782 lines
22 KiB
Python
782 lines
22 KiB
Python
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import numpy as np
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import pytest
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from pandas import DataFrame, Float64Index, Index, Int64Index, RangeIndex, Series
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import pandas._testing as tm
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def gen_obj(klass, index):
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if klass is Series:
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obj = Series(np.arange(len(index)), index=index)
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else:
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obj = DataFrame(
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np.random.randn(len(index), len(index)), index=index, columns=index
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)
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return obj
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class TestFloatIndexers:
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def check(self, result, original, indexer, getitem):
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"""
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comparator for results
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we need to take care if we are indexing on a
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Series or a frame
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"""
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if isinstance(original, Series):
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expected = original.iloc[indexer]
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else:
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if getitem:
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expected = original.iloc[:, indexer]
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else:
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expected = original.iloc[indexer]
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tm.assert_almost_equal(result, expected)
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@pytest.mark.parametrize(
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"index_func",
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[
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tm.makeStringIndex,
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tm.makeUnicodeIndex,
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tm.makeCategoricalIndex,
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tm.makeDateIndex,
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tm.makeTimedeltaIndex,
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tm.makePeriodIndex,
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],
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)
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def test_scalar_non_numeric(self, index_func, frame_or_series):
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# GH 4892
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# float_indexers should raise exceptions
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# on appropriate Index types & accessors
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i = index_func(5)
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s = gen_obj(frame_or_series, i)
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# getting
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with pytest.raises(KeyError, match="^3.0$"):
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s[3.0]
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with pytest.raises(KeyError, match="^3.0$"):
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s.loc[3.0]
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# contains
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assert 3.0 not in s
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# setting with an indexer
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if s.index.inferred_type in ["categorical"]:
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# Value or Type Error
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pass
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elif s.index.inferred_type in ["datetime64", "timedelta64", "period"]:
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# FIXME: dont leave commented-out
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# these should prob work
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# and are inconsistent between series/dataframe ATM
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# for idxr in [lambda x: x]:
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# s2 = s.copy()
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#
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# with pytest.raises(TypeError):
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# idxr(s2)[3.0] = 0
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pass
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else:
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s2 = s.copy()
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s2.loc[3.0] = 10
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assert s2.index.is_object()
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s2 = s.copy()
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s2[3.0] = 0
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assert s2.index.is_object()
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@pytest.mark.parametrize(
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"index_func",
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[
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tm.makeStringIndex,
