1037 lines
39 KiB
Python
1037 lines
39 KiB
Python
# pylint: disable-msg=E1101,W0612
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import pytest
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import numpy as np
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import pandas as pd
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import pandas.util.testing as tm
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class TestSparseSeriesIndexing(object):
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def setup_method(self, method):
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self.orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
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self.sparse = self.orig.to_sparse()
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def test_getitem(self):
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orig = self.orig
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sparse = self.sparse
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assert sparse[0] == 1
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assert np.isnan(sparse[1])
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assert sparse[3] == 3
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result = sparse[[1, 3, 4]]
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exp = orig[[1, 3, 4]].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# dense array
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result = sparse[orig % 2 == 1]
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exp = orig[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array (actuary it coerces to normal Series)
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result = sparse[sparse % 2 == 1]
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exp = orig[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array
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result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
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tm.assert_sp_series_equal(result, exp)
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def test_getitem_slice(self):
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orig = self.orig
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sparse = self.sparse
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tm.assert_sp_series_equal(sparse[:2], orig[:2].to_sparse())
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tm.assert_sp_series_equal(sparse[4:2], orig[4:2].to_sparse())
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tm.assert_sp_series_equal(sparse[::2], orig[::2].to_sparse())
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tm.assert_sp_series_equal(sparse[-5:], orig[-5:].to_sparse())
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def test_getitem_int_dtype(self):
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# GH 8292
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s = pd.SparseSeries([0, 1, 2, 3, 4, 5, 6], name='xxx')
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res = s[::2]
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exp = pd.SparseSeries([0, 2, 4, 6], index=[0, 2, 4, 6], name='xxx')
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tm.assert_sp_series_equal(res, exp)
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assert res.dtype == np.int64
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s = pd.SparseSeries([0, 1, 2, 3, 4, 5, 6], fill_value=0, name='xxx')
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res = s[::2]
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exp = pd.SparseSeries([0, 2, 4, 6], index=[0, 2, 4, 6],
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fill_value=0, name='xxx')
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tm.assert_sp_series_equal(res, exp)
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assert res.dtype == np.int64
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def test_getitem_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse(fill_value=0)
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assert sparse[0] == 1
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assert np.isnan(sparse[1])
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assert sparse[2] == 0
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assert sparse[3] == 3
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result = sparse[[1, 3, 4]]
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exp = orig[[1, 3, 4]].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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# dense array
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result = sparse[orig % 2 == 1]
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exp = orig[orig % 2 == 1].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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# sparse array (actuary it coerces to normal Series)
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result = sparse[sparse % 2 == 1]
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exp = orig[orig % 2 == 1].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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# sparse array
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result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
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tm.assert_sp_series_equal(result, exp)
