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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")),
| 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")),
| )
| 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
|
|
