import re
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import numpy as np
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import pytest
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from pandas.compat import pa_version_under7p0
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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Categorical,
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CategoricalDtype,
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DataFrame,
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DatetimeTZDtype,
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Index,
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Interval,
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IntervalDtype,
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NaT,
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Series,
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Timedelta,
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Timestamp,
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concat,
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date_range,
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option_context,
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)
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import pandas._testing as tm
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def _check_cast(df, v):
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"""
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Check if all dtypes of df are equal to v
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"""
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assert all(s.dtype.name == v for _, s in df.items())
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class TestAstype:
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def test_astype_float(self, float_frame):
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casted = float_frame.astype(int)
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expected = DataFrame(
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float_frame.values.astype(int),
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index=float_frame.index,
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columns=float_frame.columns,
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)
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tm.assert_frame_equal(casted, expected)
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casted = float_frame.astype(np.int32)
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expected = DataFrame(
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float_frame.values.astype(np.int32),
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index=float_frame.index,
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columns=float_frame.columns,
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)
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tm.assert_frame_equal(casted, expected)
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float_frame["foo"] = "5"
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casted = float_frame.astype(int)
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expected = DataFrame(
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float_frame.values.astype(int),
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index=float_frame.index,
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columns=float_frame.columns,
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)
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tm.assert_frame_equal(casted, expected)
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def test_astype_mixed_float(self, mixed_float_frame):
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# mixed casting
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casted = mixed_float_frame.reindex(columns=["A", "B"]).astype("float32")
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_check_cast(casted, "float32")
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casted = mixed_float_frame.reindex(columns=["A", "B"]).astype("float16")
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_check_cast(casted, "float16")
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def test_astype_mixed_type(self, mixed_type_frame):
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# mixed casting
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mn = mixed_type_frame._get_numeric_data().copy()
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mn["little_float"] = np.array(12345.0, dtype="float16")
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mn["big_float"] = np.array(123456789101112.0, dtype="float64")
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casted = mn.astype("float64")
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_check_cast(casted, "float64")
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casted = mn.astype("int64")
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_check_cast(casted, "int64")
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casted = mn.reindex(columns=["little_float"]).astype("float16")
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_check_cast(casted, "float16")
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casted = mn.astype("float32")
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_check_cast(casted, "float32")
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casted = mn.astype("int32")
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_check_cast(casted, "int32")
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# to object
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casted = mn.astype("O")
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_check_cast(casted, "object")
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def test_astype_with_exclude_string(self, float_frame):
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df = float_frame.copy()
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expected = float_frame.astype(int)
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df["string"] = "foo"
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casted = df.astype(int, errors="ignore")
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expected["string"] = "foo"
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tm.assert_frame_equal(casted, expected)
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df = float_frame.copy()
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expected = float_frame.astype(np.int32)
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df["string"] = "foo"
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casted = df.astype(np.int32, errors="ignore")
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expected["string"] = "foo"
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tm.assert_frame_equal(casted, expected)
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def test_astype_with_view_float(self, float_frame):
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# this is the only real reason to do it this way
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tf = np.round(float_frame).astype(np.int32)
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casted = tf.astype(np.float32, copy=False)
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# TODO(wesm): verification?
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tf = float_frame.astype(np.float64)
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casted = tf.astype(np.int64, copy=False) # noqa
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def test_astype_with_view_mixed_float(self, mixed_float_frame):
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tf = mixed_float_frame.reindex(columns=["A", "B", "C"])
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casted = tf.astype(np.int64)
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casted = tf.astype(np.float32) # noqa
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@pytest.mark.parametrize("dtype", [np.int32, np.int64])
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@pytest.mark.parametrize("val", [np.nan, np.inf])
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def test_astype_cast_nan_inf_int(self, val, dtype):
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# see GH#14265
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#
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# Check NaN and inf --> raise error when converting to int.