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def test_getitem_ellipsis(self):
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# GH 9467
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s = pd.SparseSeries([1, np.nan, 2, 0, np.nan])
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tm.assert_sp_series_equal(s[...], s)
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s = pd.SparseSeries([1, np.nan, 2, 0, np.nan], fill_value=0)
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tm.assert_sp_series_equal(s[...], s)
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def test_getitem_slice_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse[:2],
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orig[:2].to_sparse(fill_value=0))
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tm.assert_sp_series_equal(sparse[4:2],
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orig[4:2].to_sparse(fill_value=0))
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tm.assert_sp_series_equal(sparse[::2],
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orig[::2].to_sparse(fill_value=0))
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tm.assert_sp_series_equal(sparse[-5:],
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orig[-5:].to_sparse(fill_value=0))
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def test_loc(self):
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orig = self.orig
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sparse = self.sparse
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assert sparse.loc[0] == 1
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assert np.isnan(sparse.loc[1])
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result = sparse.loc[[1, 3, 4]]
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exp = orig.loc[[1, 3, 4]].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# exceeds the bounds
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result = sparse.reindex([1, 3, 4, 5])
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exp = orig.reindex([1, 3, 4, 5]).to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# padded with NaN
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assert np.isnan(result[-1])
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# dense array
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result = sparse.loc[orig % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array (actuary it coerces to normal Series)
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result = sparse.loc[sparse % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array
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result = sparse.loc[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
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tm.assert_sp_series_equal(result, exp)
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def test_loc_index(self):
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE'))
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sparse = orig.to_sparse()
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assert sparse.loc['A'] == 1
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assert np.isnan(sparse.loc['B'])
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result = sparse.loc[['A', 'C', 'D']]
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exp = orig.loc[['A', 'C', 'D']].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# dense array
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result = sparse.loc[orig % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array (actuary it coerces to normal Series)
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result = sparse.loc[sparse % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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# sparse array
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result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
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tm.assert_sp_series_equal(result, exp)
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def test_loc_index_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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assert sparse.loc['A'] == 1
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assert np.isnan(sparse.loc['B'])
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result = sparse.loc[['A', 'C', 'D']]
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exp = orig.loc[['A', 'C', 'D']].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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# dense array
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result = sparse.loc[orig % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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# sparse array (actuary it coerces to normal Series)
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result = sparse.loc[sparse % 2 == 1]
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exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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def test_loc_slice(self):
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orig = self.orig
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sparse = self.sparse
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tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse())
