405 lines
14 KiB
Python
405 lines
14 KiB
Python
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from datetime import datetime, timedelta
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from io import StringIO
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import itertools
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Series,
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Timestamp,
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compat,
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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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from pandas.core.internals import ObjectBlock
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from pandas.core.internals.blocks import IntBlock
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# Segregated collection of methods that require the BlockManager internal data
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# structure
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class TestDataFrameBlockInternals:
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def test_setitem_invalidates_datetime_index_freq(self):
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# GH#24096 altering a datetime64tz column inplace invalidates the
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# `freq` attribute on the underlying DatetimeIndex
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dti = date_range("20130101", periods=3, tz="US/Eastern")
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ts = dti[1]
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df = DataFrame({"B": dti})
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assert df["B"]._values.freq == "D"
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df.iloc[1, 0] = pd.NaT
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assert df["B"]._values.freq is None
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# check that the DatetimeIndex was not altered in place
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assert dti.freq == "D"
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assert dti[1] == ts
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def test_cast_internals(self, float_frame):
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casted = DataFrame(float_frame._mgr, dtype=int)
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expected = DataFrame(float_frame._series, dtype=int)
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tm.assert_frame_equal(casted, expected)
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casted = DataFrame(float_frame._mgr, dtype=np.int32)
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expected = DataFrame(float_frame._series, dtype=np.int32)
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tm.assert_frame_equal(casted, expected)
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def test_consolidate(self, float_frame):
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float_frame["E"] = 7.0
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consolidated = float_frame._consolidate()
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assert len(consolidated._mgr.blocks) == 1
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# Ensure copy, do I want this?
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recons = consolidated._consolidate()
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assert recons is not consolidated
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tm.assert_frame_equal(recons, consolidated)
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float_frame["F"] = 8.0
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assert len(float_frame._mgr.blocks) == 3
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return_value = float_frame._consolidate_inplace()
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assert return_value is None
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assert len(float_frame._mgr.blocks) == 1
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def test_consolidate_inplace(self, float_frame):
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frame = float_frame.copy() # noqa
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# triggers in-place consolidation
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for letter in range(ord("A"), ord("Z")):
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float_frame[chr(letter)] = chr(letter)
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def test_values_consolidate(self, float_frame):
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float_frame["E"] = 7.0
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assert not float_frame._mgr.is_consolidated()
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_ = float_frame.values
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assert float_frame._mgr.is_consolidated()
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def test_modify_values(self, float_frame):
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float_frame.values[5] = 5
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assert (float_frame.values[5] == 5).all()
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# unconsolidated
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float_frame["E"] = 7.0
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col = float_frame["E"]
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float_frame.values[6] = 6
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assert (float_frame.values[6] == 6).all()
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# check that item_cache was cleared
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assert float_frame["E"] is not col
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assert (col == 7).all()
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def test_boolean_set_uncons(self, float_frame):
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float_frame["E"] = 7.0
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expected = float_frame.values.copy()
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expected[expected > 1] = 2
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float_frame[float_frame > 1] = 2
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tm.assert_almost_equal(expected, float_frame.values)
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def test_constructor_with_convert(self):
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# this is actually mostly a test of lib.maybe_convert_objects
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# #2845
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df = DataFrame({"A": [2 ** 63 - 1]})
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result = df["A"]
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expected = Series(np.asarray([2 ** 63 - 1], np.int64), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [2 ** 63]})
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result = df["A"]
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expected = Series(np.asarray([2 ** 63], np.uint64), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [datetime(2005, 1, 1), True]})
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result = df["A"]
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expected = Series(
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np.asarray([datetime(2005, 1, 1), True], np.object_), name="A"
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)
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [None, 1]})
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result = df["A"]
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expected = Series(np.asarray([np.nan, 1], np.float_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0, 2]})
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result = df["A"]
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expected = Series(np.asarray([1.0, 2], np.float_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0 + 2.0j, 3]})
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result = df["A"]
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expected = Series(np.asarray([1.0 + 2.0j, 3], np.complex_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0 + 2.0j, 3.0]})
