1304 lines
48 KiB
Python
1304 lines
48 KiB
Python
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# pylint: disable-msg=E1101,W0612
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import operator
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import pytest
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from warnings import catch_warnings
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from numpy import nan
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import numpy as np
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import pandas as pd
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from pandas import Series, DataFrame, bdate_range, Panel
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from pandas.core.indexes.datetimes import DatetimeIndex
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from pandas.tseries.offsets import BDay
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from pandas.util import testing as tm
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from pandas.compat import lrange
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from pandas import compat
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from pandas.core.sparse import frame as spf
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from pandas._libs.sparse import BlockIndex, IntIndex
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from pandas.core.sparse.api import SparseSeries, SparseDataFrame, SparseArray
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from pandas.tests.frame.test_api import SharedWithSparse
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class TestSparseDataFrame(SharedWithSparse):
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klass = SparseDataFrame
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# SharedWithSparse tests use generic, klass-agnostic assertion
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_assert_frame_equal = staticmethod(tm.assert_sp_frame_equal)
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_assert_series_equal = staticmethod(tm.assert_sp_series_equal)
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def setup_method(self, method):
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self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
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'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
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'C': np.arange(10, dtype=np.float64),
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'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
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self.dates = bdate_range('1/1/2011', periods=10)
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self.orig = pd.DataFrame(self.data, index=self.dates)
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self.iorig = pd.DataFrame(self.data, index=self.dates)
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self.frame = SparseDataFrame(self.data, index=self.dates)
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self.iframe = SparseDataFrame(self.data, index=self.dates,
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default_kind='integer')
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self.mixed_frame = self.frame.copy(False)
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self.mixed_frame['foo'] = pd.SparseArray(['bar'] * len(self.dates))
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values = self.frame.values.copy()
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values[np.isnan(values)] = 0
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self.zorig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
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index=self.dates)
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self.zframe = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
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default_fill_value=0, index=self.dates)
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values = self.frame.values.copy()
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values[np.isnan(values)] = 2
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self.fill_orig = pd.DataFrame(values, columns=['A', 'B', 'C', 'D'],
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index=self.dates)
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self.fill_frame = SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
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default_fill_value=2,
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index=self.dates)
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self.empty = SparseDataFrame()
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def test_fill_value_when_combine_const(self):
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# GH12723
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dat = np.array([0, 1, np.nan, 3, 4, 5], dtype='float')
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df = SparseDataFrame({'foo': dat}, index=range(6))
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exp = df.fillna(0).add(2)
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res = df.add(2, fill_value=0)
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tm.assert_sp_frame_equal(res, exp)
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def test_values(self):
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empty = self.empty.values
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assert empty.shape == (0, 0)
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no_cols = SparseDataFrame(index=np.arange(10))
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mat = no_cols.values
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assert mat.shape == (10, 0)
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no_index = SparseDataFrame(columns=np.arange(10))
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mat = no_index.values
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assert mat.shape == (0, 10)
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def test_copy(self):
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cp = self.frame.copy()
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assert isinstance(cp, SparseDataFrame)
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tm.assert_sp_frame_equal(cp, self.frame)
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# as of v0.15.0
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# this is now identical (but not is_a )
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assert cp.index.identical(self.frame.index)
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def test_constructor(self):
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for col, series in compat.iteritems(self.frame):
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assert isinstance(series, SparseSeries)
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assert isinstance(self.iframe['A'].sp_index, IntIndex)
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# constructed zframe from matrix above
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assert self.zframe['A'].fill_value == 0
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tm.assert_numpy_array_equal(pd.SparseArray([1., 2., 3., 4., 5., 6.]),
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self.zframe['A'].values)
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tm.assert_numpy_array_equal(np.array([0., 0., 0., 0., 1., 2.,
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3., 4., 5., 6.]),
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self.zframe['A'].to_dense().values)
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# construct no data
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sdf = SparseDataFrame(columns=np.arange(10), index=np.arange(10))
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for col, series in compat.iteritems(sdf):
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assert isinstance(series, SparseSeries)
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# construct from nested dict
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data = {}
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for c, s in compat.iteritems(self.frame):
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data[c] = s.to_dict()
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sdf = SparseDataFrame(data)
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tm.assert_sp_frame_equal(sdf, self.frame)
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# TODO: test data is copied from inputs
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# init dict with different index
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idx = self.frame.index[:5]
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cons = SparseDataFrame(
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self.frame, index=idx, columns=self.frame.columns,
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default_fill_value=self.frame.default_fill_value,
