CLN: remove compat.lrange

Author: topper-123

Diff for: pandas/tests/dtypes/test_inference.py

@@ -16,7 +16,6 @@
 import pytz
 
 from pandas._libs import iNaT, lib, missing as libmissing
-from pandas.compat import lrange
 import pandas.util._test_decorators as td
 
 from pandas.core.dtypes import inference
@@ -1302,8 +1301,7 @@ def test_datetimeindex_from_empty_datetime64_array():
 def test_nan_to_nat_conversions():
 
     df = DataFrame(dict({
-        'A': np.asarray(
-            lrange(10), dtype='float64'),
+        'A': np.asarray(range(10), dtype='float64'),
         'B': Timestamp('20010101')
     }))
     df.iloc[3:6, :] = np.nan

Diff for: pandas/tests/frame/test_alter_axes.py

@@ -4,8 +4,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 from pandas.core.dtypes.common import (
     is_categorical_dtype, is_interval_dtype, is_object_dtype)
 
@@ -1091,13 +1089,13 @@ def test_reset_index_multiindex_col(self):
         tm.assert_frame_equal(rs, xp)
 
         rs = df.reset_index('a', col_fill=None)
-        xp = DataFrame(full, Index(lrange(3), name='d'),
+        xp = DataFrame(full, Index(range(3), name='d'),
                        columns=[['a', 'b', 'b', 'c'],
                                 ['a', 'mean', 'median', 'mean']])
         tm.assert_frame_equal(rs, xp)
 
         rs = df.reset_index('a', col_fill='blah', col_level=1)
-        xp = DataFrame(full, Index(lrange(3), name='d'),
+        xp = DataFrame(full, Index(range(3), name='d'),
                        columns=[['blah', 'b', 'b', 'c'],
                                 ['a', 'mean', 'median', 'mean']])
         tm.assert_frame_equal(rs, xp)

Diff for: pandas/tests/frame/test_analytics.py

@@ -6,7 +6,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -54,7 +53,7 @@ def assert_stat_op_calc(opname, alternative, frame, has_skipna=True,
         result = getattr(df, opname)()
         assert isinstance(result, Series)
 
-        df['a'] = lrange(len(df))
+        df['a'] = range(len(df))
         result = getattr(df, opname)()
         assert isinstance(result, Series)
         assert len(result)
@@ -1203,7 +1202,7 @@ def test_stats_mixed_type(self, float_string_frame):
         float_string_frame.skew(1)
 
     def test_sum_bools(self):
-        df = DataFrame(index=lrange(1), columns=lrange(10))
+        df = DataFrame(index=range(1), columns=range(10))
         bools = isna(df)
         assert bools.sum(axis=1)[0] == 10
 
@@ -1212,7 +1211,7 @@ def test_sum_bools(self):
 
     def test_cumsum_corner(self):
         dm = DataFrame(np.arange(20).reshape(4, 5),
-                       index=lrange(4), columns=lrange(5))
+                       index=range(4), columns=range(5))
         # ?(wesm)
         result = dm.cumsum()  # noqa
 
@@ -1327,12 +1326,12 @@ def test_count(self):
         assert isinstance(ct2, Series)
 
         # GH#423
-        df = DataFrame(index=lrange(10))
+        df = DataFrame(index=range(10))
         result = df.count(1)
         expected = Series(0, index=df.index)
         tm.assert_series_equal(result, expected)
 
-        df = DataFrame(columns=lrange(10))
+        df = DataFrame(columns=range(10))
         result = df.count(0)
         expected = Series(0, index=df.columns)
         tm.assert_series_equal(result, expected)
@@ -2137,9 +2136,9 @@ def test_dot(self):
 
         # unaligned
         df = DataFrame(np.random.randn(3, 4),
-                       index=[1, 2, 3], columns=lrange(4))
+                       index=[1, 2, 3], columns=range(4))
         df2 = DataFrame(np.random.randn(5, 3),
-                        index=lrange(5), columns=[1, 2, 3])
+                        index=range(5), columns=[1, 2, 3])
 
         with pytest.raises(ValueError, match='aligned'):
             df.dot(df2)
@@ -2197,9 +2196,9 @@ def test_matmul(self):
 
         # unaligned
         df = DataFrame(np.random.randn(3, 4),
-                       index=[1, 2, 3], columns=lrange(4))
+                       index=[1, 2, 3], columns=range(4))
         df2 = DataFrame(np.random.randn(5, 3),
-                        index=lrange(5), columns=[1, 2, 3])
+                        index=range(5), columns=[1, 2, 3])
 
         with pytest.raises(ValueError, match='aligned'):
             operator.matmul(df, df2)

