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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,52 +1,59 @@ | ||
| import pytest | ||
| import pandas as pd | ||
| import pytest | ||
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| from ..utils_test import cluster_memory, timeseries_of_size, wait | ||
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| @pytest.mark.skipif() | ||
| def test_unique(small_client): | ||
| """Find unique values""" | ||
| memory = cluster_memory(small_client) | ||
| df = timeseries_of_size(memory) | ||
| s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| result = s.unique() | ||
| wait(result, small_client, 10 * 60) | ||
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| def test_contains(small_client): | ||
| """String contains""" | ||
| memory = cluster_memory(small_client) | ||
| df = timeseries_of_size(memory) | ||
| s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| result = s.str.contains("a") | ||
| wait(result, small_client, 10 * 60) | ||
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| def test_startswith(small_client): | ||
| """String starts with""" | ||
| @pytest.fixture(params=[True, False]) | ||
| def series_with_client(request, small_client): | ||
| memory = cluster_memory(small_client) | ||
| df = timeseries_of_size(memory) | ||
| s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| result = s.str.startswith("B") | ||
| wait(result, small_client, 10 * 60) | ||
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| if request.param: | ||
| series = df.name.astype(pd.StringDtype("pyarrow")) | ||
| series = series.persist() | ||
| yield series, small_client | ||
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| def test_filter(small_client): | ||
| """How fast can we filter a DataFrame?""" | ||
| memory = cluster_memory(small_client) | ||
| df = timeseries_of_size(memory) | ||
| df.name = df.name.astype(pd.StringDtype("pyarrow")) | ||
| df = df.persist() | ||
| name = df.head(1).name.iloc[0] # Get first name that appears | ||
| result = df[df.name == name] | ||
| wait(result, small_client, 10 * 60) | ||
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| def test_value_counts(small_client): | ||
| """Value counts on string values""" | ||
| memory = cluster_memory(small_client) | ||
| df = timeseries_of_size(memory) | ||
| s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| result = s.value_counts() | ||
| wait(result, small_client, 10 * 60) | ||
| def test_unique(series_with_client): | ||
| """Find unique values""" | ||
| series, client = series_with_client | ||
| result = series.unique() | ||
| wait(result, client, 10 * 60) | ||
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| # def test_contains(small_client): | ||
| # """String contains""" | ||
| # memory = cluster_memory(small_client) | ||
| # df = timeseries_of_size(memory) | ||
| # s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| # result = s.str.contains("a") | ||
| # wait(result, small_client, 10 * 60) | ||
| # | ||
| # | ||
| # def test_startswith(small_client): | ||
| # """String starts with""" | ||
| # memory = cluster_memory(small_client) | ||
| # df = timeseries_of_size(memory) | ||
| # s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| # result = s.str.startswith("B") | ||
| # wait(result, small_client, 10 * 60) | ||
| # | ||
| # | ||
| # def test_filter(small_client): | ||
| # """How fast can we filter a DataFrame?""" | ||
| # memory = cluster_memory(small_client) | ||
| # df = timeseries_of_size(memory) | ||
| # df.name = df.name.astype(pd.StringDtype("pyarrow")) | ||
| # df = df.persist() | ||
| # name = df.head(1).name.iloc[0] # Get first name that appears | ||
| # result = df[df.name == name] | ||
| # wait(result, small_client, 10 * 60) | ||
| # | ||
| # | ||
| # def test_value_counts(small_client): | ||
| # """Value counts on string values""" | ||
| # memory = cluster_memory(small_client) | ||
| # df = timeseries_of_size(memory) | ||
| # s = df.name.astype(pd.StringDtype("pyarrow")).persist() | ||
| # result = s.value_counts() | ||
| # wait(result, small_client, 10 * 60) |