Python 从字典中添加具有映射值的新 Pandas 列
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Adding a new pandas column with mapped value from a dictionary
提问by Rick Donnelly
I'm trying do something that should be really simple in pandas, but it seems anything but. I'm trying to add a column to an existing pandas dataframe that is a mapped value based on another (existing) column. Here is a small test case:
我正在尝试在 Pandas 中做一些应该非常简单的事情，但似乎什么都不是。我正在尝试向现有的 Pandas 数据框添加一列，该列是基于另一个（现有）列的映射值。这是一个小测试用例：
import pandas as pd
equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame( {"A": [7001, 8001, 9001]} )
df["B"] = equiv(df["A"])
print(df)
I was hoping the following would result:
我希望结果如下：
A B
0 7001 1
1 8001 2
2 9001 3
Instead, I get an error telling me that equiv is not a callable function. Fair enough, it's a dictionary, but even if I wrap it in a function I still get frustration. So I tried to use a map function that seems to work with other operations, but it also is defeated by use of a dictionary:
相反，我收到一条错误消息，告诉我 equiv 不是可调用函数。很公平，它是一本字典，但即使我将它包装在一个函数中，我仍然感到沮丧。所以我尝试使用一个似乎可以与其他操作一起使用的 map 函数，但它也被字典的使用打败了：
df["B"] = df["A"].map(lambda x:equiv[x])
In this case I just get KeyError: 8001. I've read through documentation and previous posts, but have yet to come across anything that suggests how to mix dictionaries with pandas dataframes. Any suggestions would be greatly appreciated.
在这种情况下，我只得到 KeyError: 8001。我已经阅读了文档和以前的帖子，但还没有遇到任何建议如何将字典与 Pandas 数据框混合的内容。任何建议将不胜感激。
回答by CT Zhu
The right way of doing it will be df["B"] = df["A"].map(equiv).
正确的做法是df["B"] = df["A"].map(equiv)。
In [55]:
import pandas as pd
equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame( {"A": [7001, 8001, 9001]} )
df["B"] = df["A"].map(equiv)
print(df)
A B
0 7001 1
1 8001 2
2 9001 3
[3 rows x 2 columns]
And it will handle the situation when the key does not exist very nicely, considering the following example:
它会很好地处理密钥不存在的情况，考虑以下示例：
In [56]:
import pandas as pd
equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame( {"A": [7001, 8001, 9001, 10000]} )
df["B"] = df["A"].map(equiv)
print(df)
A B
0 7001 1
1 8001 2
2 9001 3
3 10000 NaN
[4 rows x 2 columns]
