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Commit 4276457

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Improved tasks 2877-2881
1 parent b665e1e commit 4276457

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5 files changed

+279
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import unittest
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import pandas as pd
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from typing import List
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def createDataframe(student_data: List[List[int]]) -> pd.DataFrame:
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column_name = ['student_id','age']
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result = pd.DataFrame(student_data, columns=column_name)
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return result
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class TestCreateDataframe(unittest.TestCase):
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def test_valid_data(self):
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student_data = [[1, 15], [2, 11], [3, 11], [4, 20]]
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expected_df = pd.DataFrame({
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'student_id': [1, 2, 3, 4],
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'age': [15, 11, 11, 20]
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})
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result_df = createDataframe(student_data)
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pd.testing.assert_frame_equal(result_df, expected_df)
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def test_empty_data(self):
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student_data = []
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expected_df = pd.DataFrame(columns=['student_id', 'age'])
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result_df = createDataframe(student_data)
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pd.testing.assert_frame_equal(result_df, expected_df)
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def test_single_row(self):
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student_data = [[5, 18]]
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expected_df = pd.DataFrame({
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'student_id': [5],
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'age': [18]
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})
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result_df = createDataframe(student_data)
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pd.testing.assert_frame_equal(result_df, expected_df)
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def test_negative_age(self):
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student_data = [[6, -10]]
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expected_df = pd.DataFrame({
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'student_id': [6],
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'age': [-10]
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})
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result_df = createDataframe(student_data)
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pd.testing.assert_frame_equal(result_df, expected_df)
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if __name__ == '__main__':
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unittest.main()
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import unittest
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import pandas as pd
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from typing import List
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def getDataframeSize(players: pd.DataFrame) -> List[int]:
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return [players.shape[0], players.shape[1]]
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class TestGetDataframeSize(unittest.TestCase):
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def test_example_case(self):
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# Example DataFrame
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data = {
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"player_id": [846, 749, 155, 583, 388, 883, 355, 247, 761, 642],
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"name": ["Mason", "Riley", "Bob", "Isabella", "Zachary", "Ava", "Violet", "Thomas", "Jack", "Charlie"],
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"age": [21, 30, 28, 32, 24, 23, 18, 27, 33, 36],
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"position": ["Forward", "Winger", "Striker", "Goalkeeper", "Midfielder", "Defender", "Striker", "Striker", "Midfielder", "Center-back"],
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"team": ["RealMadrid", "Barcelona", "ManchesterUnited", "Liverpool", "BayernMunich", "Chelsea", "Juventus", "ParisSaint-Germain", "ManchesterCity", "Arsenal"]
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}
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players = pd.DataFrame(data)
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# Expected result: 10 rows, 5 columns
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expected_output = [10, 5]
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self.assertEqual(getDataframeSize(players), expected_output)
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def test_empty_dataframe(self):
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# Empty DataFrame
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players = pd.DataFrame(columns=["player_id", "name", "age", "position", "team"])
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# Expected result: 0 rows, 5 columns
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expected_output = [0, 5]
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self.assertEqual(getDataframeSize(players), expected_output)
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def test_single_row(self):
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# DataFrame with a single row
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data = {
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"player_id": [1],
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"name": ["John"],
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"age": [25],
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"position": ["Forward"],
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"team": ["TestTeam"]
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}
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players = pd.DataFrame(data)
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# Expected result: 1 row, 5 columns
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expected_output = [1, 5]
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self.assertEqual(getDataframeSize(players), expected_output)
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def test_different_columns(self):
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# DataFrame with more columns
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data = {
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"player_id": [1, 2],
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"name": ["John", "Doe"],
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"age": [25, 30],
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"position": ["Forward", "Midfielder"],
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"team": ["TestTeam", "AnotherTeam"],
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"goals": [15, 20]
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}
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players = pd.DataFrame(data)
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# Expected result: 2 rows, 6 columns
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expected_output = [2, 6]
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self.assertEqual(getDataframeSize(players), expected_output)
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if __name__ == "__main__":
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unittest.main()
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import unittest
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import pandas as pd
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def selectFirstRows(zs: pd.DataFrame) -> pd.DataFrame:
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return zs.head(3)
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class TestSelectFirstRows(unittest.TestCase):
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def test_example_case(self):
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# Example DataFrame
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data = {
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"employee_id": [3, 90, 9, 60, 49, 43],
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"name": ["Bob", "Alice", "Tatiana", "Annabelle", "Jonathan", "Khaled"],
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"department": ["Operations", "Sales", "Engineering", "InformationTechnology", "HumanResources", "Administration"],
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"salary": [48675, 11096, 33805, 37678, 23793, 40454]
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}
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employees = pd.DataFrame(data)
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# Expected DataFrame with the first 3 rows
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expected_data = {
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"employee_id": [3, 90, 9],
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"name": ["Bob", "Alice", "Tatiana"],
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"department": ["Operations", "Sales", "Engineering"],
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"salary": [48675, 11096, 33805]
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}
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expected_output = pd.DataFrame(expected_data)
