Anyone know Python or Anaconda?

[JJ Husker]

Count me out

 

I did Fortran at UNL using punch cards.

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.

[Moiraine]

I figured it out. Will post code later in case anyone (Bob Zoogs) is curious.

[Moiraine]

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.

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

[huKSer]

Isn't it great when you solve the puzzle and feel the accomplishment?

[Moiraine]

Isn't it great when you solve the puzzle and feel the accomplishment?

 

 

Indeed