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default_kind=self.frame.default_kind, copy=True)
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reindexed = self.frame.reindex(idx)
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tm.assert_sp_frame_equal(cons, reindexed, exact_indices=False)
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# assert level parameter breaks reindex
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with pytest.raises(TypeError):
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self.frame.reindex(idx, level=0)
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repr(self.frame)
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def test_constructor_dict_order(self):
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# GH19018
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# initialization ordering: by insertion order if python>= 3.6, else
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# order by value
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d = {'b': [2, 3], 'a': [0, 1]}
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frame = SparseDataFrame(data=d)
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if compat.PY36:
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expected = SparseDataFrame(data=d, columns=list('ba'))
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else:
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expected = SparseDataFrame(data=d, columns=list('ab'))
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tm.assert_sp_frame_equal(frame, expected)
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def test_constructor_ndarray(self):
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# no index or columns
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sp = SparseDataFrame(self.frame.values)
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# 1d
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sp = SparseDataFrame(self.data['A'], index=self.dates, columns=['A'])
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tm.assert_sp_frame_equal(sp, self.frame.reindex(columns=['A']))
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# raise on level argument
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pytest.raises(TypeError, self.frame.reindex, columns=['A'],
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level=1)
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# wrong length index / columns
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with tm.assert_raises_regex(ValueError, "^Index length"):
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SparseDataFrame(self.frame.values, index=self.frame.index[:-1])
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with tm.assert_raises_regex(ValueError, "^Column length"):
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SparseDataFrame(self.frame.values, columns=self.frame.columns[:-1])
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# GH 9272
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def test_constructor_empty(self):
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sp = SparseDataFrame()
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assert len(sp.index) == 0
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assert len(sp.columns) == 0
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def test_constructor_dataframe(self):
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dense = self.frame.to_dense()
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sp = SparseDataFrame(dense)
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tm.assert_sp_frame_equal(sp, self.frame)
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def test_constructor_convert_index_once(self):
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arr = np.array([1.5, 2.5, 3.5])
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sdf = SparseDataFrame(columns=lrange(4), index=arr)
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assert sdf[0].index is sdf[1].index
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def test_constructor_from_series(self):
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# GH 2873
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x = Series(np.random.randn(10000), name='a')
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x = x.to_sparse(fill_value=0)
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assert isinstance(x, SparseSeries)
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df = SparseDataFrame(x)
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assert isinstance(df, SparseDataFrame)
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x = Series(np.random.randn(10000), name='a')
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y = Series(np.random.randn(10000), name='b')
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x2 = x.astype(float)
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x2.loc[:9998] = np.NaN
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# TODO: x_sparse is unused...fix
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x_sparse = x2.to_sparse(fill_value=np.NaN) # noqa
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# Currently fails too with weird ufunc error
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# df1 = SparseDataFrame([x_sparse, y])
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y.loc[:9998] = 0
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# TODO: y_sparse is unsused...fix
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y_sparse = y.to_sparse(fill_value=0) # noqa
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# without sparse value raises error
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# df2 = SparseDataFrame([x2_sparse, y])
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def test_constructor_from_dense_series(self):
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# GH 19393
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# series with name
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x = Series(np.random.randn(10000), name='a')
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result = SparseDataFrame(x)
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expected = x.to_frame().to_sparse()
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tm.assert_sp_frame_equal(result, expected)
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# series with no name
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x = Series(np.random.randn(10000))
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result = SparseDataFrame(x)
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expected = x.to_frame().to_sparse()
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tm.assert_sp_frame_equal(result, expected)
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def test_constructor_from_unknown_type(self):
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# GH 19393
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class Unknown(object):
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pass
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with pytest.raises(TypeError,
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message='SparseDataFrame called with unknown type '
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'"Unknown" for data argument'):
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SparseDataFrame(Unknown())
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def test_constructor_preserve_attr(self):
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# GH 13866
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arr = pd.SparseArray([1, 0, 3, 0], dtype=np.int64, fill_value=0)
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assert arr.dtype == np.int64
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assert arr.fill_value == 0
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df = pd.SparseDataFrame({'x': arr})
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assert df['x'].dtype == np.int64
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assert df['x'].fill_value == 0
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s = pd.SparseSeries(arr, name='x')
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assert s.dtype == np.int64
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assert s.fill_value == 0
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df = pd.SparseDataFrame(s)
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assert df['x'].dtype == np.int64
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assert df['x'].fill_value == 0
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df = pd.SparseDataFrame({'x': s})
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assert df['x'].dtype == np.int64
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assert df['x'].fill_value == 0
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def test_constructor_nan_dataframe(self):
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# GH 10079
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trains = np.arange(100)
