def test_5():
"""
Situation : Symmetrical data which includes negative numbers also
"""
test_file = open("data/data_for_test_mean_vs_median/test_5.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = 'None'
assert(str(suggestion) == expected_suggestion)
def test_3():
"""
Situation : A lot of initial values are very small values
"""
test_file = open("data/data_for_test_mean_vs_median/test_3.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = """{'suggestion': 'Median is very different from the Mean', 'oversight_name': 'Mean vs Median'}"""
assert(str(suggestion) == expected_suggestion)
def test_4():
"""
Situation : There are only 2 data points present
"""
test_file = open("data/data_for_test_mean_vs_median/test_4.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = 'None'
assert(str(suggestion) == expected_suggestion)
def test_1():
"""
Situation : One of the entry of data contains a number with an extra 0 in it
"""
test_file = open("data/data_for_test_mean_vs_median/test_1.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = """{'suggestion': 'Median is very different from the Mean', 'oversight_name': 'Mean vs Median'}"""
assert(str(suggestion) == expected_suggestion)
def test_6():
"""
Situation : Values having negative numbers which are large in absolute value
The skewness in this situation should be negative
"""
test_file = open("data/data_for_test_mean_vs_median/test_6.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = """{'suggestion': 'Median is very different from the Mean', 'oversight_name': 'Mean vs Median'}"""
assert(str(suggestion) == expected_suggestion)
def test_2():
"""
Situation : Values having some high peaks above the median and some low peaks
below the median due to which mean balances to be around median
"""
test_file = open("data/data_for_test_mean_vs_median/test_2.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = 'None'
assert(str(suggestion) == expected_suggestion)
def test_10():
"""
Situation : All values being 0 , this situation check if there is any zero
division errors
"""
test_file = open("data/data_for_test_mean_vs_median/test_10.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = 'None'
assert(str(suggestion) == expected_suggestion)
def test_9():
"""
Situation : Clustering example of data , with first initial values having
low value and rest of the values being high
"""
test_file = open("data/data_for_test_mean_vs_median/test_9.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = """{'suggestion': 'Median is very different from the Mean', 'oversight_name': 'Mean vs Median'}"""
assert(str(suggestion) == expected_suggestion)
def test_7():
"""
Situation : Values containing a large number of 0s in between which impacts the
mean value but not the median value
"""
test_file = open("data/data_for_test_mean_vs_median/test_7.txt", "r")
values = test_file.read().split("\n")
values = list(map(int, values))
suggestion = mean_vs_median(values)
print(suggestion)
print("Mean: " + str(statistics.mean(values)) + " Median: " + str(statistics.median(values)))
expected_suggestion = """{'suggestion': 'Median is very different from the Mean', 'oversight_name': 'Mean vs Median'}"""
assert(str(suggestion) == expected_suggestion)