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msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
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df = DataFrame([val])
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with pytest.raises(ValueError, match=msg):
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df.astype(dtype)
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def test_astype_str(self):
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# see GH#9757
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a = Series(date_range("2010-01-04", periods=5))
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b = Series(date_range("3/6/2012 00:00", periods=5, tz="US/Eastern"))
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c = Series([Timedelta(x, unit="d") for x in range(5)])
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d = Series(range(5))
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e = Series([0.0, 0.2, 0.4, 0.6, 0.8])
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df = DataFrame({"a": a, "b": b, "c": c, "d": d, "e": e})
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# Datetime-like
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result = df.astype(str)
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expected = DataFrame(
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{
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"a": list(map(str, map(lambda x: Timestamp(x)._date_repr, a._values))),
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"b": list(map(str, map(Timestamp, b._values))),
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"c": list(map(lambda x: Timedelta(x)._repr_base(), c._values)),
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"d": list(map(str, d._values)),
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"e": list(map(str, e._values)),
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}
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)
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tm.assert_frame_equal(result, expected)
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def test_astype_str_float(self):
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# see GH#11302
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result = DataFrame([np.NaN]).astype(str)
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expected = DataFrame(["nan"])
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tm.assert_frame_equal(result, expected)
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result = DataFrame([1.12345678901234567890]).astype(str)
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val = "1.1234567890123457"
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expected = DataFrame([val])
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("dtype_class", [dict, Series])
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def test_astype_dict_like(self, dtype_class):
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# GH7271 & GH16717
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a = Series(date_range("2010-01-04", periods=5))
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b = Series(range(5))
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c = Series([0.0, 0.2, 0.4, 0.6, 0.8])
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d = Series(["1.0", "2", "3.14", "4", "5.4"])
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df = DataFrame({"a": a, "b": b, "c": c, "d": d})
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original = df.copy(deep=True)
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# change type of a subset of columns
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dt1 = dtype_class({"b": "str", "d": "float32"})
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result = df.astype(dt1)
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expected = DataFrame(
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{
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"a": a,
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"b": Series(["0", "1", "2", "3", "4"]),
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"c": c,
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"d": Series([1.0, 2.0, 3.14, 4.0, 5.4], dtype="float32"),
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}
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)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(df, original)
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dt2 = dtype_class({"b": np.float32, "c": "float32", "d": np.float64})
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result = df.astype(dt2)
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expected = DataFrame(
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{
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"a": a,
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"b": Series([0.0, 1.0, 2.0, 3.0, 4.0], dtype="float32"),
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"c": Series([0.0, 0.2, 0.4, 0.6, 0.8], dtype="float32"),
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"d": Series([1.0, 2.0, 3.14, 4.0, 5.4], dtype="float64"),
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}
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)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(df, original)
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# change all columns
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dt3 = dtype_class({"a": str, "b": str, "c": str, "d": str})
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tm.assert_frame_equal(df.astype(dt3), df.astype(str))
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tm.assert_frame_equal(df, original)
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# error should be raised when using something other than column labels
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# in the keys of the dtype dict
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dt4 = dtype_class({"b": str, 2: str})
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dt5 = dtype_class({"e": str})
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msg_frame = (
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"Only a column name can be used for the key in a dtype mappings argument. "
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"'{}' not found in columns."
