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CLN: move interpolation tests from test_generic to test_missing
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3 files changed

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-576
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3 files changed

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‎pandas/tests/frame/test_missing.py

+220
Original file line numberDiff line numberDiff line change
@@ -2,6 +2,7 @@
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from __future__ import print_function
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from distutils.version import LooseVersion
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from numpy import nan, random
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import numpy as np
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@@ -18,6 +19,13 @@
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from pandas.tests.frame.common import TestData, _check_mixed_float
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def _skip_if_no_pchip():
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try:
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from scipy.interpolate import pchip_interpolate # noqa
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except ImportError:
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raise nose.SkipTest('scipy.interpolate.pchip missing')
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2129
class TestDataFrameMissingData(tm.TestCase, TestData):
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_multiprocess_can_split_ = True
@@ -436,6 +444,218 @@ def test_fill_value_when_combine_const(self):
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assert_frame_equal(res, exp)
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class TestDataFrameInterpolate(tm.TestCase, TestData):
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def test_interp_basic(self):
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df = DataFrame({'A': [1, 2, np.nan, 4],
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'B': [1, 4, 9, np.nan],
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'C': [1, 2, 3, 5],
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'D': list('abcd')})
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expected = DataFrame({'A': [1., 2., 3., 4.],
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'B': [1., 4., 9., 9.],
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'C': [1, 2, 3, 5],
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'D': list('abcd')})
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result = df.interpolate()
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assert_frame_equal(result, expected)
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result = df.set_index('C').interpolate()
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expected = df.set_index('C')
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expected.loc[3, 'A'] = 3
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expected.loc[5, 'B'] = 9
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assert_frame_equal(result, expected)
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def test_interp_bad_method(self):
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df = DataFrame({'A': [1, 2, np.nan, 4],
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'B': [1, 4, 9, np.nan],
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'C': [1, 2, 3, 5],
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'D': list('abcd')})
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with tm.assertRaises(ValueError):
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df.interpolate(method='not_a_method')
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def test_interp_combo(self):
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df = DataFrame({'A': [1., 2., np.nan, 4.],
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'B': [1, 4, 9, np.nan],
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'C': [1, 2, 3, 5],
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'D': list('abcd')})
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result = df['A'].interpolate()
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expected = Series([1., 2., 3., 4.], name='A')
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assert_series_equal(result, expected)
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result = df['A'].interpolate(downcast='infer')
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expected = Series([1, 2, 3, 4], name='A')
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assert_series_equal(result, expected)
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def test_interp_nan_idx(self):
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df = DataFrame({'A': [1, 2, np.nan, 4], 'B': [np.nan, 2, 3, 4]})
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df = df.set_index('A')
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with tm.assertRaises(NotImplementedError):
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df.interpolate(method='values')
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def test_interp_various(self):
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tm._skip_if_no_scipy()
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df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
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'C': [1, 2, 3, 5, 8, 13, 21]})
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df = df.set_index('C')
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expected = df.copy()
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result = df.interpolate(method='polynomial', order=1)
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expected.A.loc[3] = 2.66666667
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expected.A.loc[13] = 5.76923076
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assert_frame_equal(result, expected)
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result = df.interpolate(method='cubic')
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expected.A.loc[3] = 2.81621174
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expected.A.loc[13] = 5.64146581
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assert_frame_equal(result, expected)
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result = df.interpolate(method='nearest')
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expected.A.loc[3] = 2
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expected.A.loc[13] = 5
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assert_frame_equal(result, expected, check_dtype=False)
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result = df.interpolate(method='quadratic')
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expected.A.loc[3] = 2.82533638
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expected.A.loc[13] = 6.02817974
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assert_frame_equal(result, expected)
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result = df.interpolate(method='slinear')
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expected.A.loc[3] = 2.66666667
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expected.A.loc[13] = 5.76923077
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assert_frame_equal(result, expected)
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result = df.interpolate(method='zero')
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expected.A.loc[3] = 2.
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expected.A.loc[13] = 5