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tm.makeUnicodeIndex,
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tm.makeCategoricalIndex,
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tm.makeDateIndex,
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tm.makeTimedeltaIndex,
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tm.makePeriodIndex,
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],
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)
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def test_scalar_non_numeric_series_fallback(self, index_func):
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# fallsback to position selection, series only
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i = index_func(5)
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s = Series(np.arange(len(i)), index=i)
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s[3]
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with pytest.raises(KeyError, match="^3.0$"):
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s[3.0]
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def test_scalar_with_mixed(self):
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s2 = Series([1, 2, 3], index=["a", "b", "c"])
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s3 = Series([1, 2, 3], index=["a", "b", 1.5])
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# lookup in a pure string index with an invalid indexer
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with pytest.raises(KeyError, match="^1.0$"):
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s2[1.0]
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with pytest.raises(KeyError, match=r"^1\.0$"):
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s2.loc[1.0]
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result = s2.loc["b"]
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expected = 2
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assert result == expected
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# mixed index so we have label
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# indexing
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with pytest.raises(KeyError, match="^1.0$"):
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s3[1.0]
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result = s3[1]
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expected = 2
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assert result == expected
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with pytest.raises(KeyError, match=r"^1\.0$"):
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s3.loc[1.0]
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result = s3.loc[1.5]
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expected = 3
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assert result == expected
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@pytest.mark.parametrize(
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"idxr,getitem", [(lambda x: x.loc, False), (lambda x: x, True)]
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)
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@pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
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def test_scalar_integer(self, index_func, frame_or_series, idxr, getitem):
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# test how scalar float indexers work on int indexes
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# integer index
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i = index_func(5)
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obj = gen_obj(frame_or_series, i)
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# coerce to equal int
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result = idxr(obj)[3.0]
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self.check(result, obj, 3, getitem)
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if isinstance(obj, Series):
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def compare(x, y):
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assert x == y
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expected = 100
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else:
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compare = tm.assert_series_equal
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if getitem:
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expected = Series(100, index=range(len(obj)), name=3)
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else:
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expected = Series(100.0, index=range(len(obj)), name=3)
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s2 = obj.copy()
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idxr(s2)[3.0] = 100
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result = idxr(s2)[3.0]
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compare(result, expected)
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result = idxr(s2)[3]
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compare(result, expected)
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@pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
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def test_scalar_integer_contains_float(self, index_func, frame_or_series):
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# contains
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# integer index
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index = index_func(5)
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obj = gen_obj(frame_or_series, index)
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# coerce to equal int