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thresholds = [10, 20, 30, 40, 50, 60]
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tuples = [(i, j) for i in trains for j in thresholds]
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index = pd.MultiIndex.from_tuples(tuples,
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names=['trains', 'thresholds'])
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matrix = np.empty((len(index), len(trains)))
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matrix.fill(np.nan)
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df = pd.DataFrame(matrix, index=index, columns=trains, dtype=float)
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result = df.to_sparse()
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expected = pd.SparseDataFrame(matrix, index=index, columns=trains,
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dtype=float)
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tm.assert_sp_frame_equal(result, expected)
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def test_type_coercion_at_construction(self):
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# GH 15682
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result = pd.SparseDataFrame(
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{'a': [1, 0, 0], 'b': [0, 1, 0], 'c': [0, 0, 1]}, dtype='uint8',
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default_fill_value=0)
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expected = pd.SparseDataFrame(
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{'a': pd.SparseSeries([1, 0, 0], dtype='uint8'),
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'b': pd.SparseSeries([0, 1, 0], dtype='uint8'),
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'c': pd.SparseSeries([0, 0, 1], dtype='uint8')},
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default_fill_value=0)
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tm.assert_sp_frame_equal(result, expected)
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def test_dtypes(self):
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df = DataFrame(np.random.randn(10000, 4))
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df.loc[:9998] = np.nan
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sdf = df.to_sparse()
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result = sdf.get_dtype_counts()
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expected = Series({'float64': 4})
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tm.assert_series_equal(result, expected)
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def test_shape(self):
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# see gh-10452
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assert self.frame.shape == (10, 4)
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assert self.iframe.shape == (10, 4)
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assert self.zframe.shape == (10, 4)
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assert self.fill_frame.shape == (10, 4)
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def test_str(self):
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df = DataFrame(np.random.randn(10000, 4))
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df.loc[:9998] = np.nan
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sdf = df.to_sparse()
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str(sdf)
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def test_array_interface(self):
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res = np.sqrt(self.frame)
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dres = np.sqrt(self.frame.to_dense())
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tm.assert_frame_equal(res.to_dense(), dres)
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def test_pickle(self):
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def _test_roundtrip(frame, orig):
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result = tm.round_trip_pickle(frame)
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tm.assert_sp_frame_equal(frame, result)
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tm.assert_frame_equal(result.to_dense(), orig, check_dtype=False)
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_test_roundtrip(SparseDataFrame(), DataFrame())
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self._check_all(_test_roundtrip)
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def test_dense_to_sparse(self):
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df = DataFrame({'A': [nan, nan, nan, 1, 2],
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'B': [1, 2, nan, nan, nan]})
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sdf = df.to_sparse()
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assert isinstance(sdf, SparseDataFrame)
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assert np.isnan(sdf.default_fill_value)
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assert isinstance(sdf['A'].sp_index, BlockIndex)
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tm.assert_frame_equal(sdf.to_dense(), df)
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sdf = df.to_sparse(kind='integer')
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assert isinstance(sdf['A'].sp_index, IntIndex)
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df = DataFrame({'A': [0, 0, 0, 1, 2],
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'B': [1, 2, 0, 0, 0]}, dtype=float)
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sdf = df.to_sparse(fill_value=0)
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assert sdf.default_fill_value == 0
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tm.assert_frame_equal(sdf.to_dense(), df)
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def test_density(self):
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df = SparseSeries([nan, nan, nan, 0, 1, 2, 3, 4, 5, 6])
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assert df.density == 0.7
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df = SparseDataFrame({'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
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|
|
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
|
||
|
|
'C': np.arange(10),
|
||
|
|
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]})
|
||
|
|
|
||
|
|
assert df.density == 0.75
|
||
|
|
|
||
|
|
def test_sparse_to_dense(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_sparse_series_ops(self):
|
||
|
|
self._check_frame_ops(self.frame)
|
||
|
|
|
||
|
|
def test_sparse_series_ops_i(self):
|
||
|
|
self._check_frame_ops(self.iframe)
|
||
|
|
|
||
|
|
def test_sparse_series_ops_z(self):
|
||
|
|
self._check_frame_ops(self.zframe)
|
||
|
|
|
||
|
|
def test_sparse_series_ops_fill(self):
|
||
|
|
self._check_frame_ops(self.fill_frame)
|
||
|
|
|
||
|
|
def _check_frame_ops(self, frame):
|
||
|
|
|
||
|
|
def _compare_to_dense(a, b, da, db, op):
|
||
|
|
sparse_result = op(a, b)
|
||
|
|
dense_result = op(da, db)
|
||
|
|
|
||
|
|
fill = sparse_result.default_fill_value
|
||
|
|
dense_result = dense_result.to_sparse(fill_value=fill)
|
||
|
|
tm.assert_sp_frame_equal(sparse_result, dense_result,
|
||
|
|
exact_indices=False)
|
||
|
|
|
||
|
|
if isinstance(a, DataFrame) and isinstance(db, DataFrame):
|
||
|
|
mixed_result = op(a, db)
|
||
|
|
assert isinstance(mixed_result, SparseDataFrame)
|
||
|
|
tm.assert_sp_frame_equal(mixed_result, sparse_result,
|
||
|
|
exact_indices=False)
|
||
|
|
|
||
|
|
opnames = ['add', 'sub', 'mul', 'truediv', 'floordiv']
|
||
|
|
ops = [getattr(operator, name) for name in opnames]
|
||
|
|
|
||
|
|
fidx = frame.index
|
||
|
|
|
||
|
|
# time series operations
|
||
|
|
|
||
|
|
series = [frame['A'], frame['B'], frame['C'], frame['D'],
|
||
|
|
frame['A'].reindex(fidx[:7]), frame['A'].reindex(fidx[::2]),
|
||
|
|
SparseSeries(
|
||
|
|
[], index=[])]
|
||
|
|
|
||
|
|
for op in opnames:
|
||
|
|
_compare_to_dense(frame, frame[::2], frame.to_dense(),
|
||
|
|
frame[::2].to_dense(), getattr(operator, op))
|
||
|
|
|
||
|
|
# 2304, no auto-broadcasting
|
||
|
|
for i, s in enumerate(series):
|
||
|
|
f = lambda a, b: getattr(a, op)(b, axis='index')
|
||
|
|
_compare_to_dense(frame, s, frame.to_dense(), s.to_dense(), f)
|
||
|
|
|
||
|
|
# rops are not implemented
|
||
|
|
# _compare_to_dense(s, frame, s.to_dense(),
|
||
|
|
# frame.to_dense(), f)
|
||
|
|
|
||
|
|
# cross-sectional operations
|
||
|
|
series = [frame.xs(fidx[0]), frame.xs(fidx[3]), frame.xs(fidx[5]),
|
||
|
|
frame.xs(fidx[7]), frame.xs(fidx[5])[:2]]
|
||
|
|
|
||
|
|
for op in ops:
|
||
|
|
for s in series:
|
||
|
|
_compare_to_dense(frame, s, frame.to_dense(), s, op)
|
||
|
|
_compare_to_dense(s, frame, s, frame.to_dense(), op)
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
result = self.frame + self.frame.loc[:, ['A', 'B']] # noqa
|
||
|
|
|
||
|
|
def test_op_corners(self):
|
||
|
|
empty = self.empty + self.empty
|
||
|
|
assert empty.empty
|
||
|
|
|
||
|
|
foo = self.frame + self.empty
|
||
|
|
assert isinstance(foo.index, DatetimeIndex)
