210 lines
7.0 KiB
Python
210 lines
7.0 KiB
Python
import numpy as np
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import pytest
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from pandas import DataFrame, NaT, Series, Timestamp, date_range, period_range
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import pandas._testing as tm
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class TestDataFrameValues:
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def test_values(self, float_frame):
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float_frame.values[:, 0] = 5.0
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assert (float_frame.values[:, 0] == 5).all()
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def test_more_values(self, float_string_frame):
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values = float_string_frame.values
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assert values.shape[1] == len(float_string_frame.columns)
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def test_values_mixed_dtypes(self, float_frame, float_string_frame):
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frame = float_frame
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arr = frame.values
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frame_cols = frame.columns
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for i, row in enumerate(arr):
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for j, value in enumerate(row):
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col = frame_cols[j]
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if np.isnan(value):
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assert np.isnan(frame[col][i])
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else:
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assert value == frame[col][i]
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# mixed type
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arr = float_string_frame[["foo", "A"]].values
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assert arr[0, 0] == "bar"
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df = DataFrame({"complex": [1j, 2j, 3j], "real": [1, 2, 3]})
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arr = df.values
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assert arr[0, 0] == 1j
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def test_values_duplicates(self):
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df = DataFrame(
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[[1, 2, "a", "b"], [1, 2, "a", "b"]], columns=["one", "one", "two", "two"]
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)
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result = df.values
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expected = np.array([[1, 2, "a", "b"], [1, 2, "a", "b"]], dtype=object)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("constructor", [date_range, period_range])
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def test_values_casts_datetimelike_to_object(self, constructor):
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series = Series(constructor("2000-01-01", periods=10, freq="D"))
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expected = series.astype("object")
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df = DataFrame({"a": series, "b": np.random.randn(len(series))})
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result = df.values.squeeze()
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assert (result[:, 0] == expected.values).all()
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df = DataFrame({"a": series, "b": ["foo"] * len(series)})
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result = df.values.squeeze()
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assert (result[:, 0] == expected.values).all()
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def test_frame_values_with_tz(self):
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tz = "US/Central"
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df = DataFrame({"A": date_range("2000", periods=4, tz=tz)})
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result = df.values
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expected = np.array(
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[
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[Timestamp("2000-01-01", tz=tz)],
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[Timestamp("2000-01-02", tz=tz)],
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[Timestamp("2000-01-03", tz=tz)],
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[Timestamp("2000-01-04", tz=tz)],
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]
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)
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tm.assert_numpy_array_equal(result, expected)
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# two columns, homogenous
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df["B"] = df["A"]
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result = df.values
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expected = np.concatenate([expected, expected], axis=1)
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tm.assert_numpy_array_equal(result, expected)
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# three columns, heterogeneous
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est = "US/Eastern"
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df["C"] = df["A"].dt.tz_convert(est)
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new = np.array(
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[
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[Timestamp("2000-01-01T01:00:00", tz=est)],
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[Timestamp("2000-01-02T01:00:00", tz=est)],
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[Timestamp("2000-01-03T01:00:00", tz=est)],
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[Timestamp("2000-01-04T01:00:00", tz=est)],
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]
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)
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expected = np.concatenate([expected, new], axis=1)
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result = df.values
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tm.assert_numpy_array_equal(result, expected)
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def test_interleave_with_tzaware(self, timezone_frame):
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# interleave with object
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result = timezone_frame.assign(D="foo").values
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expected = np.array(
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[
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[
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Timestamp("2013-01-01 00:00:00"),
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Timestamp("2013-01-02 00:00:00"),
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Timestamp("2013-01-03 00:00:00"),
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],
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[
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Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
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NaT,
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Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
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],
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[
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Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
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NaT,
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Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
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],
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["foo", "foo", "foo"],
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],
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dtype=object,
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).T
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tm.assert_numpy_array_equal(result, expected)
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# interleave with only datetime64[ns]
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result = timezone_frame.values
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expected = np.array(
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[
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[
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Timestamp("2013-01-01 00:00:00"),
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Timestamp("2013-01-02 00:00:00"),
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Timestamp("2013-01-03 00:00:00"),
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],
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[
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Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
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NaT,
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Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
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],
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[
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Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
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NaT,
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Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
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],
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],
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dtype=object,
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).T
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tm.assert_numpy_array_equal(result, expected)
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def test_values_interleave_non_unique_cols(self):
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df = DataFrame(
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[[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]],
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columns=["x", "x"],
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index=[1, 2],
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)
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df_unique = df.copy()
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df_unique.columns = ["x", "y"]
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assert df_unique.values.shape == df.values.shape
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tm.assert_numpy_array_equal(df_unique.values[0], df.values[0])
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tm.assert_numpy_array_equal(df_unique.values[1], df.values[1])
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def test_values_numeric_cols(self, float_frame):
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float_frame["foo"] = "bar"
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values = float_frame[["A", "B", "C", "D"]].values
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assert values.dtype == np.float64
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def test_values_lcd(self, mixed_float_frame, mixed_int_frame):
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# mixed lcd
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values = mixed_float_frame[["A", "B", "C", "D"]].values
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assert values.dtype == np.float64
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values = mixed_float_frame[["A", "B", "C"]].values
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assert values.dtype == np.float32
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values = mixed_float_frame[["C"]].values
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assert values.dtype == np.float16
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# GH#10364
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# B uint64 forces float because there are other signed int types
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values = mixed_int_frame[["A", "B", "C", "D"]].values
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assert values.dtype == np.float64
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values = mixed_int_frame[["A", "D"]].values
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assert values.dtype == np.int64
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# B uint64 forces float because there are other signed int types
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values = mixed_int_frame[["A", "B", "C"]].values
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assert values.dtype == np.float64
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# as B and C are both unsigned, no forcing to float is needed
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values = mixed_int_frame[["B", "C"]].values
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assert values.dtype == np.uint64
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values = mixed_int_frame[["A", "C"]].values
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assert values.dtype == np.int32
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values = mixed_int_frame[["C", "D"]].values
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assert values.dtype == np.int64
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values = mixed_int_frame[["A"]].values
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assert values.dtype == np.int32
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values = mixed_int_frame[["C"]].values
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assert values.dtype == np.uint8
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