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)
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with pytest.raises(KeyError, match=msg_frame.format(2)):
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df.astype(dt4)
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with pytest.raises(KeyError, match=msg_frame.format("e")):
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df.astype(dt5)
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tm.assert_frame_equal(df, original)
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# if the dtypes provided are the same as the original dtypes, the
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# resulting DataFrame should be the same as the original DataFrame
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dt6 = dtype_class({col: df[col].dtype for col in df.columns})
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equiv = df.astype(dt6)
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tm.assert_frame_equal(df, equiv)
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tm.assert_frame_equal(df, original)
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# GH#16717
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# if dtypes provided is empty, the resulting DataFrame
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# should be the same as the original DataFrame
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dt7 = dtype_class({}) if dtype_class is dict else dtype_class({}, dtype=object)
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equiv = df.astype(dt7)
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tm.assert_frame_equal(df, equiv)
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tm.assert_frame_equal(df, original)
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def test_astype_duplicate_col(self):
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a1 = Series([1, 2, 3, 4, 5], name="a")
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b = Series([0.1, 0.2, 0.4, 0.6, 0.8], name="b")
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a2 = Series([0, 1, 2, 3, 4], name="a")
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df = concat([a1, b, a2], axis=1)
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result = df.astype(str)
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a1_str = Series(["1", "2", "3", "4", "5"], dtype="str", name="a")
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b_str = Series(["0.1", "0.2", "0.4", "0.6", "0.8"], dtype=str, name="b")
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a2_str = Series(["0", "1", "2", "3", "4"], dtype="str", name="a")
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expected = concat([a1_str, b_str, a2_str], axis=1)
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tm.assert_frame_equal(result, expected)
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result = df.astype({"a": "str"})
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expected = concat([a1_str, b, a2_str], axis=1)
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tm.assert_frame_equal(result, expected)
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def test_astype_duplicate_col_series_arg(self):
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# GH#44417
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vals = np.random.randn(3, 4)
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df = DataFrame(vals, columns=["A", "B", "C", "A"])
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dtypes = df.dtypes
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dtypes.iloc[0] = str
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dtypes.iloc[2] = "Float64"
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result = df.astype(dtypes)
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expected = DataFrame(
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{
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0: vals[:, 0].astype(str),
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1: vals[:, 1],
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2: pd.array(vals[:, 2], dtype="Float64"),
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3: vals[:, 3],
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}
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)
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expected.columns = df.columns
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype",
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[
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"category",
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CategoricalDtype(),
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CategoricalDtype(ordered=True),
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CategoricalDtype(ordered=False),
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CategoricalDtype(categories=list("abcdef")),
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CategoricalDtype(categories=list("edba"), ordered=False),
