dim = min(a.shape)
b = a[:dim,:dim]
result = np.vstack((np.diag(b), np.diag(np.fliplr(b))))

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array(
                [
                    [0, 1, 2, 3, 4, 5],
                    [5, 6, 7, 8, 9, 10],
                    [10, 11, 12, 13, 14, 15],
                    [15, 16, 17, 18, 19, 20],
                    [20, 21, 22, 23, 24, 25],
                ]
            )
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(8, 10)
        return a

    def generate_ans(data):
        _a = data
        a = _a
        dim = min(a.shape)
        b = a[:dim, :dim]
        result = np.vstack((np.diag(b), np.diag(np.fliplr(b))))
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(30).reshape(5,6))

dim = min(df.shape)
b = df.iloc[:dim,:dim].values
result = np.vstack((np.diag(b), np.diag(np.fliplr(b))))

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.arange(30).reshape(5,6))
        elif test_case_id == 2:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(8, 10))
        return df

    def generate_ans(data):
        _df = data
        df = _df
        dim = min(df.shape)
        b = df.iloc[:dim, :dim].values
        result = np.vstack((np.diag(b), np.diag(np.fliplr(b))))
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens


import numpy as np
X = np.random.randint(2, 10, (5, 6))

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            X = np.random.randint(2, 10, (5, 6))
        return X

    def generate_ans(data):
        _a = data
        X = _a
        result = []
        for value in X.flat:
            result.append(value)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(sorted(result), sorted(ans))
    return 1

exec_context = r"""
import numpy as np
X = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

df = pd.DataFrame(np.random.randint(2, 10, (r, c)))

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(2, 10, (5, 6)))

result = ... # put solution in this variable

result = []
for value in df.values.flat:
    result.append(value)

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            df = pd.DataFrame(np.random.randint(2, 10, (5, 6)))
        return df

    def generate_ans(data):
        _df = data
        result = []
        for value in _df.values.flat:
            result.append(value)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(sorted(result), sorted(ans))
    return 1

exec_context = r"""
import numpy as np
import pandas as pd
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
example_X = np.random.randint(2, 10, (5, 6))
def f(X = example_X):
    # return the solution in this function
    # result = f(X)
    ###

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            X = np.random.randint(2, 10, (5, 6))
        return X

    def generate_ans(data):
        _a = data
        X = _a
        result = []
        for value in X.flat:
            result.append(value)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(sorted(result), sorted(ans))
    return 1

exec_context = r"""
import numpy as np
X = test_input
def f(X):
[insert]
result = f(X)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

df = pd.DataFrame(np.random.randint(2, 10, (3, 3)))

import numpy as np
import pandas as pd
example_df = pd.DataFrame(np.random.randint(2, 10, (5, 6)))
def f(df = example_df):
    # return the solution in this function
    # result = f(df)
    ###

    result = []
    for value in df.values.flat:
        result.append(value)

    return result

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            df = pd.DataFrame(np.random.randint(2, 10, (5, 6)))
        return df

    def generate_ans(data):
        _df = data
        result = []
        for value in _df.values.flat:
            result.append(value)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(sorted(result), sorted(ans))
    return 1

exec_context = r"""
import numpy as np
import pandas as pd
df = test_input
def f(df):
[insert]
result = f(df)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


result = np.array(list(mystr), dtype = int)

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            mystr = "100110"
        elif test_case_id == 2:
            mystr = "987543"
        return mystr

    def generate_ans(data):
        _a = data
        mystr = _a
        result = np.array(list(mystr), dtype=int)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
mystr = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
mystr = "100110"

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            mystr = "100110"
        elif test_case_id == 2:
            mystr = "987543"
        return mystr

    def generate_ans(data):
        _a = data
        mystr = _a
        result = pd.Series(list(mystr), dtype=int)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_series_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
mystr = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.random.rand(8, 5)
col = 2
multiply_number = 5.2

result = ... # put solution in this variable

a[:, col-1] *= multiply_number
result = np.cumsum(a[:, col-1])

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.rand(8, 5)
            col = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))
            col = 4
            const = np.random.rand()
        return a, col, const

    def generate_ans(data):
        _a = data
        a, col, multiply_number = _a
        a[:, col - 1] *= multiply_number
        result = np.cumsum(a[:, col - 1])
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, col, multiply_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
a = pd.DataFrame(np.random.rand(8, 5))
col = 2
multiply_number = 5.2

result = ... # put solution in this variable

a.iloc[:, col-1] *= multiply_number
result = a.iloc[:, col-1].cumsum()

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(8, 5))
            col = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))
            col = 4
            const = np.random.rand()
        return a, col, const

    def generate_ans(data):
        _a = data
        a, col, multiply_number = _a
        a.iloc[:, col - 1] *= multiply_number
        result = a.iloc[:, col - 1].cumsum()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_series_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
a, col, multiply_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


array = np.array([[1, 2, 3], [4, 5, 6]]), row_index = 1, multiplier = 5.2

import numpy as np
a = np.random.rand(8, 5)
row = 2
multiply_number = 5.2

result = ... # put solution in this variable

a[row-1, :] *= multiply_number
result = np.cumsum(a[row-1, :])

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.rand(8, 5)
            row = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))
            row = 4
            const = np.random.rand()
        return a, row, const

    def generate_ans(data):
        _a = data
        a, row, multiply_number = _a
        a[row - 1, :] *= multiply_number
        result = np.cumsum(a[row - 1, :])
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, row, multiply_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
a = pd.DataFrame(np.random.rand(8, 5))
row = 2
multiply_number = 5.2

result = ... # put solution in this variable

a.iloc[row-1, :] *= multiply_number
result = a.iloc[row-1, :].cumsum()

