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openSUSE:Backports:SLE-15-SP4:FactoryCandidates
python-datashader
datashader-pr1314-fix-pandas2.2-tests.patch
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File datashader-pr1314-fix-pandas2.2-tests.patch of Package python-datashader
From 2a0177902210d3cb5ce982f9cc114c5abb852f30 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Simon=20H=C3=B8xbro=20Hansen?= <simon.hansen@me.com> Date: Wed, 31 Jan 2024 18:08:33 +0100 Subject: [PATCH 01/14] Pin pytest for now. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) Index: datashader-0.16.0/setup.py =================================================================== --- datashader-0.16.0.orig/setup.py +++ datashader-0.16.0/setup.py @@ -44,14 +44,13 @@ extras_require = { 'tests': geopandas + [ 'codecov', 'geodatasets', - 'fastparquet', # optional dependency 'flake8', 'nbconvert', 'nbformat', 'nbsmoke[verify] >0.5', 'netcdf4', 'pyarrow', - 'pytest', + 'pytest <8', # Fails lint with IPynbFile is deprecated 'pytest-benchmark', 'pytest-cov', 'rasterio', Index: datashader-0.16.0/datashader/tests/test_dask.py =================================================================== --- datashader-0.16.0.orig/datashader/tests/test_dask.py +++ datashader-0.16.0/datashader/tests/test_dask.py @@ -124,7 +124,7 @@ def test_gpu_dependencies(): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_count(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray(np.array([[5, 5], [5, 5]], dtype='i4'), coords=coords, dims=dims) @@ -140,7 +140,7 @@ def test_count(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_any(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray(np.array([[True, True], [True, True]]), coords=coords, dims=dims) @@ -155,7 +155,7 @@ def test_any(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_sum(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i32).reshape((2, 2, 5)).sum(axis=2, dtype='f8').T, @@ -173,7 +173,7 @@ def test_sum(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_first(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[0, 10], [5, 15]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds.first('i32')), out) @@ -185,7 +185,7 @@ def test_first(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_last(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[4, 14], [9, 19]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds.last('i32')), out) @@ -197,7 +197,7 @@ def test_last(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_min(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i64).reshape((2, 2, 5)).min(axis=2).astype('f8').T, @@ -211,7 +211,7 @@ def test_min(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_max(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i64).reshape((2, 2, 5)).max(axis=2).astype('f8').T, @@ -225,7 +225,7 @@ def test_max(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_min_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[0, 10], [5, 15]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds._min_row_index()), out) @@ -234,7 +234,7 @@ def test_min_row_index(ddf, npartitions) @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_max_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[4, 14], [9, 19]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds._max_row_index()), out) @@ -243,7 +243,7 @@ def test_max_row_index(ddf, npartitions) @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_min_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[-3, -1, 0, 4, nan, nan], [-13, -11, 10, 12, 14, nan]], [[-9, -7, -5, 6, 8, nan], [-19, -17, -15, 16, 18, nan]]]) @@ -258,7 +258,7 @@ def test_min_n(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_max_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[4, 0, -1, -3, nan, nan], [14, 12, 10, -11, -13, nan]], [[8, 6, -5, -7, -9, nan], [18, 16, -15, -17, -19, nan]]]) @@ -273,7 +273,7 @@ def test_max_n(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_min_n_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[0, 1, 2, 3, 4, -1], [10, 11, 12, 13, 14, -1]], [[5, 6, 7, 8, 9, -1], [15, 16, 17, 18, 19, -1]]]) @@ -288,7 +288,7 @@ def test_min_n_row_index(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_max_n_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[4, 