def test_dataframe_to_array_all_categorical_with_missing_vals(): s_1 = pd.Series([-1, 0, 2, 1, float('NaN')]) s_2 = pd.Series(['one', 'two', 'three', 'four', float('NaN')]) df = pd.concat([s_1, s_2], axis=1) dtypes = ['categorical'] * 2 metadata = dict() valmaps = du.gen_valmaps(df, dtypes, metadata) data = du.dataframe_to_array(df, valmaps) assert data.shape == df.shape assert 'float' in str(data.dtype) assert data[0, 0] == 0 assert data[1, 0] == 1 assert data[2, 0] == 3 assert data[3, 0] == 2 assert np.isnan(data[4, 0]) assert data[0, 1] == 1 assert data[1, 1] == 3 assert data[2, 1] == 2 assert data[3, 1] == 0 assert np.isnan(data[4, 1])
def test_dataframe_to_array_all_categorical_with_missing_vals(): s_1 = pd.Series([-1, 0, 2, 1, float('NaN')]) s_2 = pd.Series(['one', 'two', 'three', 'four', float('NaN')]) df = pd.concat([s_1, s_2], axis=1) dtypes = ['categorical']*2 metadata = dict() valmaps = du.gen_valmaps(df, dtypes, metadata) data = du.dataframe_to_array(df, valmaps) assert data.shape == df.shape assert 'float' in str(data.dtype) assert data[0, 0] == 0 assert data[1, 0] == 1 assert data[2, 0] == 3 assert data[3, 0] == 2 assert np.isnan(data[4, 0]) assert data[0, 1] == 1 assert data[1, 1] == 3 assert data[2, 1] == 2 assert data[3, 1] == 0 assert np.isnan(data[4, 1])
def test_dataframe_to_array_all_continuous(): n_cols = 5 df = pd.DataFrame(np.random.rand(30, n_cols)) valmaps = dict() data = du.dataframe_to_array(df, valmaps) assert data.shape == df.shape assert 'float' in str(data.dtype)