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CategoricalDtype(categories=list("edcb"), ordered=True),
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],
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ids=repr,
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)
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def test_astype_categorical(self, dtype):
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# GH#18099
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d = {"A": list("abbc"), "B": list("bccd"), "C": list("cdde")}
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df = DataFrame(d)
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result = df.astype(dtype)
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expected = DataFrame({k: Categorical(v, dtype=dtype) for k, v in d.items()})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("cls", [CategoricalDtype, DatetimeTZDtype, IntervalDtype])
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def test_astype_categoricaldtype_class_raises(self, cls):
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df = DataFrame({"A": ["a", "a", "b", "c"]})
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xpr = f"Expected an instance of {cls.__name__}"
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with pytest.raises(TypeError, match=xpr):
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df.astype({"A": cls})
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with pytest.raises(TypeError, match=xpr):
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df["A"].astype(cls)
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@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"])
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def test_astype_extension_dtypes(self, dtype):
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# GH#22578
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df = DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"])
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expected1 = DataFrame(
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{
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"a": pd.array([1, 3, 5], dtype=dtype),
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"b": pd.array([2, 4, 6], dtype=dtype),
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}
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)
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tm.assert_frame_equal(df.astype(dtype), expected1)
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tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1)
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tm.assert_frame_equal(df.astype(dtype).astype("float64"), df)
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df = DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"])
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df["b"] = df["b"].astype(dtype)
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expected2 = DataFrame(
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{"a": [1.0, 3.0, 5.0], "b": pd.array([2, 4, 6], dtype=dtype)}
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)
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tm.assert_frame_equal(df, expected2)
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tm.assert_frame_equal(df.astype(dtype), expected1)
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tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1)
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@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"])
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def test_astype_extension_dtypes_1d(self, dtype):
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# GH#22578
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df = DataFrame({"a": [1.0, 2.0, 3.0]})
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expected1 = DataFrame({"a": pd.array([1, 2, 3], dtype=dtype)})
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tm.assert_frame_equal(df.astype(dtype), expected1)
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tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1)
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df = DataFrame({"a": [1.0, 2.0, 3.0]})
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df["a"] = df["a"].astype(dtype)
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expected2 = DataFrame({"a": pd.array([1, 2, 3], dtype=dtype)})
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tm.assert_frame_equal(df, expected2)
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tm.assert_frame_equal(df.astype(dtype), expected1)
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tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1)
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@pytest.mark.parametrize("dtype", ["category", "Int64"])
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def test_astype_extension_dtypes_duplicate_col(self, dtype):