|
||
|
|
tm.assert_frame_equal(foo, self.frame * np.nan)
|
||
|
|
|
||
|
|
foo = self.empty + self.frame
|
||
|
|
tm.assert_frame_equal(foo, self.frame * np.nan)
|
||
|
|
|
||
|
|
def test_scalar_ops(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def test_getitem(self):
|
||
|
|
# 1585 select multiple columns
|
||
|
|
sdf = SparseDataFrame(index=[0, 1, 2], columns=['a', 'b', 'c'])
|
||
|
|
|
||
|
|
result = sdf[['a', 'b']]
|
||
|
|
exp = sdf.reindex(columns=['a', 'b'])
|
||
|
|
tm.assert_sp_frame_equal(result, exp)
|
||
|
|
|
||
|
|
pytest.raises(Exception, sdf.__getitem__, ['a', 'd'])
|
||
|
|
|
||
|
|
def test_iloc(self):
|
||
|
|
|
||
|
|
# 2227
|
||
|
|
result = self.frame.iloc[:, 0]
|
||
|
|
assert isinstance(result, SparseSeries)
|
||
|
|
tm.assert_sp_series_equal(result, self.frame['A'])
|
||
|
|
|
||
|
|
# preserve sparse index type. #2251
|
||
|
|
data = {'A': [0, 1]}
|
||
|
|
iframe = SparseDataFrame(data, default_kind='integer')
|
||
|
|
tm.assert_class_equal(iframe['A'].sp_index,
|
||
|
|
iframe.iloc[:, 0].sp_index)
|
||
|
|
|
||
|
|
def test_set_value(self):
|
||
|
|
|
||
|
|
# ok, as the index gets converted to object
|
||
|
|
frame = self.frame.copy()
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
res = frame.set_value('foobar', 'B', 1.5)
|
||
|
|
assert res.index.dtype == 'object'
|
||
|
|
|
||
|
|
res = self.frame
|
||
|
|
res.index = res.index.astype(object)
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
res = self.frame.set_value('foobar', 'B', 1.5)
|
||
|
|
assert res is not self.frame
|
||
|
|
assert res.index[-1] == 'foobar'
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
assert res.get_value('foobar', 'B') == 1.5
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
res2 = res.set_value('foobar', 'qux', 1.5)
|
||
|
|
assert res2 is not res
|
||
|
|
tm.assert_index_equal(res2.columns,
|
||
|
|
pd.Index(list(self.frame.columns) + ['qux']))
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
assert res2.get_value('foobar', 'qux') == 1.5
|
||
|
|
|
||
|
|
def test_fancy_index_misc(self):
|
||
|
|
# axis = 0
|
||
|
|
sliced = self.frame.iloc[-2:, :]
|
||
|
|
expected = self.frame.reindex(index=self.frame.index[-2:])
|
||
|
|
tm.assert_sp_frame_equal(sliced, expected)
|
||
|
|
|
||
|
|
# axis = 1
|
||
|
|
sliced = self.frame.iloc[:, -2:]
|
||
|
|
expected = self.frame.reindex(columns=self.frame.columns[-2:])
|
||
|
|
tm.assert_sp_frame_equal(sliced, expected)
|
||
|
|
|
||
|
|
def test_getitem_overload(self):
|
||
|
|
# slicing
|
||
|
|
sl = self.frame[:20]
|
||
|
|
tm.assert_sp_frame_equal(sl, self.frame.reindex(self.frame.index[:20]))
|
||
|
|
|
||
|
|
# boolean indexing
|
||
|
|
d = self.frame.index[5]
|
||
|
|
indexer = self.frame.index > d
|
||
|
|
|
||
|
|
subindex = self.frame.index[indexer]
|
||
|
|
subframe = self.frame[indexer]
|
||
|
|
|
||
|
|
tm.assert_index_equal(subindex, subframe.index)
|
||
|
|
pytest.raises(Exception, self.frame.__getitem__, indexer[:-1])
|
||
|
|
|
||
|
|
def test_setitem(self):
|
||
|
|
|
||
|
|
def _check_frame(frame, orig):
|
||
|
|
N = len(frame)
|
||
|
|
|
||
|
|
# insert SparseSeries
|
||
|
|
frame['E'] = frame['A']
|
||
|
|
assert isinstance(frame['E'], SparseSeries)
|
||
|
|
tm.assert_sp_series_equal(frame['E'], frame['A'],
|
||
|
|
check_names=False)
|
||
|
|
|
||
|
|
# insert SparseSeries differently-indexed
|
||
|
|
to_insert = frame['A'][::2]
|
||
|
|
frame['E'] = to_insert
|
||
|
|
expected = to_insert.to_dense().reindex(frame.index)
|
||
|
|
result = frame['E'].to_dense()
|
||
|
|
tm.assert_series_equal(result, expected, check_names=False)
|
||
|
|
assert result.name == 'E'
|
||
|
|
|
||
|
|
# insert Series
|
||
|
|
frame['F'] = frame['A'].to_dense()
|
||
|
|
assert isinstance(frame['F'], SparseSeries)
|
||
|
|
tm.assert_sp_series_equal(frame['F'], frame['A'],
|
||
|
|
check_names=False)
|
||
|
|
|
||
|
|
# insert Series differently-indexed
|
||
|
|
to_insert = frame['A'].to_dense()[::2]
|
||
|
|
frame['G'] = to_insert
|
||
|
|
expected = to_insert.reindex(frame.index)
|
||
|
|
expected.name = 'G'
|
||
|
|
tm.assert_series_equal(frame['G'].to_dense(), expected)
|
||
|
|
|
||
|
|
# insert ndarray
|
||
|
|
frame['H'] = np.random.randn(N)
|
||
|
|
assert isinstance(frame['H'], SparseSeries)
|
||
|
|
|
||
|
|
to_sparsify = np.random.randn(N)
|
||
|
|
to_sparsify[N // 2:] = frame.default_fill_value
|
||
|
|
frame['I'] = to_sparsify
|
||
|
|
assert len(frame['I'].sp_values) == N // 2
|
||
|
|
|
||
|
|
# insert ndarray wrong size
|
||
|
|
pytest.raises(Exception, frame.__setitem__, 'foo',
|
||
|
|
np.random.randn(N - 1))
|
||
|
|
|
||
|
|
# scalar value
|
||
|
|
frame['J'] = 5
|
||
|
|
assert len(frame['J'].sp_values) == N
|
||
|
|
assert (frame['J'].sp_values == 5).all()
|
||
|
|
|
||
|
|
frame['K'] = frame.default_fill_value
|
||
|
|
assert len(frame['K'].sp_values) == 0
|
||
|
|
|
||
|
|
self._check_all(_check_frame)
|
||
|
|
|
||
|
|
def test_setitem_corner(self):
|
||
|
|
self.frame['a'] = self.frame['B']
|
||
|
|
tm.assert_sp_series_equal(self.frame['a'], self.frame['B'],
|
||
|
|
check_names=False)
|
||
|
|
|
||
|
|
def test_setitem_array(self):
|
||
|
|
arr = self.frame['B']
|
||
|
|
|
||
|
|
self.frame['E'] = arr
|
||
|
|
tm.assert_sp_series_equal(self.frame['E'], self.frame['B'],
|
||
|
|
check_names=False)
|
||
|
|
|
||
|
|
self.frame['F'] = arr[:-1]
|
||
|
|
index = self.frame.index[:-1]
|
||
|
|
tm.assert_sp_series_equal(self.frame['E'].reindex(index),
|
||
|
|
self.frame['F'].reindex(index),
|
||
|
|
check_names=False)
|
||
|
|
|
||
|
|
def test_setitem_chained_no_consolidate(self):
|
||
|
|
# https://github.com/pandas-dev/pandas/pull/19268
|
||
|
|
# issuecomment-361696418
|
||
|
|
# chained setitem used to cause consolidation
|
||
|
|
sdf = pd.SparseDataFrame([[np.nan, 1], [2, np.nan]])
|
||
|
|
with pd.option_context('mode.chained_assignment', None):
|
||
|
|
sdf[0][1] = 2
|
||
|
|
assert len(sdf._data.blocks) == 2
|
||
|
|
|
||
|
|
def test_delitem(self):
|
||
|
|
A = self.frame['A']
|
||
|
|
C = self.frame['C']
|
||
|
|
|
||
|
|
del self.frame['B']
|
||
|
|
assert 'B' not in self.frame
|
||
|
|
tm.assert_sp_series_equal(self.frame['A'], A)
|
||
|
|
tm.assert_sp_series_equal(self.frame['C'], C)
|
||
|
|
|
||
|
|
del self.frame['D']
|
||
|
|
assert 'D' not in self.frame
|
||
|
|
|
||
|
|
del self.frame['A']
|
||
|
|
assert 'A' not in self.frame
|
||
|
|
|
||
|
|
def test_set_columns(self):
|
||
|
|
self.frame.columns = self.frame.columns
|
||
|
|
pytest.raises(Exception, setattr, self.frame, 'columns',
|
||
|
|
self.frame.columns[:-1])
|
||
|
|
|
||
|
|
def test_set_index(self):
|
||
|
|
self.frame.index = self.frame.index
|
||
|
|
pytest.raises(Exception, setattr, self.frame, 'index',
|
||
|
|
self.frame.index[:-1])
|
||
|
|
|
||
|
|
def test_append(self):
|
||
|
|
a = self.frame[:5]
|
||
|
|
b = self.frame[5:]
|
||
|
|
|
||
|
|
appended = a.append(b)
|
||
|
|
tm.assert_sp_frame_equal(appended, self.frame, exact_indices=False)
|
||
|
|
|
||
|
|
a = self.frame.iloc[:5, :3]
|
||
|
|
b = self.frame.iloc[5:]
|
||
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
||
|
|
# Stacklevel is set for pd.concat, not append
|
||
|
|
appended = a.append(b)
|
||
|
|
tm.assert_sp_frame_equal(appended.iloc[:, :3], self.frame.iloc[:, :3],
|
||
|
|
exact_indices=False)
|
||
|
|
|
||
|
|
a = a[['B', 'C', 'A']].head(2)
|
||
|
|
b = b.head(2)
|
||
|
|
|
||
|
|
expected = pd.SparseDataFrame({
|
||
|
|
"B": [0., 1, None, 3],
|
||
|
|
"C": [0., 1, 5, 6],
|
||
|
|
"A": [None, None, 2, 3],
|
||
|
|
"D": [None, None, 5, None],
|
||
|
|
}, index=a.index | b.index, columns=['B', 'C', 'A', 'D'])
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
appended = a.append(b, sort=False)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(appended, expected)
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
appended = a.append(b, sort=True)
|
||
|
|
|
||
|
|
tm.assert_sp_frame_equal(appended, expected[['A', 'B', 'C', 'D']])
|
||
|
|
|
||
|
|
def test_astype(self):
|
||
|
|
sparse = pd.SparseDataFrame({'A': SparseArray([1, 2, 3, 4],
|
||
|
|
dtype=np.int64),
|
||
|
|
'B': SparseArray([4, 5, 6, 7],
|
||
|
|
dtype=np.int64)})
|
||
|
|
assert sparse['A'].dtype == np.int64
|
||
|
|
assert sparse['B'].dtype == np.int64
|
||
|
|
|
||
|
|
res = sparse.astype(np.float64)
|
||
|
|
exp = pd.SparseDataFrame({'A': SparseArray([1., 2., 3., 4.],
|
||
|
|
fill_value=0.),
|
||
|
|
'B': SparseArray([4., 5., 6., 7.],
|
||
|
|
fill_value=0.)},
|
||
|
|
default_fill_value=np.nan)
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
assert res['A'].dtype == np.float64
|
||
|
|
assert res['B'].dtype == np.float64
|
||
|
|
|
||
|
|
sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
|
||
|
|
dtype=np.int64),
|
||
|
|
'B': SparseArray([0, 5, 0, 7],
|
||
|
|
dtype=np.int64)},
|
||
|
|
default_fill_value=0)
|
||
|
|
assert sparse['A'].dtype == np.int64
|
||
|
|
assert sparse['B'].dtype == np.int64
|
||
|
|
|
||
|
|
res = sparse.astype(np.float64)
|
||
|
|
exp = pd.SparseDataFrame({'A': SparseArray([0., 2., 0., 4.],
|
||
|
|
fill_value=0.),
|
||
|
|
'B': SparseArray([0., 5., 0., 7.],
|
||
|
|
fill_value=0.)},
|
||
|
|
default_fill_value=0.)
