Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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590 lines
19 KiB
590 lines
19 KiB
from copy import deepcopy
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import datetime
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import inspect
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import pydoc
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import numpy as np
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import pytest
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from pandas.compat import PY37
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from pandas.util._test_decorators import async_mark, skip_if_no
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import pandas as pd
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from pandas import Categorical, DataFrame, Series, compat, date_range, timedelta_range
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import pandas._testing as tm
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class TestDataFrameMisc:
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@pytest.mark.parametrize("attr", ["index", "columns"])
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def test_copy_index_name_checking(self, float_frame, attr):
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# don't want to be able to modify the index stored elsewhere after
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# making a copy
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ind = getattr(float_frame, attr)
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ind.name = None
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cp = float_frame.copy()
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getattr(cp, attr).name = "foo"
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assert getattr(float_frame, attr).name is None
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def test_getitem_pop_assign_name(self, float_frame):
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s = float_frame["A"]
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assert s.name == "A"
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s = float_frame.pop("A")
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assert s.name == "A"
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s = float_frame.loc[:, "B"]
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assert s.name == "B"
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s2 = s.loc[:]
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assert s2.name == "B"
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def test_get_value(self, float_frame):
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for idx in float_frame.index:
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for col in float_frame.columns:
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result = float_frame._get_value(idx, col)
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expected = float_frame[col][idx]
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tm.assert_almost_equal(result, expected)
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def test_add_prefix_suffix(self, float_frame):
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with_prefix = float_frame.add_prefix("foo#")
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expected = pd.Index([f"foo#{c}" for c in float_frame.columns])
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tm.assert_index_equal(with_prefix.columns, expected)
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with_suffix = float_frame.add_suffix("#foo")
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expected = pd.Index([f"{c}#foo" for c in float_frame.columns])
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tm.assert_index_equal(with_suffix.columns, expected)
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with_pct_prefix = float_frame.add_prefix("%")
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expected = pd.Index([f"%{c}" for c in float_frame.columns])
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tm.assert_index_equal(with_pct_prefix.columns, expected)
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with_pct_suffix = float_frame.add_suffix("%")
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expected = pd.Index([f"{c}%" for c in float_frame.columns])
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tm.assert_index_equal(with_pct_suffix.columns, expected)
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def test_get_axis(self, float_frame):
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f = float_frame
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assert f._get_axis_number(0) == 0
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assert f._get_axis_number(1) == 1
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assert f._get_axis_number("index") == 0
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assert f._get_axis_number("rows") == 0
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assert f._get_axis_number("columns") == 1
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assert f._get_axis_name(0) == "index"
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assert f._get_axis_name(1) == "columns"
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assert f._get_axis_name("index") == "index"
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assert f._get_axis_name("rows") == "index"
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assert f._get_axis_name("columns") == "columns"
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assert f._get_axis(0) is f.index
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assert f._get_axis(1) is f.columns
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with pytest.raises(ValueError, match="No axis named"):
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f._get_axis_number(2)
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with pytest.raises(ValueError, match="No axis.*foo"):
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f._get_axis_name("foo")
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with pytest.raises(ValueError, match="No axis.*None"):
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f._get_axis_name(None)
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with pytest.raises(ValueError, match="No axis named"):
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f._get_axis_number(None)
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def test_keys(self, float_frame):
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getkeys = float_frame.keys
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assert getkeys() is float_frame.columns
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def test_column_contains_raises(self, float_frame):
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with pytest.raises(TypeError, match="unhashable type: 'Index'"):
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float_frame.columns in float_frame
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def test_tab_completion(self):
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# DataFrame whose columns are identifiers shall have them in __dir__.
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df = pd.DataFrame([list("abcd"), list("efgh")], columns=list("ABCD"))
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for key in list("ABCD"):
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assert key in dir(df)
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assert isinstance(df.__getitem__("A"), pd.Series)
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# DataFrame whose first-level columns are identifiers shall have
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# them in __dir__.
