105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
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 SparseDtype
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import pandas.util.testing as tm
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class TestSeriesSubclassing:
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def test_indexing_sliced(self):
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s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"))
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res = s.loc[["a", "b"]]
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exp = tm.SubclassedSeries([1, 2], index=list("ab"))
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tm.assert_series_equal(res, exp)
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res = s.iloc[[2, 3]]
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exp = tm.SubclassedSeries([3, 4], index=list("cd"))
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tm.assert_series_equal(res, exp)
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res = s.loc[["a", "b"]]
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exp = tm.SubclassedSeries([1, 2], index=list("ab"))
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tm.assert_series_equal(res, exp)
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def test_to_frame(self):
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s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"), name="xxx")
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res = s.to_frame()
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exp = tm.SubclassedDataFrame({"xxx": [1, 2, 3, 4]}, index=list("abcd"))
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tm.assert_frame_equal(res, exp)
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def test_subclass_unstack(self):
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# GH 15564
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s = tm.SubclassedSeries([1, 2, 3, 4], index=[list("aabb"), list("xyxy")])
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res = s.unstack()
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exp = tm.SubclassedDataFrame({"x": [1, 3], "y": [2, 4]}, index=["a", "b"])
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tm.assert_frame_equal(res, exp)
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def test_subclass_empty_repr(self):
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assert "SubclassedSeries" in repr(tm.SubclassedSeries())
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@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
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class TestSparseSeriesSubclassing:
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def test_subclass_sparse_slice(self):
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# int64
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s = tm.SubclassedSparseSeries([1, 2, 3, 4, 5])
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exp = tm.SubclassedSparseSeries([2, 3, 4], index=[1, 2, 3])
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tm.assert_sp_series_equal(s.loc[1:3], exp)
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assert s.loc[1:3].dtype == SparseDtype(np.int64)
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exp = tm.SubclassedSparseSeries([2, 3], index=[1, 2])
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tm.assert_sp_series_equal(s.iloc[1:3], exp)
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assert s.iloc[1:3].dtype == SparseDtype(np.int64)
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exp = tm.SubclassedSparseSeries([2, 3], index=[1, 2])
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tm.assert_sp_series_equal(s[1:3], exp)
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assert s[1:3].dtype == SparseDtype(np.int64)
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# float64
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s = tm.SubclassedSparseSeries([1.0, 2.0, 3.0, 4.0, 5.0])
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exp = tm.SubclassedSparseSeries([2.0, 3.0, 4.0], index=[1, 2, 3])
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tm.assert_sp_series_equal(s.loc[1:3], exp)
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assert s.loc[1:3].dtype == SparseDtype(np.float64)
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exp = tm.SubclassedSparseSeries([2.0, 3.0], index=[1, 2])
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tm.assert_sp_series_equal(s.iloc[1:3], exp)
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assert s.iloc[1:3].dtype == SparseDtype(np.float64)
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exp = tm.SubclassedSparseSeries([2.0, 3.0], index=[1, 2])
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tm.assert_sp_series_equal(s[1:3], exp)
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assert s[1:3].dtype == SparseDtype(np.float64)
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def test_subclass_sparse_addition(self):
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s1 = tm.SubclassedSparseSeries([1, 3, 5])
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s2 = tm.SubclassedSparseSeries([-2, 5, 12])
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exp = tm.SubclassedSparseSeries([-1, 8, 17])
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tm.assert_sp_series_equal(s1 + s2, exp)
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s1 = tm.SubclassedSparseSeries([4.0, 5.0, 6.0])
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s2 = tm.SubclassedSparseSeries([1.0, 2.0, 3.0])
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exp = tm.SubclassedSparseSeries([5.0, 7.0, 9.0])
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tm.assert_sp_series_equal(s1 + s2, exp)
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def test_subclass_sparse_to_frame(self):
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s = tm.SubclassedSparseSeries([1, 2], index=list("ab"), name="xxx")
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res = s.to_frame()
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exp_arr = pd.SparseArray([1, 2], dtype=np.int64, kind="block", fill_value=0)
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exp = tm.SubclassedSparseDataFrame(
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{"xxx": exp_arr}, index=list("ab"), default_fill_value=0
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)
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tm.assert_sp_frame_equal(res, exp)
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# create from int dict
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res = tm.SubclassedSparseDataFrame(
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{"xxx": [1, 2]}, index=list("ab"), default_fill_value=0
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)
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tm.assert_sp_frame_equal(res, exp)
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s = tm.SubclassedSparseSeries([1.1, 2.1], index=list("ab"), name="xxx")
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res = s.to_frame()
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exp = tm.SubclassedSparseDataFrame({"xxx": [1.1, 2.1]}, index=list("ab"))
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tm.assert_sp_frame_equal(res, exp)
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