Too many values to unpack using NLTK and Pandas in Python - pandas

Question

I am trying out different things to make the NLTK's naive bayes work using the NLTK and Pandas modules, but I am getting the "too many values to unpack" error.
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import re
import nltk
### Remove cases with missing name or missing ethnicity information
def read_file():
data = pd.read_csv("C:\sample.csv", encoding="utf-8")
frame = DataFrame(data)
frame.columns = ["Name", "Gender"]
return frame
#read_file()
def gender_features(word):
return {'last_letter': word[-1]}
#gender_features()
frame = read_file()
featuresets = [(gender_features(n), gender) for (n, gender) in frame]
train_set, test_set = features[500:], featuresets[:500]
classifier = nltkNaiveBayesClassifier.train(train_set)

Answer 1

I suspect you are trying to do something bigger than name classification when using panadas.DataFrame because the DataFrame object is normally used when you have limited RAM and wants to makes use of diskspace as you iterate through the data to extract features:
a 2-dimensional labeled data structure with columns of potentially
different types. You can think of it like a spreadsheet or SQL table,
or a dict of Series objects. It is generally the most commonly used
pandas object. Like Series, DataFrame accepts many different kinds of
input:
Dict of 1D ndarrays, lists, dicts, or Series
2-D numpy.ndarray
Structured or record ndarray
A Series
Another DataFrame
I suggest you go through the pandas tutorial to learn about the library first: http://pandas.pydata.org/pandas-docs/dev/tutorials.html
And then learn about the NLTK classification from http://www.nltk.org/book/ch06.html
Firstly, there are several things wrong in how you access pandas.DataFrame object.
To iterate through the rows of the dataframe, you should do this:
# Read file into pandas dataframe
df = DataFrame(pd.read_csv('sample.csv'))
df.columns = ['name', 'gender']
for index, row in df.iterrows():
print row['name'], row['gender']
Next to train a classifier, you should do this:
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
from nltk.corpus import names
from nltk.classify import NaiveBayesClassifier as nbc
# Create a sample.csv file
male_names = [','.join([i,'m']) for i in names.words('male.txt')]
female_names = [','.join([i,'m']) for i in names.words('female.txt')]
with open('sample.csv', 'w') as fout:
fout.write('\n'.join(male_names+female_names))
# Feature extractor function.
def gender_features(word):
return {'last_letter': word[-1]}
# Read file into pandas dataframe
df = DataFrame(pd.read_csv('sample.csv'))
df.columns = ['name', 'gender']
# Extract features.
featuresets = [(gender_features(name), gender) for index, (name, gender) in df.iterrows()]
# Split train and test set
train_set, test_set = featuresets[500:], featuresets[:500]
# Train a classifier
classifier = nbc.train(train_set)
# Test classifier on "Neo"
print classifier.classify(gender_features('Neo'))
[out]:
m