def test_RVI_wrong_type(self):
"""
Negative test
data: array (unsupported type)
Checks that the function raises a TypeError if the data is passed as
an array.
"""
# 1. Arrange
data = [2, 4, np.nan, 1]
# 2. Act & 3. Assert
with self.assertRaises(TypeError):
random_value_imputation(data)
def test_RVI_series_inplace(self):
"""
Positive test
data: Correct series (example series)
Checks that random_value_interpolation removes 3 NA values from the
series.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act
random_value_imputation(ser, inplace=True)
# 3. Assert
self.assertEqual(ser.isna().sum().sum(), 0)
def test_RVI_df_inplace(self):
"""
Positive test
data: Correct dataframe (divcols)
Checks that random_value_interpolation removes 18 values from the
dataframe.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
random_value_imputation(df, inplace=True)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 0)
def test_RVI_df_invalid_distribution(self):
"""
Negative test
data: Correct dataframe (divcols)
distribution: '' (invalid value)
Checks that random_value_interpolation raises a ValueError when an
unrecognized distribution is passed
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act & Assert
with self.assertRaises(ValueError):
random_value_imputation(ser, '')
def test_RVI_df_wrong_columns(self):
"""
Negative test
data: Correct dataframe (divcols)
columns: ['z'] ('z' doesn't exist as a column in the data)
Checks that random_value_interpolation raises a ValueError if one of
the specified columns doesn't exist in the data.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act & Assert
with self.assertRaises(ValueError):
random_value_imputation(ser, columns=['z'])
def test_RVI_series_returning(self):
"""
Positive test
data: Correct series (example series)
Checks that the original series remains unmodified and that the
returned dataframe contains 0 NA values, 3 less than the original.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act
ser2 = random_value_imputation(ser)
# 3. Assert
self.assertEqual(ser.isna().sum().sum(), 3)
self.assertEqual(ser2.isna().sum().sum(), 0)
def test_RVI_df_returning(self):
"""
Positive test
data: Correct dataframe (divcols)
Checks that the original dataframe remains unmodified and that the
returned dataframe contains 0 NA values, 18 less than the original.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
df2 = random_value_imputation(df)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 18)
self.assertEqual(df2.isna().sum().sum(), 0)
