from datetime import timedelta
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import numpy as np
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import pytest
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from pandas.core.dtypes.dtypes import DatetimeTZDtype
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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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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class TestDataFrameDataTypes:
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def test_empty_frame_dtypes(self):
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empty_df = DataFrame()
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tm.assert_series_equal(empty_df.dtypes, Series(dtype=object))
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nocols_df = DataFrame(index=[1, 2, 3])
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tm.assert_series_equal(nocols_df.dtypes, Series(dtype=object))
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norows_df = DataFrame(columns=list("abc"))
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tm.assert_series_equal(norows_df.dtypes, Series(object, index=list("abc")))
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norows_int_df = DataFrame(columns=list("abc")).astype(np.int32)
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tm.assert_series_equal(
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norows_int_df.dtypes, Series(np.dtype("int32"), index=list("abc"))
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)
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df = DataFrame({"a": 1, "b": True, "c": 1.0}, index=[1, 2, 3])
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ex_dtypes = Series({"a": np.int64, "b": np.bool_, "c": np.float64})
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tm.assert_series_equal(df.dtypes, ex_dtypes)
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# same but for empty slice of df
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tm.assert_series_equal(df[:0].dtypes, ex_dtypes)
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def test_datetime_with_tz_dtypes(self):
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tzframe = 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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tzframe.iloc[1, 1] = pd.NaT
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tzframe.iloc[1, 2] = pd.NaT
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result = tzframe.dtypes.sort_index()
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expected = Series(
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[
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np.dtype("datetime64[ns]"),
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DatetimeTZDtype("ns", "US/Eastern"),
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DatetimeTZDtype("ns", "CET"),
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],
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["A", "B", "C"],
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)
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tm.assert_series_equal(result, expected)
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def test_dtypes_are_correct_after_column_slice(self):
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# GH6525
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df = DataFrame(index=range(5), columns=list("abc"), dtype=np.float_)
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tm.assert_series_equal(
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df.dtypes,
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Series({"a": np.float_, "b": np.float_, "c": np.float_}),
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)
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tm.assert_series_equal(df.iloc[:, 2:].dtypes, Series({"c": np.float_}))
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tm.assert_series_equal(
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df.dtypes,
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Series({"a": np.float_, "b": np.float_, "c": np.float_}),
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)
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@pytest.mark.parametrize(
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"data",
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[pd.NA, True],
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)
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def test_dtypes_are_correct_after_groupby_last(self, data):
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# GH46409
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df = DataFrame(
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{"id": [1, 2, 3, 4], "test": [True, pd.NA, data, False]}
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).convert_dtypes()
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result = df.groupby("id").last().test
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expected = df.set_index("id").test
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assert result.dtype == pd.BooleanDtype()
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tm.assert_series_equal(expected, result)
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def test_dtypes_gh8722(self, float_string_frame):
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float_string_frame["bool"] = float_string_frame["A"] > 0
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result = float_string_frame.dtypes
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expected = Series(
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{k: v.dtype for k, v in float_string_frame.items()}, index=result.index
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)
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tm.assert_series_equal(result, expected)
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# compat, GH 8722
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with option_context("use_inf_as_na", True):
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df = DataFrame([[1]])
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result = df.dtypes
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tm.assert_series_equal(result, Series({0: np.dtype("int64")}))
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def test_dtypes_timedeltas(self):
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df = DataFrame(
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{
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"A": Series(date_range("2012-1-1", periods=3, freq="D")),
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"B": Series([timedelta(days=i) for i in range(3)]),
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}
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)
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result = df.dtypes
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expected = Series(
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[np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")], index=list("AB")
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)
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tm.assert_series_equal(result, expected)
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df["C"] = df["A"] + df["B"]
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result = df.dtypes
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expected = Series(
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[
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np.dtype("datetime64[ns]"),
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np.dtype("timedelta64[ns]"),
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np.dtype("datetime64[ns]"),
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],
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index=list("ABC"),
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)
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tm.assert_series_equal(result, expected)
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# mixed int types
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df["D"] = 1
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result = df.dtypes
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expected = Series(
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[
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np.dtype("datetime64[ns]"),
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np.dtype("timedelta64[ns]"),
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np.dtype("datetime64[ns]"),
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np.dtype("int64"),
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],
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index=list("ABCD"),
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)
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tm.assert_series_equal(result, expected)
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def test_frame_apply_np_array_return_type(self):
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# GH 35517
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df = DataFrame([["foo"]])
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result = df.apply(lambda col: np.array("bar"))
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expected = Series(["bar"])
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tm.assert_series_equal(result, expected)
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