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# GH#24704
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a1 = Series([0, np.nan, 4], name="a")
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a2 = Series([np.nan, 3, 5], name="a")
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df = concat([a1, a2], axis=1)
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result = df.astype(dtype)
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expected = concat([a1.astype(dtype), a2.astype(dtype)], axis=1)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype", [{100: "float64", 200: "uint64"}, "category", "float64"]
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)
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def test_astype_column_metadata(self, dtype):
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# GH#19920
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columns = Index([100, 200, 300], dtype=np.uint64, name="foo")
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df = DataFrame(np.arange(15).reshape(5, 3), columns=columns)
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df = df.astype(dtype)
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tm.assert_index_equal(df.columns, columns)
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@pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"])
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def test_astype_from_object_to_datetime_unit(self, unit):
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vals = [
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["2015-01-01", "2015-01-02", "2015-01-03"],
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["2017-01-01", "2017-01-02", "2017-02-03"],
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]
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df = DataFrame(vals, dtype=object)
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with pytest.raises(TypeError, match="Cannot cast"):
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df.astype(f"M8[{unit}]")
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@pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"])
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def test_astype_from_object_to_timedelta_unit(self, unit):
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vals = [
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["1 Day", "2 Days", "3 Days"],
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["4 Days", "5 Days", "6 Days"],
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]
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df = DataFrame(vals, dtype=object)
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msg = (
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r"Cannot convert from timedelta64\[ns\] to timedelta64\[.*\]. "
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"Supported resolutions are 's', 'ms', 'us', 'ns'"
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)
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with pytest.raises(ValueError, match=msg):
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# TODO: this is ValueError while for DatetimeArray it is TypeError;
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# get these consistent
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df.astype(f"m8[{unit}]")
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@pytest.mark.parametrize("dtype", ["M8", "m8"])
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@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
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def test_astype_from_datetimelike_to_object(self, dtype, unit):
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# tests astype to object dtype
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# GH#19223 / GH#12425
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dtype = f"{dtype}[{unit}]"
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arr = np.array([[1, 2, 3]], dtype=dtype)
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df = DataFrame(arr)
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result = df.astype(object)
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assert (result.dtypes == object).all()
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if dtype.startswith("M8"):
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assert result.iloc[0, 0] == Timestamp(1, unit=unit)
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else:
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assert result.iloc[0, 0] == Timedelta(1, unit=unit)
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@pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
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@pytest.mark.parametrize("dtype", ["M8", "m8"])
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@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