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assert 3.0 in obj
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def test_scalar_float(self, frame_or_series):
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# scalar float indexers work on a float index
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index = Index(np.arange(5.0))
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s = gen_obj(frame_or_series, index)
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# assert all operations except for iloc are ok
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indexer = index[3]
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for idxr, getitem in [(lambda x: x.loc, False), (lambda x: x, True)]:
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# getting
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result = idxr(s)[indexer]
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self.check(result, s, 3, getitem)
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# setting
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s2 = s.copy()
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result = idxr(s2)[indexer]
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self.check(result, s, 3, getitem)
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# random float is a KeyError
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with pytest.raises(KeyError, match=r"^3\.5$"):
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idxr(s)[3.5]
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# contains
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assert 3.0 in s
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# iloc succeeds with an integer
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expected = s.iloc[3]
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s2 = s.copy()
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s2.iloc[3] = expected
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result = s2.iloc[3]
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self.check(result, s, 3, False)
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@pytest.mark.parametrize(
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"index_func",
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[
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tm.makeStringIndex,
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tm.makeUnicodeIndex,
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tm.makeDateIndex,
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tm.makeTimedeltaIndex,
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tm.makePeriodIndex,
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],
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)
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@pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
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def test_slice_non_numeric(self, index_func, idx, frame_or_series):
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# GH 4892
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# float_indexers should raise exceptions
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# on appropriate Index types & accessors
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index = index_func(5)
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s = gen_obj(frame_or_series, index)
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# getitem
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msg = (
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"cannot do positional indexing "
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fr"on {type(index).__name__} with these indexers \[(3|4)\.0\] of "
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"type float"
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)
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with pytest.raises(TypeError, match=msg):
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s.iloc[idx]
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msg = (
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"cannot do (slice|positional) indexing "
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fr"on {type(index).__name__} with these indexers "
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r"\[(3|4)(\.0)?\] "
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r"of type (float|int)"
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)
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for idxr in [lambda x: x.loc, lambda x: x.iloc, lambda x: x]:
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with pytest.raises(TypeError, match=msg):
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idxr(s)[idx]
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# setitem
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msg = "slice indices must be integers or None or have an __index__ method"
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with pytest.raises(TypeError, match=msg):
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s.iloc[idx] = 0
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msg = (
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"cannot do (slice|positional) indexing "
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fr"on {type(index).__name__} with these indexers "
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r"\[(3|4)(\.0)?\] "
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r"of type (float|int)"
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)
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for idxr in [lambda x: x.loc, lambda x: x]:
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with pytest.raises(TypeError, match=msg):
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idxr(s)[idx] = 0
