I have the following dataframe (RuleDF) with a column which contains a string.
Inside the string are some values (parameters) which shall be replaced
eg. param1 and param2
Rule_ID | Rule_Value |
---|---|
R-123 | column1 > param1 and column2 > param2 |
R-456 | column1 > param1 |
... | ... |
Rule_ID | Rule_Param | Param_Value |
---|---|---|
R-123 | param1 | 100 |
R-123 | param2 | 200 |
R-456 | param1 | 100 |
The result of the replacement will be something like this:
Another option is to have a new column with the replaced string
Rule_ID | Rule_Value |
---|---|
R-123 | column1 > 100 and column2 > 200 |
R-456 | column1 > 100 |
... | ... |
I appreciate any ideas. Thank you.
As @Nk03, first create a mapping dict for each Rule_ID from df2 to allow string substitution with replace()
method:
params = df2.groupby('Rule_ID') \
.apply(lambda x: dict(zip(x['Rule_Param'], x['Param_Value'].astype(str)))) \
.to_dict()
out = df1.groupby('Rule_ID') \
.apply(lambda x: x['Rule_Value'].replace(params[x.name], regex=True))
>>> params
{'R-123': {'param1': '100', 'param2': '200'}, 'R-456': {'param1': '100'}}
>>> out
Rule_ID
R-123 0 column1 > 100 and column2 > 200
R-456 1 column1 > 100
Name: Rule_Value, dtype: object
One Way:
df3 = df1.merge(df2.groupby('Rule_ID').apply(lambda x: dict(
x[['Rule_Param', 'Param_Value']].values)).reset_index(), on='Rule_ID', how='left')
df3['Rule_Value'] = df3.apply(lambda x: ' '.join(
str(x[0].get(i, i)) for i in x['Rule_Value'].split()), 1)
df3 = df3.drop(0, 1)
Rule_ID Rule_Value
0 R-123 column1 > 100 and column2 > 200
1 R-456 column1 > 100
It will create a mapping dict for each Rule_ID
from df2. We can left merge
the result with the original df1
.
Rule_ID Rule_Value \
0 R-123 column1 > param1 and column2 > param2
1 R-456 column1 > param1
0
0 {'param1': 100, 'param2': 200}
1 {'param1': 100}
It'll use the mapping dict to replace the value in the Rule_Value
column.
Drop the map_dict
column ie column 0
.
如果您要更新Rule_Value
列中的每个字符串
dataframe.at[index,'Rule_Value']='new value'
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