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