Updated script that can be controled by Nodejs web app
This commit is contained in:
mac OS
@ -0,0 +1,228 @@
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from collections import OrderedDict
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import numpy as np
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import pytest
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from pandas._config import using_pyarrow_string_dtype
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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RangeIndex,
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Series,
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)
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import pandas._testing as tm
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class TestFromDict:
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# Note: these tests are specific to the from_dict method, not for
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# passing dictionaries to DataFrame.__init__
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def test_constructor_list_of_odicts(self):
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data = [
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OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
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OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
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OrderedDict([["a", 1.5], ["d", 6]]),
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OrderedDict(),
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OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
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OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
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]
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result = DataFrame(data)
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expected = DataFrame.from_dict(
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dict(zip(range(len(data)), data)), orient="index"
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)
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tm.assert_frame_equal(result, expected.reindex(result.index))
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def test_constructor_single_row(self):
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data = [OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]])]
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result = DataFrame(data)
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expected = DataFrame.from_dict(dict(zip([0], data)), orient="index").reindex(
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result.index
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.skipif(
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using_pyarrow_string_dtype(), reason="columns inferring logic broken"
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)
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def test_constructor_list_of_series(self):
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data = [
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OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
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OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
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]
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sdict = OrderedDict(zip(["x", "y"], data))
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idx = Index(["a", "b", "c"])
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# all named
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data2 = [
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Series([1.5, 3, 4], idx, dtype="O", name="x"),
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Series([1.5, 3, 6], idx, name="y"),
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]
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result = DataFrame(data2)
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expected = DataFrame.from_dict(sdict, orient="index")
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tm.assert_frame_equal(result, expected)
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# some unnamed
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data2 = [
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Series([1.5, 3, 4], idx, dtype="O", name="x"),
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Series([1.5, 3, 6], idx),
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]
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result = DataFrame(data2)
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sdict = OrderedDict(zip(["x", "Unnamed 0"], data))
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expected = DataFrame.from_dict(sdict, orient="index")
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tm.assert_frame_equal(result, expected)
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# none named
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data = [
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OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
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OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
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OrderedDict([["a", 1.5], ["d", 6]]),
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OrderedDict(),
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OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
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OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
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]
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data = [Series(d) for d in data]
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result = DataFrame(data)
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sdict = OrderedDict(zip(range(len(data)), data))
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expected = DataFrame.from_dict(sdict, orient="index")
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tm.assert_frame_equal(result, expected.reindex(result.index))
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result2 = DataFrame(data, index=np.arange(6, dtype=np.int64))
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tm.assert_frame_equal(result, result2)
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result = DataFrame([Series(dtype=object)])
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expected = DataFrame(index=[0])
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tm.assert_frame_equal(result, expected)
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data = [
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OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
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OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
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]
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sdict = OrderedDict(zip(range(len(data)), data))
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idx = Index(["a", "b", "c"])
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data2 = [Series([1.5, 3, 4], idx, dtype="O"), Series([1.5, 3, 6], idx)]
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result = DataFrame(data2)
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expected = DataFrame.from_dict(sdict, orient="index")
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tm.assert_frame_equal(result, expected)
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def test_constructor_orient(self, float_string_frame):
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data_dict = float_string_frame.T._series
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recons = DataFrame.from_dict(data_dict, orient="index")
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expected = float_string_frame.reindex(index=recons.index)
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tm.assert_frame_equal(recons, expected)
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# dict of sequence
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a = {"hi": [32, 3, 3], "there": [3, 5, 3]}
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rs = DataFrame.from_dict(a, orient="index")
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xp = DataFrame.from_dict(a).T.reindex(list(a.keys()))
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tm.assert_frame_equal(rs, xp)
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def test_constructor_from_ordered_dict(self):
