def test_my_spread_na():
'''When key column contains NaN, do not add NaN as a column name'''
d0_test = miniTidyPy.my_spread(sample_df_na, key = 'time', value = 'value')
assert d0_test.equals(output_na), "key column contains NaN"
# when the whole dataframe contains NaN
assert miniTidyPy.my_spread(all_na, key = 'a', value = 'b').equals(pd.DataFrame({}))
def test_my_spread_wrong_key():
'''When the input key is not included, should return the original dataframe'''
# input is a list
d2_test = miniTidyPy.my_spread(sample_df, key = [], value = 'value')
assert d2_test.equals(sample_df), "key is a list"
# input is an integer
d3_test = miniTidyPy.my_spread(sample_df, key = 2, value = 'value')
assert d3_test.equals(sample_df), "key is an integer"
def test_my_spread_wrong_df():
'''When the input df is not a dataframe, should return None'''
d4_test = miniTidyPy.my_spread(2, key = 'foo', value = 'foo')
assert d4_test is None, "input dataframe type incorrect"
def test_my_spread_normal():
'''Test on a normal dataframe'''
# spread the selected column
d1_test = miniTidyPy.my_spread(sample_df, key = 'time', value = 'value')
assert d1_test.equals(output1), "normal dataframe"