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def test_loc_slice_index_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.loc['C':],
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orig.loc['C':].to_sparse(fill_value=0))
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def test_loc_slice_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.loc[2:],
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orig.loc[2:].to_sparse(fill_value=0))
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def test_iloc(self):
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orig = self.orig
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sparse = self.sparse
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assert sparse.iloc[3] == 3
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assert np.isnan(sparse.iloc[2])
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result = sparse.iloc[[1, 3, 4]]
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exp = orig.iloc[[1, 3, 4]].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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result = sparse.iloc[[1, -2, -4]]
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exp = orig.iloc[[1, -2, -4]].to_sparse()
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tm.assert_sp_series_equal(result, exp)
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with pytest.raises(IndexError):
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sparse.iloc[[1, 3, 5]]
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def test_iloc_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse(fill_value=0)
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assert sparse.iloc[3] == 3
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assert np.isnan(sparse.iloc[1])
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assert sparse.iloc[4] == 0
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result = sparse.iloc[[1, 3, 4]]
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exp = orig.iloc[[1, 3, 4]].to_sparse(fill_value=0)
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tm.assert_sp_series_equal(result, exp)
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def test_iloc_slice(self):
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
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sparse = orig.to_sparse()
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tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse())
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def test_iloc_slice_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.iloc[2:],
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orig.iloc[2:].to_sparse(fill_value=0))
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def test_at(self):
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
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sparse = orig.to_sparse()
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assert sparse.at[0] == orig.at[0]
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assert np.isnan(sparse.at[1])
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assert np.isnan(sparse.at[2])
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assert sparse.at[3] == orig.at[3]
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assert np.isnan(sparse.at[4])
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan],
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index=list('abcde'))
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sparse = orig.to_sparse()
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assert sparse.at['a'] == orig.at['a']
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assert np.isnan(sparse.at['b'])
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assert np.isnan(sparse.at['c'])
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assert sparse.at['d'] == orig.at['d']
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assert np.isnan(sparse.at['e'])
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def test_at_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0],
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index=list('abcde'))
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sparse = orig.to_sparse(fill_value=0)
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assert sparse.at['a'] == orig.at['a']
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assert np.isnan(sparse.at['b'])
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assert sparse.at['c'] == orig.at['c']
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assert sparse.at['d'] == orig.at['d']
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assert sparse.at['e'] == orig.at['e']
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def test_iat(self):
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orig = self.orig
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sparse = self.sparse
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assert sparse.iat[0] == orig.iat[0]
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assert np.isnan(sparse.iat[1])
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assert np.isnan(sparse.iat[2])
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assert sparse.iat[3] == orig.iat[3]
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assert np.isnan(sparse.iat[4])
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assert np.isnan(sparse.iat[-1])
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assert sparse.iat[-5] == orig.iat[-5]