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# GH#8425
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a = OrderedDict(
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[
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("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])),
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("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])),
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("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])),
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]
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)
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expected = DataFrame.from_dict(a, orient="columns").T
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result = DataFrame.from_dict(a, orient="index")
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tm.assert_frame_equal(result, expected)
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def test_from_dict_columns_parameter(self):
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# GH#18529
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# Test new columns parameter for from_dict that was added to make
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# from_items(..., orient='index', columns=[...]) easier to replicate
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result = DataFrame.from_dict(
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OrderedDict([("A", [1, 2]), ("B", [4, 5])]),
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orient="index",
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columns=["one", "two"],
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)
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expected = DataFrame([[1, 2], [4, 5]], index=["A", "B"], columns=["one", "two"])
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tm.assert_frame_equal(result, expected)
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msg = "cannot use columns parameter with orient='columns'"
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with pytest.raises(ValueError, match=msg):
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DataFrame.from_dict(
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{"A": [1, 2], "B": [4, 5]},
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orient="columns",
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columns=["one", "two"],
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)
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with pytest.raises(ValueError, match=msg):
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DataFrame.from_dict({"A": [1, 2], "B": [4, 5]}, columns=["one", "two"])
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@pytest.mark.parametrize(
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"data_dict, orient, expected",
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[
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({}, "index", RangeIndex(0)),
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(
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[{("a",): 1}, {("a",): 2}],
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"columns",
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Index([("a",)], tupleize_cols=False),
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),
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(
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[OrderedDict([(("a",), 1), (("b",), 2)])],
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"columns",
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Index([("a",), ("b",)], tupleize_cols=False),
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),
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([{("a", "b"): 1}], "columns", Index([("a", "b")], tupleize_cols=False)),
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],
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)
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def test_constructor_from_dict_tuples(self, data_dict, orient, expected):
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# GH#16769
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df = DataFrame.from_dict(data_dict, orient)
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result = df.columns
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tm.assert_index_equal(result, expected)
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def test_frame_dict_constructor_empty_series(self):
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s1 = Series(
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[1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)])
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)
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s2 = Series(
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[1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)])
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)
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s3 = Series(dtype=object)
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# it works!
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DataFrame({"foo": s1, "bar": s2, "baz": s3})
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DataFrame.from_dict({"foo": s1, "baz": s3, "bar": s2})
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def test_from_dict_scalars_requires_index(self):
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msg = "If using all scalar values, you must pass an index"
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with pytest.raises(ValueError, match=msg):
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DataFrame.from_dict(OrderedDict([("b", 8), ("a", 5), ("a", 6)]))
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def test_from_dict_orient_invalid(self):
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msg = (
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"Expected 'index', 'columns' or 'tight' for orient parameter. "
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"Got 'abc' instead"
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)
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with pytest.raises(ValueError, match=msg):
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DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc")
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def test_from_dict_order_with_single_column(self):
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data = {
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"alpha": {
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"value2": 123,
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"value1": 532,
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"animal": 222,
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"plant": False,
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"name": "test",
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}
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}
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result = DataFrame.from_dict(
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data,
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orient="columns",
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)
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expected = DataFrame(
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[[123], [532], [222], [False], ["test"]],
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index=["value2", "value1", "animal", "plant", "name"],
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columns=["alpha"],
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)
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tm.assert_frame_equal(result, expected)
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@ -0,0 +1,505 @@
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from collections.abc import Iterator
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from datetime import datetime
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from decimal import Decimal
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import numpy as np
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import pytest
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import pytz
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from pandas._config import using_pyarrow_string_dtype
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from pandas.compat import is_platform_little_endian
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from pandas import (
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CategoricalIndex,
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DataFrame,
