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Commit 4f82bb4

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Improved tasks 2884, 2885, 2886, 2887
1 parent f0c39f6 commit 4f82bb4

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4 files changed

+387
-0
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4 files changed

+387
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import pandas as pd
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import unittest
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# The function to be tested
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def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame:
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employees['salary'] = employees['salary'] * 2
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return employees
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# Test class
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class TestDropMissingData(unittest.TestCase):
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def test_modify_salary_column_basic_case(self):
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# Input DataFrame
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employees = pd.DataFrame({
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'name': ['Jack', 'Piper', 'Mia', 'Ulysses'],
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'salary': [19666, 74754, 62509, 54866]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'name': ['Jack', 'Piper', 'Mia', 'Ulysses'],
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'salary': [39332, 149508, 125018, 109732]
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})
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# Call the function and assert equality
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result = modifySalaryColumn(employees)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_modify_salary_column_empty_dataframe(self):
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# Input: Empty DataFrame
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employees = pd.DataFrame(columns=['name', 'salary'])
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# Expected output: Empty DataFrame
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expected_output = pd.DataFrame(columns=['name', 'salary'])
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# Call the function and assert equality
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result = modifySalaryColumn(employees)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_modify_salary_column_single_row(self):
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# Input DataFrame with a single row
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employees = pd.DataFrame({
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'name': ['Alice'],
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'salary': [50000]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'name': ['Alice'],
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'salary': [100000]
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})
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# Call the function and assert equality
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result = modifySalaryColumn(employees)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_modify_salary_column_zero_salary(self):
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# Input DataFrame with a zero salary
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employees = pd.DataFrame({
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'name': ['Bob'],
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'salary': [0]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'name': ['Bob'],
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'salary': [0]
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})
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# Call the function and assert equality
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result = modifySalaryColumn(employees)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_modify_salary_column_negative_salary(self):
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# Input DataFrame with a negative salary
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employees = pd.DataFrame({
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'name': ['Charlie'],
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'salary': [-30000]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'name': ['Charlie'],
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'salary': [-60000]
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})
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# Call the function and assert equality
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result = modifySalaryColumn(employees)
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pd.testing.assert_frame_equal(result, expected_output)
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# Run the tests
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if __name__ == '__main__':
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unittest.main()
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import pandas as pd
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import unittest
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# The function to be tested
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def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
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students.rename(columns={'id': 'student_id', 'first': 'first_name', 'last': 'last_name', 'age': 'age_in_years'}, inplace=True)
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return students
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# Test class
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class TestRenameColumns(unittest.TestCase):
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def test_rename_columns_basic_case(self):
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# Input DataFrame
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students = pd.DataFrame({
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'id': [1, 2, 3, 4, 5],
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'first': ['Mason', 'Ava', 'Taylor', 'Georgia', 'Thomas'],
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'last': ['King', 'Wright', 'Hall', 'Thompson', 'Moore'],
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'age': [6, 7, 16, 18, 10]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'student_id': [1, 2, 3, 4, 5],
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'first_name': ['Mason', 'Ava', 'Taylor', 'Georgia', 'Thomas'],
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'last_name': ['King', 'Wright', 'Hall', 'Thompson', 'Moore'],
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'age_in_years': [6, 7, 16, 18, 10]
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})
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# Call the function and assert equality
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result = renameColumns(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_rename_columns_empty_dataframe(self):
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# Input: Empty DataFrame with the correct column names
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students = pd.DataFrame(columns=['id', 'first', 'last', 'age'])
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# Expected output: Empty DataFrame with renamed columns
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expected_output = pd.DataFrame(columns=['student_id', 'first_name', 'last_name', 'age_in_years'])
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# Call the function and assert equality
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result = renameColumns(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_rename_columns_single_row(self):
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# Input DataFrame with a single row
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students = pd.DataFrame({
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'id': [10],
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'first': ['Emma'],
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'last': ['Johnson'],
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'age': [15]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'student_id': [10],
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'first_name': ['Emma'],
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'last_name': ['Johnson'],
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'age_in_years': [15]
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})
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# Call the function and assert equality
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result = renameColumns(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_rename_columns_with_different_ages(self):
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# Input DataFrame with various ages
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students = pd.DataFrame({
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'id': [101, 102],
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'first': ['Liam', 'Olivia'],
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'last': ['Brown', 'Davis'],
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'age': [21, 30]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'student_id': [101, 102],
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'first_name': ['Liam', 'Olivia'],
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'last_name': ['Brown', 'Davis'],
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'age_in_years': [21, 30]
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})
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# Call the function and assert equality
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result = renameColumns(students)
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pd.testing.assert_frame_equal(result, expected_output)
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# Run the tests
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if __name__ == '__main__':
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unittest.main()
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import pandas as pd
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import unittest
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# The function to be tested
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def changeDatatype(students: pd.DataFrame) -> pd.DataFrame:
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students['grade'] = students['grade'].astype('int64')
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return students
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# Test class
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class TestChangeDatatype(unittest.TestCase):
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def test_change_datatype_basic_case(self):
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# Input DataFrame
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students = pd.DataFrame({
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'student_id': [1, 2],
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'name': ['Ava', 'Kate'],
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'age': [6, 15],
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'grade': [73.0, 87.0]
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})
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# Expected output DataFrame with grade explicitly set as int64
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expected_output = pd.DataFrame({
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'student_id': [1, 2],
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'name': ['Ava', 'Kate'],
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'age': [6, 15],
