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(