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Select values ​​from dictionary to create new DataFrame column

I have a dictionary

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

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

I want to create a new column containing the corresponding value type_dict

, but the only thing I could think of and didn't work:

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

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

Do I really need to apply

and iterate every line, or is there a better alternative?

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1 answer


You can use here map

:

>>> 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

can take a dictionary, series or function and return a new series with displayed values. It's also very efficiently implemented (much more so than apply

, for example).

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