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javadevjavadev
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Improved tasks 2888, 2889, 2890, 2891
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‎src/test/kotlin/g2801_2900/s2888_reshape_data_concatenate/solution_test.py

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import unittest
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import pandas as pd
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from pandas.testing import assert_frame_equal
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def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
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return pd.concat([df1, df2], ignore_index=True)
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class TestConcatenateTables(unittest.TestCase):
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def test_concatenate_normal_case(self):
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# Input DataFrames
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df1 = pd.DataFrame({
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"student_id": [1, 2, 3, 4],
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"name": ["Mason", "Ava", "Taylor", "Georgia"],
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"age": [8, 6, 15, 17]
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})
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df2 = pd.DataFrame({
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"student_id": [5, 6],
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"name": ["Leo", "Alex"],
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"age": [7, 7]
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})
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# Expected Output
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expected = pd.DataFrame({
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"student_id": [1, 2, 3, 4, 5, 6],
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"name": ["Mason", "Ava", "Taylor", "Georgia", "Leo", "Alex"],
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"age": [8, 6, 15, 17, 7, 7]
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})
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# Actual Output
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result = concatenateTables(df1, df2)
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# Assert the result matches the expected DataFrame
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try:
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assert_frame_equal(result, expected)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal: {e}")
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def test_concatenate_empty_df1(self):
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# Input DataFrames
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df1 = pd.DataFrame(columns=["student_id", "name", "age"]).astype({
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"student_id": "int64",
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"name": "object",
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"age": "int64"
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})
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df2 = pd.DataFrame({
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"student_id": [5, 6],
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"name": ["Leo", "Alex"],
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"age": [7, 7]
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})
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# Expected Output
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expected = pd.DataFrame({
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"student_id": [5, 6],
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"name": ["Leo", "Alex"],
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"age": [7, 7]
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})
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# Actual Output
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result = concatenateTables(df1, df2)
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# Assert the result matches the expected DataFrame
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try:
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assert_frame_equal(result, expected)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal when df1 is empty: {e}")
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def test_concatenate_empty_df2(self):
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# Input DataFrames
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df1 = pd.DataFrame({
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"student_id": [1, 2, 3, 4],
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"name": ["Mason", "Ava", "Taylor", "Georgia"],
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"age": [8, 6, 15, 17]
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})
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df2 = pd.DataFrame(columns=["student_id", "name", "age"]).astype({
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"student_id": "int64",
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"name": "object",
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"age": "int64"
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})
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# Expected Output
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expected = df1
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# Actual Output
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result = concatenateTables(df1, df2)
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# Assert the result matches the expected DataFrame
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try:
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assert_frame_equal(result, expected)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal when df2 is empty: {e}")
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def test_concatenate_both_empty(self):
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# Input DataFrames
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df1 = pd.DataFrame(columns=["student_id", "name", "age"])
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df2 = pd.DataFrame(columns=["student_id", "name", "age"])
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# Expected Output
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expected = pd.DataFrame(columns=["student_id", "name", "age"])
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# Actual Output
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result = concatenateTables(df1, df2)
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# Assert the result matches the expected DataFrame
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try:
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assert_frame_equal(result, expected)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal when both are empty: {e}")
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def test_concatenate_different_column_order(self):
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# Input DataFrames
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df1 = pd.DataFrame({
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"student_id": [1, 2],
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"name": ["Mason", "Ava"],
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"age": [8, 6]
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})
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df2 = pd.DataFrame({
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"name": ["Leo", "Alex"],
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"age": [7, 7],
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"student_id": [5, 6]
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})
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# Expected Output
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expected = pd.DataFrame({
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"student_id": [1, 2, 5, 6],
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"name": ["Mason", "Ava", "Leo", "Alex"],
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"age": [8, 6, 7, 7]
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})
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# Actual Output
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result = concatenateTables(df1, df2)
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# Assert the result matches the expected DataFrame
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try:
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assert_frame_equal(result, expected)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal when columns are in different orders: {e}")
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# Run the tests
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if __name__ == "__main__":
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unittest.main()

