How to fix AttributeError: ‘dict‘ object has no attribute ‘iteritems‘

Error : AttributeError: 'dict' object has no attribute 'iteritems'

Solution:

Python 3 renamed dict.iteritems -> dict.items

Original Code: 

# Example of summarizing data by class value
from math import sqrt

# Split the dataset by class values, returns a dictionary
def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in separated):
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated

# Calculate the mean of a list of numbers
def mean(numbers):
    return sum(numbers)/float(len(numbers))

# Calculate the standard deviation of a list of numbers

def stdev(numbers):
    avg = mean(numbers)
    variance = sum([(x-avg)**2 for x in numbers])/ float(len(numbers)-1)
    return sqrt(variance)

# Calculate the mean,stdev and count for each column in a dataset.
def summarize_dataset(dataset):
    summaries = [(mean(column),stdev(column),len(column),len(column)) for column in zip(*dataset)]
    del(summaries[-1])
    return summaries

# Split  dataset by class then calculate statistics for each row
def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value,rows in separated.iteritems():
        summaries[class_value] = summarize_dataset(rows)
    return summaries

# Test summaring by class
dataset = [[3.393533211,2.331273381,0],
[3.110073483,1.781539638,0],
[1.343808831,3.368360954,0],
[3.582294042,4.67917911,0],
[2.280362439,2.866990263,0],
[7.423436942,4.696522875,1],
[5.745051997,3.533989803,1],
[9.172168622,2.511101045,1],
[7.792783481,3.424088941,1],
[7.939820817,0.791637231,1]]
summary = summarize_by_class(dataset)
for label in summary:
    print(label)
    for row in summary[label]:
        print(row)

Modified Code:

# Example of summarizing data by class value
from math import sqrt

# Split the dataset by class values, returns a dictionary
def separate_by_class(dataset):
    separated = dict()
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in separated):
            separated[class_value] = list()
        separated[class_value].append(vector)
    return separated

# Calculate the mean of a list of numbers
def mean(numbers):
    return sum(numbers)/float(len(numbers))

# Calculate the standard deviation of a list of numbers

def stdev(numbers):
    avg = mean(numbers)
    variance = sum([(x-avg)**2 for x in numbers])/ float(len(numbers)-1)
    return sqrt(variance)

# Calculate the mean,stdev and count for each column in a dataset.
def summarize_dataset(dataset):
    summaries = [(mean(column),stdev(column),len(column),len(column)) for column in zip(*dataset)]
    del(summaries[-1])
    return summaries

# Split  dataset by class then calculate statistics for each row
def summarize_by_class(dataset):
    separated = separate_by_class(dataset)
    summaries = dict()
    for class_value,rows in separated.items():
        summaries[class_value] = summarize_dataset(rows)
    return summaries

# Test summaring by class
dataset = [[3.393533211,2.331273381,0],
[3.110073483,1.781539638,0],
[1.343808831,3.368360954,0],
[3.582294042,4.67917911,0],
[2.280362439,2.866990263,0],
[7.423436942,4.696522875,1],
[5.745051997,3.533989803,1],
[9.172168622,2.511101045,1],
[7.792783481,3.424088941,1],
[7.939820817,0.791637231,1]]
summary = summarize_by_class(dataset)
for label in summary:
    print(label)
    for row in summary[label]:
        print(row)