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
assert res['A'].dtype == np.float64
|
||
|
|
assert res['B'].dtype == np.float64
|
||
|
|
|
||
|
|
def test_astype_bool(self):
|
||
|
|
sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
|
||
|
|
fill_value=0,
|
||
|
|
dtype=np.int64),
|
||
|
|
'B': SparseArray([0, 5, 0, 7],
|
||
|
|
fill_value=0,
|
||
|
|
dtype=np.int64)},
|
||
|
|
default_fill_value=0)
|
||
|
|
assert sparse['A'].dtype == np.int64
|
||
|
|
assert sparse['B'].dtype == np.int64
|
||
|
|
|
||
|
|
res = sparse.astype(bool)
|
||
|
|
exp = pd.SparseDataFrame({'A': SparseArray([False, True, False, True],
|
||
|
|
dtype=np.bool,
|
||
|
|
fill_value=False),
|
||
|
|
'B': SparseArray([False, True, False, True],
|
||
|
|
dtype=np.bool,
|
||
|
|
fill_value=False)},
|
||
|
|
default_fill_value=False)
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
assert res['A'].dtype == np.bool
|
||
|
|
assert res['B'].dtype == np.bool
|
||
|
|
|
||
|
|
def test_fillna(self):
|
||
|
|
df = self.zframe.reindex(lrange(5))
|
||
|
|
dense = self.zorig.reindex(lrange(5))
|
||
|
|
|
||
|
|
result = df.fillna(0)
|
||
|
|
expected = dense.fillna(0)
|
||
|
|
tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0),
|
||
|
|
exact_indices=False)
|
||
|
|
tm.assert_frame_equal(result.to_dense(), expected)
|
||
|
|
|
||
|
|
result = df.copy()
|
||
|
|
result.fillna(0, inplace=True)
|
||
|
|
expected = dense.fillna(0)
|
||
|
|
|
||
|
|
tm.assert_sp_frame_equal(result, expected.to_sparse(fill_value=0),
|
||
|
|
exact_indices=False)
|
||
|
|
tm.assert_frame_equal(result.to_dense(), expected)
|
||
|
|
|
||
|
|
result = df.copy()
|
||
|
|
result = df['A']
|
||
|
|
result.fillna(0, inplace=True)
|
||
|
|
|
||
|
|
expected = dense['A'].fillna(0)
|
||
|
|
# this changes internal SparseArray repr
|
||
|
|
# tm.assert_sp_series_equal(result, expected.to_sparse(fill_value=0))
|
||
|
|
tm.assert_series_equal(result.to_dense(), expected)
|
||
|
|
|
||
|
|
def test_fillna_fill_value(self):
|
||
|
|
df = pd.DataFrame({'A': [1, 0, 0], 'B': [np.nan, np.nan, 4]})
|
||
|
|
|
||
|
|
sparse = pd.SparseDataFrame(df)
|
||
|
|
tm.assert_frame_equal(sparse.fillna(-1).to_dense(),
|
||
|
|
df.fillna(-1), check_dtype=False)
|
||
|
|
|
||
|
|
sparse = pd.SparseDataFrame(df, default_fill_value=0)
|
||
|
|
tm.assert_frame_equal(sparse.fillna(-1).to_dense(),
|
||
|
|
df.fillna(-1), check_dtype=False)
|
||
|
|
|
||
|
|
def test_sparse_frame_pad_backfill_limit(self):
|
||
|
|
index = np.arange(10)
|
||
|
|
df = DataFrame(np.random.randn(10, 4), index=index)
|
||
|
|
sdf = df.to_sparse()
|
||
|
|
|
||
|
|
result = sdf[:2].reindex(index, method='pad', limit=5)
|
||
|
|
|
||
|
|
expected = sdf[:2].reindex(index).fillna(method='pad')
|
||
|
|
expected = expected.to_dense()
|
||
|
|
expected.values[-3:] = np.nan
|
||
|
|
expected = expected.to_sparse()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = sdf[-2:].reindex(index, method='backfill', limit=5)
|
||
|
|
|
||
|
|
expected = sdf[-2:].reindex(index).fillna(method='backfill')
|
||
|
|
expected = expected.to_dense()
|
||
|
|
expected.values[:3] = np.nan
|
||
|
|
expected = expected.to_sparse()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_sparse_frame_fillna_limit(self):
|
||
|
|
index = np.arange(10)
|
||
|
|
df = DataFrame(np.random.randn(10, 4), index=index)
|
||
|
|
sdf = df.to_sparse()
|
||
|
|
|
||
|
|
result = sdf[:2].reindex(index)
|
||
|
|
result = result.fillna(method='pad', limit=5)
|
||
|
|
|
||
|
|
expected = sdf[:2].reindex(index).fillna(method='pad')
|
||
|
|
expected = expected.to_dense()
|
||
|
|
expected.values[-3:] = np.nan
|
||
|
|
expected = expected.to_sparse()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = sdf[-2:].reindex(index)
|
||
|
|
result = result.fillna(method='backfill', limit=5)
|
||
|
|
|
||
|
|
expected = sdf[-2:].reindex(index).fillna(method='backfill')
|
||
|
|
expected = expected.to_dense()
|
||
|
|
expected.values[:3] = np.nan
|
||
|
|
expected = expected.to_sparse()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rename(self):
|
||
|
|
result = self.frame.rename(index=str)
|
||
|
|
expected = SparseDataFrame(self.data, index=self.dates.strftime(
|
||
|
|
"%Y-%m-%d %H:%M:%S"))
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.rename(columns=lambda x: '%s%d' % (x, len(x)))
|
||
|
|
data = {'A1': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
|
||
|
|
'B1': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
|
||
|
|
'C1': np.arange(10, dtype=np.float64),
|
||
|
|
'D1': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
|
||
|
|
expected = SparseDataFrame(data, index=self.dates)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_corr(self):
|
||
|
|
res = self.frame.corr()
|
||
|
|
tm.assert_frame_equal(res, self.frame.to_dense().corr())
|
||
|
|
|
||
|
|
def test_describe(self):
|
||
|
|
self.frame['foo'] = np.nan
|
||
|
|
self.frame.get_dtype_counts()
|
||
|
|
str(self.frame)
|
||
|
|
desc = self.frame.describe() # noqa
|
||
|
|
|
||
|
|
def test_join(self):
|
||
|
|
left = self.frame.loc[:, ['A', 'B']]
|
||
|
|
right = self.frame.loc[:, ['C', 'D']]
|
||
|
|
joined = left.join(right)
|
||
|
|
tm.assert_sp_frame_equal(joined, self.frame, exact_indices=False)
|
||
|
|
|
||
|
|
right = self.frame.loc[:, ['B', 'D']]
|
||
|
|
pytest.raises(Exception, left.join, right)
|
||
|
|
|
||
|
|
with tm.assert_raises_regex(ValueError,
|
||
|
|
'Other Series must have a name'):
|
||
|
|
self.frame.join(Series(
|
||
|
|
np.random.randn(len(self.frame)), index=self.frame.index))
|
||
|
|
|
||
|
|
def test_reindex(self):
|
||
|
|
