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CLN: remove compat.lrange, part 3 #26319

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May 9, 2019
Merged

CLN: remove compat.lrange, part 3 #26319

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4 changes: 1 addition & 3 deletions pandas/tests/dtypes/test_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down
6 changes: 2 additions & 4 deletions pandas/tests/frame/test_alter_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand Down Expand Up @@ -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)
Expand Down
19 changes: 9 additions & 10 deletions pandas/tests/frame/test_analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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

Expand All @@ -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

Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down
4 changes: 1 addition & 3 deletions pandas/tests/frame/test_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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)
Expand Down
39 changes: 19 additions & 20 deletions pandas/tests/frame/test_axis_select_reindex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
import numpy as np
import pytest

from pandas.compat import lrange
from pandas.errors import PerformanceWarning

import pandas as pd
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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()

Expand Down Expand Up @@ -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)

Expand Down
36 changes: 17 additions & 19 deletions pandas/tests/frame/test_combine_concat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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')
Expand Down Expand Up @@ -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.]},
Expand Down Expand Up @@ -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])
Expand Down
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