def test_input_int_in_np_median_odd():
obj = np.array([30, 53, 31, 47, 32])
df = DataFrame(obj, colindex=["AGE"], rowindex=["A", "B", "C", "D", "E"])
expected_output = [32.0]
actual_output = df.median()
assert actual_output == expected_output
def test_input_int_in_list_of_lists_median_odd():
obj = [[30, 53, 31, 47, 32], [4, 10, 2, 5, 4]]
df = DataFrame(
obj, colindex=["AGE", "ALBUMS"], rowindex=["A", "B", "C", "D", "E"]
)
expected_output = [32.0, 4.0]
actual_output = df.median()
assert actual_output == expected_output
def test_input_int_in_list_of_lists_median_even():
obj = [[30, 53, 31, 47, 32, 100], [4, 10, 2, 5, 4, 100]]
df = DataFrame(
obj,
colindex=["AGE", "ALBUMS"],
rowindex=["A", "B", "C", "D", "E", "F"],
)
expected_output = [39.5, 4.5]
actual_output = df.median()
assert actual_output == expected_output
def test_median_df():
data = {
"value1": [1, 2, 3, 4, 5, 6],
"value2": [2, 2, 2, 2, 2, 2],
"value3": [1.1, 2.2, 3.3, 4.4, 5.5, 6.6],
"value4": ["a", "b", "c", "d", "e", "f"],
}
expected_output = np.array([3.5, 2, 3.85, None])
df = DataFrame(data)
median_output = df.median()
print(median_output)
print(expected_output)
assert median_output.all() == expected_output.all()
def test_input_int_in_dict_of_np_median_odd():
obj = {
"age": np.array([30, 53, 31, 47, 32]),
"albums": np.array([4, 10, 2, 5, 4]),
}
df = DataFrame(
obj, colindex=["AGE", "ALBUMS"], rowindex=["A", "B", "C", "D", "E"]
)
expected_output = [32.0, 4.0]
actual_output = df.median()
assert actual_output == expected_output
def test_input_mixed_in_list_of_lists_median_odd():
obj = [
[30.1, 53.1, 31.1, 47.1, 32.1],
[4, 10, 2, 5, 4],
["a", "b", "c", "d", "e"],
[True, False, True, True, False],
]
df = DataFrame(
obj,
colindex=["AGE", "ALBUMS", "C", "D"],
rowindex=["A", "B", "C", "D", "E"],
)
expected_output = [32.1, 4.0, 1.0]
actual_output = df.median()
assert actual_output == expected_output
def test_input_mixed_in_dict_of_np_median_odd():
obj = {
"age": np.array([30.1, 53.1, 31.1, 47.1, 32.1]),
"albums": np.array([4, 10, 2, 5, 4]),
"C": np.array(["a", "b", "c", "d", "e"]),
"D": np.array([True, False, True, True, False]),
}
df = DataFrame(
obj,
colindex=["AGE", "ALBUMS", "C", "D"],
rowindex=["A", "B", "C", "D", "E"],
)
expected_output = [32.1, 4.0, 1.0]
actual_output = df.median()
assert actual_output == expected_output
def test_input_mixed_in_dict_of_lists_median_even():
obj = {
"age": [30.1, 53.1, 31.1, 47.1, 32.1, 100.0],
"albums": [4, 10, 2, 5, 4, 100],
"C": ["a", "b", "c", "d", "e", "f"],
"D": [True, False, True, True, False, True],
}
df = DataFrame(
obj,
colindex=["AGE", "ALBUMS", "C", "D"],
rowindex=["A", "B", "C", "D", "E", "F"],
)
expected_output = [39.6, 4.5, 1.0]
actual_output = df.median()
assert actual_output == expected_output
def test_median2():
dictionary = {
"pet": np.array(["cat", "dog", "mouse"]),
"age": np.array([1, 2, 3]),
"weight": np.array([1.0, 2.0, 3.0]),
"sick": np.array([True, True, False]),
}
df = DataFrame(dictionary)
median_collector = []
columns = []
for key in dictionary:
if dictionary[key].dtype == "float64" or \
dictionary[key].dtype == "int32":
columns.append(key)
median_collector.append(statistics.median(dictionary[key]))
expected_output = median_collector
output = df.median()
assert output == expected_output
def test_median(dictionary, expected):
myDF = DataFrame(dictionary)
assert myDF.median() == expected