python - Pandas: Assigning multiple *new* columns simultaneously

Question

I have a DataFrame df with a column containing labels for each row (in addition to some relevant data for each row). I have a dictionary labeldict with keys equal to the possible labels and values equal to 2-tuples of information related to that label. I'd like to tack two new columns onto my frame, one for each part of the 2-tuple corresponding to the label for each row.

Here is the setup:

import pandas as pd
import numpy as np

np.random.seed(1)
n = 10

labels = list('abcdef')
colors = ['red', 'green', 'blue']
sizes = ['small', 'medium', 'large']

labeldict = {c: (np.random.choice(colors), np.random.choice(sizes)) for c in labels}

df = pd.DataFrame({'label': np.random.choice(labels, n), 
                   'somedata': np.random.randn(n)})

I can get what I want by running:

df['color'], df['size'] = zip(*df['label'].map(labeldict))
print df

  label  somedata  color    size
0     b  0.196643    red  medium
1     c -1.545214  green   small
2     a -0.088104  green   small
3     c  0.852239  green   small
4     b  0.677234    red  medium
5     c -0.106878  green   small
6     a  0.725274  green   small
7     d  0.934889    red  medium
8     a  1.118297  green   small
9     c  0.055613  green   small

But how can I do this if I don't want to manually type out the two columns on the left side of the assignment? I.e. how can I create multiple new columns on the fly. For example, if I had 10-tuples in labeldict instead of 2-tuples, this would be a real pain as currently written. Here are a couple things that don't work:

# set up attrlist for later use
attrlist = ['color', 'size']

# non-working idea 1)
df[attrlist] = zip(*df['label'].map(labeldict))

# non-working idea 2)
df.loc[:, attrlist] = zip(*df['label'].map(labeldict))

This does work, but seems like a hack:

for a in attrlist:
    df[a] = 0
df[attrlist] = zip(*df['label'].map(labeldict))

Better solutions?

Answer 1

You can use merge instead:

>>> ld = pd.DataFrame(labeldict).T
>>> ld.columns = ['color', 'size']
>>> ld.index.name = 'label'
>>> df.merge(ld.reset_index(), on='label')
  label  somedata  color    size
0     b  1.462108    red  medium
1     c -2.060141  green   small
2     c  1.133769  green   small
3     c  0.042214  green   small
4     e -0.322417    red  medium
5     e -1.099891    red  medium
6     e -0.877858    red  medium
7     e  0.582815    red  medium
8     f -0.384054    red   large
9     d -0.172428    red  medium