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def test_iat_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0])
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sparse = orig.to_sparse()
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assert sparse.iat[0] == orig.iat[0]
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assert np.isnan(sparse.iat[1])
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assert sparse.iat[2] == orig.iat[2]
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assert sparse.iat[3] == orig.iat[3]
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assert sparse.iat[4] == orig.iat[4]
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assert sparse.iat[-1] == orig.iat[-1]
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assert sparse.iat[-5] == orig.iat[-5]
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def test_get(self):
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s = pd.SparseSeries([1, np.nan, np.nan, 3, np.nan])
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assert s.get(0) == 1
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assert np.isnan(s.get(1))
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assert s.get(5) is None
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s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE'))
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assert s.get('A') == 1
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assert np.isnan(s.get('B'))
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assert s.get('C') == 0
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assert s.get('XX') is None
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s = pd.SparseSeries([1, np.nan, 0, 3, 0], index=list('ABCDE'),
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fill_value=0)
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assert s.get('A') == 1
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assert np.isnan(s.get('B'))
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assert s.get('C') == 0
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assert s.get('XX') is None
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def test_take(self):
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan],
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index=list('ABCDE'))
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sparse = orig.to_sparse()
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tm.assert_sp_series_equal(sparse.take([0]),
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orig.take([0]).to_sparse())
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tm.assert_sp_series_equal(sparse.take([0, 1, 3]),
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orig.take([0, 1, 3]).to_sparse())
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tm.assert_sp_series_equal(sparse.take([-1, -2]),
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orig.take([-1, -2]).to_sparse())
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def test_take_fill_value(self):
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orig = pd.Series([1, np.nan, 0, 3, 0],
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index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.take([0]),
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orig.take([0]).to_sparse(fill_value=0))
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exp = orig.take([0, 1, 3]).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.take([0, 1, 3]), exp)
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exp = orig.take([-1, -2]).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(sparse.take([-1, -2]), exp)
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def test_reindex(self):
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orig = pd.Series([1, np.nan, np.nan, 3, np.nan],
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index=list('ABCDE'))
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sparse = orig.to_sparse()
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res = sparse.reindex(['A', 'E', 'C', 'D'])
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exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse()
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tm.assert_sp_series_equal(res, exp)
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# all missing & fill_value
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res = sparse.reindex(['B', 'E', 'C'])
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exp = orig.reindex(['B', 'E', 'C']).to_sparse()
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tm.assert_sp_series_equal(res, exp)
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orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan],
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index=list('ABCDE'))
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sparse = orig.to_sparse()
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res = sparse.reindex(['A', 'E', 'C', 'D'])
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exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse()
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tm.assert_sp_series_equal(res, exp)
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def test_fill_value_reindex(self):
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orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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res = sparse.reindex(['A', 'E', 'C', 'D'])