|
||
|
|
def _check_frame(frame):
|
||
|
|
index = frame.index
|
||
|
|
sidx = index[::2]
|
||
|
|
sidx2 = index[:5] # noqa
|
||
|
|
|
||
|
|
sparse_result = frame.reindex(sidx)
|
||
|
|
dense_result = frame.to_dense().reindex(sidx)
|
||
|
|
tm.assert_frame_equal(sparse_result.to_dense(), dense_result)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(frame.reindex(list(sidx)).to_dense(),
|
||
|
|
dense_result)
|
||
|
|
|
||
|
|
sparse_result2 = sparse_result.reindex(index)
|
||
|
|
dense_result2 = dense_result.reindex(index)
|
||
|
|
tm.assert_frame_equal(sparse_result2.to_dense(), dense_result2)
|
||
|
|
|
||
|
|
# propagate CORRECT fill value
|
||
|
|
tm.assert_almost_equal(sparse_result.default_fill_value,
|
||
|
|
frame.default_fill_value)
|
||
|
|
tm.assert_almost_equal(sparse_result['A'].fill_value,
|
||
|
|
frame['A'].fill_value)
|
||
|
|
|
||
|
|
# length zero
|
||
|
|
length_zero = frame.reindex([])
|
||
|
|
assert len(length_zero) == 0
|
||
|
|
assert len(length_zero.columns) == len(frame.columns)
|
||
|
|
assert len(length_zero['A']) == 0
|
||
|
|
|
||
|
|
# frame being reindexed has length zero
|
||
|
|
length_n = length_zero.reindex(index)
|
||
|
|
assert len(length_n) == len(frame)
|
||
|
|
assert len(length_n.columns) == len(frame.columns)
|
||
|
|
assert len(length_n['A']) == len(frame)
|
||
|
|
|
||
|
|
# reindex columns
|
||
|
|
reindexed = frame.reindex(columns=['A', 'B', 'Z'])
|
||
|
|
assert len(reindexed.columns) == 3
|
||
|
|
tm.assert_almost_equal(reindexed['Z'].fill_value,
|
||
|
|
frame.default_fill_value)
|
||
|
|
assert np.isnan(reindexed['Z'].sp_values).all()
|
||
|
|
|
||
|
|
_check_frame(self.frame)
|
||
|
|
_check_frame(self.iframe)
|
||
|
|
_check_frame(self.zframe)
|
||
|
|
_check_frame(self.fill_frame)
|
||
|
|
|
||
|
|
# with copy=False
|
||
|
|
reindexed = self.frame.reindex(self.frame.index, copy=False)
|
||
|
|
reindexed['F'] = reindexed['A']
|
||
|
|
assert 'F' in self.frame
|
||
|
|
|
||
|
|
reindexed = self.frame.reindex(self.frame.index)
|
||
|
|
reindexed['G'] = reindexed['A']
|
||
|
|
assert 'G' not in self.frame
|
||
|
|
|
||
|
|
def test_reindex_fill_value(self):
|
||
|
|
rng = bdate_range('20110110', periods=20)
|
||
|
|
|
||
|
|
result = self.zframe.reindex(rng, fill_value=0)
|
||
|
|
exp = self.zorig.reindex(rng, fill_value=0)
|
||
|
|
exp = exp.to_sparse(self.zframe.default_fill_value)
|
||
|
|
tm.assert_sp_frame_equal(result, exp)
|
||
|
|
|
||
|
|
def test_reindex_method(self):
|
||
|
|
|
||
|
|
sparse = SparseDataFrame(data=[[11., 12., 14.],
|
||
|
|
[21., 22., 24.],
|
||
|
|
[41., 42., 44.]],
|
||
|
|
index=[1, 2, 4],
|
||
|
|
columns=[1, 2, 4],
|
||
|
|
dtype=float)
|
||
|
|
|
||
|
|
# Over indices
|
||
|
|
|
||
|
|
# default method
|
||
|
|
result = sparse.reindex(index=range(6))
|
||
|
|
expected = SparseDataFrame(data=[[nan, nan, nan],
|
||
|
|
[11., 12., 14.],
|
||
|
|
[21., 22., 24.],
|
||
|
|
[nan, nan, nan],
|
||
|
|
[41., 42., 44.],
|
||
|
|
[nan, nan, nan]],
|
||
|
|
index=range(6),
|
||
|
|
columns=[1, 2, 4],
|
||
|
|
dtype=float)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# method='bfill'
|
||
|
|
result = sparse.reindex(index=range(6), method='bfill')
|
||
|
|
expected = SparseDataFrame(data=[[11., 12., 14.],
|
||
|
|
[11., 12., 14.],
|
||
|
|
[21., 22., 24.],
|
||
|
|
[41., 42., 44.],
|
||
|
|
[41., 42., 44.],
|
||
|
|
[nan, nan, nan]],
|
||
|
|
index=range(6),
|
||
|
|
columns=[1, 2, 4],
|
||
|
|
dtype=float)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# method='ffill'
|
||
|
|
result = sparse.reindex(index=range(6), method='ffill')
|
||
|
|
expected = SparseDataFrame(data=[[nan, nan, nan],
|
||
|
|
[11., 12., 14.],
|
||
|
|
[21., 22., 24.],
|
||
|
|
[21., 22., 24.],
|
||
|
|
[41., 42., 44.],
|
||
|
|
[41., 42., 44.]],
|
||
|
|
index=range(6),
|
||
|
|
columns=[1, 2, 4],
|
||
|
|
dtype=float)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# Over columns
|
||
|
|
|
||
|
|
# default method
|
||
|
|
result = sparse.reindex(columns=range(6))
|
||
|
|
expected = SparseDataFrame(data=[[nan, 11., 12., nan, 14., nan],
|
||
|
|
[nan, 21., 22., nan, 24., nan],
|
||
|
|
[nan, 41., 42., nan, 44., nan]],
|
||
|
|
index=[1, 2, 4],
|
||
|
|
columns=range(6),
|
||
|
|
dtype=float)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# method='bfill'
|
||
|
|
with pytest.raises(NotImplementedError):
|
||
|
|
sparse.reindex(columns=range(6), method='bfill')
|
||
|
|
|
||
|
|
# method='ffill'
|
||
|
|
with pytest.raises(NotImplementedError):
|
||
|
|
sparse.reindex(columns=range(6), method='ffill')
|
||
|
|
|
||
|
|
def test_take(self):
|
||
|
|
result = self.frame.take([1, 0, 2], axis=1)
|
||
|
|
expected = self.frame.reindex(columns=['B', 'A', 'C'])
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_to_dense(self):
|
||
|
|
def _check(frame, orig):
|
||
|
|
dense_dm = frame.to_dense()
|
||
|
|
tm.assert_frame_equal(frame, dense_dm)
|
||
|
|
tm.assert_frame_equal(dense_dm, orig, check_dtype=False)
|
||
|
|
|
||
|
|
self._check_all(_check)
|
||
|
|
|
||
|
|
def test_stack_sparse_frame(self):
|
||
|
|
with catch_warnings(record=True):
|
||
|
|
|
||
|
|
def _check(frame):
|
||
|
|
dense_frame = frame.to_dense() # noqa
|
||
|
|
|
||
|
|
wp = Panel.from_dict({'foo': frame})
|
||
|
|
from_dense_lp = wp.to_frame()
|
||
|
|
|
||
|
|
from_sparse_lp = spf.stack_sparse_frame(frame)
|
||
|
|
|
||
|
|
tm.assert_numpy_array_equal(from_dense_lp.values,
|
||
|
|
from_sparse_lp.values)
|
||
|
|
|
||
|
|
_check(self.frame)
|
||
|
|
_check(self.iframe)
|
||
|
|
|
||
|
|
# for now
|
||
|
|
pytest.raises(Exception, _check, self.zframe)