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def test_astype_to_datetimelike_unit(self, arr_dtype, dtype, unit):
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# tests all units from numeric origination
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# GH#19223 / GH#12425
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dtype = f"{dtype}[{unit}]"
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arr = np.array([[1, 2, 3]], dtype=arr_dtype)
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df = DataFrame(arr)
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result = df.astype(dtype)
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expected = DataFrame(arr.astype(dtype))
|
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tm.assert_frame_equal(result, expected)
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|
@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
|
def test_astype_to_datetime_unit(self, unit):
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# tests all units from datetime origination
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# GH#19223
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dtype = f"M8[{unit}]"
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arr = np.array([[1, 2, 3]], dtype=dtype)
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df = DataFrame(arr)
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ser = df.iloc[:, 0]
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idx = Index(ser)
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dta = ser._values
|
|
if unit in ["ns", "us", "ms", "s"]:
|
# GH#48928
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result = df.astype(dtype)
|
else:
|
# we use the nearest supported dtype (i.e. M8[s])
|
msg = rf"Cannot cast DatetimeArray to dtype datetime64\[{unit}\]"
|
with pytest.raises(TypeError, match=msg):
|
df.astype(dtype)
|
|
with pytest.raises(TypeError, match=msg):
|
ser.astype(dtype)
|
|
with pytest.raises(TypeError, match=msg.replace("Array", "Index")):
|
idx.astype(dtype)
|
|
with pytest.raises(TypeError, match=msg):
|
dta.astype(dtype)
|
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return
|
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exp_df = DataFrame(arr.astype(dtype))
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assert (exp_df.dtypes == dtype).all()
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tm.assert_frame_equal(result, exp_df)
|
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res_ser = ser.astype(dtype)
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exp_ser = exp_df.iloc[:, 0]
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assert exp_ser.dtype == dtype
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tm.assert_series_equal(res_ser, exp_ser)
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exp_dta = exp_ser._values
|
|
res_index = idx.astype(dtype)
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exp_index = Index(exp_ser)
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assert exp_index.dtype == dtype
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tm.assert_index_equal(res_index, exp_index)
|
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res_dta = dta.astype(dtype)
|
assert exp_dta.dtype == dtype
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tm.assert_extension_array_equal(res_dta, exp_dta)
|
|
@pytest.mark.parametrize("unit", ["ns"])
|
def test_astype_to_timedelta_unit_ns(self, unit):
|
# preserver the timedelta conversion
|
# GH#19223
|
dtype = f"m8[{unit}]"
|
arr = np.array([[1, 2, 3]], dtype=dtype)
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df = DataFrame(arr)
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result = df.astype(dtype)
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expected = DataFrame(arr.astype(dtype))
|
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("unit", ["us", "ms", "s", "h", "m", "D"])
|
def test_astype_to_timedelta_unit(self, unit):
|
# coerce to float
|
# GH#19223 until 2.0 used to coerce to float
|
dtype = f"m8[{unit}]"
|
arr = np.array([[1, 2, 3]], dtype=dtype)
|
df = DataFrame(arr)
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ser = df.iloc[:, 0]
|
tdi = Index(ser)
|
tda = tdi._values
|
|
if unit in ["us", "ms", "s"]:
|
assert (df.dtypes == dtype).all()
|
result = df.astype(dtype)
|
else:
|
# We get the nearest supported unit, i.e. "s"
|
assert (df.dtypes == "m8[s]").all()
|
|
msg = (
|
rf"Cannot convert from timedelta64\[s\] to timedelta64\[{unit}\]. "
|
"Supported resolutions are 's', 'ms', 'us', 'ns'"
|
)
|
with pytest.raises(ValueError, match=msg):
|
df.astype(dtype)
|
with pytest.raises(ValueError, match=msg):
|
ser.astype(dtype)
|
with pytest.raises(ValueError, match=msg):
|
tdi.astype(dtype)
|
with pytest.raises(ValueError, match=msg):
|
tda.astype(dtype)
|
|
return
|
|
result = df.astype(dtype)
|
# The conversion is a no-op, so we just get a copy
|
expected = df
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
|
