Merge pull request #7451 from sinhrks/skiptest

TST/CLN: centralize module check funcs

Author: jreback

pandas/stats/tests/test_moments.py

@@ -17,11 +17,6 @@
 
 N, K = 100, 10
 
-def _skip_if_no_scipy():
-    try:
-        import scipy.stats
-    except ImportError:
-        raise nose.SkipTest("no scipy.stats")
 
 class TestMoments(tm.TestCase):
 
@@ -68,7 +63,7 @@ def test_rolling_mean(self):
         self._check_moment_func(mom.rolling_mean, np.mean)
 
     def test_cmov_mean(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_mean
         except ImportError:
@@ -86,7 +81,7 @@ def test_cmov_mean(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -104,7 +99,7 @@ def test_cmov_window(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window_corner(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -128,7 +123,7 @@ def test_cmov_window_corner(self):
         self.assertEqual(len(rs), 5)
 
     def test_cmov_window_frame(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -141,7 +136,7 @@ def test_cmov_window_frame(self):
         assert_frame_equal(DataFrame(xp), rs)
 
     def test_cmov_window_na_min_periods(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -158,7 +153,7 @@ def test_cmov_window_na_min_periods(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window_regular(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -174,7 +169,7 @@ def test_cmov_window_regular(self):
             assert_series_equal(Series(xp), rs)
 
     def test_cmov_window_special(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:

pandas/tests/test_format.py

@@ -86,18 +86,6 @@ def skip_if_np_version_under1p7():
 
         raise nose.SkipTest('numpy >= 1.7 required')
 
-def _skip_if_no_pytz():
-    try:
-        import pytz
-    except ImportError:
-        raise nose.SkipTest("pytz not installed")
-
-def _skip_if_no_dateutil():
-    try:
-        import dateutil
-    except ImportError:
-        raise nose.SkipTest("dateutil not installed")
-
 
 class TestDataFrameFormatting(tm.TestCase):
     _multiprocess_can_split_ = True
@@ -2930,7 +2918,7 @@ def test_no_tz(self):
         self.assertEqual(str(ts_nanos_micros), "1970-01-01 00:00:00.000001200")
 
     def test_tz_pytz(self):
-        _skip_if_no_pytz()
+        tm._skip_if_no_pytz()
 
         import pytz
 
@@ -2944,7 +2932,7 @@ def test_tz_pytz(self):
         self.assertEqual(str(dt_datetime_us), str(Timestamp(dt_datetime_us)))
 
     def test_tz_dateutil(self):
-        _skip_if_no_dateutil()
+        tm._skip_if_no_dateutil()
         import dateutil
         utc = dateutil.tz.tzutc()
 

pandas/tests/test_frame.py

@@ -52,12 +52,6 @@
 
 from numpy.testing.decorators import slow
 
-def _skip_if_no_scipy():
-    try:
-        import scipy.stats
-    except ImportError:
-        raise nose.SkipTest("no scipy.stats module")
-
 #---------------------------------------------------------------------
 # DataFrame test cases
 
@@ -6753,28 +6747,28 @@ def _check_method(self, method='pearson', check_minp=False):
             expected.ix['A', 'B'] = expected.ix['B', 'A'] = nan
 
     def test_corr_pearson(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('pearson')
 
     def test_corr_kendall(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('kendall')
 
     def test_corr_spearman(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('spearman')
 
     def test_corr_non_numeric(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
@@ -6784,7 +6778,7 @@ def test_corr_non_numeric(self):
         assert_frame_equal(result, expected)
 
     def test_corr_nooverlap(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         # nothing in common
         for meth in ['pearson', 'kendall', 'spearman']:
@@ -6797,7 +6791,7 @@ def test_corr_nooverlap(self):
             self.assertEqual(rs.ix['B', 'B'], 1)
 
     def test_corr_constant(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         # constant --> all NA
 
@@ -10971,7 +10965,7 @@ def test_sem(self):
             nanops._USE_BOTTLENECK = True
 
     def test_skew(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import skew
 
         def alt(x):
@@ -10982,7 +10976,7 @@ def alt(x):
         self._check_stat_op('skew', alt)
 
     def test_kurt(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         from scipy.stats import kurtosis
 