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pd.testing.assert_frame_equal(selectFirstRows(employees), expected_output)
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def test_less_than_three_rows(self):
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# DataFrame with less than 3 rows
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data = {
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"employee_id": [1, 2],
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"name": ["John", "Doe"],
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"department": ["HR", "IT"],
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"salary": [50000, 60000]
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}
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employees = pd.DataFrame(data)
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# Expected DataFrame (same as input since there are fewer than 3 rows)
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expected_output = employees.copy()
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pd.testing.assert_frame_equal(selectFirstRows(employees), expected_output)
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def test_empty_dataframe(self):
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# Empty DataFrame
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employees = pd.DataFrame(columns=["employee_id", "name", "department", "salary"])
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# Expected result: Empty DataFrame with same columns
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expected_output = employees.copy()
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pd.testing.assert_frame_equal(selectFirstRows(employees), expected_output)
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def test_exactly_three_rows(self):
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# DataFrame with exactly 3 rows
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data = {
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"employee_id": [10, 20, 30],
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"name": ["Eve", "Mark", "Lily"],
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"department": ["Finance", "Operations", "Engineering"],
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"salary": [70000, 65000, 72000]
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}
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employees = pd.DataFrame(data)
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# Expected DataFrame (same as input since there are exactly 3 rows)
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expected_output = employees.copy()
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pd.testing.assert_frame_equal(selectFirstRows(employees), expected_output)
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if __name__ == "__main__":
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unittest.main()
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import unittest
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import pandas as pd
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def selectData(students: pd.DataFrame) -> pd.DataFrame:
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return students[students.student_id == 101][['name', 'age']]
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class TestSelectData(unittest.TestCase):
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def test_example_case(self):
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# Example DataFrame
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data = {
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"student_id": [101, 53, 128, 3],
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"name": ["Ulysses", "William", "Henry", "Henry"],
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"age": [13, 10, 6, 11]
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}
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students = pd.DataFrame(data)
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# Expected output DataFrame with explicit data types
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expected_data = {
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"name": pd.Series(["Ulysses"], dtype="object"),
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"age": pd.Series([13], dtype="int64")
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}
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expected_output = pd.DataFrame(expected_data)
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pd.testing.assert_frame_equal(selectData(students), expected_output, check_dtype=False)
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def test_no_matching_id(self):
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# DataFrame with no matching student_id = 101
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data = {
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"student_id": [102, 53, 128, 3],
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"name": ["John", "William", "Henry", "Doe"],
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"age": [12, 10, 6, 11]
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}
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students = pd.DataFrame(data)
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# Expected output: Empty DataFrame with columns ['name', 'age']
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expected_output = pd.DataFrame(columns=['name', 'age'])
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pd.testing.assert_frame_equal(selectData(students), expected_output, check_dtype=False)
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def test_multiple_students_with_101(self):
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# DataFrame with multiple students having student_id = 101
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data = {
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"student_id": [101, 101, 128],
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"name": ["Alice", "Bob", "Charlie"],
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"age": [20, 21, 22]
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}
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students = pd.DataFrame(data)
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# Expected output: DataFrame with both rows where student_id = 101
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expected_data = {
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"name": ["Alice", "Bob"],
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"age": [20, 21]
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}
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expected_output = pd.DataFrame(expected_data)
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pd.testing.assert_frame_equal(selectData(students), expected_output, check_dtype=False)
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def test_empty_dataframe(self):
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# Empty DataFrame with the same structure
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students = pd.DataFrame(columns=["student_id", "name", "age"])
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# Expected output: Empty DataFrame with columns ['name', 'age']
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expected_output = pd.DataFrame(columns=['name', 'age'])
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pd.testing.assert_frame_equal(selectData(students), expected_output, check_dtype=False)
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if __name__ == "__main__":
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unittest.main()
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import unittest
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import pandas as pd
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def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
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employees["bonus"] = employees["salary"] * 2
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return employees
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class TestCreateBonusColumn(unittest.TestCase):
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def test_create_bonus_column(self):
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# Example DataFrame as input
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data = {
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"name": ["Piper", "Grace", "Georgia", "Willow", "Finn", "Thomas"],
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"salary": [4548, 28150, 1103, 6593, 74576, 24433]
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}
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employees = pd.DataFrame(data)
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# Expected output DataFrame
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expected_data = {
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"name": ["Piper", "Grace", "Georgia", "Willow", "Finn", "Thomas"],
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"salary": [4548, 28150, 1103, 6593, 74576, 24433],
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"bonus": [9096, 56300, 2206, 13186, 149152, 48866]
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}
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expected_output = pd.DataFrame(expected_data)
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# Test the function
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result = createBonusColumn(employees)
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# Use pandas testing utilities to compare DataFrames
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pd.testing.assert_frame_equal(result, expected_output)
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if __name__ == '__main__':
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unittest.main()

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