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exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(res, exp)
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# includes missing and fill_value
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res = sparse.reindex(['A', 'B', 'C'])
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exp = orig.reindex(['A', 'B', 'C']).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(res, exp)
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# all missing
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orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan],
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index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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res = sparse.reindex(['A', 'E', 'C', 'D'])
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exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(res, exp)
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# all fill_value
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orig = pd.Series([0., 0., 0., 0., 0.],
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index=list('ABCDE'))
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sparse = orig.to_sparse(fill_value=0)
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res = sparse.reindex(['A', 'E', 'C', 'D'])
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exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0)
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tm.assert_sp_series_equal(res, exp)
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def test_reindex_fill_value(self):
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floats = pd.Series([1., 2., 3.]).to_sparse()
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result = floats.reindex([1, 2, 3], fill_value=0)
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expected = pd.Series([2., 3., 0], index=[1, 2, 3]).to_sparse()
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tm.assert_sp_series_equal(result, expected)
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def test_reindex_nearest(self):
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s = pd.Series(np.arange(10, dtype='float64')).to_sparse()
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target = [0.1, 0.9, 1.5, 2.0]
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actual = s.reindex(target, method='nearest')
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expected = pd.Series(np.around(target), target).to_sparse()
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tm.assert_sp_series_equal(expected, actual)
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actual = s.reindex(target, method='nearest', tolerance=0.2)
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expected = pd.Series([0, 1, np.nan, 2], target).to_sparse()
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tm.assert_sp_series_equal(expected, actual)
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actual = s.reindex(target, method='nearest',
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tolerance=[0.3, 0.01, 0.4, 3])
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expected = pd.Series([0, np.nan, np.nan, 2], target).to_sparse()
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tm.assert_sp_series_equal(expected, actual)
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def tests_indexing_with_sparse(self):
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# GH 13985
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for kind in ['integer', 'block']:
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for fill in [True, False, np.nan]:
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arr = pd.SparseArray([1, 2, 3], kind=kind)
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indexer = pd.SparseArray([True, False, True], fill_value=fill,
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dtype=bool)
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tm.assert_sp_array_equal(pd.SparseArray([1, 3], kind=kind),
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arr[indexer])
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s = pd.SparseSeries(arr, index=['a', 'b', 'c'],
|
|
dtype=np.float64)
|
|
exp = pd.SparseSeries([1, 3], index=['a', 'c'],
|
|
dtype=np.float64, kind=kind)
|
|
tm.assert_sp_series_equal(s[indexer], exp)
|
|
tm.assert_sp_series_equal(s.loc[indexer], exp)
|
|
tm.assert_sp_series_equal(s.iloc[indexer], exp)
|
|
|
|
indexer = pd.SparseSeries(indexer, index=['a', 'b', 'c'])
|
|
tm.assert_sp_series_equal(s[indexer], exp)
|
|
tm.assert_sp_series_equal(s.loc[indexer], exp)
|
|
|
|
msg = ("iLocation based boolean indexing cannot use an "
|
|
"indexable as a mask")
|
|
with tm.assert_raises_regex(ValueError, msg):
|
|
s.iloc[indexer]
|
|
|
|
|
|
class TestSparseSeriesMultiIndexing(TestSparseSeriesIndexing):
|
|
|
|
def setup_method(self, method):
|
|
# Mi with duplicated values
|
|
idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0),
|
|
('C', 0), ('C', 1)])
|
|
self.orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=idx)
|
|
self.sparse = self.orig.to_sparse()
|
|
|
|
def test_getitem_multi(self):
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
assert sparse[0] == orig[0]
|
|
assert np.isnan(sparse[1])
|
|
assert sparse[3] == orig[3]
|
|
|
|
