Stop using np.{builtin}, and fix bugs due to the previous confusion #8996
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This is the case for x in {int, bool, str, float, complex, object}.
Using the np.{x} version is deceptive as it suggests that there is a
difference. This change doesn't affect any external behaviour. The
`long` type is missing in python 3, so np.long is still useful
Additionally, make the fixes needed to avoid an incoming deprecation in numpy/numpy#9505
eric-wieser
commented
Aug 6, 2017
| @@ -3136,17 +3136,17 @@ def get_type(item, atype=int): | |||
| def get_justify(colname, column, precision): | |||
| ntype = column.dtype | |||
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| if np.issubdtype(ntype, str) or np.issubdtype(ntype, bytes): | |||
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This didn't catch unicode on python 2, but did on python 3
eric-wieser
commented
Aug 6, 2017
| fixed_width = int(ntype.str[2:]) | ||
| length = max(len(colname), fixed_width) | ||
| return 0, length+padding, "%s" # left justify | ||
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| if np.issubdtype(ntype, np.int): |
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This was false for ntype == np.uint8
eric-wieser
commented
Aug 6, 2017
| length = max(len(colname), | ||
| np.max(list(map(len, list(map(str, column)))))) | ||
| return 1, length+padding, "%d" # right justify | ||
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| if np.issubdtype(ntype, np.float): |
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This worked correctly, but will raise a warning in numpy 1.14 if numpy/numpy#9505 is merged
dstansby
approved these changes
Aug 6, 2017
|
Thanks @eric-wieser ! |
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For
xin{int, bool, str, float, complex, object},np.{x}andbuiltins.{x}are one and the same:Using the
np.{x}version is deceptive as it suggests that there is a difference, when there is not. This change doesn't affect any external behaviour.Similar to numpy/numpy#9517