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(8, 5))
            row = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))
            row = 4
            const = np.random.rand()
        return a, row, const

    def generate_ans(data):
        _a = data
        a, row, multiply_number = _a
        a.iloc[row - 1, :] *= multiply_number
        result = a.iloc[row - 1, :].cumsum()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_series_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
a, row, multiply_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.random.rand(8, 5)
row = 2
divide_number = 5.2

result = ... # put solution in this variable

a[row-1, :] /= divide_number
result = np.multiply.reduce(a[row-1, :])

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.rand(8, 5)
            row = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))
            row = 4
            const = np.random.rand() + 1
        return a, row, const

    def generate_ans(data):
        _a = data
        a, row, divide_number = _a
        a[row - 1, :] /= divide_number
        result = np.multiply.reduce(a[row - 1, :])
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, row, divide_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
a = pd.DataFrame(np.random.rand(8, 5))
row = 2
divide_number = 5.2

result = ... # put solution in this variable

a.iloc[row-1, :] /= divide_number
result = a.iloc[row-1, :].prod()

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(8, 5))
            row = 2
            const = 5.2
        elif test_case_id == 2:
            np.random.seed(42)
            a = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))
            row = 4
            const = np.random.rand() + 1
        return a, row, const

    def generate_ans(data):
        _a = data
        a, row, divide_number = _a
        a.iloc[row - 1, :] /= divide_number
        result = a.iloc[row - 1, :].prod()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
a, row, divide_number = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.array([[0,1,0,0], [0,0,1,0], [0,1,1,0], [1,0,0,1]])

result = ... # put solution in this variable

def LI_vecs(M):
    dim = M.shape[0]
    LI=[M[0]]
    for i in range(dim):
        tmp=[]
        for r in LI:
            tmp.append(r)
        tmp.append(M[i])                #set tmp=LI+[M[i]]
        if np.linalg.matrix_rank(tmp)>len(LI):    #test if M[i] is linearly independent from all (row) vectors in LI
            LI.append(M[i])             #note that matrix_rank does not need to take in a square matrix
    return LI                           #return set of linearly independent (row) vectors
result = LI_vecs(a)

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 1, 0], [1, 0, 0, 1]])
        elif test_case_id == 2:
            a = np.array(
                [
                    [0, 1, 0, 0, 1, 1],
                    [0, 0, 1, 0, 0, 1],
                    [0, 1, 1, 0, 0, 0],
                    [1, 0, 1, 0, 0, 1],
                    [1, 1, 0, 1, 0, 0],
                ]
            )
        elif test_case_id == 3:
            a = np.array(
                [
                    [0, 1, 0, 0, 1, 1],
                    [0, 1, 0, 0, 1, 1],
                    [0, 1, 0, 0, 1, 1],
                    [1, 0, 1, 0, 0, 1],
                    [1, 1, 0, 1, 0, 0],
                ]
            )
        return a

    def generate_ans(data):
        _a = data
        a = _a
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    result = np.array(result)
    if result.shape[1] == ans.shape[1]:
        assert np.linalg.matrix_rank(ans) == np.linalg.matrix_rank(
            result
        ) and np.linalg.matrix_rank(result) == len(result)
        assert len(np.unique(result, axis=0)) == len(result)
        for arr in result:
            assert np.any(np.all(ans == arr, axis=1))
    else:
        assert (
            np.linalg.matrix_rank(ans) == np.linalg.matrix_rank(result)
            and np.linalg.matrix_rank(result) == result.shape[1]
        )
        assert np.unique(result, axis=1).shape[1] == result.shape[1]
        for i in range(result.shape[1]):
            assert np.any(np.all(ans == result[:, i].reshape(-1, 1), axis=0))
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
a = pd.DataFrame([[0,1,0,0], [0,0,1,0], [0,1,1,0], [1,0,0,1]])

result = ... # put solution in this variable

def LI_vecs(M):
    dim = M.shape[0]
    LI=[M.iloc[0]]
    for i in range(dim):
        tmp=[]
        for r in LI:
            tmp.append(r)
        tmp.append(M.iloc[i])                #set tmp=LI+[M.iloc[i]]
        if np.linalg.matrix_rank(np.array(tmp))>len(LI):    #test if M.iloc[i] is linearly independent from all (row) vectors in LI
            LI.append(M.iloc[i])             #note that matrix_rank does not need to take in a square matrix
    return pd.DataFrame(LI)                           #return DataFrame of linearly independent (row) vectors
result = LI_vecs(a)

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = pd.DataFrame([[0, 1, 0, 0], [0, 0, 1, 0], [0, 1, 1, 0], [1, 0, 0, 1]])
        elif test_case_id == 2:
            a = pd.DataFrame(
                [
                    [0, 1, 0, 0, 1, 1],
                    [0, 0, 1, 0, 0, 1],
                    [0, 1, 1, 0, 0, 0],
                    [1, 0, 1, 0, 0, 1],
                    [1, 1, 0, 1, 0, 0],
                ]
            )
        elif test_case_id == 3:
            a = pd.DataFrame(
                [
                    [0, 1, 0, 0, 1, 1],
                    [0, 1, 0, 0, 1, 1],
                    [0, 1, 0, 0, 1, 1],
                    [1, 0, 1, 0, 0, 1],
                    [1, 1, 0, 1, 0, 0],
                ]
            )
        return a

