CLN: remove compat.lrange

pandas/compat/__init__.py

@@ -4,9 +4,6 @@
 
 Cross-compatible functions for different versions of Python.
 
-Key items to import for compatible code:
-* lists: lrange()
-
 Other items:
 * platform checker
 """
@@ -19,11 +16,6 @@
 PYPY = platform.python_implementation() == 'PyPy'
 
 
-# list-producing versions of the major Python iterating functions
-def lrange(*args, **kwargs):
-    return list(range(*args, **kwargs))
-
-
 # ----------------------------------------------------------------------------
 # functions largely based / taken from the six module
 

pandas/plotting/_converter.py

@@ -12,7 +12,6 @@
 from pandas._libs import lib, tslibs
 from pandas._libs.tslibs import resolution
 from pandas._libs.tslibs.frequencies import FreqGroup, get_freq
-from pandas.compat import lrange
 
 from pandas.core.dtypes.common import (
     is_datetime64_ns_dtype, is_float, is_float_dtype, is_integer,
@@ -1029,7 +1028,7 @@ def __call__(self):
             base = self.base
             (d, m) = divmod(vmin, base)
             vmin = (d + 1) * base
-            locs = lrange(vmin, vmax + 1, base)
+            locs = list(range(vmin, vmax + 1, base))
         return locs
 
     def autoscale(self):

pandas/plotting/_core.py

@@ -8,7 +8,6 @@
 
 from pandas._config import get_option
 
-from pandas.compat import lrange
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, cache_readonly
 
@@ -583,9 +582,9 @@ def _get_xticks(self, convert_period=False):
                 x = self.data.index._mpl_repr()
             else:
                 self._need_to_set_index = True
-                x = lrange(len(index))
+                x = list(range(len(index)))
         else:
-            x = lrange(len(index))
+            x = list(range(len(index)))
 
         return x
 

pandas/plotting/_misc.py

@@ -1,7 +1,6 @@
 # being a bit too dynamic
 import numpy as np
 
-from pandas.compat import lrange
 from pandas.util._decorators import deprecate_kwarg
 
 from pandas.core.dtypes.missing import notna
@@ -81,8 +80,8 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
         rdelta_ext = (rmax_ - rmin_) * range_padding / 2.
         boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
 
-    for i, a in zip(lrange(n), df.columns):
-        for j, b in zip(lrange(n), df.columns):
+    for i, a in enumerate(df.columns):
+        for j, b in enumerate(df.columns):
             ax = axes[i, j]
 
             if i == j:
@@ -420,7 +419,7 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
                           for sampling in samplings])
     if fig is None:
         fig = plt.figure()
-    x = lrange(samples)
+    x = list(range(samples))
     axes = []
     ax1 = fig.add_subplot(2, 3, 1)
     ax1.set_xlabel("Sample")
@@ -532,7 +531,7 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
             raise ValueError('Length of xticks must match number of columns')
         x = xticks
     else:
-        x = lrange(ncols)
+        x = list(range(ncols))
 
     if ax is None:
         ax = plt.gca()

pandas/plotting/_style.py

@@ -4,8 +4,6 @@
 
 import numpy as np
 
-from pandas.compat import lrange
-
 from pandas.core.dtypes.common import is_list_like
 
 
@@ -49,7 +47,7 @@ def random_color(column):
                 rs = com.random_state(column)
                 return rs.rand(3).tolist()
 
-            colors = [random_color(num) for num in lrange(num_colors)]
+            colors = [random_color(num) for num in range(num_colors)]
         else:
             raise ValueError("color_type must be either 'default' or 'random'")
 

pandas/tests/arrays/categorical/test_missing.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 from pandas.core.dtypes.dtypes import CategoricalDtype
 
 from pandas import Categorical, Index, isna
@@ -16,7 +14,7 @@ class TestCategoricalMissing:
     def test_na_flags_int_categories(self):
         # #1457
 