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def test_slice_integer(self):
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# same as above, but for Integer based indexes
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# these coerce to a like integer
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# oob indicates if we are out of bounds
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# of positional indexing
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for index, oob in [
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(Int64Index(range(5)), False),
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(RangeIndex(5), False),
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(Int64Index(range(5)) + 10, True),
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]:
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# s is an in-range index
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s = Series(range(5), index=index)
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# getitem
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for idx in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]:
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result = s.loc[idx]
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# these are all label indexing
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# except getitem which is positional
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# empty
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if oob:
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indexer = slice(0, 0)
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else:
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indexer = slice(3, 5)
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self.check(result, s, indexer, False)
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# getitem out-of-bounds
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for idx in [slice(-6, 6), slice(-6.0, 6.0)]:
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result = s.loc[idx]
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# these are all label indexing
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# except getitem which is positional
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# empty
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if oob:
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indexer = slice(0, 0)
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else:
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indexer = slice(-6, 6)
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self.check(result, s, indexer, False)
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# positional indexing
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msg = (
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"cannot do slice indexing "
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fr"on {type(index).__name__} with these indexers \[-6\.0\] of "
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"type float"
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)
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with pytest.raises(TypeError, match=msg):
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s[slice(-6.0, 6.0)]
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# getitem odd floats
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for idx, res1 in [
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(slice(2.5, 4), slice(3, 5)),
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(slice(2, 3.5), slice(2, 4)),
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(slice(2.5, 3.5), slice(3, 4)),
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]:
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result = s.loc[idx]
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if oob:
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res = slice(0, 0)
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else:
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res = res1
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self.check(result, s, res, False)
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# positional indexing
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msg = (
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"cannot do slice indexing "
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fr"on {type(index).__name__} with these indexers \[(2|3)\.5\] of "
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"type float"
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)
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with pytest.raises(TypeError, match=msg):
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s[idx]
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@pytest.mark.parametrize("idx", [slice(2, 4.0), slice(2.0, 4), slice(2.0, 4.0)])
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def test_integer_positional_indexing(self, idx):
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"""make sure that we are raising on positional indexing
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w.r.t. an integer index
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"""
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s = Series(range(2, 6), index=range(2, 6))
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result = s[2:4]
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expected = s.iloc[2:4]
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tm.assert_series_equal(result, expected)
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klass = RangeIndex
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msg = (
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"cannot do (slice|positional) indexing "
