., ColG, ColH (shape: 80514, 8 - no index)
DataFrameB: Date, Code, Col1, Col2, Col3, ..., Col15, Col16 (shape: 859, 16 - no index)
Do I need to correct my join statement? Or is there another, better way to get the intersection (or inner join) of these two DataFrames?
我需要更正我的加入声明吗？或者是否有另一种更好的方法来获得这两个 DataFrame 的交集（或内部连接）？
采纳答案by JAB
use mergeif you are not joining on the index:
使用merge，如果你没有在指数加盟：
merged = pd.merge(DataFrameA,DataFrameB, on=['Code','Date'])
Follow up to question below:
跟进以下问题：
Here is a reproducible example:
这是一个可重现的示例：
import pandas as pd
# create some timestamps for date column
i = pd.to_datetime(pd.date_range('20140601',periods=2))
#create two dataframes to merge
df = pd.DataFrame({'code': ['ABC','EFG'], 'date':i,'col1': [10,100]})
df2 = pd.DataFrame({'code': ['ABC','EFG'], 'date':i,'col2': [10,200]})
#merge on columns (default join is inner)
pd.merge(df, df2, on =['code','date'])
This results is:
这个结果是：
code col1 date col2
0 ABC 10 2014-06-01 10
1 EFG 100 2014-06-02 200
What happens when you run this code?
运行此代码时会发生什么？
回答by Jeru Luke
Here is another way of performing join. Unlike the answer verified, this is a more general answer applicable to all other types of join.
这是另一种表演方式join。与已验证的答案不同，这是适用于所有其他类型的 join的更通用的答案。
Inner Join
内部联接
inner joincan also be performed by explicitly mentioning it as follows in how:
inner join也可以通过在以下内容中明确提及它来执行how：
pd.merge(df1, df2, on='filename', how='inner')
The same methodology aplies for the other types of join:
相同的方法适用于其他类型的连接：
OuterJoin
外连接
pd.merge(df1, df2, on='filename', how='outer')
Left Join
左加入
pd.merge(df1, df2, on='filename', how='left')
Right Join
右加入
pd.merge(df1, df2, on='filename', how='right')
