如何用字典键替换数据框列值? [英] How to replace dataframe column values with dictionary keys?

问题描述

假设我有一本字典:

dict = {"1" : "A", "2" : "B" , "3" : "C"}

和一个数据框

df = pd.DataFrame()
df["ID"] = pd.Series(["A","B","C"])
df["Desc"] = pd.Series(["Fruits","Vegs","Meat"])

数据框将如下所示:

如何用字典键替换列df["ID"]中的值，以便在df["ID"]中使用1,2,3而不是A,B,C?

How would I replace values in column df["ID"] with dictionary keys so that I have 1,2,3 in df["ID"] instead of A,B,C?

推荐答案

首先创建一个反向映射:

First create a reverse mapping:

In [363]: dict2 = {v : k for k, v in dict_.items()}

这里所做的假设是您的价值观是唯一的.现在您可以使用pd.Series.replace:

The assumption made here is that your values are unique. Now you can use pd.Series.replace:

In [367]: df.ID = df.ID.replace(dict2); df
Out[367]: 
  ID    Desc
0  1  Fruits
1  2    Vegs
2  3    Meat

---

使用pd.Series.map的替代解决方案:

---

Alternative solution with pd.Series.map:

In [380]: df.ID = df.ID.map(dict2); df
Out[380]: 
  ID    Desc
0  1  Fruits
1  2    Vegs
2  3    Meat

此外，我建议您使用不同于dict的名称，因为已经有一个内置名称.

Also, I recommend you use a different name than dict, because there's already a builtin with that name.

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