################################################################################################################
#this is to convert the raw latitude data in my pandas dictionary into "buckets" or ranges as google calls them.
#################################################################################################################

LATITUDE_RANGES = zip(xrange(32, 44), xrange(33, 45))
#the above code I changed and replaced xrange with just range since xrange is already deprecated python3.
#could this be the problem? using range instead of xrange? see below for my conundrum.


def select_and_transform_features(source_df):
  selected_examples = pd.DataFrame()
  selected_examples["median_income"] = source_df["median_income"]
  for r in LATITUDE_RANGES:
    selected_examples["latitude_%d_to_%d" % r] = source_df["latitude"].apply(
      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)
  return selected_examples



#####################################################################################################################################
#these two are to run the above function and convert may exiting training and validation data sets into ranges or buckets for latitude
######################################################################################################################################
selected_training_examples = select_and_transform_features(training_examples)
selected_validation_examples = select_and_transform_features(validation_examples)



###########################
#this is the training model
###########################
_ = train_model(
    learning_rate=0.01,
    steps=500,
    batch_size=5,
    training_examples=selected_training_examples,
    training_targets=training_targets,
    validation_examples=selected_validation_examples,
    validation_targets=validation_targets)


#############
#THE PROBLEM
#############

#oki so here is how I understand the problem. When I run the training model it throws this error
#ValueError: Feature latitude_32_to_33 is not in features dictionary.
#So I called selected_training_examples  and  selected_validation_examples
#here's what I found. If I run
#  selected_training_examples = select_and_transform_features(training_examples)
#then I get the proper data set when I call selected_training_examples which yields all the feature "buckets" including Feature #latitude_32_to_33
#but when I run the next function
#selected_validation_examples = select_and_transform_features(validation_examples)
#it yields no buckets or ranges resulting in the
#ValueError: Feature latitude_32_to_33 is not in features dictionary.
# so I next tried disabling the first function selected_training_examples = select_and_transform_features(training_examples)
#and I just ran the second function selected_validation_examples = select_and_transform_features(validation_examples).
#If I do this, I then get the desired dataset for selected_validation_examples .
#the problem now is running the first function no longer gives me the "buckets" and I'm back to where I began? I guess my question is #how are the two functions affecting each other? and preventing the other from giving me the datasets I need? If I run them together?
#Thanks in advance!