英文:
Match values in pandas dataframe and replace with matched values from master table
问题
我想要将主表中的值与映射表中的详细信息进行匹配和替换，而不使用for循环。
主表：
| Case | Path1 | Path2 | Path3 |
|---|---|---|---|
| 1 | a | c | d |
| 2 | b | c | a |
| 3 | c | a | e |
| 4 | b | d | e |
| 5 | d | b | a |
映射表：
| factor | detail |
|---|---|
| a | 样本A |
| b | 样本B |
| c | 样本C |
| d | 样本D |
| e | 样本E |
| f | 样本F |
我希望输出如下所示。
结果：
| Case | Path1 | Path2 | Path3 |
|---|---|---|---|
| 1 | 样本A | 样本C | 样本D |
| 2 | 样本B | 样本C | 样本A |
| 3 | 样本C | 样本A | 样本E |
| 4 | 样本B | 样本D | 样本E |
| 5 | 样本D | 样本B | 样本A |
英文:
I would like to match and replace values from Main Table to detail in Mapping Table without using for-loop.
Main Table:
| Case | Path1 | Path2 | Path3 |
|---|---|---|---|
| 1 | a | c | d |
| 2 | b | c | a |
| 3 | c | a | e |
| 4 | b | d | e |
| 5 | d | b | a |
Mapping Table:
| factor | detail |
|---|---|
| a | sample A |
| b | sample B |
| c | sample C |
| d | sample D |
| e | sample E |
| f | sample F |
I would like the output to be like this.
Result:
| Case | Path1 | Path2 | Path3 |
|---|---|---|---|
| 1 | sample A | sample C | sample D |
| 2 | sample B | sample C | sample A |
| 3 | sample C | sample A | sample E |
| 4 | sample B | sample D | sample E |
| 5 | sample D | sample B | sample A |
答案1
得分: 2
你可以使用replace：
# df -> 主表# dmap -> 映射表cols = df.filter(like='Path').columnsdf[cols] = df[cols].replace(dmap.set_index('factor')['detail'])print(df)# 输出Case Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
英文:
You can use replace:
# df -> main table# dmap -> map tablecols = df.filter(like='Path').columnsdf[cols] = df[cols].replace(dmap.set_index('factor')['detail'])print(df)# OutputCase Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
答案2
得分: 1
Use Series.map by all columns without first - if no match get NaNs:
df1.iloc[:, 1:] = df1.iloc[:, 1:].apply(lambda x: x.map(df2.set_index('factor')['detail']))
Or DataFrame.replace - if no match get original value:
df1.iloc[:, 1:] = df1.iloc[:, 1:].replace(df2.set_index('factor')['detail'])print (df1)Case Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
If want update only columns starting Path use Series.update with DataFrame.filter and DataFrame.replace:
df1.update(df1.filter(regex=r'^Path').replace(df2.set_index('factor')['detail']))print (df1)Case Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
英文:
Use Series.map by all columns without first - if no match get NaNs:
df1.iloc[:, 1:] = df1.iloc[:, 1:].apply(lambda x: x.map(df2.set_index('factor')['detail']))
Or DataFrame.replace - if no match get original value:
df1.iloc[:, 1:] = df1.iloc[:, 1:].replace(df2.set_index('factor')['detail'])print (df1)Case Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
If want update only columns starting Path use Series.update with DataFrame.filter and DataFrame.replace:
df1.update(df1.filter(regex=r'^Path').replace(df2.set_index('factor')['detail']))print (df1)Case Path1 Path2 Path30 1 sample A sample C sample D1 2 sample B sample C sample A2 3 sample C sample A sample E3 4 sample B sample D sample E4 5 sample D sample B sample A
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