def test_astype_to_incorrect_datetimelike(self, unit):
|
# trying to astype a m to a M, or vice-versa
|
# GH#19224
|
dtype = f"M8[{unit}]"
|
other = f"m8[{unit}]"
|
|
df = DataFrame(np.array([[1, 2, 3]], dtype=dtype))
|
msg = "|".join(
|
[
|
# BlockManager path
|
rf"Cannot cast DatetimeArray to dtype timedelta64\[{unit}\]",
|
# ArrayManager path
|
"cannot astype a datetimelike from "
|
rf"\[datetime64\[ns\]\] to \[timedelta64\[{unit}\]\]",
|
]
|
)
|
with pytest.raises(TypeError, match=msg):
|
df.astype(other)
|
|
msg = "|".join(
|
[
|
# BlockManager path
|
rf"Cannot cast TimedeltaArray to dtype datetime64\[{unit}\]",
|
# ArrayManager path
|
"cannot astype a timedelta from "
|
rf"\[timedelta64\[ns\]\] to \[datetime64\[{unit}\]\]",
|
]
|
)
|
df = DataFrame(np.array([[1, 2, 3]], dtype=other))
|
with pytest.raises(TypeError, match=msg):
|
df.astype(dtype)
|
|
def test_astype_arg_for_errors(self):
|
# GH#14878
|
|
df = DataFrame([1, 2, 3])
|
|
msg = (
|
"Expected value of kwarg 'errors' to be one of "
|
"['raise', 'ignore']. Supplied value is 'True'"
|
)
|
with pytest.raises(ValueError, match=re.escape(msg)):
|
df.astype(np.float64, errors=True)
|
|
df.astype(np.int8, errors="ignore")
|
|
def test_astype_invalid_conversion(self):
|
# GH#47571
|
df = DataFrame({"a": [1, 2, "text"], "b": [1, 2, 3]})
|
|
msg = (
|
"invalid literal for int() with base 10: 'text': "
|
"Error while type casting for column 'a'"
|
)
|
|
with pytest.raises(ValueError, match=re.escape(msg)):
|
df.astype({"a": int})
|
|
def test_astype_arg_for_errors_dictlist(self):
|
# GH#25905
|
df = DataFrame(
|
[
|
{"a": "1", "b": "16.5%", "c": "test"},
|
{"a": "2.2", "b": "15.3", "c": "another_test"},
|
]
|
)
|
expected = DataFrame(
|
[
|
{"a": 1.0, "b": "16.5%", "c": "test"},
|
{"a": 2.2, "b": "15.3", "c": "another_test"},
|
]
|
)
|
type_dict = {"a": "float64", "b": "float64", "c": "object"}
|
|
result = df.astype(dtype=type_dict, errors="ignore")
|
|
tm.assert_frame_equal(result, expected)
|
|
def test_astype_dt64tz(self, timezone_frame):
|
# astype
|
expected = np.array(
|
[
|
[
|
Timestamp("2013-01-01 00:00:00"),
|
Timestamp("2013-01-02 00:00:00"),
|
Timestamp("2013-01-03 00:00:00"),
|
],
|
[
|
Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
|
NaT,
|
Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
|
],
|
[
|
Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
|
NaT,
|
Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
|
],
|
],
|
dtype=object,
|
).T
|
expected = DataFrame(
|
expected,
|
index=timezone_frame.index,
|
columns=timezone_frame.columns,
|
dtype=object,
|
)
|
result = timezone_frame.astype(object)
|
tm.assert_frame_equal(result, expected)
|
|
msg = "Cannot use .astype to convert from timezone-aware dtype to timezone-"
|
with pytest.raises(TypeError, match=msg):
|
# dt64tz->dt64 deprecated
|
timezone_frame.astype("datetime64[ns]")
|
|
def test_astype_dt64tz_to_str(self, timezone_frame):
|
# str formatting
|
result = timezone_frame.astype(str)
|
expected = DataFrame(
|
[
|
[
|
"2013-01-01",
|
"2013-01-01 00:00:00-05:00",
|
"2013-01-01 00:00:00+01:00",
|
],
|
["2013-01-02", "NaT", "NaT"],
|
[
|
"2013-01-03",
|
"2013-01-03 00:00:00-05:00",
|
"2013-01-03 00:00:00+01:00",
|
],
|
],
|
columns=timezone_frame.columns,
|
)
|
tm.assert_frame_equal(result, expected)
|
|
with option_context("display.max_columns", 20):
|
result = str(timezone_frame)
|
assert (
|
"0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00"
|
) in result
|
assert (
|
"1 2013-01-02 NaT NaT"
|
) in result
|
assert (
|
"2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00"
|
) in result
|
|
def test_astype_empty_dtype_dict(self):
|
# issue mentioned further down in the following issue's thread
|
# https://github.com/pandas-dev/pandas/issues/33113
|
df = DataFrame()
|
result = df.astype({})
|
tm.assert_frame_equal(result, df)
|
assert result is not df
|
|
@pytest.mark.parametrize(
|
"data, dtype",
|
[
|
(["x", "y", "z"], "string[python]"),
|
pytest.param(
|
["x", "y", "z"],
|
"string[pyarrow]",
|
marks=td.skip_if_no("pyarrow"),
|
),
|
(["x", "y", "z"], "category"),
|
(3 * [Timestamp("2020-01-01", tz="UTC")], None),
|
(3 * [Interval(0, 1)], None),
|
],
|
)
|
@pytest.mark.parametrize("errors", ["raise", "ignore"])
|
def test_astype_ignores_errors_for_extension_dtypes(self, data, dtype, errors):
|
# https://github.com/pandas-dev/pandas/issues/35471
|
df = DataFrame(Series(data, dtype=dtype))
|
if errors == "ignore":
|
expected = df
|
result = df.astype(float, errors=errors)
|
tm.assert_frame_equal(result, expected)
|
else:
|
msg = "(Cannot cast)|(could not convert)"
|
with pytest.raises((ValueError, TypeError), match=msg):
|
df.astype(float, errors=errors)
|
|
def test_astype_tz_conversion(self):
|
# GH 35973
|
val = {"tz": date_range("2020-08-30", freq="d", periods=2, tz="Europe/London")}
|
df = DataFrame(val)
|
result = df.astype({"tz": "datetime64[ns, Europe/Berlin]"})
|
|
expected = df
|
expected["tz"] = expected["tz"].dt.tz_convert("Europe/Berlin")
