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How to use test_floats method in assertpy

Best Python code snippet using assertpy_python

test_cleansing.py

Source:test_cleansing.py

1import pytest2import numpy as np3import pandas as pd4from housinglib.cleansing import drop_precision, fill_na_real, data_cleaning5def test_drop_precision():6    """Checks if drop_precision function works correctly: drops precision,7    keeps shape intact and doesn't change values."""8    rng = np.random.default_rng()9    test_floats = pd.DataFrame(rng.random((100, 10), dtype='float64'),10                               columns=['f_' + str(n) for n in range(10)])11    test_ints = pd.DataFrame(rng.integers(0, 10, (100, 10), dtype='int64'),12                             columns=['i_' + str(n) for n in range(10)])13    test_df = pd.concat([test_ints, test_floats], axis=1)14    result = drop_precision(test_df)15    assert result.shape == test_df.shape16    old_int_cols = test_df.select_dtypes(include='int64').columns17    new_int_cols = result.select_dtypes(include='int32').columns18    assert (old_int_cols == new_int_cols).all()19    old_float_cols = test_df.select_dtypes(include='float64').columns20    new_float_cols = result.select_dtypes(include='float32').columns21    assert (old_float_cols == new_float_cols).all()22    assert np.allclose(test_df.values, result.values)23def test_fill_na_real():24    """Checks if fill_na_real function works correctly: fills all NaNs, doesn't change shape25     and fills with correct values."""26    rng = np.random.default_rng()27    test_floats = pd.DataFrame(rng.random((100, 10), dtype='float64'),28                               columns=['f_' + str(n) for n in range(10)])29    test_ints = pd.DataFrame(rng.integers(0, 10, (100, 10), dtype='int32'),30                             columns=['i_' + str(n) for n in range(10)])31    test_df = pd.concat([test_floats, test_ints], axis=1)32    nan_idx_x = rng.choice(100, size=250)33    nan_idx_y = rng.choice(20, size=250)34    for i in range(250):35        test_df.iloc[nan_idx_x[i], nan_idx_y[i]] = np.NaN36    fill_values = pd.concat([test_df.iloc[:, :10].mean(),37                             test_df.iloc[:, 10:].median()], axis=0)38    result = fill_na_real(test_df)39    assert result.shape[0] == test_df.shape[0]40    assert result.notna().all(axis=None)41    for i in range(250):42        assert result.iloc[nan_idx_x[i], nan_idx_y[i]] == fill_values[nan_idx_y[i]]43@pytest.mark.parametrize('lower_precision',44                         [True, False])45def test_data_cleaning(lower_precision):46    """Checks if processed DataFrame does not make anything exceptionally bad with data.47    Also checks if 'lower_precision' parameter is working."""48    raw = pd.read_table('./data/raw/AmesHousing.txt', index_col=0)49    df = data_cleaning(raw, lower_precision)50    assert df.shape[0] > 051    assert df.shape[1] > 052    assert df.notna().all(axis=None)53    if lower_precision:54        assert 'int64' not in df.dtypes.values...

test_xor16.py

Source:test_xor16.py

1from pyxorfilter import Xor162import random3def test_xor16_int():4    xor_filter = Xor16(100)5    test_lst = random.sample(range(0, 1000), 100)6    xor_filter.populate(test_lst.copy())7    for i in test_lst:8        assert xor_filter.contains(i) == True9    for i in random.sample(range(1000, 3000), 500):10        assert xor_filter.contains(i) == False11def test_xor16_int_iterable():12    xor_filter = Xor16(100)13    xor_filter.populate(range(50))14    for i in range(50):15        assert xor_filter.contains(i) == True16def test_xor16_strings():17    xor_filter = Xor16(10)18    test_str = ["ã", "/dev/null; touch /tmp/blns.fail ; echo", "à¤", "Normal", "122"]19    xor_filter.populate(test_str.copy())20    for test in test_str:21        assert xor_filter.contains(test) == True22    test_str2 = ["æ", "à¤", "12", "delta"]23    for i in test_str2:24        assert xor_filter.contains(i) == False25def test_xor16_floats():26    xor_filter = Xor16(10)27    test_floats = [1.23, 9999.88, 323.43, 0.0]28    xor_filter.populate(test_floats.copy())29    for i in test_floats:30        assert xor_filter.contains(i) == True31    test_floats2 = [-1.23, 1.0, 0.1, 676.5, 1.234]32    for i in test_floats2:33        assert xor_filter.contains(i) == False34def test_xor16_all():35    xor_filter = Xor16(5)36    test_str = ["string", 51, 0.0, 12.3]37    xor_filter.populate(test_str.copy())38    for i in test_str:39        assert xor_filter.contains(i) == True40    test_str2 = [12, "à¥ª", 0.1]41    for i in test_str2:...

test_xor8.py

Source:test_xor8.py

1from pyxorfilter import Xor82from random import sample3def test_xor8_int():4    xor_filter = Xor8(50)5    xor_filter.populate([_ for _ in range(50)])6    for i in range(50):7        assert xor_filter.contains(i) == True8def test_xor8_int_iterable():9    xor_filter = Xor8(50)10    xor_filter.populate(range(50))11    for i in range(50):12        assert xor_filter.contains(i) == True13def test_xor8_strings():14    xor_filter = Xor8(10)15    test_str = ["ã", "/dev/null; touch /tmp/blns.fail ; echo", "à¤", "Normal", "122"]16    xor_filter.populate(test_str.copy())17    for i in test_str:18        assert xor_filter.contains(i) == True19def test_xor8_floats():20    xor_filter = Xor8(10)21    test_floats = [1.23, 9999.88, 323.43, 0.0]22    xor_filter.populate(test_floats.copy())23    for i in test_floats:24        assert xor_filter.contains(i) == True25def test_xor8_all():26    xor_filter = Xor8(5)27    test_str = ["string", 51, 0.0, 12.3]28    xor_filter.populate(test_str.copy())29    for i in test_str:...

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