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101 lines
2.6 KiB
101 lines
2.6 KiB
import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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NaT,
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date_range,
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)
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@pytest.fixture
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def datetime_frame() -> DataFrame:
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"""
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Fixture for DataFrame of floats with DatetimeIndex
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Columns are ['A', 'B', 'C', 'D']
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"""
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return DataFrame(
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np.random.default_rng(2).standard_normal((100, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=date_range("2000-01-01", periods=100, freq="B"),
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)
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@pytest.fixture
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def float_string_frame():
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"""
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Fixture for DataFrame of floats and strings with index of unique strings
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Columns are ['A', 'B', 'C', 'D', 'foo'].
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"""
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df = DataFrame(
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np.random.default_rng(2).standard_normal((30, 4)),
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index=Index([f"foo_{i}" for i in range(30)], dtype=object),
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columns=Index(list("ABCD"), dtype=object),
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)
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df["foo"] = "bar"
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return df
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@pytest.fixture
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def mixed_float_frame():
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"""
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Fixture for DataFrame of different float types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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df = DataFrame(
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{
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col: np.random.default_rng(2).random(30, dtype=dtype)
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for col, dtype in zip(
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list("ABCD"), ["float32", "float32", "float32", "float64"]
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)
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},
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index=Index([f"foo_{i}" for i in range(30)], dtype=object),
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)
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# not supported by numpy random
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df["C"] = df["C"].astype("float16")
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return df
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@pytest.fixture
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def mixed_int_frame():
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"""
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Fixture for DataFrame of different int types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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"""
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return DataFrame(
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{
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col: np.ones(30, dtype=dtype)
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for col, dtype in zip(list("ABCD"), ["int32", "uint64", "uint8", "int64"])
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},
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index=Index([f"foo_{i}" for i in range(30)], dtype=object),
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)
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@pytest.fixture
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def timezone_frame():
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"""
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Fixture for DataFrame of date_range Series with different time zones
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Columns are ['A', 'B', 'C']; some entries are missing
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A B C
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0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00
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1 2013-01-02 NaT NaT
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2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00
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"""
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df = DataFrame(
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{
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"A": date_range("20130101", periods=3),
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"B": date_range("20130101", periods=3, tz="US/Eastern"),
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"C": date_range("20130101", periods=3, tz="CET"),
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}
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)
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df.iloc[1, 1] = NaT
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df.iloc[1, 2] = NaT
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return df
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