CLN: remove uses of compat.lrange, part I

Author: topper-123

pandas/core/generic.py

@@ -15,7 +15,7 @@
 from pandas._config import config
 
 from pandas._libs import Timestamp, iNaT, properties
-from pandas.compat import lrange, lzip, set_function_name, to_str
+from pandas.compat import lzip, set_function_name, to_str
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
@@ -1101,7 +1101,7 @@ def rename(self, *args, **kwargs):
         result = self if inplace else self.copy(deep=copy)
 
         # start in the axis order to eliminate too many copies
-        for axis in lrange(self._AXIS_LEN):
+        for axis in range(self._AXIS_LEN):
             v = axes.get(self._AXIS_NAMES[axis])
             if v is None:
                 continue
@@ -1294,7 +1294,7 @@ class  name
             # is specified
             result = self if inplace else self.copy(deep=copy)
 
-            for axis in lrange(self._AXIS_LEN):
+            for axis in range(self._AXIS_LEN):
                 v = axes.get(self._AXIS_NAMES[axis])
                 if v is sentinel:
                     continue

pandas/core/indexes/multi.py

@@ -9,7 +9,7 @@
 
 from pandas._libs import (
     Timestamp, algos as libalgos, index as libindex, lib, tslibs)
-from pandas.compat import lrange, lzip
+from pandas.compat import lzip
 from pandas.compat.numpy import function as nv
 from pandas.errors import PerformanceWarning, UnsortedIndexError
 from pandas.util._decorators import Appender, cache_readonly, deprecate_kwarg
@@ -1913,7 +1913,7 @@ def drop(self, codes, level=None, errors='raise'):
                 if isinstance(loc, int):
                     inds.append(loc)
                 elif isinstance(loc, slice):
-                    inds.extend(lrange(loc.start, loc.stop))
+                    inds.extend(range(loc.start, loc.stop))
                 elif com.is_bool_indexer(loc):
                     if self.lexsort_depth == 0:
                         warnings.warn('dropping on a non-lexsorted multi-index'

pandas/core/indexes/range.py

@@ -7,7 +7,6 @@
 
 from pandas._libs import index as libindex, lib
 import pandas.compat as compat
-from pandas.compat import lrange
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, cache_readonly
 
@@ -292,7 +291,7 @@ def has_duplicates(self):
         return False
 
     def tolist(self):
-        return lrange(self._start, self._stop, self._step)
+        return list(range(self._start, self._stop, self._step))
 
     @Appender(_index_shared_docs['_shallow_copy'])
     def _shallow_copy(self, values=None, **kwargs):

pandas/core/internals/construction.py

@@ -9,7 +9,7 @@
 
 from pandas._libs import lib
 from pandas._libs.tslibs import IncompatibleFrequency
-from pandas.compat import lmap, lrange, raise_with_traceback
+from pandas.compat import lmap, raise_with_traceback
 
 from pandas.core.dtypes.cast import (
     construct_1d_arraylike_from_scalar, construct_1d_ndarray_preserving_na,
@@ -339,7 +339,7 @@ def get_names_from_index(data):
     if not has_some_name:
         return ibase.default_index(len(data))
 
-    index = lrange(len(data))
+    index = list(range(len(data)))
     count = 0
     for i, s in enumerate(data):
         n = getattr(s, 'name', None)

pandas/core/reshape/concat.py

@@ -452,7 +452,7 @@ def _get_new_axes(self):
                                      "to {length}".format(length=ndim - 1))
 
             # ufff...
-            indices = compat.lrange(ndim)
+            indices = list(range(ndim))
             indices.remove(self.axis)
 
             for i, ax in zip(indices, self.join_axes):

pandas/core/reshape/pivot.py

@@ -1,6 +1,5 @@
 import numpy as np
 
-from pandas.compat import lrange
 from pandas.util._decorators import Appender, Substitution
 
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
@@ -303,7 +302,7 @@ def _all_key(key):
         row_margin = row_margin.stack()
 