-        categories = lrange(10)
+        categories = list(range(10))
         labels = np.random.randint(0, 10, 20)
         labels[::5] = -1
 

pandas/tests/frame/test_duplicates.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 from pandas import DataFrame, Series
 import pandas.util.testing as tm
 
@@ -88,8 +86,8 @@ def test_drop_duplicates():
                     'B': ['one', 'one', 'two', 'two',
                           'two', 'two', 'one', 'two'],
                     'C': [1, 1, 2, 2, 2, 2, 1, 2],
-                    'D': lrange(8)})
-
+                    'D': range(8),
+                    })
     # single column
     result = df.drop_duplicates('AAA')
     expected = df[:2]
@@ -211,8 +209,8 @@ def test_drop_duplicates_for_take_all():
                     'B': ['one', 'one', 'two', 'two',
                           'two', 'two', 'one', 'two'],
                     'C': [1, 1, 2, 2, 2, 2, 1, 2],
-                    'D': lrange(8)})
-
+                    'D': range(8),
+                    })
     # single column
     result = df.drop_duplicates('AAA')
     expected = df.iloc[[0, 1, 2, 6]]
@@ -246,8 +244,8 @@ def test_drop_duplicates_tuple():
                     'B': ['one', 'one', 'two', 'two',
                           'two', 'two', 'one', 'two'],
                     'C': [1, 1, 2, 2, 2, 2, 1, 2],
-                    'D': lrange(8)})
-
+                    'D': range(8),
+                    })
     # single column
     result = df.drop_duplicates(('AA', 'AB'))
     expected = df[:2]
@@ -292,8 +290,8 @@ def test_drop_duplicates_NA():
                     'B': ['one', 'one', 'two', 'two',
                           'two', 'two', 'one', 'two'],
                     'C': [1.0, np.nan, np.nan, np.nan, 1., 1., 1, 1.],
-                    'D': lrange(8)})
-
+                    'D': range(8),
+                    })
     # single column
     result = df.drop_duplicates('A')
     expected = df.loc[[0, 2, 3]]
@@ -327,8 +325,8 @@ def test_drop_duplicates_NA():
                     'B': ['one', 'one', 'two', 'two',
                           'two', 'two', 'one', 'two'],
                     'C': [1.0, np.nan, np.nan, np.nan, 1., 1., 1, 1.],
-                    'D': lrange(8)})
-
+                    'D': range(8),
+                    })
     # single column
     result = df.drop_duplicates('C')
     expected = df[:2]
@@ -398,8 +396,8 @@ def test_drop_duplicates_inplace():
                       'B': ['one', 'one', 'two', 'two',
                             'two', 'two', 'one', 'two'],
                       'C': [1, 1, 2, 2, 2, 2, 1, 2],
-                      'D': lrange(8)})
-
+                      'D': range(8),
+                      })
     # single column
     df = orig.copy()
     df.drop_duplicates('A', inplace=True)

pandas/tests/indexes/datetimes/test_timezones.py

@@ -11,7 +11,6 @@
 import pytz
 
 from pandas._libs.tslibs import conversion, timezones
-from pandas.compat import lrange
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -958,7 +957,7 @@ def test_dti_with_timezone_repr(self, tzstr):
     def test_dti_take_dont_lose_meta(self, tzstr):
         rng = date_range('1/1/2000', periods=20, tz=tzstr)
 
-        result = rng.take(lrange(5))
+        result = rng.take(range(5))
         assert result.tz == rng.tz
         assert result.freq == rng.freq
 

pandas/tests/indexes/multi/test_analytics.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
 from pandas.compat.numpy import _np_version_under1p17
 
 import pandas as pd
@@ -32,8 +31,8 @@ def test_groupby(idx):
 
 
 def test_truncate():
-    major_axis = Index(lrange(4))
-    minor_axis = Index(lrange(2))
+    major_axis = Index(list(range(4)))
+    minor_axis = Index(list(range(2)))
 
     major_codes = np.array([0, 0, 1, 2, 3, 3])
     minor_codes = np.array([0, 1, 0, 1, 0, 1])