@@ -11334,7 +11328,7 @@ def test_cumprod(self):
         df.cumprod(1)
 
     def test_rank(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import rankdata
 
         self.frame['A'][::2] = np.nan
@@ -11426,7 +11420,7 @@ def test_rank2(self):
         assert_frame_equal(df.rank(), exp)
 
     def test_rank_na_option(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import rankdata
 
         self.frame['A'][::2] = np.nan

pandas/tests/test_generic.py

@@ -1,17 +1,14 @@
 # pylint: disable-msg=E1101,W0612
 
 from datetime import datetime, timedelta
-import operator
 import nose
-import copy
 import numpy as np
 from numpy import nan
 import pandas as pd
 
 from pandas import (Index, Series, DataFrame, Panel,
                     isnull, notnull,date_range, _np_version_under1p7)
 from pandas.core.index import Index, MultiIndex
-from pandas.tseries.index import Timestamp, DatetimeIndex
 
 import pandas.core.common as com
 
@@ -25,13 +22,6 @@
 import pandas.util.testing as tm
 
 
-def _skip_if_no_scipy():
-    try:
-        import scipy.interpolate
-    except ImportError:
-        raise nose.SkipTest('scipy.interpolate missing')
-
-
 def _skip_if_no_pchip():
     try:
         from scipy.interpolate import pchip_interpolate
@@ -491,7 +481,7 @@ def test_interpolate(self):
         self.assertRaises(ValueError, non_ts.interpolate, method='time')
 
     def test_interp_regression(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         _skip_if_no_pchip()
 
         ser = Series(np.sort(np.random.uniform(size=100)))
@@ -509,7 +499,7 @@ def test_interpolate_corners(self):
         s = Series([]).interpolate()
         assert_series_equal(s.interpolate(), s)
 
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([np.nan, np.nan])
         assert_series_equal(s.interpolate(method='polynomial', order=1), s)
 
@@ -544,7 +534,7 @@ def test_nan_interpolate(self):
         expected = Series([0., 1., 2., 3.])
         assert_series_equal(result, expected)
 
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         result = s.interpolate(method='polynomial', order=1)
         assert_series_equal(result, expected)
 
@@ -561,14 +551,14 @@ def test_nan_str_index(self):
         assert_series_equal(result, expected)
 
     def test_interp_quad(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
         result = sq.interpolate(method='quadratic')
         expected = Series([1., 4., 9., 16.], index=[1, 2, 3, 4])
         assert_series_equal(result, expected)
 
     def test_interp_scipy_basic(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([1, 3, np.nan, 12, np.nan, 25])
         # slinear
         expected = Series([1., 3., 7.5, 12., 18.5, 25.])
@@ -611,7 +601,7 @@ def test_interp_limit(self):
 
     def test_interp_all_good(self):
         # scipy
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([1, 2, 3])
         result = s.interpolate(method='polynomial', order=1)
         assert_series_equal(result, s)
@@ -629,18 +619,18 @@ def test_interp_multiIndex(self):
         result = s.interpolate()
         assert_series_equal(result, expected)
 
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         with tm.assertRaises(ValueError):
             s.interpolate(method='polynomial', order=1)
 
     def test_interp_nonmono_raise(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([1, np.nan, 3], index=[0, 2, 1])
         with tm.assertRaises(ValueError):
             s.interpolate(method='krogh')
 
     def test_interp_datetime64(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         df = Series([1, np.nan, 3], index=date_range('1/1/2000', periods=3))
         result = df.interpolate(method='nearest')
         expected = Series([1., 1., 3.], index=date_range('1/1/2000', periods=3))
@@ -768,7 +758,7 @@ def test_interp_nan_idx(self):
             df.interpolate(method='values')
 
     def test_interp_various(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                         'C': [1, 2, 3, 5, 8, 13, 21]})
         df = df.set_index('C')
@@ -810,7 +800,7 @@ def test_interp_various(self):
         assert_frame_equal(result, expected)
 
     def test_interp_alt_scipy(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                         'C': [1, 2, 3, 5, 8, 13, 21]})
         result = df.interpolate(method='barycentric')
@@ -850,7 +840,7 @@ def test_interp_rowwise(self):
         assert_frame_equal(result, expected)
 
         # scipy route
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         result = df.interpolate(axis=1, method='values')
         assert_frame_equal(result, expected)
 
@@ -871,7 +861,7 @@ def test_interp_leading_nans(self):
         expected['B'].loc[3] = -3.75
         assert_frame_equal(result, expected)
 
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         result = df.interpolate(method='polynomial', order=1)
         assert_frame_equal(result, expected)
 
@@ -1022,15 +1012,15 @@ def test_describe_objects(self):
         assert_frame_equal(df[['C2', 'C3']].describe(), df[['C3']].describe())
 
     def test_no_order(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([0, 1, np.nan, 3])
         with tm.assertRaises(ValueError):
             s.interpolate(method='polynomial')
         with tm.assertRaises(ValueError):
             s.interpolate(method='spline')
 
     def test_spline(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
         result = s.interpolate(method='spline', order=1)
         expected = Series([1., 2., 3., 4., 5., 6., 7.])