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'grade': [73, 87]
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})
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expected_output['grade'] = expected_output['grade'].astype('int64')
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# Call the function and assert equality
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result = changeDatatype(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_change_datatype_empty_dataframe(self):
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# Input: Empty DataFrame with the correct columns
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students = pd.DataFrame(columns=['student_id', 'name', 'age', 'grade'])
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# Expected output: Empty DataFrame with the same columns and grade set to Int64 dtype
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expected_output = pd.DataFrame(columns=['student_id', 'name', 'age', 'grade'])
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expected_output['grade'] = expected_output['grade'].astype('int64')
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# Call the function and assert equality
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result = changeDatatype(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_change_datatype_with_negative_grades(self):
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# Input DataFrame with negative grades
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students = pd.DataFrame({
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'student_id': [3, 4],
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'name': ['Liam', 'Olivia'],
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'age': [12, 10],
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'grade': [-45.0, -88.0]
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})
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# Expected output DataFrame with grades as integers
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expected_output = pd.DataFrame({
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'student_id': [3, 4],
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'name': ['Liam', 'Olivia'],
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'age': [12, 10],
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'grade': [-45, -88]
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})
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expected_output['grade'] = expected_output['grade'].astype('int64')
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# Call the function and assert equality
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result = changeDatatype(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_change_datatype_with_decimal_grades(self):
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# Input DataFrame with decimal grades that will truncate
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students = pd.DataFrame({
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'student_id': [5, 6],
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'name': ['Ella', 'Noah'],
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'age': [14, 17],
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'grade': [95.6, 78.9]
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})
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# Expected output DataFrame with truncated grades as integers
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expected_output = pd.DataFrame({
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'student_id': [5, 6],
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'name': ['Ella', 'Noah'],
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'age': [14, 17],
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'grade': [95, 78]
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})
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expected_output['grade'] = expected_output['grade'].astype('int64')
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# Call the function and assert equality
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result = changeDatatype(students)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_change_datatype_single_row(self):
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# Input DataFrame with a single row
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students = pd.DataFrame({
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'student_id': [7],
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'name': ['James'],
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'age': [11],
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'grade': [80.0]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'student_id': [7],
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'name': ['James'],
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'age': [11],
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'grade': [80]
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})
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expected_output['grade'] = expected_output['grade'].astype('int64')
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# Call the function and assert equality
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result = changeDatatype(students)
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pd.testing.assert_frame_equal(result, expected_output)
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# Run the tests
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if __name__ == '__main__':
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unittest.main()
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import pandas as pd
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import unittest
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# The updated function to be tested
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def fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:
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products['quantity'].fillna(0, inplace=True)
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products['quantity'] = products['quantity'].astype(int) # Ensure the quantity is of type int
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return products
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# Test class
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class TestFillMissingValues(unittest.TestCase):
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def test_fill_missing_values_basic_case(self):
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# Input DataFrame with missing values in quantity
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products = pd.DataFrame({
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'name': ['Wristwatch', 'WirelessEarbuds', 'GolfClubs', 'Printer'],
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'quantity': [None, None, 779, 849],
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'price': [135, 821, 9319, 3051]
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})
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# Expected output DataFrame
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expected_output = pd.DataFrame({
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'name': ['Wristwatch', 'WirelessEarbuds', 'GolfClubs', 'Printer'],
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'quantity': [0, 0, 779, 849],
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'price': [135, 821, 9319, 3051]
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})
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# Call the function and assert equality
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result = fillMissingValues(products)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_fill_missing_values_no_missing(self):
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# Input DataFrame with no missing values
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products = pd.DataFrame({
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'name': ['Laptop', 'Mouse', 'Keyboard'],
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'quantity': [10, 5, 0],
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'price': [1000, 50, 30]
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})
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# Expected output should be the same as input
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expected_output = products.copy()
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# Call the function and assert equality
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result = fillMissingValues(products)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_fill_missing_values_empty_dataframe(self):
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# Input: Empty DataFrame
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products = pd.DataFrame(columns=['name', 'quantity', 'price'])
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# Expected output: Empty DataFrame with specified dtypes
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expected_output = pd.DataFrame(columns=['name', 'quantity', 'price'], dtype='object')
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expected_output['quantity'] = expected_output['quantity'].astype('int64')
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# Call the function and assert equality
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result = fillMissingValues(products)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_fill_missing_values_empty_dataframe(self):
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# Input: Empty DataFrame
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products = pd.DataFrame(columns=['name', 'quantity', 'price'])
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# Expected output: Empty DataFrame
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expected_output = pd.DataFrame(columns=['name', 'quantity', 'price'])
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expected_output['quantity'] = expected_output['quantity'].astype('int64')
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# Call the function and assert equality
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result = fillMissingValues(products)
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pd.testing.assert_frame_equal(result, expected_output)
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def test_fill_missing_values_all_none(self):
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# Input DataFrame with all None in quantity
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products = pd.DataFrame({
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'name': ['Item1', 'Item2', 'Item3'],
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'quantity': [None, None, None],
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'price': [100, 200, 300]
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})
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# Expected output DataFrame with quantity filled with 0
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expected_output = pd.DataFrame({
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'name': ['Item1', 'Item2', 'Item3'],
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'quantity': [0, 0, 0],
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'price': [100, 200, 300]
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})
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# Call the function and assert equality
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result = fillMissingValues(products)
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pd.testing.assert_frame_equal(result, expected_output)
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# Run the tests
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if __name__ == '__main__':
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unittest.main()

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