Diff for: pandas/tests/frame/test_api.py

@@ -4,8 +4,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 import pandas as pd
 from pandas import (
     Categorical, DataFrame, Series, SparseDataFrame, compat, date_range,
@@ -234,7 +232,7 @@ def test_itertuples(self, float_frame):
             self._assert_series_equal(s, expected)
 
         df = self.klass({'floats': np.random.randn(5),
-                         'ints': lrange(5)}, columns=['floats', 'ints'])
+                         'ints': range(5)}, columns=['floats', 'ints'])
 
         for tup in df.itertuples(index=False):
             assert isinstance(tup[1], int)

Diff for: pandas/tests/frame/test_axis_select_reindex.py

@@ -3,7 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
 from pandas.errors import PerformanceWarning
 
 import pandas as pd
@@ -393,44 +392,44 @@ def test_reindex_fill_value(self):
         df = DataFrame(np.random.randn(10, 4))
 
         # axis=0
-        result = df.reindex(lrange(15))
+        result = df.reindex(list(range(15)))
         assert np.isnan(result.values[-5:]).all()
 
-        result = df.reindex(lrange(15), fill_value=0)
-        expected = df.reindex(lrange(15)).fillna(0)
+        result = df.reindex(range(15), fill_value=0)
+        expected = df.reindex(range(15)).fillna(0)
         assert_frame_equal(result, expected)
 
         # axis=1
-        result = df.reindex(columns=lrange(5), fill_value=0.)
+        result = df.reindex(columns=range(5), fill_value=0.)
         expected = df.copy()
         expected[4] = 0.
         assert_frame_equal(result, expected)
 
-        result = df.reindex(columns=lrange(5), fill_value=0)
+        result = df.reindex(columns=range(5), fill_value=0)
         expected = df.copy()
         expected[4] = 0
         assert_frame_equal(result, expected)
 
-        result = df.reindex(columns=lrange(5), fill_value='foo')
+        result = df.reindex(columns=range(5), fill_value='foo')
         expected = df.copy()
         expected[4] = 'foo'
         assert_frame_equal(result, expected)
 
         # reindex_axis
         with tm.assert_produces_warning(FutureWarning):
-            result = df.reindex_axis(lrange(15), fill_value=0., axis=0)
-        expected = df.reindex(lrange(15)).fillna(0)
+            result = df.reindex_axis(range(15), fill_value=0., axis=0)
+        expected = df.reindex(range(15)).fillna(0)
         assert_frame_equal(result, expected)
 
         with tm.assert_produces_warning(FutureWarning):
-            result = df.reindex_axis(lrange(5), fill_value=0., axis=1)
-        expected = df.reindex(columns=lrange(5)).fillna(0)
+            result = df.reindex_axis(range(5), fill_value=0., axis=1)
+        expected = df.reindex(columns=range(5)).fillna(0)
         assert_frame_equal(result, expected)
 
         # other dtypes
         df['foo'] = 'foo'
-        result = df.reindex(lrange(15), fill_value=0)
-        expected = df.reindex(lrange(15)).fillna(0)
+        result = df.reindex(range(15), fill_value=0)
+        expected = df.reindex(range(15)).fillna(0)
         assert_frame_equal(result, expected)
 
     def test_reindex_dups(self):
@@ -1025,7 +1024,7 @@ def test_reindex_boolean(self):
         assert reindexed.values.dtype == np.object_
         assert isna(reindexed[0][1])
 
-        reindexed = frame.reindex(columns=lrange(3))
+        reindexed = frame.reindex(columns=range(3))
         assert reindexed.values.dtype == np.object_
         assert isna(reindexed[1]).all()
 
@@ -1093,22 +1092,22 @@ def test_reindex_with_nans(self):
     def test_reindex_multi(self):
         df = DataFrame(np.random.randn(3, 3))
 
-        result = df.reindex(index=lrange(4), columns=lrange(4))
-        expected = df.reindex(lrange(4)).reindex(columns=lrange(4))
+        result = df.reindex(index=range(4), columns=range(4))
+        expected = df.reindex(list(range(4))).reindex(columns=range(4))
 
         assert_frame_equal(result, expected)
 
         df = DataFrame(np.random.randint(0, 10, (3, 3)))
 
-        result = df.reindex(index=lrange(4), columns=lrange(4))
-        expected = df.reindex(lrange(4)).reindex(columns=lrange(4))
+        result = df.reindex(index=range(4), columns=range(4))
+        expected = df.reindex(list(range(4))).reindex(columns=range(4))
 
         assert_frame_equal(result, expected)
 
         df = DataFrame(np.random.randint(0, 10, (3, 3)))
 
-        result = df.reindex(index=lrange(2), columns=lrange(2))
-        expected = df.reindex(lrange(2)).reindex(columns=lrange(2))
+        result = df.reindex(index=range(2), columns=range(2))
+        expected = df.reindex(range(2)).reindex(columns=range(2))
 
         assert_frame_equal(result, expected)
 

