def test_input_int_in_np_mean():
obj = np.array([30, 53, 31, 47, 32])
df = DataFrame(obj, colindex=["AGE"], rowindex=["A", "B", "C", "D", "E"])
expected_output = [38.6]
actual_output = df.mean()
assert actual_output == expected_output
def test_input_mixed_in_np_mean():
obj = np.array([30, 53.0, "31", True, 32])
df = DataFrame(obj, colindex=["AGE"], rowindex=["A", "B", "C", "D", "E"])
expected_output = []
actual_output = df.mean()
assert actual_output == expected_output
def test_input_int_in_dict_of_lists_mean():
obj = {"age": [30, 53, 31, 47, 32], "albums": [4, 10, 2, 5, 4]}
df = DataFrame(
obj, colindex=["AGE", "ALBUMS"], rowindex=["A", "B", "C", "D", "E"]
)
expected_output = [38.6, 5.0]
actual_output = df.mean()
assert actual_output == expected_output
def test_mean_df():
data = {
"value1": [1, 2, 3, 4, 5, 6],
"value2": [2, 4, 6, 2, 10, 0],
"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, 4, 3.85, None])
df = DataFrame(data)
mean_output = df.mean()
print(mean_output)
print(expected_output)
assert mean_output.all() == expected_output.all()
def test_input_mixed_in_dict_of_np_mean():
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 = [38.7, 5.0, 0.6]
actual_output = df.mean()
assert actual_output == expected_output
def test_input_mixed_in_list_of_lists_mean():
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 = [38.7, 5.0, 0.6]
actual_output = df.mean()
assert actual_output == expected_output
def test_mean(dictionary, expected):
myDF = DataFrame(dictionary)
assert myDF.mean() == expected