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Index,
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Interval,
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RangeIndex,
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Series,
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date_range,
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)
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import pandas._testing as tm
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class TestFromRecords:
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def test_from_records_dt64tz_frame(self):
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# GH#51162 don't lose tz when calling from_records with DataFrame input
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dti = date_range("2016-01-01", periods=10, tz="US/Pacific")
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df = DataFrame({i: dti for i in range(4)})
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with tm.assert_produces_warning(FutureWarning):
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res = DataFrame.from_records(df)
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tm.assert_frame_equal(res, df)
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def test_from_records_with_datetimes(self):
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# this may fail on certain platforms because of a numpy issue
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# related GH#6140
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if not is_platform_little_endian():
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pytest.skip("known failure of test on non-little endian")
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# construction with a null in a recarray
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# GH#6140
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expected = DataFrame({"EXPIRY": [datetime(2005, 3, 1, 0, 0), None]})
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arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
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dtypes = [("EXPIRY", "<M8[ns]")]
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recarray = np.rec.fromarrays(arrdata, dtype=dtypes)
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result = DataFrame.from_records(recarray)
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tm.assert_frame_equal(result, expected)
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# coercion should work too
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arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
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dtypes = [("EXPIRY", "<M8[m]")]
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recarray = np.rec.fromarrays(arrdata, dtype=dtypes)
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result = DataFrame.from_records(recarray)
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# we get the closest supported unit, "s"
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expected["EXPIRY"] = expected["EXPIRY"].astype("M8[s]")
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tm.assert_frame_equal(result, expected)
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@pytest.mark.skipif(
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using_pyarrow_string_dtype(), reason="dtype checking logic doesn't work"
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)
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def test_from_records_sequencelike(self):
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df = DataFrame(
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{
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"A": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float64
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),
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"A1": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float64
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),
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"B": np.array(np.arange(6), dtype=np.int64),
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"C": ["foo"] * 6,
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"D": np.array([True, False] * 3, dtype=bool),
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"E": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float32
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),
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"E1": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float32
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),
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"F": np.array(np.arange(6), dtype=np.int32),
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}
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)
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# this is actually tricky to create the recordlike arrays and
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# have the dtypes be intact
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blocks = df._to_dict_of_blocks()
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tuples = []
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columns = []
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dtypes = []
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for dtype, b in blocks.items():
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columns.extend(b.columns)
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dtypes.extend([(c, np.dtype(dtype).descr[0][1]) for c in b.columns])
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for i in range(len(df.index)):
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tup = []
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for _, b in blocks.items():
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tup.extend(b.iloc[i].values)
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tuples.append(tuple(tup))
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recarray = np.array(tuples, dtype=dtypes).view(np.rec.recarray)
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recarray2 = df.to_records()
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lists = [list(x) for x in tuples]
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# tuples (lose the dtype info)
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result = DataFrame.from_records(tuples, columns=columns).reindex(
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columns=df.columns
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)
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# created recarray and with to_records recarray (have dtype info)
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result2 = DataFrame.from_records(recarray, columns=columns).reindex(
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columns=df.columns
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)
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result3 = DataFrame.from_records(recarray2, columns=columns).reindex(
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columns=df.columns
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)
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# list of tuples (no dtype info)
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result4 = DataFrame.from_records(lists, columns=columns).reindex(
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columns=df.columns
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)
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tm.assert_frame_equal(result, df, check_dtype=False)
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tm.assert_frame_equal(result2, df)
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tm.assert_frame_equal(result3, df)
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tm.assert_frame_equal(result4, df, check_dtype=False)
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# tuples is in the order of the columns
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result = DataFrame.from_records(tuples)
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tm.assert_index_equal(result.columns, RangeIndex(8))
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# test exclude parameter & we are casting the results here (as we don't
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# have dtype info to recover)
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columns_to_test = [columns.index("C"), columns.index("E1")]
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exclude = list(set(range(8)) - set(columns_to_test))