3, 2, 1, 0, -1], [14, 13, 12, 11, 10, -1]], [[9, 8, 7, 6, 5, -1], [19, 18, 17, 16, 15, -1]]]) @@ -303,7 +303,7 @@ def test_max_n_row_index(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_first_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[0, -1, -3, 4, nan, nan], [10, -11, 12, -13, 14, nan]], [[-5, 6, -7, 8, -9, nan], [-15, 16, -17, 18, -19, nan]]]) @@ -319,7 +319,7 @@ def test_first_n(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_last_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[4, -3, -1, 0, nan, nan], [14, -13, 12, -11, 10, nan]], [[-9, 8, -7, 6, -5, nan], [-19, 18, -17, 16, -15, nan]]]) @@ -334,7 +334,7 @@ def test_last_n(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_count(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[2, 1, 1, 1], [1, 1, 2, 1]], [[1, 2, 1, 1], [1, 1, 1, 2]]], dtype=np.uint32) assert_eq_ndarray(c.points(ddf, 'x', 'y', ds.by('cat2')).data, sol) @@ -347,7 +347,7 @@ def test_categorical_count(ddf, npartiti @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_min(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_int = np.array([[[0, 1, 2, 3], [12, 13, 10, 11]], [[8, 5, 6, 7], [16, 17, 18, 15]]], dtype=np.float64) @@ -361,7 +361,7 @@ def test_categorical_min(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_max(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_int = np.array([[[4, 1, 2, 3], [12, 13, 14, 11]], [[8, 9, 6, 7], [16, 17, 18, 19]]], dtype=np.float64) @@ -375,7 +375,7 @@ def test_categorical_max(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_min_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[0, 4, nan], [1, nan, nan], [nan, nan, nan], [3, nan, nan]], [[12, nan, nan], [13, nan, nan], [10, 14, nan], [11, nan, nan]]], @@ -393,7 +393,7 @@ def test_categorical_min_n(ddf, npartiti @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_max_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[4, 0, nan], [1, nan, nan], [nan, nan, nan], [3, nan, nan]], [[12, nan, nan], [13, nan, nan], [14, 10, nan], [11, nan, nan]]], @@ -411,7 +411,7 @@ def test_categorical_max_n(ddf, npartiti @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_min_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[0, 1, 2, 3], [12, 13, 10, 11]], [[8, 5, 6, 7], [16, 17, 18, 15]]]) agg = c.points(ddf, 'x', 'y', ds.by('cat2', ds._min_row_index())) @@ -421,7 +421,7 @@ def test_categorical_min_row_index(ddf, @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_max_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[4, 1, 2, 3], [12, 13, 14, 11]], [[8, 9, 6, 7], [16, 17, 18, 19]]]) agg = c.points(ddf, 'x', 'y', ds.by('cat2', ds._max_row_index())) @@ -431,7 +431,7 @@ def test_categorical_max_row_index(ddf, @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_min_n_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[0, 4, -1], [1, -1, -1], [2, -1, -1], [3, -1, -1]], [[12, -1, -1], [13, -1, -1], [10, 14, -1], [11, -1, -1]]], @@ -449,7 +449,7 @@ def test_categorical_min_n_row_index(ddf @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_max_n_row_index(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[4, 0, -1], [1, -1, -1], [2, -1, -1], [3, -1, -1]], [[12, -1, -1], [13, -1, -1], [14, 10, -1], [11, -1, -1]]], @@ -467,7 +467,7 @@ def test_categorical_max_n_row_index(ddf @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_first(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[0, -1, nan, -3], [12, -13, 10, -11]], @@ -481,7 +481,7 @@ def test_categorical_first(ddf, npartiti @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_last(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[4, -1, nan, -3], [12, -13, 14, -11]], @@ -495,7 +495,7 @@ def test_categorical_last(ddf, npartitio @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_first_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[0, 4, nan], [-1, nan, nan], [nan, nan, nan], [-3, nan, nan]], [[12, nan, nan], [-13, nan, nan], [10, 14, nan], [-11, nan, nan]]], @@ -513,7 +513,7 @@ def test_categorical_first_n(ddf, nparti @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_last_n(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions solution = np.array([[[[4, 