    def generate_ans(data):
        _a = data
        a = _a
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    result = np.array(result)
    if result.shape[1] == ans.shape[1]:
        assert np.linalg.matrix_rank(ans) == np.linalg.matrix_rank(
            result
        ) and np.linalg.matrix_rank(result) == len(result)
        assert len(np.unique(result, axis=0)) == len(result)
        for arr in result:
            assert np.any(np.all(ans == arr, axis=1))
    else:
        assert (
            np.linalg.matrix_rank(ans) == np.linalg.matrix_rank(result)
            and np.linalg.matrix_rank(result) == result.shape[1]
        )
        assert np.unique(result, axis=1).shape[1] == result.shape[1]
        for i in range(result.shape[1]):
            assert np.any(np.all(ans == result[:, i].reshape(-1, 1), axis=0))
    return 1

def test_execution(solution: str):
    exec_context = r"""
import pandas as pd
import numpy as np
a = test_input
[insert]
"""
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = a.shape[1]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))
        return df

    def generate_ans(data):
        _df = data
        df = _df
        result = df.shape[1]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


a = np.random.normal(0, 1, 100); b = np.random.normal(0, 1, 80)

import numpy as np
import scipy.stats
a = np.random.randn(40)
b = 4*np.random.randn(50)

p_value = ... # put solution in this variable

_, p_value = scipy.stats.ttest_ind(a, b,  equal_var = False)

import numpy as np
import copy
import scipy.stats

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.randn(40)
            b = 4 * np.random.randn(50)
        return a, b

    def generate_ans(data):
        _a = data
        a, b = _a
        _, p_value = scipy.stats.ttest_ind(a, b, equal_var=False)
        return p_value

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert abs(ans - result) <= 1e-5
    return 1

exec_context = r"""
import numpy as np
import scipy.stats
a, b = test_input
[insert]
result = p_value
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
import scipy.stats
a = pd.Series(np.random.randn(40))
b = pd.Series(4*np.random.randn(50))

p_value = ... # put solution in this variable

import pandas as pd
import numpy as np
import scipy.stats

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = pd.Series(np.random.randn(40))
            b = pd.Series(4 * np.random.randn(50))
        return a, b

    def generate_ans(data):
        _a = data
        a, b = _a
        _, p_value = scipy.stats.ttest_ind(a, b, equal_var=False)
        return p_value

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(test_input)
    return test_input, expected_result

def exec_test(result, ans):
    assert abs(ans - result) <= 1e-5
    return 1

exec_context = r"""
import pandas as pd
import scipy.stats
a, b = test_input
[insert]
result = p_value
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


_, p_value = scipy.stats.ttest_ind(a, b,  equal_var = False, nan_policy = 'omit')

import numpy as np
import copy
import scipy.stats

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = np.random.randn(40)
            b = 4 * np.random.randn(50)
        elif test_case_id == 2:
            np.random.seed(43)
            a = np.random.randn(40)
            b = 4 * np.random.randn(50)
            a[10] = np.nan
            b[20] = np.nan
        return a, b

    def generate_ans(data):
        _a = data
        a, b = _a
        _, p_value = scipy.stats.ttest_ind(a, b, equal_var=False, nan_policy="omit")
        return p_value

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert abs(ans - result) <= 1e-5
    return 1

exec_context = r"""
import numpy as np
import scipy.stats
a, b = test_input
[insert]
result = p_value
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
import scipy.stats
a = pd.Series(np.random.randn(40))
b = pd.Series(4 * np.random.randn(50))

p_value = ... # put solution in this variable

import pandas as pd
import numpy as np
import scipy.stats

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            a = pd.Series(np.random.randn(40))
            b = pd.Series(4 * np.random.randn(50))
        elif test_case_id == 2:
            np.random.seed(43)
            a = pd.Series(np.random.randn(40))
            b = pd.Series(4 * np.random.randn(50))
            a[10] = np.nan
            b[20] = np.nan
        return a, b

    def generate_ans(data):
        a, b = data
        _, p_value = scipy.stats.ttest_ind(a.dropna(), b.dropna(), equal_var=False)
        return p_value

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(test_input)
    return test_input, expected_result

def test_execution(solution: str):
    exec_context = """
import pandas as pd
import numpy as np
import scipy.stats
a, b = test_input
[insert]
result = p_value
"""
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert abs(test_env["result"] - expected_result) <= 1e-5


dims = np.maximum(B.max(0),A.max(0))+1
output = A[~np.in1d(np.ravel_multi_index(A.T,dims),np.ravel_multi_index(B.T,dims))]

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            A = np.asarray([[1, 1, 1], [1, 1, 2], [1, 1, 3], [1, 1, 4]])
            B = np.asarray(
                [
                    [0, 0, 0],
                    [1, 0, 2],
                    [1, 0, 3],
                    [1, 0, 4],
                    [1, 1, 0],
                    [1, 1, 1],
                    [1, 1, 4],
                ]
            )
        elif test_case_id == 2:
            np.random.seed(42)
            A = np.random.randint(0, 2, (10, 3))
            B = np.random.randint(0, 2, (20, 3))
        elif test_case_id == 3:
            A = np.asarray([[1, 1, 1], [1, 1, 4]])
            B = np.asarray(
                [
                    [0, 0, 0],
                    [1, 0, 2],
                    [1, 0, 3],
                    [1, 0, 4],
                    [1, 1, 0],
                    [1, 1, 1],
                    [1, 1, 4],
                ]
            )
        return A, B

    def generate_ans(data):
        _a = data
        A, B = _a
        dims = np.maximum(B.max(0), A.max(0)) + 1
        output = A[
            ~np.in1d(np.ravel_multi_index(A.T, dims), np.ravel_multi_index(B.T, dims))
        ]
        return output