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result = df["A"]
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expected = Series(np.asarray([1.0 + 2.0j, 3.0], np.complex_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0 + 2.0j, True]})
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result = df["A"]
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expected = Series(np.asarray([1.0 + 2.0j, True], np.object_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0, None]})
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result = df["A"]
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expected = Series(np.asarray([1.0, np.nan], np.float_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [1.0 + 2.0j, None]})
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result = df["A"]
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expected = Series(np.asarray([1.0 + 2.0j, np.nan], np.complex_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [2.0, 1, True, None]})
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result = df["A"]
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expected = Series(np.asarray([2.0, 1, True, None], np.object_), name="A")
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tm.assert_series_equal(result, expected)
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df = DataFrame({"A": [2.0, 1, datetime(2006, 1, 1), None]})
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result = df["A"]
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expected = Series(
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np.asarray([2.0, 1, datetime(2006, 1, 1), None], np.object_), name="A"
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)
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tm.assert_series_equal(result, expected)
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def test_construction_with_mixed(self, float_string_frame):
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# test construction edge cases with mixed types
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# f7u12, this does not work without extensive workaround
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data = [
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[datetime(2001, 1, 5), np.nan, datetime(2001, 1, 2)],
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[datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 1)],
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]
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df = DataFrame(data)
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# check dtypes
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result = df.dtypes
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expected = Series({"datetime64[ns]": 3})
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# mixed-type frames
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float_string_frame["datetime"] = datetime.now()
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float_string_frame["timedelta"] = timedelta(days=1, seconds=1)
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assert float_string_frame["datetime"].dtype == "M8[ns]"
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assert float_string_frame["timedelta"].dtype == "m8[ns]"
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result = float_string_frame.dtypes
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expected = Series(
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[np.dtype("float64")] * 4
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+ [
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np.dtype("object"),
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np.dtype("datetime64[ns]"),
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np.dtype("timedelta64[ns]"),
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],
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index=list("ABCD") + ["foo", "datetime", "timedelta"],
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)
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tm.assert_series_equal(result, expected)
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def test_construction_with_conversions(self):
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# convert from a numpy array of non-ns timedelta64
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arr = np.array([1, 2, 3], dtype="timedelta64[s]")
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df = DataFrame(index=range(3))
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df["A"] = arr
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expected = DataFrame(
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{"A": pd.timedelta_range("00:00:01", periods=3, freq="s")}, index=range(3)
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)
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tm.assert_frame_equal(df, expected)
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expected = DataFrame(
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{
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"dt1": Timestamp("20130101"),
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"dt2": date_range("20130101", periods=3),
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# 'dt3' : date_range('20130101 00:00:01',periods=3,freq='s'),
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},
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index=range(3),
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)
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df = DataFrame(index=range(3))
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df["dt1"] = np.datetime64("2013-01-01")
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df["dt2"] = np.array(
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["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]"
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)
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# df['dt3'] = np.array(['2013-01-01 00:00:01','2013-01-01
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# 00:00:02','2013-01-01 00:00:03'],dtype='datetime64[s]')
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tm.assert_frame_equal(df, expected)
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def test_constructor_compound_dtypes(self):
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# GH 5191
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# compound dtypes should raise not-implementederror
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def f(dtype):
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data = list(itertools.repeat((datetime(2001, 1, 1), "aa", 20), 9))
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return DataFrame(data=data, columns=["A", "B", "C"], dtype=dtype)
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msg = "compound dtypes are not implemented in the DataFrame constructor"
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with pytest.raises(NotImplementedError, match=msg):
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f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")])
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# these work (though results may be unexpected)
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f("int64")
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f("float64")
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# 10822
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# invalid error message on dt inference
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if not compat.is_platform_windows():
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f("M8[ns]")
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def test_pickle(self, float_string_frame, timezone_frame):
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empty_frame = DataFrame()
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unpickled = tm.round_trip_pickle(float_string_frame)
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tm.assert_frame_equal(float_string_frame, unpickled)
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# buglet
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float_string_frame._mgr.ndim
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# empty