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df = pd.DataFrame(
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[list("abcd"), list("efgh")],
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columns=pd.MultiIndex.from_tuples(list(zip("ABCD", "EFGH"))),
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)
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for key in list("ABCD"):
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assert key in dir(df)
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for key in list("EFGH"):
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assert key not in dir(df)
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assert isinstance(df.__getitem__("A"), pd.DataFrame)
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def test_not_hashable(self):
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empty_frame = DataFrame()
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df = DataFrame([1])
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msg = "'DataFrame' objects are mutable, thus they cannot be hashed"
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with pytest.raises(TypeError, match=msg):
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hash(df)
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with pytest.raises(TypeError, match=msg):
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hash(empty_frame)
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def test_column_name_contains_unicode_surrogate(self):
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# GH 25509
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colname = "\ud83d"
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df = DataFrame({colname: []})
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# this should not crash
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assert colname not in dir(df)
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assert df.columns[0] == colname
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def test_new_empty_index(self):
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df1 = DataFrame(np.random.randn(0, 3))
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df2 = DataFrame(np.random.randn(0, 3))
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df1.index.name = "foo"
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assert df2.index.name is None
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def test_array_interface(self, float_frame):
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with np.errstate(all="ignore"):
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result = np.sqrt(float_frame)
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assert isinstance(result, type(float_frame))
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assert result.index is float_frame.index
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assert result.columns is float_frame.columns
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tm.assert_frame_equal(result, float_frame.apply(np.sqrt))
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def test_get_agg_axis(self, float_frame):
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cols = float_frame._get_agg_axis(0)
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assert cols is float_frame.columns
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idx = float_frame._get_agg_axis(1)
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assert idx is float_frame.index
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msg = r"Axis must be 0 or 1 \(got 2\)"
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with pytest.raises(ValueError, match=msg):
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float_frame._get_agg_axis(2)
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def test_nonzero(self, float_frame, float_string_frame):
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empty_frame = DataFrame()
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assert empty_frame.empty
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assert not float_frame.empty
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assert not float_string_frame.empty
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# corner case
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df = DataFrame({"A": [1.0, 2.0, 3.0], "B": ["a", "b", "c"]}, index=np.arange(3))
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del df["A"]
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assert not df.empty
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def test_iteritems(self):
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df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"])
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for k, v in df.items():
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assert isinstance(v, DataFrame._constructor_sliced)
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def test_items(self):
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# GH 17213, GH 13918
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cols = ["a", "b", "c"]
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df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=cols)
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for c, (k, v) in zip(cols, df.items()):
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assert c == k
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assert isinstance(v, Series)
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assert (df[k] == v).all()
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def test_iter(self, float_frame):
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assert tm.equalContents(list(float_frame), float_frame.columns)
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def test_iterrows(self, float_frame, float_string_frame):
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for k, v in float_frame.iterrows():
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exp = float_frame.loc[k]
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tm.assert_series_equal(v, exp)
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for k, v in float_string_frame.iterrows():
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exp = float_string_frame.loc[k]
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tm.assert_series_equal(v, exp)
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def test_iterrows_iso8601(self):
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# GH 19671
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s = DataFrame(
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{
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"non_iso8601": ["M1701", "M1802", "M1903", "M2004"],
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"iso8601": date_range("2000-01-01", periods=4, freq="M"),
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}
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)
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for k, v in s.iterrows():
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exp = s.loc[k]
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tm.assert_series_equal(v, exp)
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def test_iterrows_corner(self):
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# gh-12222
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df = DataFrame(
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{
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"a": [datetime.datetime(2015, 1, 1)],
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"b": [None],
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"c": [None],
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"d": [""],
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"e": [[]],
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"f": [set()],
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"g": [{}],
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}
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)
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expected = Series(
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[datetime.datetime(2015, 1, 1), None, None, "", [], set(), {}],
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index=list("abcdefg"),
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name=0,
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dtype="object",
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)
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_, result = next(df.iterrows())
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tm.assert_series_equal(result, expected)
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def test_itertuples(self, float_frame):