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    if ans.shape[0]:
        np.testing.assert_array_equal(result, ans)
    else:
        result = result.reshape(0)
        ans = ans.reshape(0)
        np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
A, B = test_input
[insert]
result = output
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            A = pd.DataFrame([[1, 1, 1], [1, 1, 2], [1, 1, 3], [1, 1, 4]], columns=['a', 'b', 'c'])
            B = pd.DataFrame([[0, 0, 0], [1, 0, 2], [1, 0, 3], [1, 0, 4], [1, 1, 0], [1, 1, 1], [1, 1, 4]], columns=['a', 'b', 'c'])
        elif test_case_id == 2:
            np.random.seed(42)
            A = pd.DataFrame(np.random.randint(0, 2, (10, 3)), columns=['a', 'b', 'c'])
            B = pd.DataFrame(np.random.randint(0, 2, (20, 3)), columns=['a', 'b', 'c'])
        elif test_case_id == 3:
            A = pd.DataFrame([[1, 1, 1], [1, 1, 4]], columns=['a', 'b', 'c'])
            B = pd.DataFrame([[0, 0, 0], [1, 0, 2], [1, 0, 3], [1, 0, 4], [1, 1, 0], [1, 1, 1], [1, 1, 4]], columns=['a', 'b', 'c'])
        return A, B

    def generate_ans(data):
        _a = data
        A, B = _a
        output = A.merge(B, indicator=True, how='outer').query('_merge == "left_only"').drop('_merge', axis=1)
        return output

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result.reset_index(drop=True), ans.reset_index(drop=True))
    return 1

def test_execution(solution: str):
    exec_context = r"""
import pandas as pd
A, B = test_input
[insert]
result = output
"""
    code = exec_context.replace("[insert]", solution)
    for i in range(3):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.arange(12).reshape(3, 4)

a = ... # put solution in this variable

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.arange(12).reshape(3, 4)
        elif test_case_id == 2:
            a = np.ones((3, 3))
        return a

    def generate_ans(data):
        _a = data
        a = _a
        a = np.delete(a, 2, axis=1)
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4))

df = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.arange(12).reshape(3, 4))
        elif test_case_id == 2:
            df = pd.DataFrame(np.ones((3, 3)))
        return df

    def generate_ans(data):
        _df = data
        df = _df.drop(columns=_df.columns[2])
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.arange(12).reshape(3, 4)
        elif test_case_id == 2:
            a = np.ones((4, 4))
        return a

    def generate_ans(data):
        _a = data
        a = _a
        a = np.delete(a, 2, axis=0)
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4))

# put solution in this variable
df = ...

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.arange(12).reshape(3, 4))
        elif test_case_id == 2:
            df = pd.DataFrame(np.ones((4, 4)))
        return df

    def generate_ans(data):
        _df = data
        df = _df.drop(2)
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


temp = np.array([0, 2])
a = np.delete(a, temp, axis = 1)

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.arange(12).reshape(3, 4)
        elif test_case_id == 2:
            a = np.ones((6, 6))
        return a

    def generate_ans(data):
        _a = data
        a = _a
        temp = np.array([0, 2])
        a = np.delete(a, temp, axis=1)
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D'])

# put solution in this variable
df = ...

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
        elif test_case_id == 2:
            df = pd.DataFrame(np.ones((6, 6)), columns=list('ABCDEF'))
        return df

    def generate_ans(data):
        df = data
        df = df.drop(columns=['A', 'C'])
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.arange(12).reshape(3, 4)
del_col = np.array([1, 2, 4, 5])

result = ... # put solution in this variable

mask = (del_col <= a.shape[1])
del_col = del_col[mask] - 1
result = np.delete(a, del_col, axis=1)

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.arange(12).reshape(3, 4)
            del_col = np.array([1, 2, 4, 5])
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10))
            del_col = np.random.randint(0, 15, (4,))
        return a, del_col

    def generate_ans(data):
        _a = data
        a, del_col = _a
        mask = del_col <= a.shape[1]
        del_col = del_col[mask] - 1
        result = np.delete(a, del_col, axis=1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, del_col = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4))
del_col = np.array([1, 2, 4, 5])

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.arange(12).reshape(3, 4))
            del_col = np.array([1, 2, 4, 5])
        elif test_case_id == 2:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(np.random.randint(5, 10), np.random.randint(6, 10)))
            del_col = np.random.randint(0, 15, (4,))
        return df, del_col

    def generate_ans(data):
        df, del_col = data
        mask = del_col < df.shape[1]
        del_col = del_col[mask]
        result = df.drop(del_col, axis=1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df, del_col = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.array([[1,2],[3,4]])

pos = 1
element = [3,5]

a = ... # put solution in this variable

a = np.insert(a, pos, element, axis = 0)

