def urban_percent(data):
calculates median using helper function, then returns
list of values ("low" or "high") based on whether the
UrbanPop is above or below the median
"""
urban_pop = []
urban_level = []
for column in data:
urban_pop.append(int(column[3]))
if int(column[3]) > calculate_median(urban_pop):
urban_level.append("high")
else:
urban_level.append("low")
return urban_level
def urban_percent(data):
"""
calculates median using helper function, then returns
list of values ("low" or "high") based on whether the
UrbanPop is above or below the median
"""
urban_pop = []
urban_level = []
for column in data:
urban_pop.append(int(column[3]))
if int(column[3]) > calculate_median(urban_pop):
urban_level.append("high")
else:
urban_level.append("low")
return urban_level
plt.hist(crime, facecolor='green', label='murders')
plt.title("Murder Rate Histogram")
plt.xlabel("murder rates")
plt.ylabel("# of murders")
plt.legend()
plt.show()
if __name__ == '__main__':
data = import_data(my_file)
headings = seperate_headings_from_data(data)
murder = get_basic_statistics(data)
stats = calculate_statistics(murder)
min_max = calculate_min_and_max(murder)
states = get_state(murder, min_max, data)
print "\nMurder Statistics"
print "-----------------"
print "Mean: {}".format((stats)[0])
print "Median: {}".format((stats)[1])
print "Std. Deviation: {}".format((stats)[2])
print "Highest crime rate: {} with a rate of {}".format(
(states)[0], (min_max)[0])
print "Lowest crime rate: {} with a rate of {}\n".format(
(states)[1], (min_max)[1])
add_data(data, urban_percent(data))
updated_data = add_headings_to_data(data, headings)
export_data(updated_file, updated_data)
print create_frequency_distribution(murder)
print calculate_median(murder) == numpy.median(murder)
create_histogram(murder)
"""
plt.hist(crime, facecolor='green', label='murders')
plt.title("Murder Rate Histogram")
plt.xlabel("murder rates")
plt.ylabel("# of murders")
plt.legend()
plt.show()
if __name__ == '__main__':
data = import_data(my_file)
headings = seperate_headings_from_data(data)
murder = get_basic_statistics(data)
stats = calculate_statistics(murder)
min_max = calculate_min_and_max(murder)
states = get_state(murder, min_max, data)
print "\nMurder Statistics"
print "-----------------"
print "Mean: {}".format((stats)[0])
print "Median: {}".format((stats)[1])
print "Std. Deviation: {}".format((stats)[2])
print "Highest crime rate: {} with a rate of {}".format(
(states)[0], (min_max)[0])
print "Lowest crime rate: {} with a rate of {}\n".format(
(states)[1], (min_max)[1])
add_data(data, urban_percent(data))
updated_data = add_headings_to_data(data, headings)
export_data(updated_file, updated_data)
print create_frequency_distribution(murder)
print calculate_median(murder) == numpy.median(murder)
create_histogram(murder)
