python pandas column dtype=object 导致合并失败：DtypeWarning: Columns have mixed types
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python pandas column dtype=object causing merge to fail with: DtypeWarning: Columns have mixed types
提问by jeangelj
I am trying to merge two dataframes df1, df2on Customer_IDcolumn. It seems that Customer_IDhas the same data type (object) in both.
我正在尝试合并列df1, df2上的两个数据框Customer_ID。似乎两者Customer_ID具有相同的数据类型 ( object)。
df1:
df1：
Customer_ID | Flag
12345 A
df2:
df2：
Customer_ID | Transaction_Value
12345 258478
When I merge the two tables:
当我合并两个表时：
new_df = df2.merge(df1, on='Customer_ID', how='left')
For some Customer_IDs it worked and for others it didn't. FOr this example, I would get this result:
对于某些 Customer_ID，它有效，而对于其他 Customer_ID 则无效。对于这个例子，我会得到这个结果：
Customer_ID | Transaction_Value | Flag
12345 258478 NaN
I checked the data types and they are the same:
我检查了数据类型，它们是相同的：
df1.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 873353 entries, 0 to 873352
Data columns (total 2 columns):
Customer_ID 873353 non-null object
Flag 873353 non-null object
dtypes: object(2)
memory usage: 20.0+ MB
df2.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 873353 entries, 0 to 873352
Data columns (total 2 columns):
Customer_ID 873353 non-null object
Transaction_Value 873353 int64
dtypes: object(2)
memory usage: 20.0+ MB
When I uploaded df1, I did get this message:
当我上传 df1 时，我确实收到了这条消息：
C:\Users\xxx\AppData\Local\Continuum\Anaconda2\lib\site-packages\IPython\core\interactiveshell.py:2717: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
When I wanted to check, if a customer ID exists, I realized that I have to specify it differently in the two dataframes.
当我想检查客户 ID 是否存在时，我意识到我必须在两个数据框中以不同的方式指定它。
df1.loc[df1['Customer_ID'] == 12345]
df2.loc[df2['Customer_ID'] == '12345']
回答by piRSquared
Customer_IDis of dtype==objectin both cases... But that doesn't mean that the individual elements are the same type. You need to make both stror int
Customer_ID是dtype==object在这两种情况下......但是，这并不意味着单个元素都是同一类型。你需要使两者str或int
Using int
使用 int
dtype = dict(Customer_ID=int)
df1.astype(dtype).merge(df2.astype(dtype), 'left')
Customer_ID Flag Transaction_Value
0 12345 A 258478
Using str
使用 str
dtype = dict(Customer_ID=str)
df1.astype(dtype).merge(df2.astype(dtype), 'left')
Customer_ID Flag Transaction_Value
0 12345 A 258478
回答by Ega Dharmawan
I think i have found the easiest way to merge between two data frame without changing the dtypes.
我想我已经找到了在不更改 dtypes 的情况下合并两个数据框的最简单方法。
final = pd.concat([df1, df2], axis=1, sort=False)
Hope it helps :)
希望能帮助到你 ：）