tm.assert_sp_series_equal(sparse['A'], orig['A'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse['B'], orig['B'].to_sparse())
|
|
|
|
result = sparse[[1, 3, 4]]
|
|
exp = orig[[1, 3, 4]].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# dense array
|
|
result = sparse[orig % 2 == 1]
|
|
exp = orig[orig % 2 == 1].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# sparse array (actuary it coerces to normal Series)
|
|
result = sparse[sparse % 2 == 1]
|
|
exp = orig[orig % 2 == 1].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# sparse array
|
|
result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
def test_getitem_multi_tuple(self):
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
assert sparse['C', 0] == orig['C', 0]
|
|
assert np.isnan(sparse['A', 1])
|
|
assert np.isnan(sparse['B', 0])
|
|
|
|
def test_getitems_slice_multi(self):
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
tm.assert_sp_series_equal(sparse[2:], orig[2:].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['B':], orig.loc['B':].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['C':], orig.loc['C':].to_sparse())
|
|
|
|
tm.assert_sp_series_equal(sparse.loc['A':'B'],
|
|
orig.loc['A':'B'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[:'B'], orig.loc[:'B'].to_sparse())
|
|
|
|
def test_loc(self):
|
|
# need to be override to use different label
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
tm.assert_sp_series_equal(sparse.loc['A'],
|
|
orig.loc['A'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['B'],
|
|
orig.loc['B'].to_sparse())
|
|
|
|
result = sparse.loc[[1, 3, 4]]
|
|
exp = orig.loc[[1, 3, 4]].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# exceeds the bounds
|
|
result = sparse.loc[[1, 3, 4, 5]]
|
|
exp = orig.loc[[1, 3, 4, 5]].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# single element list (GH 15447)
|
|
result = sparse.loc[['A']]
|
|
exp = orig.loc[['A']].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# dense array
|
|
result = sparse.loc[orig % 2 == 1]
|
|
exp = orig.loc[orig % 2 == 1].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# sparse array (actuary it coerces to normal Series)
|
|
result = sparse.loc[sparse % 2 == 1]
|
|
exp = orig.loc[orig % 2 == 1].to_sparse()
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
# sparse array
|
|
result = sparse.loc[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
|
|
tm.assert_sp_series_equal(result, exp)
|
|
|
|
def test_loc_multi_tuple(self):
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
assert sparse.loc['C', 0] == orig.loc['C', 0]
|
|
assert np.isnan(sparse.loc['A', 1])
|
|
assert np.isnan(sparse.loc['B', 0])
|
|
|
|
def test_loc_slice(self):
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
tm.assert_sp_series_equal(sparse.loc['A':], orig.loc['A':].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['B':], orig.loc['B':].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['C':], orig.loc['C':].to_sparse())
|
|
|
|
tm.assert_sp_series_equal(sparse.loc['A':'B'],
|
|
orig.loc['A':'B'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[:'B'], orig.loc[:'B'].to_sparse())
|
|
|
|
def test_reindex(self):
|
|
# GH 15447
|
|
orig = self.orig
|
|
sparse = self.sparse
|
|
|
|
res = sparse.reindex([('A', 0), ('C', 1)])
|
|
exp = orig.reindex([('A', 0), ('C', 1)]).to_sparse()
|
|
tm.assert_sp_series_equal(res, exp)
|
|
|
|
# On specific level:
|
|
res = sparse.reindex(['A', 'C', 'B'], level=0)
|
|
exp = orig.reindex(['A', 'C', 'B'], level=0).to_sparse()
|
|
tm.assert_sp_series_equal(res, exp)
|
|
|
|
# single element list (GH 15447)
|
|
res = sparse.reindex(['A'], level=0)
|
|
exp = orig.reindex(['A'], level=0).to_sparse()
|
|
tm.assert_sp_series_equal(res, exp)
|
|
|
|
with pytest.raises(TypeError):
|
|
# Incomplete keys are not accepted for reindexing:
|
|
sparse.reindex(['A', 'C'])
|
|
|
|
# "copy" argument:
|
|
res = sparse.reindex(sparse.index, copy=True)
|
|
exp = orig.reindex(orig.index, copy=True).to_sparse()
|
|
tm.assert_sp_series_equal(res, exp)
|
|
assert sparse is not res
|
|
|
|
|
|
class TestSparseDataFrameIndexing(object):
|
|
|
|
def test_getitem(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
tm.assert_sp_series_equal(sparse['x'], orig['x'].to_sparse())
|
|
tm.assert_sp_frame_equal(sparse[['x']], orig[['x']].to_sparse())
|
|
tm.assert_sp_frame_equal(sparse[['z', 'x']],
|
|
orig[['z', 'x']].to_sparse())
|
|
|
|
tm.assert_sp_frame_equal(sparse[[True, False, True, True]],
|
|
orig[[True, False, True, True]].to_sparse())
|
|
|
|
tm.assert_sp_frame_equal(sparse.iloc[[1, 2]],
|
|
orig.iloc[[1, 2]].to_sparse())
|
|
|
|
def test_getitem_fill_value(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
|
|
tm.assert_sp_series_equal(sparse['y'],
|
|
orig['y'].to_sparse(fill_value=0))
|
|
|
|
exp = orig[['x']].to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse[['x']], exp)
|
|
|
|
exp = orig[['z', 'x']].to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse[['z', 'x']], exp)
|
|
|
|
indexer = [True, False, True, True]
|
|
exp = orig[indexer].to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse[indexer], exp)
|
|
|
|
exp = orig.iloc[[1, 2]].to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse.iloc[[1, 2]], exp)
|
|
|
|
def test_loc(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
assert sparse.loc[0, 'x'] == 1
|
|
assert np.isnan(sparse.loc[1, 'z'])
|
|
assert sparse.loc[2, 'z'] == 4
|
|
|
|