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unpickled = tm.round_trip_pickle(empty_frame)
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repr(unpickled)
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# tz frame
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unpickled = tm.round_trip_pickle(timezone_frame)
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tm.assert_frame_equal(timezone_frame, unpickled)
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def test_consolidate_datetime64(self):
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# numpy vstack bug
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data = (
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"starting,ending,measure\n"
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"2012-06-21 00:00,2012-06-23 07:00,77\n"
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"2012-06-23 07:00,2012-06-23 16:30,65\n"
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"2012-06-23 16:30,2012-06-25 08:00,77\n"
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"2012-06-25 08:00,2012-06-26 12:00,0\n"
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"2012-06-26 12:00,2012-06-27 08:00,77\n"
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)
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df = pd.read_csv(StringIO(data), parse_dates=[0, 1])
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ser_starting = df.starting
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ser_starting.index = ser_starting.values
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ser_starting = ser_starting.tz_localize("US/Eastern")
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ser_starting = ser_starting.tz_convert("UTC")
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ser_starting.index.name = "starting"
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ser_ending = df.ending
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ser_ending.index = ser_ending.values
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ser_ending = ser_ending.tz_localize("US/Eastern")
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ser_ending = ser_ending.tz_convert("UTC")
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ser_ending.index.name = "ending"
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df.starting = ser_starting.index
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df.ending = ser_ending.index
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tm.assert_index_equal(pd.DatetimeIndex(df.starting), ser_starting.index)
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tm.assert_index_equal(pd.DatetimeIndex(df.ending), ser_ending.index)
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def test_is_mixed_type(self, float_frame, float_string_frame):
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assert not float_frame._is_mixed_type
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assert float_string_frame._is_mixed_type
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def test_stale_cached_series_bug_473(self):
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# this is chained, but ok
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with option_context("chained_assignment", None):
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Y = DataFrame(
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np.random.random((4, 4)),
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index=("a", "b", "c", "d"),
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columns=("e", "f", "g", "h"),
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)
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repr(Y)
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Y["e"] = Y["e"].astype("object")
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Y["g"]["c"] = np.NaN
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repr(Y)
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result = Y.sum() # noqa
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exp = Y["g"].sum() # noqa
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assert pd.isna(Y["g"]["c"])
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def test_strange_column_corruption_issue(self):
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# FIXME: dont leave commented-out
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# (wesm) Unclear how exactly this is related to internal matters
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df = DataFrame(index=[0, 1])
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df[0] = np.nan
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wasCol = {}
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for i, dt in enumerate(df.index):
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for col in range(100, 200):
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if col not in wasCol:
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wasCol[col] = 1
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df[col] = np.nan
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df[col][dt] = i
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myid = 100
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first = len(df.loc[pd.isna(df[myid]), [myid]])
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second = len(df.loc[pd.isna(df[myid]), [myid]])
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assert first == second == 0
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def test_constructor_no_pandas_array(self):
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# Ensure that PandasArray isn't allowed inside Series
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# See https://github.com/pandas-dev/pandas/issues/23995 for more.
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arr = Series([1, 2, 3]).array
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result = DataFrame({"A": arr})
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expected = DataFrame({"A": [1, 2, 3]})
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tm.assert_frame_equal(result, expected)
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assert isinstance(result._mgr.blocks[0], IntBlock)
|
||
|
|
|
||
|
|
def test_add_column_with_pandas_array(self):
|
||
|
|
# GH 26390
|
||
|
|
df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
|
||
|
|
df["c"] = pd.arrays.PandasArray(np.array([1, 2, None, 3], dtype=object))
|
||
|
|
df2 = DataFrame(
|
||
|
|
{
|
||
|
|
"a": [1, 2, 3, 4],
|
||
|
|
"b": ["a", "b", "c", "d"],
|
||
|
|
"c": pd.arrays.PandasArray(np.array([1, 2, None, 3], dtype=object)),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
assert type(df["c"]._mgr.blocks[0]) == ObjectBlock
|
||
|
|
assert type(df2["c"]._mgr.blocks[0]) == ObjectBlock
|
||
|
|
tm.assert_frame_equal(df, df2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_update_inplace_sets_valid_block_values():
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/33457
|
||
|
|
df = DataFrame({"a": Series([1, 2, None], dtype="category")})
|
||
|
|
|
||
|
|
# inplace update of a single column
|
||
|
|
df["a"].fillna(1, inplace=True)
|
||
|
|
|
||
|
|
# check we havent put a Series into any block.values
|
||
|
|
assert isinstance(df._mgr.blocks[0].values, Categorical)
|
||
|
|
|
||
|
|
# smoketest for OP bug from GH#35731
|
||
|
|
assert df.isnull().sum().sum() == 0
|
||
|
|
|
||
|
|
|
||
|
|
def test_nonconsolidated_item_cache_take():
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/35521
|
||
|
|
|
||
|
|
# create non-consolidated dataframe with object dtype columns
|
||
|
|
df = DataFrame()
|
||
|
|
df["col1"] = Series(["a"], dtype=object)
|
||
|
|
df["col2"] = Series([0], dtype=object)
|
||
|
|
|
||
|
|
# access column (item cache)
|
||
|
|
df["col1"] == "A"
|
||
|
|
# take operation
|
||
|
|
# (regression was that this consolidated but didn't reset item cache,
|
||
|
|
# resulting in an invalid cache and the .at operation not working properly)
|
||
|
|
df[df["col2"] == 0]
|
||
|
|
|
||
|
|
# now setting value should update actual dataframe
|
||
|
|
df.at[0, "col1"] = "A"
|
||
|
|
|
||
|
|
expected = DataFrame({"col1": ["A"], "col2": [0]}, dtype=object)
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
assert df.at[0, "col1"] == "A"
|