I have a DataFrame with a column containing labels for each row (in addition to some relevant data for each row). I have a dictionary 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 of 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?