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array([[1, 2], [3, 4]])
            pos = 1
            element = [3, 5]
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(100, 10)
            pos = np.random.randint(0, 99)
            element = np.random.rand(10)
        return a, pos, element

    def generate_ans(data):
        _a = data
        a, pos, element = _a
        a = np.insert(a, pos, element, axis=0)
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, pos, element = test_input
[insert]
result = a
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "insert" in tokens

import pandas as pd
import numpy as np

df = pd.DataFrame([[1,2], [3,4]], columns=['A', 'B'])

pos = 1
element = pd.DataFrame([[3,5]], columns=['A', 'B'])

df = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
            pos = 1
            element = pd.DataFrame([[3, 5]], columns=['A', 'B'])
        elif test_case_id == 2:
            np.random.seed(42)
            data = np.random.randint(1, 100, (100, 2))
            df = pd.DataFrame(data, columns=['A', 'B'])
            pos = np.random.randint(0, 99)
            element = pd.DataFrame([np.random.randint(1, 100, 2)], columns=['A', 'B'])
        return df, pos, element

    def generate_ans(data):
        df, pos, element = data
        df = pd.concat([df.iloc[:pos], element, df.iloc[pos:]]).reset_index(drop=True)
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def test_execution(solution: str):
    exec_context = r"""
import pandas as pd
import numpy as np
df, pos, element = test_input
[insert]
result = df
"""
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        pd.testing.assert_frame_equal(test_env["result"], expected_result)


import numpy as np
pairs = [(2, 3), (3, 4), (4, 5)]
array_of_arrays = np.array([np.arange(a*b).reshape(a,b) for (a, b) in pairs])

result = ... # put solution in this variable

import copy
result = copy.deepcopy(array_of_arrays)

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            pairs = [(2, 3), (3, 4), (4, 5)]
            array_of_arrays = np.array(
                [np.arange(a * b).reshape(a, b) for (a, b) in pairs], dtype=object
            )
        return pairs, array_of_arrays

    def generate_ans(data):
        _a = data
        pairs, array_of_arrays = _a
        return array_of_arrays

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert id(result) != id(ans)
    for arr1, arr2 in zip(result, ans):
        assert id(arr1) != id(arr2)
        np.testing.assert_array_equal(arr1, arr2)
    return 1

exec_context = r"""
import numpy as np
pairs, array_of_arrays = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
pairs = [(2, 3), (3, 4), (4, 5)]
dataframe_of_series = pd.DataFrame({f'Series_{i}': pd.Series(np.arange(a*b)) for i, (a, b) in enumerate(pairs)})

result = ... # put solution in this variable

import copy
result = copy.deepcopy(dataframe_of_series)

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            pairs = [(2, 3), (3, 4), (4, 5)]
            dataframe_of_series = pd.DataFrame({f'Series_{i}': pd.Series(np.arange(a*b)) for i, (a, b) in enumerate(pairs)})
        return pairs, dataframe_of_series

    def generate_ans(data):
        _a = data
        pairs, dataframe_of_series = _a
        return dataframe_of_series

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert id(result) != id(ans)
    for series1, series2 in zip(result.items(), ans.items()):
        assert id(series1[1]) != id(series2[1])
        pd.testing.assert_series_equal(series1[1], series2[1])
    return 1

exec_context = r"""
import pandas as pd
pairs, dataframe_of_series = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis = 0)

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0)
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(3, 4)
        elif test_case_id == 3:
            a = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
        elif test_case_id == 4:
            a = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 3]])
        elif test_case_id == 5:
            a = np.array([[1, 1, 1], [2, 2, 1], [1, 1, 1]])
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.isclose(a, a[0], atol=0).all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np
df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0))

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0))
        elif test_case_id == 2:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(3, 4))
        elif test_case_id == 3:
            df = pd.DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
        elif test_case_id == 4:
            df = pd.DataFrame([[1, 1, 1], [1, 1, 1], [1, 1, 3]])
        elif test_case_id == 5:
            df = pd.DataFrame([[1, 1, 1], [2, 2, 1], [1, 1, 1]])
        return df

    def generate_ans(data):
        df = data
        result = df.eq(df.iloc[0]).all().all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens


import numpy as np
a = np.repeat(np.arange(1, 6).reshape(-1, 1), 3, axis = 1)

result = ... # put solution in this variable

result =np.isclose(a, a[:, 0].reshape(-1, 1), atol=0).all()

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0)
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(3, 4)
        elif test_case_id == 3:
            a = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
        elif test_case_id == 4:
            a = np.array([[1, 1, 1], [1, 1, 2], [1, 1, 3]])
        elif test_case_id == 5:
            a = np.array([[1, 1, 1], [2, 2, 1], [3, 3, 1]])
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.isclose(a, a[:, 0].reshape(-1, 1), atol=0).all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np
df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(-1, 1), 3, axis = 1))

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0))
        elif test_case_id == 2:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(3, 4))
        elif test_case_id == 3:
            df = pd.DataFrame(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]))
        elif test_case_id == 4:
            df = pd.DataFrame(np.array([[1, 1, 1], [1, 1, 2], [1, 1, 3]]))
        elif test_case_id == 5:
            df = pd.DataFrame(np.array([[1, 1, 1], [2, 2, 1], [3, 3, 1]]))
        return df

    def generate_ans(data):
        df = data
        result = df.eq(df.iloc[:, 0], axis=0).all().all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens


import numpy as np
example_a = np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis = 0)
def f(a = example_a):
    # return the solution in this function
    # result = f(a)
    ###

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0)
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(3, 4)
        elif test_case_id == 3:
            a = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
        elif test_case_id == 4:
            a = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 3]])
        elif test_case_id == 5:
            a = np.array([[1, 1, 1], [2, 2, 1], [1, 1, 1]])
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.isclose(a, a[0], atol=0).all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import numpy as np
a = test_input
def f(a):
[insert]
result = f(a)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np
example_df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis = 0))
def f(df = example_df):
    # return the solution in this function
    # result = f(df)
    ###