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fr"on {klass.__name__} with these indexers \[(2|4)\.0\] of "
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"type float"
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)
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with pytest.raises(TypeError, match=msg):
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s[idx]
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with pytest.raises(TypeError, match=msg):
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s.iloc[idx]
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@pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
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def test_slice_integer_frame_getitem(self, index_func):
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# similar to above, but on the getitem dim (of a DataFrame)
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index = index_func(5)
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s = DataFrame(np.random.randn(5, 2), index=index)
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# getitem
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|
for idx in [slice(0.0, 1), slice(0, 1.0), slice(0.0, 1.0)]:
|
||
|
|
|
||
|
|
result = s.loc[idx]
|
||
|
|
indexer = slice(0, 2)
|
||
|
|
self.check(result, s, indexer, False)
|
||
|
|
|
||
|
|
# positional indexing
|
||
|
|
msg = (
|
||
|
|
"cannot do slice indexing "
|
||
|
|
fr"on {type(index).__name__} with these indexers \[(0|1)\.0\] of "
|
||
|
|
"type float"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s[idx]
|
||
|
|
|
||
|
|
# getitem out-of-bounds
|
||
|
|
for idx in [slice(-10, 10), slice(-10.0, 10.0)]:
|
||
|
|
|
||
|
|
result = s.loc[idx]
|
||
|
|
self.check(result, s, slice(-10, 10), True)
|
||
|
|
|
||
|
|
# positional indexing
|
||
|
|
msg = (
|
||
|
|
"cannot do slice indexing "
|
||
|
|
fr"on {type(index).__name__} with these indexers \[-10\.0\] of "
|
||
|
|
"type float"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s[slice(-10.0, 10.0)]
|
||
|
|
|
||
|
|
# getitem odd floats
|
||
|
|
for idx, res in [
|
||
|
|
(slice(0.5, 1), slice(1, 2)),
|
||
|
|
(slice(0, 0.5), slice(0, 1)),
|
||
|
|
(slice(0.5, 1.5), slice(1, 2)),
|
||
|
|
]:
|
||
|
|
|
||
|
|
result = s.loc[idx]
|
||
|
|
self.check(result, s, res, False)
|
||
|
|
|
||
|
|
# positional indexing
|
||
|
|
msg = (
|
||
|
|
"cannot do slice indexing "
|
||
|
|
fr"on {type(index).__name__} with these indexers \[0\.5\] of "
|
||
|
|
"type float"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s[idx]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
|
||
|
|
@pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
|
||
|
|
def test_float_slice_getitem_with_integer_index_raises(self, idx, index_func):
|
||
|
|
|
||
|
|
# similar to above, but on the getitem dim (of a DataFrame)
|
||
|
|
index = index_func(5)
|
||
|
|
|
||
|
|
s = DataFrame(np.random.randn(5, 2), index=index)
|
||
|
|
|
||
|
|
# setitem
|
||
|
|
sc = s.copy()
|
||
|
|
sc.loc[idx] = 0
|
||
|
|
result = sc.loc[idx].values.ravel()
|
||
|
|
assert (result == 0).all()
|
||
|
|
|
||
|
|
# positional indexing
|
||
|
|
msg = (
|
||
|
|
"cannot do slice indexing "
|
||
|
|
fr"on {type(index).__name__} with these indexers \[(3|4)\.0\] of "
|
||
|
|
"type float"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s[idx] = 0
|
||
|
|
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
s[idx]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
|
||
|
|
def test_slice_float(self, idx, frame_or_series):
|
||
|
|
|
||
|
|
# same as above, but for floats
|
||
|
|
index = Index(np.arange(5.0)) + 0.1
|
||
|
|
s = gen_obj(frame_or_series, index)
|
||
|
|
|
||
|
|
expected = s.iloc[3:4]
|
||
|
|
for idxr in [lambda x: x.loc, lambda x: x]:
|
||
|
|
|
||
|
|
# getitem
|
||
|
|
result = idxr(s)[idx]
|
||
|
|
assert isinstance(result, type(s))
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
# setitem
|
||
|
|
s2 = s.copy()
|
||
|
|
idxr(s2)[idx] = 0
|
||
|
|
result = idxr(s2)[idx].values.ravel()
|
||
|
|
assert (result == 0).all()
|
||
|
|
|
||
|
|
def test_floating_index_doc_example(self):
|
||
|
|
|
||
|
|
index = Index([1.5, 2, 3, 4.5, 5])
|
||
|
|
s = Series(range(5), index=index)
|
||
|
|
assert s[3] == 2
|
||
|
|
assert s.loc[3] == 2
|
||
|
|
assert s.loc[3] == 2
|
||
|
|
assert s.iloc[3] == 3
|
||
|
|
|
||
|
|
def test_floating_misc(self):
|
||
|
|
|
||
|
|
# related 236
|
||
|
|
# scalar/slicing of a float index
|
||
|
|
s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64)
|
||
|
|
|
||
|
|
# label based slicing
|
||
|
|
result1 = s[1.0:3.0]
|
||
|
|
result2 = s.loc[1.0:3.0]
|
||
|
|
result3 = s.loc[1.0:3.0]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
|
||
|
|
# exact indexing when found
|
||
|
|
result1 = s[5.0]
|
||
|
|
result2 = s.loc[5.0]
|
||
|
|
result3 = s.loc[5.0]
|
||
|
|
assert result1 == result2
|
||
|
|
assert result1 == result3
|
||
|
|
|
||
|
|
result1 = s[5]
|
||
|
|
result2 = s.loc[5]
|
||
|
|
result3 = s.loc[5]
|
||
|
|
assert result1 == result2
|
||
|
|
assert result1 == result3
|
||
|
|
|
||
|
|
assert s[5.0] == s[5]
|
||
|
|
|
||
|
|
# value not found (and no fallbacking at all)
|
||
|
|
|
||
|
|
# scalar integers
|
||
|
|
with pytest.raises(KeyError, match=r"^4$"):
|
||
|
|
s.loc[4]
|
||
|
|
with pytest.raises(KeyError, match=r"^4$"):
|
||
|
|
s.loc[4]
|
||
|
|
with pytest.raises(KeyError, match=r"^4$"):
|
||
|
|
s[4]
|
||
|
|
|
||
|
|
# fancy floats/integers create the correct entry (as nan)
|
||
|
|
# fancy tests
|
||
|
|
expected = Series([2, 0], index=Float64Index([5.0, 0.0]))
|
||
|
|
for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float
|
||
|
|
tm.assert_series_equal(s[fancy_idx], expected)
|
||
|
|
tm.assert_series_equal(s.loc[fancy_idx], expected)
|
||
|
|
tm.assert_series_equal(s.loc[fancy_idx], expected)
|
||
|
|
|
||
|
|
