熊猫数据框-合并两个基于“ InStr”> 0的数据框

Question

I have two DataFrames in Python Pandas.

Data stored in the cells are as follows:

DF1
- DatabaseId    Integer
- DatabaseName  String

DF2
- CreateString  String

I want to apply the column DataBaseID to any record in DF2 where the DF1.DatabaseName exists within the context of Create String.

Example:
DatabaseName = "UserDB"        CreateString = "This create string would fail"
DatabaseName = "UserDB"        CreateString = "This create string has UserDB in it"

The first record would fail and not be included in the resulting set. The second record would succeed and would be in the resulting set.

I've researched a variety of options including .isin , and .contains , but these have not worked. This seems to be a 'controlled' Cartesian join with an 'if match found success' condition. But I haven't been able to find a way to do this, and it efficiently.

Total list size needing to be evaluated are between 100K and 500K each.

UPDATE Added More Example Data:

>>> DF1.head(10)
DatabaseID     DatabaseName
0              DB1
1              DB2
2              DB3
3              DB4
...

>>> DF2.head(10)
CreateString
None
None
None
CREATE VIEW DB1.Table1 AS LOC…
None
REPLACE VIEW DB3.Table3...
CREATE VIEW DB3.Table10 AS SELE...
CREATE VIEW DB55.Table999 AS SELEC...
...

Desired Result
DatabaseID      DatabaseName        CreateText
0               DB1                 CREATE VIEW DB1.Table1 AS LOC…
2               DB3                 REPLACE VIEW DB3.Table3...
2               DB3                 CREATE VIEW DB3.Table10 AS SELE...
...
etc...
...

Answer 1

UPDATE: how to parse table name:

In [100]: df2['TableName'] = df2.CreateString.str.extract('\s+(\w+\.\w+)\s+', expand=True)

In [101]: df2
Out[101]:
                            CreateString DatabaseName      TableName
0                                   None          NaN            NaN
1                                   None          NaN            NaN
2                                   None          NaN            NaN
3         CREATE VIEW DB1.Table1 AS LOC…          DB1     DB1.Table1
4                                   None          NaN            NaN
5            REPLACE VIEW DB3.Table3 ...          DB3     DB3.Table3
6     CREATE VIEW DB3.Table10 AS SELE...          DB3    DB3.Table10
7  CREATE VIEW DB55.Table999 AS SELEC...         DB55  DB55.Table999

Original answer:

you can do it this way:

In [83]: df2['DatabaseName'] = df2.CreateString.str.extract('\s+(\w+)\.\w+\s+', expand=True)

In [84]: pd.merge(df2, df1, on='DatabaseName', how='left')
Out[84]:
                            CreateString DatabaseName  DatabaseID
0                                   None          NaN         NaN
1                                   None          NaN         NaN
2                                   None          NaN         NaN
3         CREATE VIEW DB1.Table1 AS LOC…          DB1         0.0
4                                   None          NaN         NaN
5            REPLACE VIEW DB3.Table3 ...          DB3         2.0
6     CREATE VIEW DB3.Table10 AS SELE...          DB3         2.0
7  CREATE VIEW DB55.Table999 AS SELEC...         DB55         NaN