tm.assert_sp_series_equal(sparse.loc[0], orig.loc[0].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[1], orig.loc[1].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[2, :],
|
|
orig.loc[2, :].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[2, :],
|
|
orig.loc[2, :].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[:, 'y'],
|
|
orig.loc[:, 'y'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[:, 'y'],
|
|
orig.loc[:, 'y'].to_sparse())
|
|
|
|
result = sparse.loc[[1, 2]]
|
|
exp = orig.loc[[1, 2]].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[[1, 2], :]
|
|
exp = orig.loc[[1, 2], :].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[:, ['x', 'z']]
|
|
exp = orig.loc[:, ['x', 'z']].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[[0, 2], ['x', 'z']]
|
|
exp = orig.loc[[0, 2], ['x', 'z']].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# exceeds the bounds
|
|
result = sparse.reindex([1, 3, 4, 5])
|
|
exp = orig.reindex([1, 3, 4, 5]).to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# dense array
|
|
result = sparse.loc[orig.x % 2 == 1]
|
|
exp = orig.loc[orig.x % 2 == 1].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# sparse array (actuary it coerces to normal Series)
|
|
result = sparse.loc[sparse.x % 2 == 1]
|
|
exp = orig.loc[orig.x % 2 == 1].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# sparse array
|
|
result = sparse.loc[pd.SparseArray(sparse.x % 2 == 1, dtype=bool)]
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
def test_loc_index(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4]],
|
|
index=list('abc'), columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
assert sparse.loc['a', 'x'] == 1
|
|
assert np.isnan(sparse.loc['b', 'z'])
|
|
assert sparse.loc['c', 'z'] == 4
|
|
|
|
tm.assert_sp_series_equal(sparse.loc['a'], orig.loc['a'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['b'], orig.loc['b'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['b', :],
|
|
orig.loc['b', :].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc['b', :],
|
|
orig.loc['b', :].to_sparse())
|
|
|
|
tm.assert_sp_series_equal(sparse.loc[:, 'z'],
|
|
orig.loc[:, 'z'].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.loc[:, 'z'],
|
|
orig.loc[:, 'z'].to_sparse())
|
|
|
|
result = sparse.loc[['a', 'b']]
|
|
exp = orig.loc[['a', 'b']].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[['a', 'b'], :]
|
|
exp = orig.loc[['a', 'b'], :].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[:, ['x', 'z']]
|
|
exp = orig.loc[:, ['x', 'z']].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.loc[['c', 'a'], ['x', 'z']]
|
|
exp = orig.loc[['c', 'a'], ['x', 'z']].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# dense array
|
|
result = sparse.loc[orig.x % 2 == 1]
|
|
exp = orig.loc[orig.x % 2 == 1].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# sparse array (actuary it coerces to normal Series)
|
|
result = sparse.loc[sparse.x % 2 == 1]
|
|
exp = orig.loc[orig.x % 2 == 1].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
# sparse array
|
|
result = sparse.loc[pd.SparseArray(sparse.x % 2 == 1, dtype=bool)]
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
def test_loc_slice(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
tm.assert_sp_frame_equal(sparse.loc[2:], orig.loc[2:].to_sparse())
|
|
|
|
def test_iloc(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4]])
|
|
sparse = orig.to_sparse()
|
|
|
|
assert sparse.iloc[1, 1] == 3
|
|
assert np.isnan(sparse.iloc[2, 0])
|
|
|
|
tm.assert_sp_series_equal(sparse.iloc[0], orig.loc[0].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.iloc[1], orig.loc[1].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.iloc[2, :],
|
|
orig.iloc[2, :].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.iloc[2, :],
|
|
orig.iloc[2, :].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.iloc[:, 1],
|
|
orig.iloc[:, 1].to_sparse())
|
|
tm.assert_sp_series_equal(sparse.iloc[:, 1],
|
|
orig.iloc[:, 1].to_sparse())
|
|
|
|
result = sparse.iloc[[1, 2]]
|
|
exp = orig.iloc[[1, 2]].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.iloc[[1, 2], :]
|
|
exp = orig.iloc[[1, 2], :].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.iloc[:, [1, 0]]
|
|
exp = orig.iloc[:, [1, 0]].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
result = sparse.iloc[[2], [1, 0]]
|
|
exp = orig.iloc[[2], [1, 0]].to_sparse()
|
|
tm.assert_sp_frame_equal(result, exp)
|
|
|
|
with pytest.raises(IndexError):
|
|
sparse.iloc[[1, 3, 5]]
|
|
|
|
def test_iloc_slice(self):
|
|
orig = pd.DataFrame([[1, np.nan, np.nan],
|
|
[2, 3, np.nan],
|
|
[np.nan, np.nan, 4]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
tm.assert_sp_frame_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse())
|
|
|
|
def test_at(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
assert sparse.at['A', 'x'] == orig.at['A', 'x']
|
|
assert np.isnan(sparse.at['B', 'z'])
|
|
assert np.isnan(sparse.at['C', 'y'])
|
|
assert sparse.at['D', 'x'] == orig.at['D', 'x']
|
|
|
|
def test_at_fill_value(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
assert sparse.at['A', 'x'] == orig.at['A', 'x']
|
|
assert np.isnan(sparse.at['B', 'z'])
|
|
assert np.isnan(sparse.at['C', 'y'])
|
|
assert sparse.at['D', 'x'] == orig.at['D', 'x']
|
|
|
|
def test_iat(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
assert sparse.iat[0, 0] == orig.iat[0, 0]
|
|
assert np.isnan(sparse.iat[1, 2])
|
|
assert np.isnan(sparse.iat[2, 1])
|
|