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.repeat(np.arange(1, 6).reshape(1, -1), 3, axis=0))
        elif test_case_id == 2:
            np.random.seed(42)
            df = pd.DataFrame(np.random.rand(3, 4))
        elif test_case_id == 3:
            df = pd.DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
        elif test_case_id == 4:
            df = pd.DataFrame([[1, 1, 1], [1, 1, 1], [1, 1, 3]])
        elif test_case_id == 5:
            df = pd.DataFrame([[1, 1, 1], [2, 2, 1], [1, 1, 1]])
        return df

    def generate_ans(data):
        df = data
        result = df.eq(df.iloc[0]).all().all()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
def f(df):
[insert]
result = f(df)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(5):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens


def ecdf_result(x):
    xs = np.sort(x)
    ys = np.arange(1, len(xs)+1)/float(len(xs))
    return ys
result = ecdf_result(grades)

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = np.array(
                (
                    93.5,
                    93,
                    60.8,
                    94.5,
                    82,
                    87.5,
                    91.5,
                    99.5,
                    86,
                    93.5,
                    92.5,
                    78,
                    76,
                    69,
                    94.5,
                    89.5,
                    92.8,
                    78,
                    65.5,
                    98,
                    98.5,
                    92.3,
                    95.5,
                    76,
                    91,
                    95,
                    61,
                )
            )
        elif test_case_id == 2:
            np.random.seed(42)
            grades = (np.random.rand(50) - 0.5) * 100
        return grades

    def generate_ans(data):
        _a = data
        grades = _a

        def ecdf_result(x):
            xs = np.sort(x)
            ys = np.arange(1, len(xs) + 1) / float(len(xs))
            return ys

        result = ecdf_result(grades)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert np.allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
grades = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def ecdf_result(x):
    xs = x.sort_values()
    ys = pd.Series(np.arange(1, len(xs)+1)/float(len(xs)), index=xs.index)
    return ys
result = ecdf_result(grades)

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = pd.Series(
                (
                    93.5,
                    93,
                    60.8,
                    94.5,
                    82,
                    87.5,
                    91.5,
                    99.5,
                    86,
                    93.5,
                    92.5,
                    78,
                    76,
                    69,
                    94.5,
                    89.5,
                    92.8,
                    78,
                    65.5,
                    98,
                    98.5,
                    92.3,
                    95.5,
                    76,
                    91,
                    95,
                    61,
                )
            )
        elif test_case_id == 2:
            np.random.seed(42)
            grades = pd.Series((np.random.rand(50) - 0.5) * 100)
        return grades

    def generate_ans(data):
        _a = data
        grades = _a

        def ecdf_result(x):
            xs = x.sort_values()
            ys = pd.Series(np.arange(1, len(xs) + 1) / float(len(xs)), index=xs.index)
            return ys

        result = ecdf_result(grades)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result.equals(ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
grades = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


def ecdf_result(x):
    xs = np.sort(x)
    ys = np.arange(1, len(xs)+1)/float(len(xs))
    return xs, ys
resultx, resulty = ecdf_result(grades)
result = np.zeros_like(eval, dtype=float)
for i, element in enumerate(eval):
    if element < resultx[0]:
        result[i] = 0
    elif element >= resultx[-1]:
        result[i] = 1
    else:
        result[i] = resulty[(resultx > element).argmax()-1]

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = np.array(
                (
                    93.5,
                    93,
                    60.8,
                    94.5,
                    82,
                    87.5,
                    91.5,
                    99.5,
                    86,
                    93.5,
                    92.5,
                    78,
                    76,
                    69,
                    94.5,
                    89.5,
                    92.8,
                    78,
                    65.5,
                    98,
                    98.5,
                    92.3,
                    95.5,
                    76,
                    91,
                    95,
                    61,
                )
            )
            eval = np.array([88, 87, 62])
        elif test_case_id == 2:
            np.random.seed(42)
            grades = (np.random.rand(50) - 0.5) * 100
            eval = np.random.randint(10, 90, (5,))
        return grades, eval

    def generate_ans(data):
        _a = data
        grades, eval = _a

        def ecdf_result(x):
            xs = np.sort(x)
            ys = np.arange(1, len(xs) + 1) / float(len(xs))
            return xs, ys

        resultx, resulty = ecdf_result(grades)
        result = np.zeros_like(eval, dtype=float)
        for i, element in enumerate(eval):
            if element < resultx[0]:
                result[i] = 0
            elif element >= resultx[-1]:
                result[i] = 1
            else:
                result[i] = resulty[(resultx > element).argmax() - 1]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert np.allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
grades, eval = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def ecdf_result(x):
    xs = x.sort_values()
    ys = np.arange(1, len(xs)+1)/float(len(xs))
    return xs, ys
resultx, resulty = ecdf_result(grades)
result = pd.Series(np.zeros_like(eval, dtype=float))
for i, element in enumerate(eval):
    if element < resultx.iloc[0]:
        result.iloc[i] = 0
    elif element >= resultx.iloc[-1]:
        result.iloc[i] = 1
    else:
        result.iloc[i] = resulty[(resultx > element).argmax()-1]

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = pd.Series([93.5,93,60.8,94.5,82,87.5,91.5,99.5,86,93.5,92.5,78,76,69,94.5,89.5,92.8,78,65.5,98,98.5,92.3,95.5,76,91,95,61])
            eval = pd.Series([88, 87, 62])
        elif test_case_id == 2:
            np.random.seed(42)
            grades = pd.Series((np.random.rand(50) - 0.5) * 100)
            eval = pd.Series(np.random.randint(10, 90, (5,)))
        return grades, eval

    def generate_ans(data):
        _a = data
        grades, eval = _a

        def ecdf_result(x):
            xs = x.sort_values()
            ys = np.arange(1, len(xs) + 1) / float(len(xs))
            return xs, ys

        resultx, resulty = ecdf_result(grades)
        result = pd.Series(np.zeros_like(eval, dtype=float))
        for i, element in enumerate(eval):
            if element < resultx.iloc[0]:
                result.iloc[i] = 0
            elif element >= resultx.iloc[-1]:
                result.iloc[i] = 1
            else:
                result.iloc[i] = resulty[(resultx > element).argmax() - 1]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert np.allclose(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
grades, eval = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


def ecdf_result(x):
    xs = np.sort(x)
    ys = np.arange(1, len(xs)+1)/float(len(xs))
    return xs, ys
resultx, resulty = ecdf_result(grades)
t = (resulty > threshold).argmax()
low = resultx[0]
high = resultx[t]