|
||
|
|
pytest.raises(Exception, _check, self.fill_frame)
|
||
|
|
|
||
|
|
def test_transpose(self):
|
||
|
|
|
||
|
|
def _check(frame, orig):
|
||
|
|
transposed = frame.T
|
||
|
|
untransposed = transposed.T
|
||
|
|
tm.assert_sp_frame_equal(frame, untransposed)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(frame.T.to_dense(), orig.T)
|
||
|
|
tm.assert_frame_equal(frame.T.T.to_dense(), orig.T.T)
|
||
|
|
tm.assert_sp_frame_equal(frame, frame.T.T, exact_indices=False)
|
||
|
|
|
||
|
|
self._check_all(_check)
|
||
|
|
|
||
|
|
def test_shift(self):
|
||
|
|
|
||
|
|
def _check(frame, orig):
|
||
|
|
shifted = frame.shift(0)
|
||
|
|
exp = orig.shift(0)
|
||
|
|
tm.assert_frame_equal(shifted.to_dense(), exp)
|
||
|
|
|
||
|
|
shifted = frame.shift(1)
|
||
|
|
exp = orig.shift(1)
|
||
|
|
tm.assert_frame_equal(shifted, exp)
|
||
|
|
|
||
|
|
shifted = frame.shift(-2)
|
||
|
|
exp = orig.shift(-2)
|
||
|
|
tm.assert_frame_equal(shifted, exp)
|
||
|
|
|
||
|
|
shifted = frame.shift(2, freq='B')
|
||
|
|
exp = orig.shift(2, freq='B')
|
||
|
|
exp = exp.to_sparse(frame.default_fill_value,
|
||
|
|
kind=frame.default_kind)
|
||
|
|
tm.assert_frame_equal(shifted, exp)
|
||
|
|
|
||
|
|
shifted = frame.shift(2, freq=BDay())
|
||
|
|
exp = orig.shift(2, freq=BDay())
|
||
|
|
exp = exp.to_sparse(frame.default_fill_value,
|
||
|
|
kind=frame.default_kind)
|
||
|
|
tm.assert_frame_equal(shifted, exp)
|
||
|
|
|
||
|
|
self._check_all(_check)
|
||
|
|
|
||
|
|
def test_count(self):
|
||
|
|
dense_result = self.frame.to_dense().count()
|
||
|
|
|
||
|
|
result = self.frame.count()
|
||
|
|
tm.assert_series_equal(result, dense_result)
|
||
|
|
|
||
|
|
result = self.frame.count(axis=None)
|
||
|
|
tm.assert_series_equal(result, dense_result)
|
||
|
|
|
||
|
|
result = self.frame.count(axis=0)
|
||
|
|
tm.assert_series_equal(result, dense_result)
|
||
|
|
|
||
|
|
result = self.frame.count(axis=1)
|
||
|
|
dense_result = self.frame.to_dense().count(axis=1)
|
||
|
|
|
||
|
|
# win32 don't check dtype
|
||
|
|
tm.assert_series_equal(result, dense_result, check_dtype=False)
|
||
|
|
|
||
|
|
def _check_all(self, check_func):
|
||
|
|
check_func(self.frame, self.orig)
|
||
|
|
check_func(self.iframe, self.iorig)
|
||
|
|
check_func(self.zframe, self.zorig)
|
||
|
|
check_func(self.fill_frame, self.fill_orig)
|
||
|
|
|
||
|
|
def test_numpy_transpose(self):
|
||
|
|
sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a'])
|
||
|
|
result = np.transpose(np.transpose(sdf))
|
||
|
|
tm.assert_sp_frame_equal(result, sdf)
|
||
|
|
|
||
|
|
msg = "the 'axes' parameter is not supported"
|
||
|
|
tm.assert_raises_regex(ValueError, msg, np.transpose, sdf, axes=1)
|
||
|
|
|
||
|
|
def test_combine_first(self):
|
||
|
|
df = self.frame
|
||
|
|
|
||
|
|
result = df[::2].combine_first(df)
|
||
|
|
result2 = df[::2].combine_first(df.to_dense())
|
||
|
|
|
||
|
|
expected = df[::2].to_dense().combine_first(df.to_dense())
|
||
|
|
expected = expected.to_sparse(fill_value=df.default_fill_value)
|
||
|
|
|
||
|
|
tm.assert_sp_frame_equal(result, result2)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_combine_add(self):
|
||
|
|
df = self.frame.to_dense()
|
||
|
|
df2 = df.copy()
|
||
|
|
df2['C'][:3] = np.nan
|
||
|
|
df['A'][:3] = 5.7
|
||
|
|
|
||
|
|
result = df.to_sparse().add(df2.to_sparse(), fill_value=0)
|
||
|
|
expected = df.add(df2, fill_value=0).to_sparse()
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_isin(self):
|
||
|
|
sparse_df = DataFrame({'flag': [1., 0., 1.]}).to_sparse(fill_value=0.)
|
||
|
|
xp = sparse_df[sparse_df.flag == 1.]
|
||
|
|
rs = sparse_df[sparse_df.flag.isin([1.])]
|
||
|
|
tm.assert_frame_equal(xp, rs)
|
||
|
|
|
||
|
|
def test_sparse_pow_issue(self):
|
||
|
|
# 2220
|
||
|
|
df = SparseDataFrame({'A': [1.1, 3.3], 'B': [2.5, -3.9]})
|
||
|
|
|
||
|
|
# note : no error without nan
|
||
|
|
df = SparseDataFrame({'A': [nan, 0, 1]})
|
||
|
|
|
||
|
|
# note that 2 ** df works fine, also df ** 1
|
||
|
|
result = 1 ** df
|
||
|
|
|
||
|
|
r1 = result.take([0], 1)['A']
|
||
|
|
r2 = result['A']
|
||
|
|
|
||
|
|
assert len(r2.sp_values) == len(r1.sp_values)
|
||
|
|
|
||
|
|
def test_as_blocks(self):
|
||
|
|
df = SparseDataFrame({'A': [1.1, 3.3], 'B': [nan, -3.9]},
|
||
|
|
dtype='float64')
|
||
|
|
|
||
|
|
# deprecated 0.21.0
|
||
|
|
with tm.assert_produces_warning(FutureWarning,
|
||
|
|
check_stacklevel=False):
|
||
|
|
df_blocks = df.blocks
|
||
|
|
assert list(df_blocks.keys()) == ['float64']
|
||
|
|
tm.assert_frame_equal(df_blocks['float64'], df)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason='nan column names in _init_dict problematic '
|
||
|
|
'(GH 16894)')
|
||
|
|
def test_nan_columnname(self):
|
||
|
|
# GH 8822
|
||
|
|
nan_colname = DataFrame(Series(1.0, index=[0]), columns=[nan])
|
||
|
|
nan_colname_sparse = nan_colname.to_sparse()
|
||
|
|
assert np.isnan(nan_colname_sparse.columns[0])
|
||
|
|
|
||
|
|
def test_isna(self):
|
||
|
|
# GH 8276
|
||
|
|
df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
|
||
|
|
'B': [0, np.nan, np.nan, 2, np.nan]})
|
||
|
|
|
||
|
|
res = df.isna()
|
||
|
|
exp = pd.SparseDataFrame({'A': [True, True, False, False, True],
|
||
|
|
'B': [False, True, True, False, True]},
|
||
|
|
default_fill_value=True)
|
||
|
|
exp._default_fill_value = np.nan
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
|
||
|
|
# if fill_value is not nan, True can be included in sp_values
|
||
|
|
df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
|
||
|
|
'B': [0, np.nan, 0, 2, np.nan]},
|
||
|
|
default_fill_value=0.)