0, nan], [-1, nan, nan], [nan, nan, nan], [-3, nan, nan]], [[12, nan, nan], [-13, nan, nan], [14, 10, nan], [-11, nan, nan]]], @@ -533,7 +533,7 @@ def test_categorical_last_n(ddf, npartit def test_where_max(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[16, 6], [11, 1]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds.where(ds.max('i32'), 'reverse')), out) @@ -554,7 +554,7 @@ def test_where_max(ddf, npartitions): def test_where_min(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray([[20, 10], [15, 5]], coords=coords, dims=dims) assert_eq_xr(c.points(ddf, 'x', 'y', ds.where(ds.min('i32'), 'reverse')), out) @@ -575,7 +575,7 @@ def test_where_min(ddf, npartitions): def test_where_max_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = np.array([[[ 4, 0, 1, 3, -1, -1], [14, 12, 10, 11, 13, -1]], @@ -608,7 +608,7 @@ def test_where_max_n(ddf, npartitions): def test_where_min_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = np.array([[[3, 1, 0, 4, -1, -1], [13, 11, 10, 12, 14, -1]], @@ -639,7 +639,7 @@ def test_where_min_n(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_where_first(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions # Note reductions like ds.where(ds.first('i32'), 'reverse') are supported, # but the same results can be achieved using the simpler ds.first('reverse') @@ -660,7 +660,7 @@ def test_where_first(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_where_last(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions # Note reductions like ds.where(ds.last('i32'), 'reverse') are supported, # but the same results can be achieved using the simpler ds.last('reverse') @@ -683,7 +683,7 @@ def test_where_last(ddf, npartitions): def test_where_first_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = np.array([[[ 0, 1, 3, 4, -1, -1], [10, 11, 12, 13, 14, -1]], @@ -716,7 +716,7 @@ def test_where_first_n(ddf, npartitions) def test_where_last_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = np.array([[[ 4, 3, 1, 0, -1, -1], [14, 13, 12, 11, 10, -1]], @@ -747,7 +747,7 @@ def test_where_last_n(ddf, npartitions): @pytest.mark.parametrize('ddf', [_ddf]) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_summary_by(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions # summary(by) @@ -785,7 +785,7 @@ def test_summary_by(ddf, npartitions): def test_summary_where_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_min_n_rowindex = np.array([[[3, 1, 0, 4, -1], [13, 11, 10, 12, 14]], @@ -821,7 +821,7 @@ def test_summary_where_n(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_mean(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i32).reshape((2, 2, 5)).mean(axis=2, dtype='f8').T, @@ -838,7 +838,7 @@ def test_mean(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_var(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i32).reshape((2, 2, 5)).var(axis=2, dtype='f8').T, @@ -855,7 +855,7 @@ def test_var(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_std(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions out = xr.DataArray( values(df_pd.i32).reshape((2, 2, 5)).std(axis=2, dtype='f8').T, @@ -872,7 +872,7 @@ def test_std(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_count_cat(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[5, 0, 0, 0], [0, 0, 5, 0]], @@ -930,7 +930,7 @@ def test_count_cat(ddf, npartitions): @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_sum(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[ 10, nan, nan, nan], [nan, nan, 60, nan]], @@ -977,7 +977,7 @@ def test_categorical_sum_binning(ddf, np pytest.skip( "The categorical binning of 'sum' reduction is yet supported on the GPU" ) - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[8.0, nan, nan, nan], [nan, nan, 60.0, nan]], @@ -1000,7 +1000,7 @@ def test_categorical_sum_binning(ddf, np @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_mean(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[ 2, nan, nan, nan], [nan, nan, 12, nan]], @@ -1036,7 +1036,7 @@ def test_categorical_mean_binning(ddf, n pytest.skip( "The categorical binning of 'mean' reduction is yet supported on the GPU" ) - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[ 2, nan, nan, nan], [nan, nan, 12, nan]], @@ -1057,7 +1057,7 @@ def test_categorical_mean_binning(ddf, n @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_var(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.array([[[ 2.5, nan, nan, nan], [ nan, nan, 2., nan]], @@ -1099,7 +1099,7 @@ def test_categorical_var(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_categorical_std(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol = np.sqrt(np.array([ [[ 2.5, nan, nan, nan], @@ -1143,7 +1143,7 @@ def test_categorical_std(ddf, npartition @pytest.mark.parametrize('ddf', ddfs) @pytest.mark.parametrize('npartitions', [1, 2, 3, 4]) def test_multiple_aggregates(ddf, npartitions): - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions agg = c.points(ddf, 'x', 'y', ds.summary(f64_std=ds.std('f64'), @@ -2365,7 +2365,7 @@ def test_canvas_size(): def test_dataframe_dtypes(ddf, npartitions): # Issue #1235. ddf['dates'] = pd.Series(['2007-07-13']*20, dtype='datetime64[ns]') - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions ds.Canvas(2, 2).points(ddf, 'x', 'y', ds.count()) @@ -2408,7 +2408,7 @@ def test_dask_categorical_counts(on_gpu) def test_categorical_where_max(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray([[[4, 1, -1, 3], [12, 13, 14, 11]], [[8, 5, 6, 7], [16, 17, 18, 15]]], @@ -2430,7 +2430,7 @@ def test_categorical_where_max(ddf, npar def test_categorical_where_min(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray([[[0, 1, -1, 3], [12, 13, 10, 11]], [[8, 9, 6, 7], [16, 17, 18, 19]]], @@ -2452,7 +2452,7 @@ def test_categorical_where_min(ddf, npar def test_categorical_where_first(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray([[[0, 1, -1, 3], [12, 13, 10, 11]], [[8, 5, 6, 7], [16, 17, 18, 15]]], @@ -2474,7 +2474,7 @@ def test_categorical_where_first(ddf, np def test_categorical_where_last(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray([[[4, 1, -1, 3], [12, 13, 14, 11]], [[8, 9, 6, 7], [16, 17, 18, 19]]], @@ -2496,7 +2496,7 @@ def test_categorical_where_last(ddf, npa def test_categorical_where_max_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray( [[[[4, 0, -1], [1, -1, -1], [-1, -1, -1], [3, -1, -1]], @@ -2534,7 +2534,7 @@ def test_categorical_where_max_n(ddf, np def test_categorical_where_min_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray( [[[[0, 4, -1], [1, -1, -1], [-1, -1, -1], [3, -1, -1]], @@ -2572,7 +2572,7 @@ def test_categorical_where_min_n(ddf, np def test_categorical_where_first_n(ddf, npartitions): # Important to test with npartitions > 2 to have multiple combination stages. # Identical results to equivalent pandas test. - ddf = ddf.repartition(npartitions) + ddf = ddf.repartition(npartitions=npartitions) assert ddf.npartitions == npartitions sol_rowindex = xr.DataArray( [[[[0, 4, -1], [1, -1, -1], [-1, -1, -1], [3, -1, -1]], Index: datashader-0.16.0/datashader/tests/test_datatypes.py =================================================================== --- datashader-0.16.0.orig/datashader/tests/test_datatypes.py +++ datashader-0.16.0/datashader/tests/test_datatypes.py @@ -835,6 +835,12 @@ class TestRaggedMethods(eb.BaseMethodsTe def test_where_series(self): pass + @pytest.mark.xfail(reason="not currently supported") + def test_duplicated(self, data): + # Added in Pandas 2.2 + # https://github.com/pandas-dev/pandas/pull/55255 + super().test_duplicated(data) + class TestRaggedPrinting(eb.BasePrintingTests): @pytest.mark.skip(reason="Can't autoconvert ragged array to numpy array") def test_dataframe_repr(self): @@ -873,6 +879,11 @@ class TestRaggedMissing(eb.BaseMissingTe def test_fillna_series_method(self): pass + @pytest.mark.skip(reason="Can't fill with nested sequences") + def test_ffill_limit_area(self): + # Added in Pandas 2.2 + pass + class TestRaggedReshaping(eb.BaseReshapingTests): @pytest.mark.skip(reason="__setitem__ not supported") @@ -886,3 +897,15 @@ class TestRaggedReshaping(eb.BaseReshapi @pytest.mark.skip(reason="transpose with numpy array elements seems not supported") def test_transpose_frame(self): pass + + @pytest.mark.skipif( + Version(pd.__version__) == Version("2.2.0"), reason="Regression in Pandas 2.2" + ) + def test_merge_on_extension_array(self, data): + super().test_merge_on_extension_array(data) + + @pytest.mark.skipif( + Version(pd.