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Commit4f82bb4

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Improved tasks 2884, 2885, 2886, 2887
1 parentf0c39f6 commit4f82bb4

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4 files changed

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4 files changed

+387
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lines changed
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importpandasaspd
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importunittest
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# The function to be tested
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defmodifySalaryColumn(employees:pd.DataFrame)->pd.DataFrame:
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employees['salary']=employees['salary']*2
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returnemployees
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# Test class
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classTestDropMissingData(unittest.TestCase):
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deftest_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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deftest_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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deftest_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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deftest_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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deftest_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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importpandasaspd
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importunittest
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# The function to be tested
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defrenameColumns(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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returnstudents
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# Test class
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classTestRenameColumns(unittest.TestCase):
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deftest_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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deftest_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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deftest_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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deftest_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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importpandasaspd
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importunittest
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# The function to be tested
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defchangeDatatype(students:pd.DataFrame)->pd.DataFrame:
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students['grade']=students['grade'].astype('int64')
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returnstudents
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# Test class
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classTestChangeDatatype(unittest.TestCase):
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deftest_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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deftest_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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deftest_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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deftest_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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deftest_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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importpandasaspd
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importunittest
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# The updated function to be tested
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deffillMissingValues(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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returnproducts
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# Test class
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classTestFillMissingValues(unittest.TestCase):
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deftest_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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deftest_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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deftest_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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deftest_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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deftest_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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