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for i, tup in enumerate(float_frame.itertuples()):
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s = DataFrame._constructor_sliced(tup[1:])
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s.name = tup[0]
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expected = float_frame.iloc[i, :].reset_index(drop=True)
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tm.assert_series_equal(s, expected)
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df = DataFrame(
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{"floats": np.random.randn(5), "ints": range(5)}, columns=["floats", "ints"]
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)
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for tup in df.itertuples(index=False):
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assert isinstance(tup[1], int)
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df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
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dfaa = df[["a", "a"]]
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assert list(dfaa.itertuples()) == [(0, 1, 1), (1, 2, 2), (2, 3, 3)]
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# repr with int on 32-bit/windows
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if not (compat.is_platform_windows() or compat.is_platform_32bit()):
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assert (
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repr(list(df.itertuples(name=None)))
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== "[(0, 1, 4), (1, 2, 5), (2, 3, 6)]"
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)
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tup = next(df.itertuples(name="TestName"))
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assert tup._fields == ("Index", "a", "b")
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assert (tup.Index, tup.a, tup.b) == tup
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assert type(tup).__name__ == "TestName"
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df.columns = ["def", "return"]
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tup2 = next(df.itertuples(name="TestName"))
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assert tup2 == (0, 1, 4)
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assert tup2._fields == ("Index", "_1", "_2")
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df3 = DataFrame({"f" + str(i): [i] for i in range(1024)})
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# will raise SyntaxError if trying to create namedtuple
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tup3 = next(df3.itertuples())
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assert isinstance(tup3, tuple)
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if PY37:
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assert hasattr(tup3, "_fields")
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else:
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assert not hasattr(tup3, "_fields")
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# GH 28282
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df_254_columns = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(254)}])
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result_254_columns = next(df_254_columns.itertuples(index=False))
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assert isinstance(result_254_columns, tuple)
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assert hasattr(result_254_columns, "_fields")
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df_255_columns = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(255)}])
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result_255_columns = next(df_255_columns.itertuples(index=False))
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assert isinstance(result_255_columns, tuple)
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# Dataframes with >=255 columns will fallback to regular tuples on python < 3.7
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if PY37:
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assert hasattr(result_255_columns, "_fields")
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else:
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assert not hasattr(result_255_columns, "_fields")
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def test_sequence_like_with_categorical(self):
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# GH 7839
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# make sure can iterate
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df = DataFrame(
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{"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
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)
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df["grade"] = Categorical(df["raw_grade"])
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# basic sequencing testing
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result = list(df.grade.values)
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expected = np.array(df.grade.values).tolist()
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tm.assert_almost_equal(result, expected)
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# iteration
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for t in df.itertuples(index=False):
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str(t)
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for row, s in df.iterrows():
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str(s)
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for c, col in df.items():
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str(s)
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def test_len(self, float_frame):
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assert len(float_frame) == len(float_frame.index)
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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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# single block corner case
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arr = float_frame[["A", "B"]].values
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expected = float_frame.reindex(columns=["A", "B"]).values
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tm.assert_almost_equal(arr, expected)
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def test_to_numpy(self):
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df = pd.DataFrame({"A": [1, 2], "B": [3, 4.5]})
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expected = np.array([[1, 3], [2, 4.5]])
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result = df.to_numpy()
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tm.assert_numpy_array_equal(result, expected)
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def test_to_numpy_dtype(self):
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df = pd.DataFrame({"A": [1, 2], "B": [3, 4.5]})
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expected = np.array([[1, 3], [2, 4]], dtype="int64")
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result = df.to_numpy(dtype="int64")
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tm.assert_numpy_array_equal(result, expected)
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def test_to_numpy_copy(self):
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arr = np.random.randn(4, 3)
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df = pd.DataFrame(arr)
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assert df.values.base is arr
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assert df.to_numpy(copy=False).base is arr
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assert df.to_numpy(copy=True).base is not arr
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def test_to_numpy_mixed_dtype_to_str(self):
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# https://github.com/pandas-dev/pandas/issues/35455
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df = pd.DataFrame([[pd.Timestamp("2020-01-01 00:00:00"), 100.0]])
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result = df.to_numpy(dtype=str)
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expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str)
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tm.assert_numpy_array_equal(result, expected)
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def test_swapaxes(self):
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df = DataFrame(np.random.randn(10, 5))
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tm.assert_frame_equal(df.T, df.swapaxes(0, 1))
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tm.assert_frame_equal(df.T, df.swapaxes(1, 0))