__version__) == Version("2.2.0"), reason="Regression in Pandas 2.2" + ) + def test_merge_on_extension_array_duplicates(self, data): + super().test_merge_on_extension_array_duplicates(data) Index: datashader-0.16.0/pyproject.toml =================================================================== --- datashader-0.16.0.orig/pyproject.toml +++ datashader-0.16.0/pyproject.toml @@ -18,3 +18,16 @@ target-version = "py39" [tool.ruff.per-file-ignores] "test_mpl_ext.py" = ["E402"] # Module level import not at top of file + +[tool.pytest.ini_options] +addopts = ["--pyargs", "--doctest-modules", "--doctest-ignore-import-errors", "--strict-config", "--strict-markers"] +norecursedirs = 'doc .git dist build _build .ipynb_checkpoints' +minversion = "7" +xfail_strict = true +log_cli_level = "INFO" +# skipping any notebooks that require extra deps +nbsmoke_skip_run = ".*tiling.ipynb$\n.*streaming-aggregation.ipynb$\n.*8_Geography.ipynb$" +filterwarnings = [ + "ignore:Passing a (SingleBlockManager|BlockManager) to (Series|GeoSeries|DataFrame|GeoDataFrame) is deprecated:DeprecationWarning", # https://github.com/holoviz/spatialpandas/issues/137 + "ignore:Accessing the underlying geometries through the `.data`:DeprecationWarning:dask_geopandas.core", # https://github.com/geopandas/dask-geopandas/issues/264 +] Index: datashader-0.16.0/datashader/tests/test_geopandas.py =================================================================== --- datashader-0.16.0.orig/datashader/tests/test_geopandas.py +++ datashader-0.16.0/datashader/tests/test_geopandas.py @@ -200,7 +200,7 @@ def test_lines_spatialpandas(geom_type, def test_points_geopandas(geom_type): df = geopandas.read_file(geodatasets.get_path("nybb")) - df["geometry"] = df["geometry"].sample_points(100, seed=93814) # multipoint + df["geometry"] = df["geometry"].sample_points(100, rng=93814) # multipoint if geom_type == "point": df = df.explode(index_parts=False) # Multipoint -> point. unique_geom_type = df["geometry"].geom_type.unique() @@ -218,7 +218,7 @@ def test_points_geopandas(geom_type): def test_points_dask_geopandas(geom_type, npartitions): df = geopandas.read_file(geodatasets.get_path("nybb")) - df["geometry"] = df["geometry"].sample_points(100, seed=93814) # multipoint + df["geometry"] = df["geometry"].sample_points(100, rng=93814) # multipoint if geom_type == "point": df = df.explode(index_parts=False) # Multipoint -> point. unique_geom_type = df["geometry"].geom_type.unique() @@ -240,7 +240,7 @@ def test_points_dask_geopandas(geom_type def test_points_spatialpandas(geom_type, npartitions): df = geopandas.read_file(geodatasets.get_path("nybb")) - df["geometry"] = df["geometry"].sample_points(100, seed=93814) # multipoint + df["geometry"] = df["geometry"].sample_points(100, rng=93814) # multipoint if geom_type == "point": df = df.explode(index_parts=False) # Multipoint -> point. unique_geom_type = df["geometry"].geom_type.unique() Index: datashader-0.16.0/datashader/tests/test_pipeline.py =================================================================== --- datashader-0.16.0.orig/datashader/tests/test_pipeline.py +++ datashader-0.16.0/datashader/tests/test_pipeline.py @@ -9,7 +9,7 @@ import datashader.transfer_functions as df = pd.DataFrame({'x': np.array(([0.] * 10 + [1] * 10)), 'y': np.array(([0.] * 5 + [1] * 5 + [0] * 5 + [1] * 5)), 'f64': np.arange(20, dtype='f8')}) -df.f64.iloc[2] = np.nan +df.loc['f64', 2] = np.nan cvs = ds.Canvas(plot_width=2, plot_height=2, x_range=(0, 1), y_range=(0, 1)) cvs10 = ds.Canvas(plot_width=10, plot_height=10, x_range=(0, 1), y_range=(0, 1)) Index: datashader-0.16.0/datashader/datatypes.py =================================================================== --- datashader-0.16.0.orig/datashader/datatypes.py +++ datashader-0.16.0/datashader/datatypes.py @@ -649,6 +649,10 @@ Invalid indices for take with allow_fill dtype = np.dtype(object) if dtype is None else np.dtype(dtype) return np.asarray(self.tolist(), dtype=dtype) + def duplicated(self, *args, **kwargs): + msg = "duplicated is not implemented for RaggedArray" + raise NotImplementedError(msg) + @jit(nopython=True, nogil=True) def _eq_ragged_ragged(start_indices1,
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