pandas 如何用字典键替换数据框列值？
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How to replace dataframe column values with dictionary keys?
提问by farheen
Suppose I have a dictionary:
假设我有一本字典：
dict = {"1" : "A", "2" : "B" , "3" : "C"}
and a data frame
和一个数据框
df = pd.DataFrame()
df["ID"] = pd.Series(["A","B","C"])
df["Desc"] = pd.Series(["Fruits","Vegs","Meat"])
The dataframe will look like this:
数据框将如下所示：
How would I replace values in column df["ID"]with dictionary keys so that I have 1,2,3in df["ID"]instead of A,B,C?
我将如何df["ID"]用字典键替换列中的值，以便我使用1,2,3indf["ID"]而不是A,B,C？
回答by cs95
First create a reverse mapping:
首先创建一个反向映射：
In [363]: dict2 = {v : k for k, v in dict_.items()}
The assumption made here is that your values are unique. Now you can use pd.Series.replace:
这里的假设是你的价值观是独一无二的。现在您可以使用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
Alternative solution with pd.Series.map:
替代解决方案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
Also, I recommend you use a different name than dict, because there's already a builtin with that name.
另外，我建议您使用与 不同的名称dict，因为已经有一个具有该名称的内置函数。
回答by YOBEN_S
Or you can just base on pandas .
或者你可以只基于 pandas 。
df.ID=df.ID.map((pd.DataFrame(data=d,index=['Value',]).T.reset_index().set_index('Value'))['index'])
Out[23]:
ID Desc
0 1 Fruits
1 2 Vegs
2 3 Meat
回答by Gayatri
Another way to do this would be:
另一种方法是：
dict1 = pd.DataFrame(dict.items())
dict1.columns = ['ID_1',"ID"]
merge = pd.merge(df,dict1)
del merge['ID']
merge = merge.rename(columns={'ID_1': 'ID'})
Desc ID
0 Fruits 1
1 Vegs 2
2 Meat 3