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.parametrize("tz", ["UTC", "Europe/Berlin"])
|
def test_astype_tz_object_conversion(self, tz):
|
# GH 35973
|
val = {"tz": date_range("2020-08-30", freq="d", periods=2, tz="Europe/London")}
|
expected = DataFrame(val)
|
|
# convert expected to object dtype from other tz str (independently tested)
|
result = expected.astype({"tz": f"datetime64[ns, {tz}]"})
|
result = result.astype({"tz": "object"})
|
|
# do real test: object dtype to a specified tz, different from construction tz.
|
result = result.astype({"tz": "datetime64[ns, Europe/London]"})
|
tm.assert_frame_equal(result, expected)
|
|
def test_astype_dt64_to_string(self, frame_or_series, tz_naive_fixture):
|
# GH#41409
|
tz = tz_naive_fixture
|
|
dti = date_range("2016-01-01", periods=3, tz=tz)
|
dta = dti._data
|
dta[0] = NaT
|
|
obj = frame_or_series(dta)
|
result = obj.astype("string")
|
|
# Check that Series/DataFrame.astype matches DatetimeArray.astype
|
expected = frame_or_series(dta.astype("string"))
|
tm.assert_equal(result, expected)
|
|
item = result.iloc[0]
|
if frame_or_series is DataFrame:
|
item = item.iloc[0]
|
assert item is pd.NA
|
|
# For non-NA values, we should match what we get for non-EA str
|
alt = obj.astype(str)
|
assert np.all(alt.iloc[1:] == result.iloc[1:])
|
|
def test_astype_td64_to_string(self, frame_or_series):
|
# GH#41409
|
tdi = pd.timedelta_range("1 Day", periods=3)
|
obj = frame_or_series(tdi)
|
|
expected = frame_or_series(["1 days", "2 days", "3 days"], dtype="string")
|
result = obj.astype("string")
|
tm.assert_equal(result, expected)
|
|
def test_astype_bytes(self):
|
# GH#39474
|
result = DataFrame(["foo", "bar", "baz"]).astype(bytes)
|
assert result.dtypes[0] == np.dtype("S3")
|
|
@pytest.mark.parametrize(
|
"index_slice",
|
[
|
np.s_[:2, :2],
|
np.s_[:1, :2],
|
np.s_[:2, :1],
|
np.s_[::2, ::2],
|
np.s_[::1, ::2],
|
np.s_[::2, ::1],
|
],
|
)
|
def test_astype_noncontiguous(self, index_slice):
|
# GH#42396
|
data = np.arange(16).reshape(4, 4)
|
df = DataFrame(data)
|
|
result = df.iloc[index_slice].astype("int16")
|
expected = df.iloc[index_slice]
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
def test_astype_retain_attrs(self, any_numpy_dtype):
|
# GH#44414
|
df = DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]})
|
df.attrs["Location"] = "Michigan"
|
|
result = df.astype({"a": any_numpy_dtype}).attrs
|
expected = df.attrs
|
|
tm.assert_dict_equal(expected, result)
|
|
|
class TestAstypeCategorical:
|
def test_astype_from_categorical3(self):
|
df = DataFrame({"cats": [1, 2, 3, 4, 5, 6], "vals": [1, 2, 3, 4, 5, 6]})
|
cats = Categorical([1, 2, 3, 4, 5, 6])
|
exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
|
df["cats"] = df["cats"].astype("category")
|
tm.assert_frame_equal(exp_df, df)
|
|
def test_astype_from_categorical4(self):
|
df = DataFrame(
|
{"cats": ["a", "b", "b", "a", "a", "d"], "vals": [1, 2, 3, 4, 5, 6]}
|
)
|
cats = Categorical(["a", "b", "b", "a", "a", "d"])
|
exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]})
|
df["cats"] = df["cats"].astype("category")
|
tm.assert_frame_equal(exp_df, df)
|
|
def test_categorical_astype_to_int(self, any_int_dtype):
|
# GH#39402
|
|
df = DataFrame(data={"col1": pd.array([2.0, 1.0, 3.0])})
|
df.col1 = df.col1.astype("category")
|
df.col1 = df.col1.astype(any_int_dtype)
|
expected = DataFrame({"col1": pd.array([2, 1, 3], dtype=any_int_dtype)})
|
tm.assert_frame_equal(df, expected)
|
|
def test_astype_categorical_to_string_missing(self):
|
# https://github.com/pandas-dev/pandas/issues/41797
|
df = DataFrame(["a", "b", np.nan])
|
expected = df.astype(str)
|
cat = df.astype("category")
|
result = cat.astype(str)
|
tm.assert_frame_equal(result, expected)
|
|
|
class IntegerArrayNoCopy(pd.core.arrays.IntegerArray):
|
# GH 42501
|
|
def copy(self):
|
assert False
|
|
|
class Int16DtypeNoCopy(pd.Int16Dtype):
|
# GH 42501
|
|
@classmethod
|
def construct_array_type(cls):
|
return IntegerArrayNoCopy
|
|
|
def test_frame_astype_no_copy():
|
# GH 42501
|
df = DataFrame({"a": [1, 4, None, 5], "b": [6, 7, 8, 9]}, dtype=object)
|
result = df.astype({"a": Int16DtypeNoCopy()}, copy=False)
|
|
assert result.a.dtype == pd.Int16Dtype()
|
assert np.shares_memory(df.b.values, result.b.values)
|
|
|
@pytest.mark.skipif(pa_version_under7p0, reason="pyarrow is required for this test")
|
@pytest.mark.parametrize("dtype", ["int64", "Int64"])
|
def test_astype_copies(dtype):
|
# GH#50984
|
df = DataFrame({"a": [1, 2, 3]}, dtype=dtype)
|
result = df.astype("int64[pyarrow]", copy=True)
|
df.iloc[0, 0] = 100
|
expected = DataFrame({"a": [1, 2, 3]}, dtype="int64[pyarrow]")
|
tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize("val", [None, 1, 1.5, np.nan, NaT])
|
def test_astype_to_string_not_modifying_input(string_storage, val):
|
# GH#51073
|
df = DataFrame({"a": ["a", "b", val]})
|
expected = df.copy()
|
with option_context("mode.string_storage", string_storage):
|
df.astype("string", copy=False)
|
tm.assert_frame_equal(df, expected)
|
