英文:
Check 3 columns in 3 different dataframes and create a new column
问题
我有三个类似这样的数据框：
| ID | Name |
|---|---|
| First | row |
| Second | row |
所有的数据框都有一个共同的ID列。有一个主数据框，其他两个需要与之进行比较。
我想要实现的逻辑是：
1）如果第二个数据框的ID与第一个匹配，则创建一个列并给它一个常量'A'的值。
2）如果第三个数据框的ID与第一个匹配，在相同的列中给它一个常量'B'的值。
3）如果它在其中一个中都没有出现，在相同的列中给它一个常量'I'的值。
我正在尝试使其更高效，而不是创建两个for循环。任何帮助将不胜感激！
英文:
I have three dataframes like this
| ID | Name |
|---|---|
| First | row |
| Second | row |
All the dataframe have ID column in common. There is a main dataframe against which the other two needs to be compared.
Logic I want to implement:
- if ID's of second dataframe matches with the first, then create a column and give it a constant 'A' value.
- if ID's of third dataframe matches with the first, in the same column give it a constant 'B' value.
- if it doesn't appear in either of them,in the same column give it a constant 'I'
I am trying to make it efficient instead of creating two for loops. Any help will be appreciated!
答案1
得分: 1
以下是您想要的内容：
import pandas as pd
df1 = pd.DataFrame({
'ID': [1, 2, 3],
'name': [
"阅读文档",
"在提问之前检查是否已有相同问题",
"创建一个MRE"
]
})
df2 = pd.DataFrame({
'ID': [1],
'url': ["http://pandas.pydata.org/docs/"]
})
df3 = pd.DataFrame({
'ID': [2],
'url': ["https://stackoverflow.com/search"]
})
df1['new_col'] = df1['ID'].apply(lambda x: 'A' if x in df2['ID'].values else 'B' if x in df3['ID'].values else 'I')
# 输出:
ID name new_col
0 1 阅读文档 A
1 2 在提问之前检查是否已有相同问题 B
2 3 创建一个MRE I
英文:
Here is what it appears you want:
import pandas as pd
df1 = pd.DataFrame({
'ID': [1, 2, 3],
'name': [
"read the documentation",
"check your question hasn't been asked before",
"create an MRE"
]
})
df2 = pd.DataFrame({
'ID': [1],
'url': ["http://pandas.pydata.org/docs/"]
})
df3 = pd.DataFrame({
'ID': [2],
'url': ["https://stackoverflow.com/search"]
})
df1['new_col'] = df1['ID'].apply(lambda x: 'A' if x in df2['ID'].values else 'B' if x in df3['ID'].values else 'I')
# Output:
ID name new_col
0 1 read the documentation A
1 2 check your question hasn't been asked before B
2 3 create an MRE I
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