assert sparse.iat[2, 0] == orig.iat[2, 0]
|
|
|
|
assert np.isnan(sparse.iat[-1, -2])
|
|
assert sparse.iat[-1, -1] == orig.iat[-1, -1]
|
|
|
|
def test_iat_fill_value(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
assert sparse.iat[0, 0] == orig.iat[0, 0]
|
|
assert np.isnan(sparse.iat[1, 2])
|
|
assert np.isnan(sparse.iat[2, 1])
|
|
assert sparse.iat[2, 0] == orig.iat[2, 0]
|
|
|
|
assert np.isnan(sparse.iat[-1, -2])
|
|
assert sparse.iat[-1, -1] == orig.iat[-1, -1]
|
|
|
|
def test_take(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
tm.assert_sp_frame_equal(sparse.take([0]),
|
|
orig.take([0]).to_sparse())
|
|
tm.assert_sp_frame_equal(sparse.take([0, 1]),
|
|
orig.take([0, 1]).to_sparse())
|
|
tm.assert_sp_frame_equal(sparse.take([-1, -2]),
|
|
orig.take([-1, -2]).to_sparse())
|
|
|
|
def test_take_fill_value(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
|
|
exp = orig.take([0]).to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse.take([0]), exp)
|
|
|
|
exp = orig.take([0, 1]).to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse.take([0, 1]), exp)
|
|
|
|
exp = orig.take([-1, -2]).to_sparse(fill_value=0)
|
|
exp._default_fill_value = np.nan
|
|
tm.assert_sp_frame_equal(sparse.take([-1, -2]), exp)
|
|
|
|
def test_reindex(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
res = sparse.reindex(['A', 'C', 'B'])
|
|
exp = orig.reindex(['A', 'C', 'B']).to_sparse()
|
|
tm.assert_sp_frame_equal(res, exp)
|
|
|
|
orig = pd.DataFrame([[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse()
|
|
|
|
res = sparse.reindex(['A', 'C', 'B'])
|
|
exp = orig.reindex(['A', 'C', 'B']).to_sparse()
|
|
tm.assert_sp_frame_equal(res, exp)
|
|
|
|
def test_reindex_fill_value(self):
|
|
orig = pd.DataFrame([[1, np.nan, 0],
|
|
[2, 3, np.nan],
|
|
[0, np.nan, 4],
|
|
[0, np.nan, 5]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
|
|
res = sparse.reindex(['A', 'C', 'B'])
|
|
exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
|
|
tm.assert_sp_frame_equal(res, exp)
|
|
|
|
# all missing
|
|
orig = pd.DataFrame([[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan],
|
|
[np.nan, np.nan, np.nan]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
|
|
res = sparse.reindex(['A', 'C', 'B'])
|
|
exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
|
|
tm.assert_sp_frame_equal(res, exp)
|
|
|
|
# all fill_value
|
|
orig = pd.DataFrame([[0, 0, 0],
|
|
[0, 0, 0],
|
|
[0, 0, 0],
|
|
[0, 0, 0]],
|
|
index=list('ABCD'), columns=list('xyz'))
|
|
sparse = orig.to_sparse(fill_value=0)
|
|
|
|
res = sparse.reindex(['A', 'C', 'B'])
|
|
exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
|
|
tm.assert_sp_frame_equal(res, exp)
|
|
|
|
|
|
class TestMultitype(object):
|
|
|
|
def setup_method(self, method):
|
|
self.cols = ['string', 'int', 'float', 'object']
|
|
|
|
self.string_series = pd.SparseSeries(['a', 'b', 'c'])
|
|
self.int_series = pd.SparseSeries([1, 2, 3])
|
|
self.float_series = pd.SparseSeries([1.1, 1.2, 1.3])
|
|
self.object_series = pd.SparseSeries([[], {}, set()])
|
|
self.sdf = pd.SparseDataFrame({
|
|
'string': self.string_series,
|
|
'int': self.int_series,
|
|
'float': self.float_series,
|
|
'object': self.object_series,
|
|
})
|
|
self.sdf = self.sdf[self.cols]
|
|
self.ss = pd.SparseSeries(['a', 1, 1.1, []], index=self.cols)
|
|
|
|
def test_frame_basic_dtypes(self):
|
|
for _, row in self.sdf.iterrows():
|
|
assert row.dtype == object
|
|
tm.assert_sp_series_equal(self.sdf['string'], self.string_series,
|
|
check_names=False)
|
|
tm.assert_sp_series_equal(self.sdf['int'], self.int_series,
|
|
check_names=False)
|
|
tm.assert_sp_series_equal(self.sdf['float'], self.float_series,
|
|
check_names=False)
|
|
tm.assert_sp_series_equal(self.sdf['object'], self.object_series,
|
|
check_names=False)
|
|
|
|
def test_frame_indexing_single(self):
|
|
tm.assert_sp_series_equal(self.sdf.iloc[0],
|
|
pd.SparseSeries(['a', 1, 1.1, []],
|
|
index=self.cols),
|
|
check_names=False)
|
|
tm.assert_sp_series_equal(self.sdf.iloc[1],
|
|
pd.SparseSeries(['b', 2, 1.2, {}],
|
|
index=self.cols),
|
|
check_names=False)
|
|
tm.assert_sp_series_equal(self.sdf.iloc[2],
|
|
pd.SparseSeries(['c', 3, 1.3, set()],
|
|
index=self.cols),
|
|
check_names=False)
|
|
|
|
def test_frame_indexing_multiple(self):
|
|
tm.assert_sp_frame_equal(self.sdf, self.sdf[:])
|
|
tm.assert_sp_frame_equal(self.sdf, self.sdf.loc[:])
|
|
tm.assert_sp_frame_equal(self.sdf.iloc[[1, 2]],
|
|
pd.SparseDataFrame({
|
|
'string': self.string_series.iloc[[1, 2]],
|
|
'int': self.int_series.iloc[[1, 2]],
|
|
'float': self.float_series.iloc[[1, 2]],
|
|
'object': self.object_series.iloc[[1, 2]]
|
|
}, index=[1, 2])[self.cols])
|
|
tm.assert_sp_frame_equal(self.sdf[['int', 'string']],
|
|
pd.SparseDataFrame({
|
|
'int': self.int_series,
|
|
'string': self.string_series,
|
|
}))
|
|
|
|
def test_series_indexing_single(self):
|
|
for i, idx in enumerate(self.cols):
|
|
assert self.ss.iloc[i] == self.ss[idx]
|
|
tm.assert_class_equal(self.ss.iloc[i], self.ss[idx],
|
|
obj="series index")
|
|
|
|
assert self.ss['string'] == 'a'
|
|
assert self.ss['int'] == 1
|
|
assert self.ss['float'] == 1.1
|
|
assert self.ss['object'] == []
|
|
|
|
def test_series_indexing_multiple(self):
|
|
tm.assert_sp_series_equal(self.ss.loc[['string', 'int']],
|
|
pd.SparseSeries(['a', 1],
|
|
index=['string', 'int']))
|
|
tm.assert_sp_series_equal(self.ss.loc[['string', 'object']],
|
|
pd.SparseSeries(['a', []],
|
|
index=['string', 'object']))
|