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result = DataFrame.from_records(tuples, exclude=exclude)
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result.columns = [columns[i] for i in sorted(columns_to_test)]
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tm.assert_series_equal(result["C"], df["C"])
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tm.assert_series_equal(result["E1"], df["E1"])
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def test_from_records_sequencelike_empty(self):
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# empty case
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result = DataFrame.from_records([], columns=["foo", "bar", "baz"])
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assert len(result) == 0
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tm.assert_index_equal(result.columns, Index(["foo", "bar", "baz"]))
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result = DataFrame.from_records([])
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assert len(result) == 0
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assert len(result.columns) == 0
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def test_from_records_dictlike(self):
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# test the dict methods
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df = DataFrame(
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{
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"A": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float64
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),
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"A1": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float64
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),
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"B": np.array(np.arange(6), dtype=np.int64),
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"C": ["foo"] * 6,
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"D": np.array([True, False] * 3, dtype=bool),
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"E": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float32
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),
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"E1": np.array(
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np.random.default_rng(2).standard_normal(6), dtype=np.float32
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),
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"F": np.array(np.arange(6), dtype=np.int32),
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}
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)
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# columns is in a different order here than the actual items iterated
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# from the dict
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blocks = df._to_dict_of_blocks()
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columns = []
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for b in blocks.values():
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columns.extend(b.columns)
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asdict = dict(df.items())
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asdict2 = {x: y.values for x, y in df.items()}
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# dict of series & dict of ndarrays (have dtype info)
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results = []
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results.append(DataFrame.from_records(asdict).reindex(columns=df.columns))
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results.append(
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DataFrame.from_records(asdict, columns=columns).reindex(columns=df.columns)
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)
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results.append(
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DataFrame.from_records(asdict2, columns=columns).reindex(columns=df.columns)
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)
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for r in results:
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tm.assert_frame_equal(r, df)
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def test_from_records_with_index_data(self):
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 3)), columns=["A", "B", "C"]
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)
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data = np.random.default_rng(2).standard_normal(10)
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with tm.assert_produces_warning(FutureWarning):
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df1 = DataFrame.from_records(df, index=data)
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tm.assert_index_equal(df1.index, Index(data))
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def test_from_records_bad_index_column(self):
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 3)), columns=["A", "B", "C"]
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)
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# should pass
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with tm.assert_produces_warning(FutureWarning):
|
||||
df1 = DataFrame.from_records(df, index=["C"])
|
||||
tm.assert_index_equal(df1.index, Index(df.C))
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning):
|
||||
df1 = DataFrame.from_records(df, index="C")
|
||||
tm.assert_index_equal(df1.index, Index(df.C))
|
||||
|
||||
# should fail
|
||||
msg = "|".join(
|
||||
[
|
||||
r"'None of \[2\] are in the columns'",
|
||||
]
|
||||
)
|
||||
with pytest.raises(KeyError, match=msg):
|
||||
with tm.assert_produces_warning(FutureWarning):
|
||||
DataFrame.from_records(df, index=[2])
|
||||
with pytest.raises(KeyError, match=msg):
|
||||
with tm.assert_produces_warning(FutureWarning):
|
||||
DataFrame.from_records(df, index=2)
|
||||
|
||||
def test_from_records_non_tuple(self):
|
||||
class Record:
|
||||
def __init__(self, *args) -> None:
|
||||
self.args = args
|
||||
|
||||
def __getitem__(self, i):
|
||||
return self.args[i]
|
||||
|
||||
def __iter__(self) -> Iterator:
|
||||
return iter(self.args)
|
||||
|
||||
recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)]
|
||||
tups = [tuple(rec) for rec in recs]
|
||||
|
||||
result = DataFrame.from_records(recs)
|
||||
expected = DataFrame.from_records(tups)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_len0_with_columns(self):
|
||||
# GH#2633
|
||||
result = DataFrame.from_records([], index="foo", columns=["foo", "bar"])
|
||||
expected = Index(["bar"])
|
||||
|
||||
assert len(result) == 0
|
||||
assert result.index.name == "foo"
|
||||
tm.assert_index_equal(result.columns, expected)
|
||||
|
||||
def test_from_records_series_list_dict(self):
|
||||
# GH#27358
|
||||
expected = DataFrame([[{"a": 1, "b": 2}, {"a": 3, "b": 4}]]).T
|
||||
data = Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]])
|
||||
result = DataFrame.from_records(data)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_series_categorical_index(self):
|
||||
# GH#32805
|
||||
index = CategoricalIndex(
|
||||
[Interval(-20, -10), Interval(-10, 0), Interval(0, 10)]
|
||||
)
|
||||
series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index)
|
||||
frame = DataFrame.from_records(series_of_dicts, index=index)
|
||||
expected = DataFrame(
|
||||
{"a": [1, 2, np.nan], "b": [np.nan, np.nan, 3]}, index=index
|
||||
)
|
||||
tm.assert_frame_equal(frame, expected)
|
||||
|
||||
def test_frame_from_records_utc(self):
|
||||
rec = {"datum": 1.5, "begin_time": datetime(2006, 4, 27, tzinfo=pytz.utc)}
|
||||
|
||||
# it works
|
||||
DataFrame.from_records([rec], index="begin_time")
|
||||
|
||||
def test_from_records_to_records(self):
|
||||
# from numpy documentation
|
||||
arr = np.zeros((2,), dtype=("i4,f4,S10"))
|
||||
arr[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
|
||||
|
||||
DataFrame.from_records(arr)
|
||||
|
||||
index = Index(np.arange(len(arr))[::-1])
|
||||
indexed_frame = DataFrame.from_records(arr, index=index)
|
||||
tm.assert_index_equal(indexed_frame.index, index)
|
||||
|
||||
# without names, it should go to last ditch
|
||||
arr2 = np.zeros((2, 3))
|
||||
tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2))
|
||||
|
||||
# wrong length
|
||||
msg = "|".join(
|
||||
[
|
||||
r"Length of values \(2\) does not match length of index \(1\)",
|
||||
]
|
||||
)
|
||||
with pytest.raises(ValueError, match=msg):
|
||||
DataFrame.from_records(arr, index=index[:-1])
|
||||
|
||||
indexed_frame = DataFrame.from_records(arr, index="f1")
