Anyone know Python or Anaconda?

Oh, okay. The pandas page makes it look like it would be something like:

Code: 
grouped = df.groupby('lastName')
for name, group in grouped:
    doTheThings(name, group)
Possibly? Or even:

Code: 
df = df.groupby('lastName').apply(doTheThings)
(source)
 
huKSer said:
Count me out

I did Fortran at UNL using punch cards.
Click to expand...
Ha, me too. Remember those stacks of cards? Never did interest me.

We used to play a poker game called Anaconda. Deal seven cards, pass 3 to your right, discard 2, put your 5 cards face down in order then roll them 1 at a time...betting every step of the way. Fun times.

 
BAM!

The blob after dct = {} (that stands for dictionary, dirty people) is the part I needed help with and then the rest I figured out from that.

Code: 
import nltk
import pickle
import pandas

raw_data = {
    'first_name': ['Bob', 'Teacher', 'Moiraine', 'Alastair', 'Knap', 'Matilda'],
    'last_name': ['Zoogs', 'CD', 'Damodred', 'Damodred', 'Plc', 'Plc'],
    'gender': ['Male','Male','Female','Male','Male','Female']
     }

df = pandas.DataFrame(raw_data, columns = ['first_name', 'last_name', 'gender'])
last_name_dict = dict(iter(df.groupby("last_name")))

def gender_features(word):
    return {'first_letter': word[0],
            'last_letter': word[-1] }

dct = {}
for last in df.last_name.unique():
    dct[last] = []
    for first,gender in zip(last_name_dict[last].first_name,last_name_dict[last].gender):
        dct[last].append((gender_features(first),gender))       

# TRAIN in a loop
traindct = {}
class_dct = {}
for last in df.last_name.unique():
    traindct['train_set_%s' % last] = dct[last][0:] 
    class_dct['Classif_%s'% last] = nltk.NaiveBayesClassifier.train(traindct['train_set_%s' % last])

# CLASSIFY Test:
Classif_Zoogs.classify(gender_features('Rob'))
>>> Male
Classif_Damodred.classify(gender_features('Elaine'))
>>> Female
 
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