pandas/tests/indexes/multi/test_constructor.py

@@ -4,7 +4,6 @@
 import pytest
 
 from pandas._libs.tslib import Timestamp
-from pandas.compat import lrange
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 
@@ -383,7 +382,7 @@ def test_from_product_empty_two_levels(first, second):
 def test_from_product_empty_three_levels(N):
     # GH12258
     names = ['A', 'B', 'C']
-    lvl2 = lrange(N)
+    lvl2 = list(range(N))
     result = MultiIndex.from_product([[], lvl2, []], names=names)
     expected = MultiIndex(levels=[[], lvl2, []],
                           codes=[[], [], []], names=names)

pandas/tests/indexes/multi/test_drop.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
 from pandas.errors import PerformanceWarning
 
 import pandas as pd
@@ -73,9 +72,10 @@ def test_droplevel_with_names(idx):
     assert dropped.name == 'second'
 
     index = MultiIndex(
-        levels=[Index(lrange(4)), Index(lrange(4)), Index(lrange(4))],
-        codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
-            [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])],
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+               np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+               np.array([1, 0, 1, 1, 0, 0, 1, 0])],
         names=['one', 'two', 'three'])
     dropped = index.droplevel(0)
     assert dropped.names == ('two', 'three')
@@ -87,9 +87,10 @@ def test_droplevel_with_names(idx):
 
 def test_droplevel_list():
     index = MultiIndex(
-        levels=[Index(lrange(4)), Index(lrange(4)), Index(lrange(4))],
-        codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
-            [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])],
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+               np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+               np.array([1, 0, 1, 1, 0, 0, 1, 0])],
         names=['one', 'two', 'three'])
 
     dropped = index[:2].droplevel(['three', 'one'])

pandas/tests/indexes/multi/test_equivalence.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 import pandas as pd
 from pandas import Index, MultiIndex, Series
 import pandas.util.testing as tm
@@ -96,17 +94,21 @@ def test_equals_multi(idx):
     assert not idx.equals(idx[-1])
 
     # different number of levels
-    index = MultiIndex(levels=[Index(lrange(4)), Index(lrange(4)), Index(
-        lrange(4))], codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]), np.array(
-            [0, 1, 0, 0, 0, 1, 0, 1]), np.array([1, 0, 1, 1, 0, 0, 1, 0])])
+    index = MultiIndex(levels=[Index(list(range(4))),
+                               Index(list(range(4))),
+                               Index(list(range(4)))],
+                       codes=[np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                              np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                              np.array([1, 0, 1, 1, 0, 0, 1, 0])],
+                       )
 
     index2 = MultiIndex(levels=index.levels[:-1], codes=index.codes[:-1])
     assert not index.equals(index2)
     assert not index.equal_levels(index2)
 
     # levels are different
-    major_axis = Index(lrange(4))
-    minor_axis = Index(lrange(2))
+    major_axis = Index(list(range(4)))
+    minor_axis = Index(list(range(2)))
 
     major_codes = np.array([0, 0, 1, 2, 2, 3])
     minor_codes = np.array([0, 1, 0, 0, 1, 0])
@@ -178,14 +180,14 @@ def test_is_():
     mi2.set_names(["E", "F"], inplace=True)
     assert mi.is_(mi2)
     # levels are inherent properties, they change identity
-    mi3 = mi2.set_levels([lrange(10), lrange(10)])
+    mi3 = mi2.set_levels([list(range(10)), list(range(10))])
     assert not mi3.is_(mi2)
     # shouldn't change
     assert mi2.is_(mi)
     mi4 = mi3.view()
 
     # GH 17464 - Remove duplicate MultiIndex levels
-    mi4.set_levels([lrange(10), lrange(10)], inplace=True)
+    mi4.set_levels([list(range(10)), list(range(10))], inplace=True)
     assert not mi4.is_(mi3)
     mi5 = mi.view()
     mi5.set_levels(mi5.levels, inplace=True)