Mapping values from series to all elements in a dataframe

Question

I have something like the following dataframe:

import pandas as pd
import numpy as np

df = pd.DataFrame()
df['ID1'] = [0, 1, 4, 6, 7]
df['ID2'] = [6, 2, 3, 4, np.nan]
df['ID3'] = [np.nan, np.nan, 7, np.nan, np.nan]
>>> df
   ID1  ID2  ID3
0    0    6  NaN
1    1    2  NaN
2    4    3    7
3    6    4  NaN
4    7  NaN  NaN

And the following set of keys:

keys = pd.Series(['Orange', 'Blue', 'Purple', 'Red', 'Green', 'Pink', 'Brown', 'Black'],
                 name='ID')
>>> keys
0    Orange
1      Blue
2    Purple
3       Red
4     Green
5      Pink
6     Brown
7     Black
Name: ID, dtype: object

I want to replace each element in the dataframe with it's appropriate key. I can do this column by column using the 'map function':

>>> df['ID1'].map(keys)
0    Orange
1      Blue
2     Green
3     Brown
4     Black
Name: ID1, dtype: object

However, how can I do this to my entire dataframe at once? I would think that this is where 'applymap' would come in, but I seem to be using it incorrectly as I get an error message when I try df.applymap(keys).

Any suggestions on how I can do this?

Alexander · Accepted Answer · 2016-02-17 23:43:13Z

1

This is a good use case of a lambda function with apply where you map each column in the dataframe.

>>> df.apply(lambda col: col.map(keys))
      ID1     ID2    ID3
0  Orange   Brown    NaN
1    Blue  Purple    NaN
2   Green     Red  Black
3   Brown   Green    NaN
4   Black     NaN    NaN

answered Feb 17, 2016 at 23:43

Alexander

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