|
||||
|
||||
# what to do?
|
||||
records = indexed_frame.to_records()
|
||||
assert len(records.dtype.names) == 3
|
||||
|
||||
records = indexed_frame.to_records(index=False)
|
||||
assert len(records.dtype.names) == 2
|
||||
assert "index" not in records.dtype.names
|
||||
|
||||
def test_from_records_nones(self):
|
||||
tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)]
|
||||
|
||||
df = DataFrame.from_records(tuples, columns=["a", "b", "c", "d"])
|
||||
assert np.isnan(df["c"][0])
|
||||
|
||||
def test_from_records_iterator(self):
|
||||
arr = np.array(
|
||||
[(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5.0, 5.0, 6, 6), (7.0, 7.0, 8, 8)],
|
||||
dtype=[
|
||||
("x", np.float64),
|
||||
("u", np.float32),
|
||||
("y", np.int64),
|
||||
("z", np.int32),
|
||||
],
|
||||
)
|
||||
df = DataFrame.from_records(iter(arr), nrows=2)
|
||||
xp = DataFrame(
|
||||
{
|
||||
"x": np.array([1.0, 3.0], dtype=np.float64),
|
||||
"u": np.array([1.0, 3.0], dtype=np.float32),
|
||||
"y": np.array([2, 4], dtype=np.int64),
|
||||
"z": np.array([2, 4], dtype=np.int32),
|
||||
}
|
||||
)
|
||||
tm.assert_frame_equal(df.reindex_like(xp), xp)
|
||||
|
||||
# no dtypes specified here, so just compare with the default
|
||||
arr = [(1.0, 2), (3.0, 4), (5.0, 6), (7.0, 8)]
|
||||
df = DataFrame.from_records(iter(arr), columns=["x", "y"], nrows=2)
|
||||
tm.assert_frame_equal(df, xp.reindex(columns=["x", "y"]), check_dtype=False)
|
||||
|
||||
def test_from_records_tuples_generator(self):
|
||||
def tuple_generator(length):
|
||||
for i in range(length):
|
||||
letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
|
||||
yield (i, letters[i % len(letters)], i / length)
|
||||
|
||||
columns_names = ["Integer", "String", "Float"]
|
||||
columns = [
|
||||
[i[j] for i in tuple_generator(10)] for j in range(len(columns_names))
|
||||
]
|
||||
data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
|
||||
expected = DataFrame(data, columns=columns_names)
|
||||
|
||||
generator = tuple_generator(10)
|
||||
result = DataFrame.from_records(generator, columns=columns_names)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_lists_generator(self):
|
||||
def list_generator(length):
|
||||
for i in range(length):
|
||||
letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
|
||||
yield [i, letters[i % len(letters)], i / length]
|
||||
|
||||
columns_names = ["Integer", "String", "Float"]
|
||||
columns = [
|
||||
[i[j] for i in list_generator(10)] for j in range(len(columns_names))
|
||||
]
|
||||
data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
|
||||
expected = DataFrame(data, columns=columns_names)
|
||||
|
||||
generator = list_generator(10)
|
||||
result = DataFrame.from_records(generator, columns=columns_names)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_columns_not_modified(self):
|
||||
tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)]
|
||||
|
||||
columns = ["a", "b", "c"]
|
||||
original_columns = list(columns)
|
||||
|
||||
DataFrame.from_records(tuples, columns=columns, index="a")
|
||||
|
||||
assert columns == original_columns
|
||||
|
||||
def test_from_records_decimal(self):
|
||||
tuples = [(Decimal("1.5"),), (Decimal("2.5"),), (None,)]
|
||||
|
||||
df = DataFrame.from_records(tuples, columns=["a"])
|
||||
assert df["a"].dtype == object
|
||||
|
||||
df = DataFrame.from_records(tuples, columns=["a"], coerce_float=True)
|
||||
assert df["a"].dtype == np.float64
|
||||
assert np.isnan(df["a"].values[-1])
|
||||
|
||||
def test_from_records_duplicates(self):