Diff for: pandas/tests/frame/test_combine_concat.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lrange
-
 import pandas as pd
 from pandas import DataFrame, Index, Series, Timestamp, date_range
 import pandas.util.testing as tm
@@ -216,41 +214,41 @@ def test_append_dtypes(self):
         # row appends of different dtypes (so need to do by-item)
         # can sometimes infer the correct type
 
-        df1 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(5))
+        df1 = DataFrame({'bar': Timestamp('20130101')}, index=range(5))
         df2 = DataFrame()
         result = df1.append(df2)
         expected = df1.copy()
         assert_frame_equal(result, expected)
 
-        df1 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(1))
-        df2 = DataFrame({'bar': 'foo'}, index=lrange(1, 2))
+        df1 = DataFrame({'bar': Timestamp('20130101')}, index=range(1))
+        df2 = DataFrame({'bar': 'foo'}, index=range(1, 2))
         result = df1.append(df2)
         expected = DataFrame({'bar': [Timestamp('20130101'), 'foo']})
         assert_frame_equal(result, expected)
 
-        df1 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(1))
-        df2 = DataFrame({'bar': np.nan}, index=lrange(1, 2))
+        df1 = DataFrame({'bar': Timestamp('20130101')}, index=range(1))
+        df2 = DataFrame({'bar': np.nan}, index=range(1, 2))
         result = df1.append(df2)
         expected = DataFrame(
             {'bar': Series([Timestamp('20130101'), np.nan], dtype='M8[ns]')})
         assert_frame_equal(result, expected)
 
-        df1 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(1))
-        df2 = DataFrame({'bar': np.nan}, index=lrange(1, 2), dtype=object)
+        df1 = DataFrame({'bar': Timestamp('20130101')}, index=range(1))
+        df2 = DataFrame({'bar': np.nan}, index=range(1, 2), dtype=object)
         result = df1.append(df2)
         expected = DataFrame(
             {'bar': Series([Timestamp('20130101'), np.nan], dtype='M8[ns]')})
         assert_frame_equal(result, expected)
 
-        df1 = DataFrame({'bar': np.nan}, index=lrange(1))
-        df2 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(1, 2))
+        df1 = DataFrame({'bar': np.nan}, index=range(1))
+        df2 = DataFrame({'bar': Timestamp('20130101')}, index=range(1, 2))
         result = df1.append(df2)
         expected = DataFrame(
             {'bar': Series([np.nan, Timestamp('20130101')], dtype='M8[ns]')})
         assert_frame_equal(result, expected)
 
-        df1 = DataFrame({'bar': Timestamp('20130101')}, index=lrange(1))
-        df2 = DataFrame({'bar': 1}, index=lrange(1, 2), dtype=object)
+        df1 = DataFrame({'bar': Timestamp('20130101')}, index=range(1))
+        df2 = DataFrame({'bar': 1}, index=range(1, 2), dtype=object)
         result = df1.append(df2)
         expected = DataFrame({'bar': Series([Timestamp('20130101'), 1])})
         assert_frame_equal(result, expected)
@@ -379,7 +377,7 @@ def test_join_str_datetime(self):
         str_dates = ['20120209', '20120222']
         dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
 
-        A = DataFrame(str_dates, index=lrange(2), columns=['aa'])
+        A = DataFrame(str_dates, index=range(2), columns=['aa'])
         C = DataFrame([[1, 2], [3, 4]], index=str_dates, columns=dt_dates)
 
         tst = A.join(C, on='aa')
@@ -535,12 +533,12 @@ def test_concat_astype_dup_col(self):
 class TestDataFrameCombineFirst():
 
     def test_combine_first_mixed(self):
-        a = Series(['a', 'b'], index=lrange(2))
-        b = Series(lrange(2), index=lrange(2))
+        a = Series(['a', 'b'], index=range(2))
+        b = Series(range(2), index=range(2))
         f = DataFrame({'A': a, 'B': b})
 
-        a = Series(['a', 'b'], index=lrange(5, 7))
-        b = Series(lrange(2), index=lrange(5, 7))
+        a = Series(['a', 'b'], index=range(5, 7))
+        b = Series(range(2), index=range(5, 7))
         g = DataFrame({'A': a, 'B': b})
 
         exp = pd.DataFrame({'A': list('abab'), 'B': [0., 1., 0., 1.]},
@@ -861,7 +859,7 @@ def test_concat_datetime_datetime64_frame(self):
         df2_obj = DataFrame.from_records(rows, columns=['date', 'test'])
 
         ind = date_range(start="2000/1/1", freq="D", periods=10)
-        df1 = DataFrame({'date': ind, 'test': lrange(10)})
+        df1 = DataFrame({'date': ind, 'test': range(10)})
 
         # it works!
         pd.concat([df1, df2_obj])