|
||
|
|
res = df.isna()
|
||
|
|
assert isinstance(res, pd.SparseDataFrame)
|
||
|
|
exp = pd.DataFrame({'A': [False, False, False, False, True],
|
||
|
|
'B': [False, True, False, False, True]})
|
||
|
|
tm.assert_frame_equal(res.to_dense(), exp)
|
||
|
|
|
||
|
|
def test_notna(self):
|
||
|
|
# GH 8276
|
||
|
|
df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
|
||
|
|
'B': [0, np.nan, np.nan, 2, np.nan]})
|
||
|
|
|
||
|
|
res = df.notna()
|
||
|
|
exp = pd.SparseDataFrame({'A': [False, False, True, True, False],
|
||
|
|
'B': [True, False, False, True, False]},
|
||
|
|
default_fill_value=False)
|
||
|
|
exp._default_fill_value = np.nan
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
|
||
|
|
# if fill_value is not nan, True can be included in sp_values
|
||
|
|
df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
|
||
|
|
'B': [0, np.nan, 0, 2, np.nan]},
|
||
|
|
default_fill_value=0.)
|
||
|
|
res = df.notna()
|
||
|
|
assert isinstance(res, pd.SparseDataFrame)
|
||
|
|
exp = pd.DataFrame({'A': [True, True, True, True, False],
|
||
|
|
'B': [True, False, True, True, False]})
|
||
|
|
tm.assert_frame_equal(res.to_dense(), exp)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSparseDataFrameArithmetic(object):
|
||
|
|
|
||
|
|
def test_numeric_op_scalar(self):
|
||
|
|
df = pd.DataFrame({'A': [nan, nan, 0, 1, ],
|
||
|
|
'B': [0, 1, 2, nan],
|
||
|
|
'C': [1., 2., 3., 4.],
|
||
|
|
'D': [nan, nan, nan, nan]})
|
||
|
|
sparse = df.to_sparse()
|
||
|
|
|
||
|
|
tm.assert_sp_frame_equal(sparse + 1, (df + 1).to_sparse())
|
||
|
|
|
||
|
|
def test_comparison_op_scalar(self):
|
||
|
|
# GH 13001
|
||
|
|
df = pd.DataFrame({'A': [nan, nan, 0, 1, ],
|
||
|
|
'B': [0, 1, 2, nan],
|
||
|
|
'C': [1., 2., 3., 4.],
|
||
|
|
'D': [nan, nan, nan, nan]})
|
||
|
|
sparse = df.to_sparse()
|
||
|
|
|
||
|
|
# comparison changes internal repr, compare with dense
|
||
|
|
res = sparse > 1
|
||
|
|
assert isinstance(res, pd.SparseDataFrame)
|
||
|
|
tm.assert_frame_equal(res.to_dense(), df > 1)
|
||
|
|
|
||
|
|
res = sparse != 0
|
||
|
|
assert isinstance(res, pd.SparseDataFrame)
|
||
|
|
tm.assert_frame_equal(res.to_dense(), df != 0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestSparseDataFrameAnalytics(object):
|
||
|
|
def setup_method(self, method):
|
||
|
|
self.data = {'A': [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
|
||
|
|
'B': [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
|
||
|
|
'C': np.arange(10, dtype=float),
|
||
|
|
'D': [0, 1, 2, 3, 4, 5, nan, nan, nan, nan]}
|
||
|
|
|
||
|
|
self.dates = bdate_range('1/1/2011', periods=10)
|
||
|
|
|
||
|
|
self.frame = SparseDataFrame(self.data, index=self.dates)
|
||
|
|
|
||
|
|
def test_cumsum(self):
|
||
|
|
expected = SparseDataFrame(self.frame.to_dense().cumsum())
|
||
|
|
|
||
|
|
result = self.frame.cumsum()
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.cumsum(axis=None)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = self.frame.cumsum(axis=0)
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_numpy_cumsum(self):
|
||
|
|
result = np.cumsum(self.frame)
|
||
|
|
expected = SparseDataFrame(self.frame.to_dense().cumsum())
|
||
|
|
tm.assert_sp_frame_equal(result, expected)
|
||
|
|
|
||
|
|
msg = "the 'dtype' parameter is not supported"
|
||
|
|
tm.assert_raises_regex(ValueError, msg, np.cumsum,
|
||
|
|
self.frame, dtype=np.int64)
|
||
|
|
|
||
|
|
msg = "the 'out' parameter is not supported"
|
||
|
|
tm.assert_raises_regex(ValueError, msg, np.cumsum,
|
||
|
|
self.frame, out=result)
|
||
|
|
|
||
|
|
def test_numpy_func_call(self):
|
||
|
|
# no exception should be raised even though
|
||
|
|
# numpy passes in 'axis=None' or `axis=-1'
|
||
|
|
funcs = ['sum', 'cumsum', 'var',
|
||
|
|
'mean', 'prod', 'cumprod',
|
||
|
|
'std', 'min', 'max']
|
||
|
|
for func in funcs:
|
||
|
|
getattr(np, func)(self.frame)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason='Wrong SparseBlock initialization '
|
||
|
|
'(GH 17386)')
|
||
|
|
def test_quantile(self):
|
||
|
|
# GH 17386
|
||
|
|
data = [[1, 1], [2, 10], [3, 100], [nan, nan]]
|
||
|
|
q = 0.1
|
||
|
|
|
||
|
|
sparse_df = SparseDataFrame(data)
|
||
|
|
result = sparse_df.quantile(q)
|
||
|
|
|
||
|
|
dense_df = DataFrame(data)
|
||
|
|
dense_expected = dense_df.quantile(q)
|
||
|
|
sparse_expected = SparseSeries(dense_expected)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, dense_expected)
|
||
|
|
tm.assert_sp_series_equal(result, sparse_expected)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason='Wrong SparseBlock initialization '
|
||
|
|
'(GH 17386)')
|
||
|
|
def test_quantile_multi(self):
|
||
|
|
# GH 17386
|
||
|
|
data = [[1, 1], [2, 10], [3, 100], [nan, nan]]
|
||
|
|
q = [0.1, 0.5]
|
||
|
|
|
||
|
|
sparse_df = SparseDataFrame(data)
|
||
|
|
result = sparse_df.quantile(q)
|
||
|
|
|
||
|
|
dense_df = DataFrame(data)
|
||
|
|
dense_expected = dense_df.quantile(q)
|
||
|
|
sparse_expected = SparseDataFrame(dense_expected)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, dense_expected)
|
||
|
|
tm.assert_sp_frame_equal(result, sparse_expected)
|
||
|
|
|
||
|
|
def test_assign_with_sparse_frame(self):
|
||
|
|
# GH 19163
|
||
|
|
df = pd.DataFrame({"a": [1, 2, 3]})
|
||
|
|
res = df.to_sparse(fill_value=False).assign(newcol=False)
|
||
|
|
exp = df.assign(newcol=False).to_sparse(fill_value=False)
|
||
|
|
|
||
|
|
tm.assert_sp_frame_equal(res, exp)
|
||
|
|
|
||
|
|
for column in res.columns:
|
||
|
|
assert type(res[column]) is SparseSeries
|