|
||||
result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
|
||||
|
||||
expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
|
||||
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_set_index_name(self):
|
||||
def create_dict(order_id):
|
||||
return {
|
||||
"order_id": order_id,
|
||||
"quantity": np.random.default_rng(2).integers(1, 10),
|
||||
"price": np.random.default_rng(2).integers(1, 10),
|
||||
}
|
||||
|
||||
documents = [create_dict(i) for i in range(10)]
|
||||
# demo missing data
|
||||
documents.append({"order_id": 10, "quantity": 5})
|
||||
|
||||
result = DataFrame.from_records(documents, index="order_id")
|
||||
assert result.index.name == "order_id"
|
||||
|
||||
# MultiIndex
|
||||
result = DataFrame.from_records(documents, index=["order_id", "quantity"])
|
||||
assert result.index.names == ("order_id", "quantity")
|
||||
|
||||
def test_from_records_misc_brokenness(self):
|
||||
# GH#2179
|
||||
|
||||
data = {1: ["foo"], 2: ["bar"]}
|
||||
|
||||
result = DataFrame.from_records(data, columns=["a", "b"])
|
||||
exp = DataFrame(data, columns=["a", "b"])
|
||||
tm.assert_frame_equal(result, exp)
|
||||
|
||||
# overlap in index/index_names
|
||||
|
||||
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
|
||||
|
||||
result = DataFrame.from_records(data, index=["a", "b", "c"])
|
||||
exp = DataFrame(data, index=["a", "b", "c"])
|
||||
tm.assert_frame_equal(result, exp)
|
||||
|
||||
def test_from_records_misc_brokenness2(self):
|
||||
# GH#2623
|
||||
rows = []
|
||||
rows.append([datetime(2010, 1, 1), 1])
|
||||
rows.append([datetime(2010, 1, 2), "hi"]) # test col upconverts to obj
|
||||
result = DataFrame.from_records(rows, columns=["date", "test"])
|
||||
expected = DataFrame(
|
||||
{"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
assert result.dtypes["test"] == np.dtype(object)
|
||||
|
||||
def test_from_records_misc_brokenness3(self):
|
||||
rows = []
|
||||
rows.append([datetime(2010, 1, 1), 1])
|
||||
rows.append([datetime(2010, 1, 2), 1])
|
||||
result = DataFrame.from_records(rows, columns=["date", "test"])
|
||||
expected = DataFrame(
|
||||
{"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
|
||||
)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_empty(self):
|
||||
# GH#3562
|
||||
result = DataFrame.from_records([], columns=["a", "b", "c"])
|
||||
expected = DataFrame(columns=["a", "b", "c"])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
result = DataFrame.from_records([], columns=["a", "b", "b"])
|
||||
expected = DataFrame(columns=["a", "b", "b"])
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_from_records_empty_with_nonempty_fields_gh3682(self):
|
||||
a = np.array([(1, 2)], dtype=[("id", np.int64), ("value", np.int64)])
|
||||
df = DataFrame.from_records(a, index="id")
|
||||
|
||||
ex_index = Index([1], name="id")
|
||||
expected = DataFrame({"value": [2]}, index=ex_index, columns=["value"])
|
||||
tm.assert_frame_equal(df, expected)
|
||||
|
||||
b = a[:0]
|
||||
df2 = DataFrame.from_records(b, index="id")
|
||||
tm.assert_frame_equal(df2, df.iloc[:0])
|
||||
|
||||
def test_from_records_empty2(self):
|
||||
# GH#42456
|
||||
dtype = [("prop", int)]
|
||||
shape = (0, len(dtype))
|
||||
arr = np.empty(shape, dtype=dtype)
|
||||
|
||||
result = DataFrame.from_records(arr)
|
||||
expected = DataFrame({"prop": np.array([], dtype=int)})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
alt = DataFrame(arr)
|
||||
tm.assert_frame_equal(alt, expected)
|
||||
Reference in New Issue
Block a user