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tm.assert_frame_equal(df, df.swapaxes(0, 0))
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msg = "No axis named 2 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.swapaxes(2, 5)
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def test_axis_aliases(self, float_frame):
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f = float_frame
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# reg name
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expected = f.sum(axis=0)
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result = f.sum(axis="index")
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tm.assert_series_equal(result, expected)
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expected = f.sum(axis=1)
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result = f.sum(axis="columns")
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tm.assert_series_equal(result, expected)
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def test_class_axis(self):
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# GH 18147
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# no exception and no empty docstring
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assert pydoc.getdoc(DataFrame.index)
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assert pydoc.getdoc(DataFrame.columns)
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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_repr_with_mi_nat(self, float_string_frame):
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df = DataFrame(
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{"X": [1, 2]}, index=[[pd.NaT, pd.Timestamp("20130101")], ["a", "b"]]
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)
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result = repr(df)
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expected = " X\nNaT a 1\n2013-01-01 b 2"
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assert result == expected
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def test_items_names(self, float_string_frame):
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for k, v in float_string_frame.items():
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assert v.name == k
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def test_series_put_names(self, float_string_frame):
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series = float_string_frame._series
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for k, v in series.items():
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assert v.name == k
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def test_empty_nonzero(self):
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df = DataFrame([1, 2, 3])
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assert not df.empty
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df = DataFrame(index=[1], columns=[1])
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assert not df.empty
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df = DataFrame(index=["a", "b"], columns=["c", "d"]).dropna()
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assert df.empty
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assert df.T.empty
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empty_frames = [
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DataFrame(),
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DataFrame(index=[1]),
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DataFrame(columns=[1]),
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DataFrame({1: []}),
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]
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for df in empty_frames:
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assert df.empty
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assert df.T.empty
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def test_with_datetimelikes(self):
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df = DataFrame(
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{
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"A": date_range("20130101", periods=10),
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"B": timedelta_range("1 day", periods=10),
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}
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)
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t = df.T
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result = t.dtypes.value_counts()
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expected = Series({np.dtype("object"): 10})
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tm.assert_series_equal(result, expected)
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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_deepcopy(self, float_frame):
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cp = deepcopy(float_frame)
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series = cp["A"]
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series[:] = 10
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for idx, value in series.items():
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assert float_frame["A"][idx] != value
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def test_inplace_return_self(self):
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# GH 1893
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data = DataFrame(
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{"a": ["foo", "bar", "baz", "qux"], "b": [0, 0, 1, 1], "c": [1, 2, 3, 4]}
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)
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|
def _check_f(base, f):
|
|
result = f(base)
|
|
assert result is None
|
|
|
|
# -----DataFrame-----
|
|
|
|
# set_index
|
|
f = lambda x: x.set_index("a", inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# reset_index
|
|
f = lambda x: x.reset_index(inplace=True)
|
|
_check_f(data.set_index("a"), f)
|
|
|
|
# drop_duplicates
|
|
f = lambda x: x.drop_duplicates(inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# sort
|
|
f = lambda x: x.sort_values("b", inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# sort_index
|
|
f = lambda x: x.sort_index(inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# fillna
|
|
f = lambda x: x.fillna(0, inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# replace
|
|
f = lambda x: x.replace(1, 0, inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# rename
|
|
f = lambda x: x.rename({1: "foo"}, inplace=True)
|
|
_check_f(data.copy(), f)
|
|
|
|
# -----Series-----
|
|
d = data.copy()["c"]
|
|
|
|
# reset_index
|
|
f = lambda x: x.reset_index(inplace=True, drop=True)
|
|
_check_f(data.set_index("a")["c"], f)
|
|
|
|
# fillna
|
|
f = lambda x: x.fillna(0, inplace=True)
|
|
_check_f(d.copy(), f)
|
|
|
|
# replace
|
|
f = lambda x: x.replace(1, 0, inplace=True)
|
|
_check_f(d.copy(), f)
|
|
|
|
# rename
|
|
f = lambda x: x.rename({1: "foo"}, inplace=True)
|
|
_check_f(d.copy(), f)
|
|
|
|
@async_mark()
|
|
async def test_tab_complete_warning(self, ip):
|
|
# GH 16409
|
|
pytest.importorskip("IPython", minversion="6.0.0")
|
|
from IPython.core.completer import provisionalcompleter
|
|
|
|
code = "import pandas as pd; df = pd.DataFrame()"
|
|
await ip.run_code(code)
|
|
|
|
# TODO: remove it when Ipython updates
|
|
# GH 33567, jedi version raises Deprecation warning in Ipython
|
|
import jedi
|
|
|
|
if jedi.__version__ < "0.17.0":
|
|
warning = tm.assert_produces_warning(None)
|
|
else:
|
|
warning = tm.assert_produces_warning(
|
|
DeprecationWarning, check_stacklevel=False
|
|
)
|
|
with warning:
|
|
with provisionalcompleter("ignore"):
|
|
list(ip.Completer.completions("df.", 1))
|
|
|
|
def test_attrs(self):
|
|
df = pd.DataFrame({"A": [2, 3]})
|
|
assert df.attrs == {}
|
|
df.attrs["version"] = 1
|
|
|
|
result = df.rename(columns=str)
|
|
assert result.attrs == {"version": 1}
|
|
|
|
def test_cache_on_copy(self):
|
|
# GH 31784 _item_cache not cleared on copy causes incorrect reads after updates
|
|
df = DataFrame({"a": [1]})
|
|
|
|
df["x"] = [0]
|
|
df["a"]
|
|
|
|
df.copy()
|
|
|
|
df["a"].values[0] = -1
|
|
|
|
tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0]}))
|
|
|
|
df["y"] = [0]
|
|
|
|
assert df["a"].values[0] == -1
|
|
tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0], "y": [0]}))
|
|
|
|
@skip_if_no("jinja2")
|
|
def test_constructor_expanddim_lookup(self):
|
|
# GH#33628 accessing _constructor_expanddim should not
|
|
# raise NotImplementedError
|
|
df = DataFrame()
|
|
|
|
inspect.getmembers(df)
|
|
|
|
with pytest.raises(NotImplementedError, match="Not supported for DataFrames!"):
|
|
df._constructor_expanddim(np.arange(27).reshape(3, 3, 3))
|
|
|