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = np.array(
                (
                    93.5,
                    93,
                    60.8,
                    94.5,
                    82,
                    87.5,
                    91.5,
                    99.5,
                    86,
                    93.5,
                    92.5,
                    78,
                    76,
                    69,
                    94.5,
                    89.5,
                    92.8,
                    78,
                    65.5,
                    98,
                    98.5,
                    92.3,
                    95.5,
                    76,
                    91,
                    95,
                    61,
                )
            )
            threshold = 0.5
        elif test_case_id == 2:
            np.random.seed(42)
            grades = (np.random.rand(50) - 0.5) * 100
            threshold = 0.6
        return grades, threshold

    def generate_ans(data):
        _a = data
        grades, threshold = _a

        def ecdf_result(x):
            xs = np.sort(x)
            ys = np.arange(1, len(xs) + 1) / float(len(xs))
            return xs, ys

        resultx, resulty = ecdf_result(grades)
        t = (resulty > threshold).argmax()
        low = resultx[0]
        high = resultx[t]
        return [low, high]

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
grades, threshold = test_input
[insert]
result = [low, high]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def ecdf_result(x):
    xs = x.sort_values()
    ys = np.arange(1, len(xs)+1)/float(len(xs))
    return xs, ys
resultx, resulty = ecdf_result(grades)
t = (resulty > threshold).argmax()
low = resultx.iloc[0]
high = resultx.iloc[t]

import numpy as np
import pandas as pd
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            grades = pd.Series(
                (
                    93.5,
                    93,
                    60.8,
                    94.5,
                    82,
                    87.5,
                    91.5,
                    99.5,
                    86,
                    93.5,
                    92.5,
                    78,
                    76,
                    69,
                    94.5,
                    89.5,
                    92.8,
                    78,
                    65.5,
                    98,
                    98.5,
                    92.3,
                    95.5,
                    76,
                    91,
                    95,
                    61,
                )
            )
            threshold = 0.5
        elif test_case_id == 2:
            np.random.seed(42)
            grades = pd.Series((np.random.rand(50) - 0.5) * 100)
            threshold = 0.6
        return grades, threshold

    def generate_ans(data):
        _a = data
        grades, threshold = _a

        def ecdf_result(x):
            xs = x.sort_values()
            ys = np.arange(1, len(xs) + 1) / float(len(xs))
            return xs, ys

        resultx, resulty = ecdf_result(grades)
        t = (resulty > threshold).argmax()
        low = resultx.iloc[0]
        high = resultx.iloc[t]
        return [low, high]

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
import pandas as pd
grades, threshold = test_input
[insert]
result = [low, high]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
array = np.array([1, 2, 3])

import tensorflow as tf
import numpy as np
a = np.ones([2,3,4])

a_tf = ... # put solution in this variable

import numpy as np
import pandas as pd
import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.ones([2, 3, 4])
        elif test_case_id == 2:
            a = np.array([1, 1, 4, 5, 1, 4])
        return a

    def generate_ans(data):
        _a = data
        a = _a
        a_tf = tf.convert_to_tensor(a)
        return a_tf

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    assert tensor_equal(result, ans)
    return 1

exec_context = r"""
import tensorflow as tf
import numpy as np
a = test_input
[insert]
result = a_tf
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
df = pd.DataFrame(np.ones([2,3]))

df_np = ... # put solution in this variable

import pandas as pd
import numpy as np

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(np.ones([2, 3]))
        elif test_case_id == 2:
            df = pd.DataFrame({'A': [1, 1, 4, 5, 1, 4]})
        return df

    def generate_ans(data):
        _df = data
        df = _df
        df_np = df.to_numpy()
        return df_np

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(test_input)
    return test_input, expected_result

def test_execution(solution: str):
    exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df_np
"""
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(exec_context.replace("[insert]", solution), test_env)
        assert np.array_equal(test_env["result"], expected_result)


import numpy as np
a = np.array([4, 1, 0, 8, 5, 2])

result = ... # put solution in this variable

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array([4, 1, 0, 8, 5, 2])
        elif test_case_id == 2:
            np.random.seed(42)
            a = (np.random.rand(100) - 0.5) * 100
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.argsort(a)[::-1][: len(a)]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
s = pd.Series([4, 1, 0, 8, 5, 2])

