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CLN: (re-)enable infer_dtype to catch complex #25382

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Merged
merged 3 commits into from
Feb 21, 2019
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CLN: (re-)enable infer_dtype to catch complex #25382

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CLN: (re-)enable infer_dtype to catch complex
h-vetinari Feb 20, 2019
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Merge remote-tracking branch 'upstream/master' into infer_complex
h-vetinari Feb 20, 2019
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Review (jreback)
h-vetinari Feb 20, 2019
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4 changes: 4 additions & 0 deletions pandas/_libs/lib.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -939,6 +939,7 @@ _TYPE_MAP = {
'float32': 'floating',
'float64': 'floating',
'f': 'floating',
'complex64': 'complex',
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what system actually hits this? AFAIK numpy doesn't use this on a regular basis

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Well, it's as easy to use as float32 on a 64-bit platform. I just added it for completeness.

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ok see if u can come up with a test then maybe have to force numpy to construct it

'complex128': 'complex',
'c': 'complex',
'string': 'string' if PY2 else 'bytes',
Expand Down Expand Up @@ -1305,6 +1306,9 @@ def infer_dtype(value: object, skipna: object=None) -> str:
elif is_decimal(val):
return 'decimal'

elif is_complex(val):
return 'complex'

elif util.is_float_object(val):
if is_float_array(values):
return 'floating'
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24 changes: 24 additions & 0 deletions pandas/tests/dtypes/test_inference.py
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Expand Up @@ -618,6 +618,30 @@ def test_decimals(self):
result = lib.infer_dtype(arr, skipna=True)
assert result == 'decimal'

def test_complex(self):
# gets cast to complex on array construction
arr = np.array([1.0, 2.0, 1 + 1j])
result = lib.infer_dtype(arr, skipna=True)
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does skipna=False matter anywhere here?

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No, because np.nan is compatible with complex. The only thing is that not passing skipna yields a warning.

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the parametrize over skipna

assert result == 'complex'

arr = np.array([1.0, 2.0, 1 + 1j], dtype='O')
result = lib.infer_dtype(arr, skipna=True)
assert result == 'mixed'

# gets cast to complex on array construction
arr = np.array([1, np.nan, 1 + 1j])
result = lib.infer_dtype(arr, skipna=True)
assert result == 'complex'

arr = np.array([1.0, np.nan, 1 + 1j], dtype='O')
result = lib.infer_dtype(arr, skipna=True)
assert result == 'mixed'

# complex with nans stays complex
arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype='O')
result = lib.infer_dtype(arr, skipna=True)
assert result == 'complex'

def test_string(self):
pass

Expand Down