def test_LOCF_wrong_type(self):
"""
Negative test
data: array (unsupported type)
Checks that the locf 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):
locf(data)
def test_LOCF_df_inplace(self):
"""
Positive test
data: Correct dataframe (divcols)
Checks that locf removes 5 NA values from the dataframe.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
locf(df, inplace=True)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 13)
def test_LOCF_series_inplace(self):
"""
Positive test
data: Correct Series (example series)
Checks that locf removes 2 NA values from the series.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act
locf(ser, inplace=True)
# 3. Assert
self.assertEqual(ser.isna().sum(), 1)
def test_LOCF_df_inplace_columns(self):
"""
Positive test
data: Correct dataframe (divcols)
columns: ['f', 'g']
Checks that locf removes 2 NA values from the specified columns.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
locf(df, columns=['f', 'g'], inplace=True)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 16)
def test_LOCF_df_inplace_wrong_column(self):
"""
Negative test
data: Correct dataframe (divcols)
columns: ['f', 'g', 'z'] ('z' doesn't exist in the data)
Checks that the locf raises a ValueError if the data is passed as an
array.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
locf(df, columns=['f', 'g', 'z'], inplace=True)
def test_LOCF_col_for_series(self):
"""
Negative test
data: Correct series (example_series)
columns: ['a'] (series can't have columns)
Checks that the function raises a ValueError if a column is passed
for a series.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
locf(ser, columns=['a'])
def test_LOCF_series_returning(self):
"""
Positive test
data: Correct Series (example series)
Checks that the original series remains unmodified and that the
returned series contains 1 NA value, 2 less than the original.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act
ser2 = locf(ser)
# 3. Assert
self.assertEqual(ser.isna().sum(), 3)
self.assertEqual(ser2.isna().sum(), 1)
def test_LOCF_df_returning(self):
"""
Positive test
data: Correct dataframe (divcols)
Checks that the original dataframe remains unmodified and that the
returned dataframe contains 13 NA values, 5 less than the original.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
df2 = locf(df)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 18)
self.assertEqual(df2.isna().sum().sum(), 13)
def test_LOCF_series_returning_fill_leading(self):
"""
Positive test
data: Correct Series (example series)
fill_leading: True
Checks that the original series remains unmodified and that the
returned series contains 0 NA values.
"""
# 1. Arrange
ser = generate_example_series()
# 2. Act
ser2 = locf(ser, fill_leading=True)
# 3. Assert
self.assertEqual(ser.isna().sum(), 3)
self.assertEqual(ser2.isna().sum(), 0)
def test_LOCF_df_returning_columns_fill_leading(self):
"""
Positive test
data: Correct dataframe (divcols)
columns: ['f', 'g']
fill_leading: True
Checks that the original dataframe remains unmodified and that the
returned dataframe contains 14 NA values, 4 less than the original.
"""
# 1. Arrange
df = generate_example_df_divcols()
# 2. Act
df2 = locf(df, columns=['f', 'g'], fill_leading=True)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 18)
self.assertEqual(df2.isna().sum().sum(), 14)