114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
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"""
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these are systematically testing all of the args to value_counts
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with different size combinations. This is to ensure stability of the sorting
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and proper parameter handling
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"""
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from itertools import product
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import numpy as np
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import pytest
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from pandas import DataFrame, Grouper, MultiIndex, Series, date_range, to_datetime
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import pandas._testing as tm
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# our starting frame
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def seed_df(seed_nans, n, m):
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np.random.seed(1234)
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days = date_range("2015-08-24", periods=10)
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frame = DataFrame(
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{
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"1st": np.random.choice(list("abcd"), n),
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"2nd": np.random.choice(days, n),
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"3rd": np.random.randint(1, m + 1, n),
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}
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)
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if seed_nans:
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frame.loc[1::11, "1st"] = np.nan
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frame.loc[3::17, "2nd"] = np.nan
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frame.loc[7::19, "3rd"] = np.nan
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frame.loc[8::19, "3rd"] = np.nan
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frame.loc[9::19, "3rd"] = np.nan
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return frame
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# create input df, keys, and the bins
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binned = []
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ids = []
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for seed_nans in [True, False]:
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for n, m in product((100, 1000), (5, 20)):
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df = seed_df(seed_nans, n, m)
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bins = None, np.arange(0, max(5, df["3rd"].max()) + 1, 2)
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keys = "1st", "2nd", ["1st", "2nd"]
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for k, b in product(keys, bins):
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binned.append((df, k, b, n, m))
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ids.append(f"{k}-{n}-{m}")
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@pytest.mark.slow
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@pytest.mark.parametrize("df, keys, bins, n, m", binned, ids=ids)
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@pytest.mark.parametrize("isort", [True, False])
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@pytest.mark.parametrize("normalize", [True, False])
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@pytest.mark.parametrize("sort", [True, False])
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@pytest.mark.parametrize("ascending", [True, False])
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@pytest.mark.parametrize("dropna", [True, False])
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def test_series_groupby_value_counts(
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df, keys, bins, n, m, isort, normalize, sort, ascending, dropna
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):
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def rebuild_index(df):
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arr = list(map(df.index.get_level_values, range(df.index.nlevels)))
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df.index = MultiIndex.from_arrays(arr, names=df.index.names)
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return df
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kwargs = {
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"normalize": normalize,
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"sort": sort,
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"ascending": ascending,
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"dropna": dropna,
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"bins": bins,
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}
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gr = df.groupby(keys, sort=isort)
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left = gr["3rd"].value_counts(**kwargs)
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gr = df.groupby(keys, sort=isort)
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right = gr["3rd"].apply(Series.value_counts, **kwargs)
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right.index.names = right.index.names[:-1] + ["3rd"]
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# have to sort on index because of unstable sort on values
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left, right = map(rebuild_index, (left, right)) # xref GH9212
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tm.assert_series_equal(left.sort_index(), right.sort_index())
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def test_series_groupby_value_counts_with_grouper():
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# GH28479
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df = DataFrame(
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{
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"Timestamp": [
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1565083561,
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1565083561 + 86400,
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1565083561 + 86500,
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1565083561 + 86400 * 2,
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1565083561 + 86400 * 3,
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1565083561 + 86500 * 3,
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1565083561 + 86400 * 4,
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],
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"Food": ["apple", "apple", "banana", "banana", "orange", "orange", "pear"],
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}
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).drop([3])
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df["Datetime"] = to_datetime(df["Timestamp"].apply(lambda t: str(t)), unit="s")
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dfg = df.groupby(Grouper(freq="1D", key="Datetime"))
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# have to sort on index because of unstable sort on values xref GH9212
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result = dfg["Food"].value_counts().sort_index()
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expected = dfg["Food"].apply(Series.value_counts).sort_index()
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expected.index.names = result.index.names
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tm.assert_series_equal(result, expected)
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