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Update NA repr #30821

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Merged
merged 9 commits into from
Jan 9, 2020
Merged

Update NA repr #30821

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12aa3d3
Update NA repr
TomAugspurger Jan 8, 2020
197efa9
na
TomAugspurger Jan 8, 2020
2b0ad63
fixup
TomAugspurger Jan 8, 2020
a148e45
fixups
TomAugspurger Jan 8, 2020
40e8e9e
fixups
TomAugspurger Jan 8, 2020
e92ad57
Merge remote-tracking branch 'upstream/master' into na-repr
TomAugspurger Jan 9, 2020
24995d8
doctests
TomAugspurger Jan 9, 2020
3c1e367
fixup script
TomAugspurger Jan 9, 2020
c51c5d7
fixup
TomAugspurger Jan 9, 2020
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5 changes: 1 addition & 4 deletions pandas/_libs/missing.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -354,10 +354,7 @@ class NAType(C_NAType):
return NAType._instance

def __repr__(self) -> str:
return "NA"

def __str__(self) -> str:
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Python will automatically use __repr__ if one isn't defined, so this was redundant.

return "NA"
return "<NA>"

def __bool__(self):
raise TypeError("boolean value of NA is ambiguous")
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2 changes: 1 addition & 1 deletion pandas/io/formats/format.py
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Expand Up @@ -1230,7 +1230,7 @@ def _format(x):
if x is None:
return "None"
elif x is NA:
return "NA"
return formatter(x)
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This means that the formatter function needs to be able to handle NAs, which now is maybe not the case?

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Will do a PR for this, I think this breaks geopandas

elif x is NaT or np.isnat(x):
return "NaT"
except (TypeError, ValueError):
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2 changes: 1 addition & 1 deletion pandas/tests/arrays/test_boolean.py
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Expand Up @@ -335,7 +335,7 @@ def test_astype():
tm.assert_numpy_array_equal(result, expected)

result = arr.astype("str")
expected = np.array(["True", "False", "NA"], dtype="object")
expected = np.array(["True", "False", "<NA>"], dtype="object")
tm.assert_numpy_array_equal(result, expected)

# no missing values
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10 changes: 5 additions & 5 deletions pandas/tests/arrays/test_integer.py
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Expand Up @@ -90,17 +90,17 @@ def test_repr_dtype(dtype, expected):

def test_repr_array():
result = repr(integer_array([1, None, 3]))
expected = "<IntegerArray>\n[1, NA, 3]\nLength: 3, dtype: Int64"
expected = "<IntegerArray>\n[1, <NA>, 3]\nLength: 3, dtype: Int64"
assert result == expected


def test_repr_array_long():
data = integer_array([1, 2, None] * 1000)
expected = (
"<IntegerArray>\n"
"[ 1, 2, NA, 1, 2, NA, 1, 2, NA, 1,\n"
"[ 1, 2, <NA>, 1, 2, <NA>, 1, 2, <NA>, 1,\n"
" ...\n"
" NA, 1, 2, NA, 1, 2, NA, 1, 2, NA]\n"
" <NA>, 1, 2, <NA>, 1, 2, <NA>, 1, 2, <NA>]\n"
"Length: 3000, dtype: Int64"
)
result = repr(data)
Expand Down Expand Up @@ -673,7 +673,7 @@ def test_to_numpy_na_raises(self, dtype):

def test_astype_str(self):
a = pd.array([1, 2, None], dtype="Int64")
expected = np.array(["1", "2", "NA"], dtype=object)
expected = np.array(["1", "2", "<NA>"], dtype=object)

tm.assert_numpy_array_equal(a.astype(str), expected)
tm.assert_numpy_array_equal(a.astype("str"), expected)
Expand All @@ -683,7 +683,7 @@ def test_frame_repr(data_missing):

df = pd.DataFrame({"A": data_missing})
result = repr(df)
expected = " A\n0 NA\n1 1"
expected = " A\n0 <NA>\n1 1"
assert result == expected


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