def test_set_npartitions(num_partitions): NPartitions.put(num_partitions) data = np.random.randint(0, 100, size=(2**16, 2**8)) df = pd.DataFrame(data) part_shape = df._query_compiler._modin_frame._partitions.shape assert part_shape[0] == num_partitions and part_shape[1] == min( num_partitions, 8)
def test_runtime_change_npartitions(left_num_partitions, right_num_partitions): NPartitions.put(left_num_partitions) data = np.random.randint(0, 100, size=(2**16, 2**8)) left_df = pd.DataFrame(data) part_shape = left_df._query_compiler._modin_frame._partitions.shape assert part_shape[0] == left_num_partitions and part_shape[1] == min( left_num_partitions, 8) NPartitions.put(right_num_partitions) right_df = pd.DataFrame(data) part_shape = right_df._query_compiler._modin_frame._partitions.shape assert part_shape[0] == right_num_partitions and part_shape[1] == min( right_num_partitions, 8)
import pytest import pandas import matplotlib import modin.pandas as pd from modin.pandas.test.utils import ( df_equals, test_data_values, test_data_keys, eval_general, test_data, create_test_dfs, ) from modin.config import NPartitions NPartitions.put(4) # Force matplotlib to not use any Xwindows backend. matplotlib.use("Agg") @pytest.mark.parametrize( "other", [ lambda df: 4, lambda df, axis: df.iloc[0] if axis == "columns" else list(df[df.columns[0]]), ], ids=["scalar", "series_or_list"], ) @pytest.mark.parametrize("axis", ["rows", "columns"]) @pytest.mark.parametrize(