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            s = pd.Series([4, 1, 0, 8, 5, 2])
        elif test_case_id == 2:
            np.random.seed(42)
            s = pd.Series((np.random.rand(100) - 0.5) * 100)
        return s

    def generate_ans(data):
        _s = data
        s = _s
        result = s.argsort()[::-1][:len(s)]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
s = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array([4, 1, 0, 8, 5, 2])
        elif test_case_id == 2:
            np.random.seed(42)
            a = (np.random.rand(100) - 0.5) * 100
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.argsort(a)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            s = pd.Series([4, 1, 0, 8, 5, 2])
        elif test_case_id == 2:
            np.random.seed(42)
            s = pd.Series((np.random.rand(100) - 0.5) * 100)
        return s

    def generate_ans(data):
        _s = data
        s = _s
        result = s.argsort()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
s = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
a = np.array([4, 1, 0, 8, 5, 2])
N = 3

result = ... # put solution in this variable

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array([4, 1, 0, 8, 5, 2])
            N = 3
        elif test_case_id == 2:
            np.random.seed(42)
            a = (np.random.rand(100) - 0.5) * 100
            N = np.random.randint(1, 25)
        return a, N

    def generate_ans(data):
        _a = data
        a, N = _a
        result = np.argsort(a)[::-1][:N]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a, N = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

import pandas as pd
s = pd.Series([4, 1, 0, 8, 5, 2])
N = 3

result = ... # put solution in this variable

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            s = pd.Series([4, 1, 0, 8, 5, 2])
            N = 3
        elif test_case_id == 2:
            np.random.seed(42)
            data = (np.random.rand(100) - 0.5) * 100
            s = pd.Series(data)
            N = np.random.randint(1, 25)
        return s, N

    def generate_ans(data):
        _s = data
        s, N = _s
        result = s.nlargest(N).index.tolist()
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert result == ans
    return 1

exec_context = r"""
import pandas as pd
s, N = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


import numpy as np
A = np.arange(16).reshape(4, 4)
n = 5

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            A = np.arange(16).reshape(4, 4)
            n = 5
        elif test_case_id == 2:
            np.random.seed(42)
            dim = np.random.randint(10, 15)
            A = np.random.rand(dim, dim)
            n = np.random.randint(3, 8)
        return A, n

    def generate_ans(data):
        _a = data
        A, n = _a
        result = np.linalg.matrix_power(A, n)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert type(result) == np.ndarray
    np.testing.assert_allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
A, n = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "matrix" not in tokens

import pandas as pd
import numpy as np
DF = pd.DataFrame(np.arange(16).reshape(4, 4))
n = 5

result = ... # put solution in this variable

import numpy as np
import pandas as pd
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = np.arange(16).reshape(4, 4)
            DF = pd.DataFrame(data)
            n = 5
        elif test_case_id == 2:
            np.random.seed(42)
            dim = np.random.randint(10, 15)
            data = np.random.rand(dim, dim)
            DF = pd.DataFrame(data)
            n = np.random.randint(3, 8)
        return DF, n

    def generate_ans(data):
        _df, n = data
        result = _df.pow(n)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    pd.testing.assert_frame_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
DF, n = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "matrix" not in tokens


result = a.reshape(a.shape[0]//2, 2, a.shape[1]//2, 2).swapaxes(1, 2).transpose(1, 0, 2, 3).reshape(-1, 2, 2)

import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array(
                [[1, 5, 9, 13], [2, 6, 10, 14], [3, 7, 11, 15], [4, 8, 12, 16]]
            )
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(100, 200)
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = (
            a.reshape(a.shape[0] // 2, 2, a.shape[1] // 2, 2)
            .swapaxes(1, 2)
            .transpose(1, 0, 2, 3)
            .reshape(-1, 2, 2)
        )
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12],
    'D': [13, 14, 15, 16]
})

result = ... # put solution in this variable

result = df.to_numpy().reshape(df.shape[0]//2, 2, df.shape[1]//2, 2).swapaxes(1, 2).transpose(1, 0, 2, 3).reshape(-1, 2, 2)

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame({
                'A': [1, 2, 3, 4],
                'B': [5, 6, 7, 8],
                'C': [9, 10, 11, 12],
                'D': [13, 14, 15, 16]
            })
        elif test_case_id == 2:
            np.random.seed(42)
            data = np.random.rand(36, 72)
            df = pd.DataFrame(data)
        return df

    def generate_ans(data):
        _df = data
        df = _df.to_numpy()
        result = (
            df.reshape(df.shape[0] // 2, 2, df.shape[1] // 2, 2)
            .swapaxes(1, 2)
            .transpose(1, 0, 2, 3)
            .reshape(-1, 2, 2)
        )
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens


result = np.lib.stride_tricks.sliding_window_view(a, window_shape=(2,2)).reshape(-1, 2, 2)

import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = np.array(
                [[1, 5, 9, 13], [2, 6, 10, 14], [3, 7, 11, 15], [4, 8, 12, 16]]
            )
        elif test_case_id == 2:
            np.random.seed(42)
            a = np.random.rand(100, 200)
        return a

    def generate_ans(data):
        _a = data
        a = _a
        result = np.lib.stride_tricks.sliding_window_view(
            a, window_shape=(2, 2)
        ).reshape(-1, 2, 2)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12],
    'D': [13, 14, 15, 16]
})

result = ... # put solution in this variable

result = np.lib.stride_tricks.sliding_window_view(df.to_numpy(), window_shape=(2,2)).reshape(-1, 2, 2)

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame({
                'A': [1, 2, 3, 4],
                'B': [5, 6, 7, 8],
                'C': [9, 10, 11, 12],
                'D': [13, 14, 15, 16]
            })
        elif test_case_id == 2:
            np.random.seed(42)
            data = np.random.rand(100, 200)
            df = pd.DataFrame(data)
        return df

    def generate_ans(data):
        _df = data
        df = _df.to_numpy()
        result = np.lib.stride_tricks.sliding_window_view(
            df, window_shape=(2, 2)
        ).reshape(-1, 2, 2)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    np.testing.assert_array_equal(result, ans)
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "while" not in tokens and "for" not in tokens