expected = Series([2, 0], index=Index([5, 0], dtype="int64"))
|
||
|
|
for fancy_idx in [[5, 0], np.array([5, 0])]: # int
|
||
|
|
tm.assert_series_equal(s[fancy_idx], expected)
|
||
|
|
tm.assert_series_equal(s.loc[fancy_idx], expected)
|
||
|
|
tm.assert_series_equal(s.loc[fancy_idx], expected)
|
||
|
|
|
||
|
|
# all should return the same as we are slicing 'the same'
|
||
|
|
result1 = s.loc[2:5]
|
||
|
|
result2 = s.loc[2.0:5.0]
|
||
|
|
result3 = s.loc[2.0:5]
|
||
|
|
result4 = s.loc[2.1:5]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
tm.assert_series_equal(result1, result4)
|
||
|
|
|
||
|
|
# previously this did fallback indexing
|
||
|
|
result1 = s[2:5]
|
||
|
|
result2 = s[2.0:5.0]
|
||
|
|
result3 = s[2.0:5]
|
||
|
|
result4 = s[2.1:5]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
tm.assert_series_equal(result1, result4)
|
||
|
|
|
||
|
|
result1 = s.loc[2:5]
|
||
|
|
result2 = s.loc[2.0:5.0]
|
||
|
|
result3 = s.loc[2.0:5]
|
||
|
|
result4 = s.loc[2.1:5]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
tm.assert_series_equal(result1, result4)
|
||
|
|
|
||
|
|
# combined test
|
||
|
|
result1 = s.loc[2:5]
|
||
|
|
result2 = s.loc[2:5]
|
||
|
|
result3 = s[2:5]
|
||
|
|
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
|
||
|
|
# list selection
|
||
|
|
result1 = s[[0.0, 5, 10]]
|
||
|
|
result2 = s.loc[[0.0, 5, 10]]
|
||
|
|
result3 = s.loc[[0.0, 5, 10]]
|
||
|
|
result4 = s.iloc[[0, 2, 4]]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
tm.assert_series_equal(result1, result4)
|
||
|
|
|
||
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
||
|
|
s[[1.6, 5, 10]]
|
||
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
||
|
|
s.loc[[1.6, 5, 10]]
|
||
|
|
|
||
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
||
|
|
s[[0, 1, 2]]
|
||
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
||
|
|
s.loc[[0, 1, 2]]
|
||
|
|
|
||
|
|
result1 = s.loc[[2.5, 5]]
|
||
|
|
result2 = s.loc[[2.5, 5]]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0]))
|
||
|
|
|
||
|
|
result1 = s[[2.5]]
|
||
|
|
result2 = s.loc[[2.5]]
|
||
|
|
result3 = s.loc[[2.5]]
|
||
|
|
tm.assert_series_equal(result1, result2)
|
||
|
|
tm.assert_series_equal(result1, result3)
|
||
|
|
tm.assert_series_equal(result1, Series([1], index=[2.5]))
|
||
|
|
|
||
|
|
def test_floating_tuples(self):
|
||
|
|
# see gh-13509
|
||
|
|
s = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name="foo")
|
||
|
|
|
||
|
|
result = s[0.0]
|
||
|
|
assert result == (1, 1)
|
||
|
|
|
||
|
|
expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name="foo")
|
||
|
|
s = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name="foo")
|
||
|
|
|
||
|
|
result = s[0.0]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_float64index_slicing_bug(self):
|
||
|
|
# GH 5557, related to slicing a float index
|
||
|
|
ser = {
|
||
|
|
256: 2321.0,
|
||
|
|
1: 78.0,
|
||
|
|
2: 2716.0,
|
||
|
|
3: 0.0,
|
||
|
|
4: 369.0,
|
||
|
|
5: 0.0,
|
||
|
|
6: 269.0,
|
||
|
|
7: 0.0,
|
||
|
|
8: 0.0,
|
||
|
|
9: 0.0,
|
||
|
|
10: 3536.0,
|
||
|
|
11: 0.0,
|
||
|
|
12: 24.0,
|
||
|
|
13: 0.0,
|
||
|
|
14: 931.0,
|
||
|
|
15: 0.0,
|
||
|
|
16: 101.0,
|
||
|
|
17: 78.0,
|
||
|
|
18: 9643.0,
|
||
|
|
19: 0.0,
|
||
|
|
20: 0.0,
|
||
|
|
21: 0.0,
|
||
|
|
22: 63761.0,
|
||
|
|
23: 0.0,
|
||
|
|
24: 446.0,
|
||
|
|
25: 0.0,
|
||
|
|
26: 34773.0,
|
||
|
|
27: 0.0,
|
||
|
|
28: 729.0,
|
||
|
|
29: 78.0,
|
||
|
|
30: 0.0,
|
||
|
|
31: 0.0,
|
||
|
|
32: 3374.0,
|
||
|
|
33: 0.0,
|
||
|
|
34: 1391.0,
|
||
|
|
35: 0.0,
|
||
|
|
36: 361.0,
|
||
|
|
37: 0.0,
|
||
|
|
38: 61808.0,
|
||
|
|
39: 0.0,
|
||
|
|
40: 0.0,
|
||
|
|
41: 0.0,
|
||
|
|
42: 6677.0,
|
||
|
|
43: 0.0,
|
||
|
|
44: 802.0,
|
||
|
|
45: 0.0,
|
||
|
|
46: 2691.0,
|
||
|
|
47: 0.0,
|
||
|
|
48: 3582.0,
|
||
|
|
49: 0.0,
|
||
|
|
50: 734.0,
|
||
|
|
51: 0.0,
|
||
|
|
52: 627.0,
|
||
|
|
53: 70.0,
|
||
|
|
54: 2584.0,
|
||
|
|
55: 0.0,
|
||
|
|
56: 324.0,
|
||
|
|
57: 0.0,
|
||
|
|
58: 605.0,
|
||
|
|
59: 0.0,
|
||
|
|
60: 0.0,
|
||
|
|
61: 0.0,
|
||
|
|
62: 3989.0,
|
||
|
|
63: 10.0,
|
||
|
|
64: 42.0,
|
||
|
|
65: 0.0,
|
||
|
|
66: 904.0,
|
||
|
|
67: 0.0,
|
||
|
|
68: 88.0,
|
||
|
|
69: 70.0,
|
||
|
|
70: 8172.0,
|
||
|
|
71: 0.0,
|
||
|
|
72: 0.0,
|
||
|
|
73: 0.0,
|
||
|
|
74: 64902.0,
|
||
|
|
75: 0.0,
|
||
|
|
76: 347.0,
|
||
|
|
77: 0.0,
|
||
|
|
78: 36605.0,
|
||
|
|
79: 0.0,
|
||
|
|
80: 379.0,
|
||
|
|
81: 70.0,
|
||
|
|
82: 0.0,
|
||
|
|
83: 0.0,
|
||
|
|
84: 3001.0,
|
||
|
|
85: 0.0,
|
||
|
|
86: 1630.0,
|
||
|
|
87: 7.0,
|
||
|
|
88: 364.0,
|
||
|
|
89: 0.0,
|
||
|
|
90: 67404.0,
|
||
|
|
91: 9.0,
|
||
|
|
92: 0.0,
|
||
|
|
93: 0.0,
|
||
|
|
94: 7685.0,
|
||
|
|
95: 0.0,
|
||
|
|
96: 1017.0,
|
||
|
|
97: 0.0,
|
||
|
|
98: 2831.0,
|
||
|
|
99: 0.0,
|
||
|
|
100: 2963.0,
|
||
|
|
101: 0.0,
|
||
|
|
102: 854.0,
|
||
|
|
103: 0.0,
|
||
|
|
104: 0.0,
|
||
|
|
105: 0.0,
|
||
|
|
106: 0.0,
|
||
|
|
107: 0.0,
|
||
|
|
108: 0.0,
|
||
|
|
109: 0.0,
|
||
|
|
110: 0.0,
|
||
|
|
111: 0.0,
|
||
|
|
112: 0.0,
|
||
|
|
113: 0.0,
|
||
|
|
114: 0.0,
|
||
|
|
115: 0.0,
|
||
|
|
116: 0.0,
|
||
|
|
117: 0.0,
|
||
|
|
118: 0.0,
|
||
|
|
119: 0.0,
|
||
|
|
120: 0.0,
|
||
|
|
121: 0.0,
|
||
|
|
122: 0.0,
|
||
|
|
123: 0.0,
|
||
|
|
124: 0.0,
|
||
|
|
125: 0.0,
|
||
|
|
126: 67744.0,
|
||
|
|
127: 22.0,
|
||
|
|
128: 264.0,
|
||
|
|
129: 0.0,
|
||
|
|
260: 197.0,
|
||
|
|
268: 0.0,
|
||
|
|
265: 0.0,
|
||
|
|
269: 0.0,
|
||
|
|
261: 0.0,
|
||
|
|
266: 1198.0,
|
||
|
|
267: 0.0,
|
||
|
|
262: 2629.0,
|
||
|
|
258: 775.0,
|
||
|
|
257: 0.0,
|
||
|
|
263: 0.0,
|
||
|
|
259: 0.0,
|
||
|
|
264: 163.0,
|
||
|
|
250: 10326.0,
|
||
|
|
251: 0.0,
|
||
|
|
252: 1228.0,
|
||
|
|
253: 0.0,
|
||
|
|
254: 2769.0,
|
||
|
|
255: 0.0,
|
||
|
|
}
|
||
|
|
|
||
|
|
# smoke test for the repr
|
||
|
|
s = Series(ser)
|
||
|
|
result = s.value_counts()
|
||
|
|
str(result)
|