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assert_frame_equal(result, expected, check_dtype=False)
531+
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result = df.interpolate(method='quadratic')
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expected.A.loc[3] = 2.82533638
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expected.A.loc[13] = 6.02817974
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assert_frame_equal(result, expected)
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def test_interp_alt_scipy(self):
538+
tm._skip_if_no_scipy()
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df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
540+
'C': [1, 2, 3, 5, 8, 13, 21]})
541+
result = df.interpolate(method='barycentric')
542+
expected = df.copy()
543+
expected.ix[2, 'A'] = 3
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expected.ix[5, 'A'] = 6
545+
assert_frame_equal(result, expected)
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547+
result = df.interpolate(method='barycentric', downcast='infer')
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assert_frame_equal(result, expected.astype(np.int64))
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result = df.interpolate(method='krogh')
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expectedk = df.copy()
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expectedk['A'] = expected['A']
553+
assert_frame_equal(result, expectedk)
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_skip_if_no_pchip()
556+
import scipy
557+
result = df.interpolate(method='pchip')
558+
expected.ix[2, 'A'] = 3
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560+
if LooseVersion(scipy.__version__) >= '0.17.0':
561+
expected.ix[5, 'A'] = 6.0
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else:
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expected.ix[5, 'A'] = 6.125
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assert_frame_equal(result, expected)
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def test_interp_rowwise(self):
568+
df = DataFrame({0: [1, 2, np.nan, 4],
569+
1: [2, 3, 4, np.nan],
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2: [np.nan, 4, 5, 6],
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3: [4, np.nan, 6, 7],
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4: [1, 2, 3, 4]})
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result = df.interpolate(axis=1)
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expected = df.copy()
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expected.loc[3, 1] = 5
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expected.loc[0, 2] = 3
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expected.loc[1, 3] = 3
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expected[4] = expected[4].astype(np.float64)
579+
assert_frame_equal(result, expected)
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581+
# scipy route
582+
tm._skip_if_no_scipy()
583+
result = df.interpolate(axis=1, method='values')
584+
assert_frame_equal(result, expected)
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586+
result = df.interpolate(axis=0)
587+
expected = df.interpolate()
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assert_frame_equal(result, expected)
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def test_rowwise_alt(self):
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df = DataFrame({0: [0, .5, 1., np.nan, 4, 8, np.nan, np.nan, 64],
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1: [1, 2, 3, 4, 3, 2, 1, 0, -1]})
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df.interpolate(axis=0)
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def test_interp_leading_nans(self):
596+
df = DataFrame({"A": [np.nan, np.nan, .5, .25, 0],
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"B": [np.nan, -3, -3.5, np.nan, -4]})
598+
result = df.interpolate()
599+
expected = df.copy()
600+
expected['B'].loc[3] = -3.75
601+
assert_frame_equal(result, expected)
602+
603+
tm._skip_if_no_scipy()
604+
result = df.interpolate(method='polynomial', order=1)
605+
assert_frame_equal(result, expected)
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def test_interp_raise_on_only_mixed(self):
608+
df = DataFrame({'A': [1, 2, np.nan, 4],
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'B': ['a', 'b', 'c', 'd'],
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'C': [np.nan, 2, 5, 7],
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'D': [np.nan, np.nan, 9, 9],
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'E': [1, 2, 3, 4]})
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with tm.assertRaises(TypeError):
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df.interpolate(axis=1)
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def test_interp_inplace(self):
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df = DataFrame({'a': [1., 2., np.nan, 4.]})
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expected = DataFrame({'a': [1., 2., 3., 4.]})
619+
result = df.copy()
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result['a'].interpolate(inplace=True)
621+
assert_frame_equal(result, expected)
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623+
result = df.copy()
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result['a'].interpolate(inplace=True, downcast='infer')
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assert_frame_equal(result, expected.astype('int64'))
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def test_interp_inplace_row(self):
628+
# GH 10395
629+
result = DataFrame({'a': [1., 2., 3., 4.],
630+
'b': [np.nan, 2., 3., 4.],
631+
'c': [3, 2, 2, 2]})
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expected = result.interpolate(method='linear', axis=1, inplace=False)
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result.interpolate(method='linear', axis=1, inplace=True)
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assert_frame_equal(result, expected)
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def test_interp_ignore_all_good(self):
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# GH
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df = DataFrame({'A': [1, 2, np.nan, 4],
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'B': [1, 2, 3, 4],
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'C': [1., 2., np.nan, 4.],
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'D': [1., 2., 3., 4.]})
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expected = DataFrame({'A': np.array(
643+
[1, 2, 3, 4], dtype='float64'),
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'B': np.array(
645+
[1, 2, 3, 4], dtype='int64'),
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'C': np.array(
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[1., 2., 3, 4.], dtype='float64'),
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'D': np.array(
649+
[1., 2., 3., 4.], dtype='float64')})
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result = df.interpolate(downcast=None)
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assert_frame_equal(result, expected)
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# all good
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result = df[['B', 'D']].interpolate(downcast=None)
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assert_frame_equal(result, df[['B', 'D']])
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439659
if __name__ == '__main__':
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import nose
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nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],

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