CI: ASV fixups

Author: TomAugspurger

asv_bench/benchmarks/algorithms.py

@@ -1,10 +1,12 @@
-import warnings
 from importlib import import_module
 
 import numpy as np
+
 import pandas as pd
 from pandas.util import testing as tm
 
+from .pandas_vb_common import setup  # noqa: F401
+
 for imp in ['pandas.util', 'pandas.tools.hashing']:
     try:
         hashing = import_module(imp)
@@ -73,10 +75,6 @@ def setup(self):
         self.uniques = tm.makeStringIndex(1000).values
         self.all = self.uniques.repeat(10)
 
-    def time_match_string(self):
-        with warnings.catch_warnings(record=True):
-            pd.match(self.all, self.uniques)
-
 
 class Hashing(object):
 
@@ -114,6 +112,3 @@ def time_series_timedeltas(self, df):
 
     def time_series_dates(self, df):
         hashing.hash_pandas_object(df['dates'])
-
-
-from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/frame_methods.py

@@ -1,9 +1,12 @@
 import string
 
 import numpy as np
+
+from pandas import (
+    DataFrame, MultiIndex, NaT, Series, date_range, isnull, period_range)
 import pandas.util.testing as tm
-from pandas import (DataFrame, Series, MultiIndex, date_range, period_range,
-                    isnull, NaT)
+
+from .pandas_vb_common import setup  # noqa: F401
 
 
 class GetNumericData(object):
@@ -60,9 +63,6 @@ def time_reindex_axis1(self):
     def time_reindex_both_axes(self):
         self.df.reindex(index=self.idx, columns=self.idx)
 
-    def time_reindex_both_axes_ix(self):
-        self.df.ix[self.idx, self.idx]
-
     def time_reindex_upcast(self):
         self.df2.reindex(np.random.permutation(range(1200)))
 
@@ -521,6 +521,3 @@ def time_series_describe(self):
 
     def time_dataframe_describe(self):
         self.df.describe()
-
-
-from .pandas_vb_common import setup  # noqa: F401

asv_bench/benchmarks/groupby.py

@@ -1,13 +1,16 @@
-import warnings
-from string import ascii_letters
-from itertools import product
 from functools import partial
+from itertools import product
+from string import ascii_letters
+import warnings
 
 import numpy as np
-from pandas import (DataFrame, Series, MultiIndex, date_range, period_range,
-                    TimeGrouper, Categorical, Timestamp)
+
+from pandas import (
+    Categorical, DataFrame, MultiIndex, Series, TimeGrouper, Timestamp,
+    date_range, period_range)
 import pandas.util.testing as tm
 
+from .pandas_vb_common import setup  # noqa: F401
 
 method_blacklist = {
     'object': {'median', 'prod', 'sem', 'cumsum', 'sum', 'cummin', 'mean',
@@ -210,7 +213,7 @@ def time_multi_int_nunique(self, df):
 
 class AggFunctions(object):
 
-    def setup_cache():
+    def setup_cache(self):
         N = 10**5
         fac1 = np.array(['A', 'B', 'C'], dtype='O')
         fac2 = np.array(['one', 'two'], dtype='O')
@@ -535,6 +538,3 @@ def setup(self):
 
     def time_first(self):
         self.df_nans.groupby('key').transform('first')
-
-
-from .pandas_vb_common import setup  # noqa: F401

pandas/core/sparse/scipy_sparse.py

@@ -58,15 +58,7 @@ def _get_label_to_i_dict(labels, sort_labels=False):
             return (d)
 
         def _get_index_subset_to_coord_dict(index, subset, sort_labels=False):
-            def robust_get_level_values(i):
-                # if index has labels (that are not None) use those,
-                # else use the level location
-                try:
-                    return index.get_level_values(index.names[i])
-                except KeyError:
-                    return index.get_level_values(i)
-
-            ilabels = list(zip(*[robust_get_level_values(i) for i in subset]))
+            ilabels = list(zip(*[index._get_level_values(i) for i in subset]))
             labels_to_i = _get_label_to_i_dict(ilabels,
                                                sort_labels=sort_labels)
             labels_to_i = Series(labels_to_i)

pandas/io/formats/html.py

@@ -161,15 +161,7 @@ def write_result(self, buf):
             _classes.extend(self.classes)
 
         if self.notebook:
-            div_style = ''
-            try:
-                import IPython
-                if IPython.__version__ < LooseVersion('3.0.0'):
-                    div_style = ' style="max-width:1500px;overflow:auto;"'
-            except (ImportError, AttributeError):
-                pass
-
-            self.write('<div{style}>'.format(style=div_style))
+            self.write('<div>')
 
         self.write_style()
 

pandas/tests/sparse/frame/test_to_from_scipy.py

@@ -1,5 +1,6 @@
 import pytest
 import numpy as np
+import pandas as pd
 from pandas.util import testing as tm
 from pandas import SparseDataFrame, SparseSeries
 from pandas.core.sparse.api import SparseDtype
@@ -168,3 +169,16 @@ def test_from_scipy_fillna(spmatrix):
         expected[col].fill_value = -1
 
     tm.assert_sp_frame_equal(sdf, expected)
+
+
+def test_index_names_multiple_nones():
+    # https://github.com/pandas-dev/pandas/pull/24092
+    sparse = pytest.importorskip("scipy.sparse")
+
+    s = (pd.Series(1, index=pd.MultiIndex.from_product([['A', 'B'], [0, 1]]))
+           .to_sparse())
+    result, _, _ = s.to_coo()
+    assert isinstance(result, sparse.coo_matrix)
+    result = result.toarray()
+    expected = np.ones((2, 2), dtype=int)
+    tm.assert_numpy_array_equal(result, expected)