         # slight hack
-        new_order = [len(cols)] + lrange(len(cols))
+        new_order = [len(cols)] + list(range(len(cols)))
         row_margin.index = row_margin.index.reorder_levels(new_order)
     else:
         row_margin = Series(np.nan, index=result.columns)

pandas/io/pytables.py

@@ -18,7 +18,6 @@
 
 from pandas._libs import lib, writers as libwriters
 from pandas._libs.tslibs import timezones
-from pandas.compat import lrange
 from pandas.errors import PerformanceWarning
 
 from pandas.core.dtypes.common import (
@@ -4101,7 +4100,7 @@ def delete(self, where=None, start=None, stop=None, **kwargs):
             # we must remove in reverse order!
             pg = groups.pop()
             for g in reversed(groups):
-                rows = sorted_series.take(lrange(g, pg))
+                rows = sorted_series.take(range(g, pg))
                 table.remove_rows(start=rows[rows.index[0]
                                              ], stop=rows[rows.index[-1]] + 1)
                 pg = g

pandas/tests/groupby/test_groupby.py

@@ -6,7 +6,7 @@
 import numpy as np
 import pytest
 
-from pandas.compat import lmap, lrange, lzip
+from pandas.compat import lmap, lzip
 from pandas.errors import PerformanceWarning
 
 import pandas as pd
@@ -84,7 +84,7 @@ def test_groupby_nonobject_dtype(mframe, df_mixed_floats):
 
     # GH 3911, mixed frame non-conversion
     df = df_mixed_floats.copy()
-    df['value'] = lrange(len(df))
+    df['value'] = range(len(df))
 
     def max_value(group):
         return group.loc[group['value'].idxmax()]
@@ -249,11 +249,11 @@ def test_len():
 
 def test_basic_regression():
     # regression
-    T = [1.0 * x for x in lrange(1, 10) * 10][:1095]
-    result = Series(T, lrange(0, len(T)))
+    T = [1.0 * x for x in list(range(1, 10)) * 10][:1095]
+    result = Series(T, index=range(len(T)))
 
     groupings = np.random.random((1100, ))
-    groupings = Series(groupings, lrange(0, len(groupings))) * 10.
+    groupings = Series(groupings, index=range(len(groupings))) * 10.
 
     grouped = result.groupby(groupings)
     grouped.mean()
@@ -320,9 +320,9 @@ def f3(x):
         else:
             return y
 
-    df = DataFrame({'a': [1, 2, 2, 2], 'b': lrange(4), 'c': lrange(5, 9)})
+    df = DataFrame({'a': [1, 2, 2, 2], 'b': range(4), 'c': range(5, 9)})
 
-    df2 = DataFrame({'a': [3, 2, 2, 2], 'b': lrange(4), 'c': lrange(5, 9)})
+    df2 = DataFrame({'a': [3, 2, 2, 2], 'b': range(4), 'c': range(5, 9)})
 
     # correct result
     result1 = df.groupby('a').apply(f1)
@@ -875,7 +875,7 @@ def test_mutate_groups():
         'cat1': ['a'] * 8 + ['b'] * 6,
         'cat2': ['c'] * 2 + ['d'] * 2 + ['e'] * 2 + ['f'] * 2 + ['c'] * 2 +
         ['d'] * 2 + ['e'] * 2,
-        'cat3': lmap(lambda x: 'g%s' % x, lrange(1, 15)),
+        'cat3': lmap(lambda x: 'g%s' % x, range(1, 15)),
         'val': np.random.randint(100, size=14),
     })
 
@@ -1063,8 +1063,8 @@ def test_groupby_mixed_type_columns():
 def test_cython_grouper_series_bug_noncontig():
     arr = np.empty((100, 100))
     arr.fill(np.nan)
-    obj = Series(arr[:, 0], index=lrange(100))
-    inds = np.tile(lrange(10), 10)
+    obj = Series(arr[:, 0], index=range(100))
+    inds = np.tile(range(10), 10)
 
     result = obj.groupby(inds).agg(Series.median)
     assert result.isna().all()
@@ -1086,7 +1086,7 @@ def test_series_grouper_noncontig_index():
 
 def test_convert_objects_leave_decimal_alone():
 
-    s = Series(lrange(5))
+    s = Series(range(5))
     labels = np.array(['a', 'b', 'c', 'd', 'e'], dtype='O')
 
     def convert_fast(x):
@@ -1217,7 +1217,7 @@ def test_groupby_nat_exclude():
 
 
 def test_groupby_2d_malformed():
-    d = DataFrame(index=lrange(2))
+    d = DataFrame(index=range(2))
     d['group'] = ['g1', 'g2']
     d['zeros'] = [0, 0]
     d['ones'] = [1, 1]