‎src/test/kotlin/g2801_2900/s2889_reshape_data_pivot/solution_test.py

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import unittest
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import pandas as pd
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from pandas.testing import assert_frame_equal
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# Method to be tested
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def pivotTable(weather: pd.DataFrame) -> pd.DataFrame:
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result = weather.pivot(index='month', columns='city', values='temperature')
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result.columns.name = None
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return result.reset_index()
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# Unit Test Class
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class TestPivotTable(unittest.TestCase):
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def test_pivot_table(self):
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# Input DataFrame
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input_data = {
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"city": ["Jacksonville", "Jacksonville", "Jacksonville", "Jacksonville", "Jacksonville",
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"ElPaso", "ElPaso", "ElPaso", "ElPaso", "ElPaso"],
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"month": ["January", "February", "March", "April", "May",
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"January", "February", "March", "April", "May"],
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"temperature": [13, 23, 38, 5, 34, 20, 6, 26, 2, 43]
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}
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weather = pd.DataFrame(input_data)
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# Expected Output DataFrame
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expected_data = {
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"month": ["April", "February", "January", "March", "May"],
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"ElPaso": [2, 6, 20, 26, 43],
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"Jacksonville": [5, 23, 13, 38, 34]
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}
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expected_df = pd.DataFrame(expected_data)
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# Actual Output
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result_df = pivotTable(weather)
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# Assert the DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal: {e}")
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def test_empty_dataframe(self):
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# Test for an empty input DataFrame
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weather = pd.DataFrame(columns=["city", "month", "temperature"])
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expected_df = pd.DataFrame(columns=["month"])
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# Actual Output
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result_df = pivotTable(weather)
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# Assert the DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal for empty input: {e}")
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def test_single_row_dataframe(self):
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# Test for a single row input DataFrame
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input_data = {
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"city": ["ElPaso"],
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"month": ["January"],
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"temperature": [20]
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}
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weather = pd.DataFrame(input_data)
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# Expected Output DataFrame
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expected_data = {
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"month": ["January"],
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"ElPaso": [20]
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}
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expected_df = pd.DataFrame(expected_data)
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# Actual Output
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result_df = pivotTable(weather)
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# Assert the DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal for single row input: {e}")
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# Run the tests
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if __name__ == "__main__":
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unittest.main()

‎src/test/kotlin/g2801_2900/s2890_reshape_data_melt/solution_test.py

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import unittest
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import pandas as pd
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from pandas.testing import assert_frame_equal
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def meltTable(report: pd.DataFrame) -> pd.DataFrame:
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return report.melt(id_vars='product', var_name='quarter', value_name='sales')
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# Unit Test Class
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class TestMeltTable(unittest.TestCase):
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def test_melt_table(self):
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# Input DataFrame
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input_data = {
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"product": ["Umbrella", "SleepingBag"],
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"quarter_1": [417, 800],
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"quarter_2": [224, 936],
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"quarter_3": [379, 93],
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"quarter_4": [611, 875]
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}
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report = pd.DataFrame(input_data)
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# Expected Output DataFrame
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expected_data = {
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"product": ["Umbrella", "SleepingBag", "Umbrella", "SleepingBag", "Umbrella", "SleepingBag", "Umbrella", "SleepingBag"],
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"quarter": ["quarter_1", "quarter_1", "quarter_2", "quarter_2", "quarter_3", "quarter_3", "quarter_4", "quarter_4"],
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"sales": [417, 800, 224, 936, 379, 93, 611, 875]
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}
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expected_df = pd.DataFrame(expected_data)
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# Actual Output
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result_df = meltTable(report)
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# Assert DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal: {e}")
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def test_empty_dataframe(self):
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# Test with an empty DataFrame
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report = pd.DataFrame(columns=["product", "quarter_1", "quarter_2", "quarter_3", "quarter_4"])
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expected_df = pd.DataFrame(columns=["product", "quarter", "sales"])
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# Actual Output
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result_df = meltTable(report)
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# Assert DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal for empty input: {e}")
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def test_single_row_dataframe(self):
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# Test with a single row DataFrame
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input_data = {
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"product": ["Umbrella"],
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"quarter_1": [417],
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"quarter_2": [224],
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"quarter_3": [379],
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"quarter_4": [611]
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}
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report = pd.DataFrame(input_data)
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# Expected Output DataFrame
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expected_data = {
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"product": ["Umbrella", "Umbrella", "Umbrella", "Umbrella"],
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"quarter": ["quarter_1", "quarter_2", "quarter_3", "quarter_4"],
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"sales": [417, 224, 379, 611]
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}
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expected_df = pd.DataFrame(expected_data)
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# Actual Output
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result_df = meltTable(report)
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# Assert DataFrames are equal
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try:
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assert_frame_equal(result_df, expected_df)
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except AssertionError as e:
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self.fail(f"DataFrames are not equal for single row input: {e}")
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# Run the tests
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if __name__ == "__main__":
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unittest.main()

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