从字典中选择值以创建新的DataFrame列 [英] Select values from dictionary to create a new DataFrame column

问题描述

我有字典

type_dict = {3: 'foo', 4: 'bar',5: 'foobar', 6: 'foobarbar'}

和带有以下列的DataFrame:

and a DataFrame with the following column:

>>> df.type
0     3
1     4
2     5
3     6
4     3
5     4
6     5
7     6
8     3

我想创建一个包含相应的type_dict值的新列，但是以下是我唯一想到的并且无法正常工作的情况:

I want to create a new column containing the corresponding type_dict value, but the following was the only thing I could come up and was not working:

>>> type_dict[df.type]
TypeError: 'Series' objects are mutable, thus they cannot be hashed

>>> type_dict[df.type.values]
TypeError: unhashable type: 'numpy.ndarray'

我真的需要apply并遍历每一行，还是有更有效的选择?

Do I really need to apply and iterate through each row, or is there a more efficient alternative?

推荐答案

您可以使用 map 此处:

You could use map here:

>>> df['type'].map(type_dict)
0          foo
1          bar
2       foobar
3    foobarbar
4          foo
5          bar
6       foobar
7    foobarbar
8          foo
Name: type, dtype: object

map可以采用字典，系列或函数，并返回具有映射值的新系列.它也非常有效地实现了(例如，比apply要多得多).

map can take a dictionary, Series or function and return a new Series with the mapped values. It is also very efficiently implemented (much more so than apply, for example).

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