解决 Pandas 中的 Windows-numpy astype(int) 错误

Question

I have a codebase I've been developing on a Mac (and running on Linux machines) based largely on pandas (and therefore numpy). Very commonly I type-cast with astype(int) .

Recently a Windows-based developer joined our team. In an effort to make the code base more platform-independent, we're trying to gracefully tackle this tricky issue whereby numpy uses a 32-bit type instead of the 64-bit type, which breaks longer integers.

On a Mac, we see:

ipdb> ids.astype(int)
id
1818726176      1818726176  
1881879486      1881879486  
2590366906      2590366906  
284399109       284399109   
299981685       299981685   
370708200       370708200   
387277023371    387277023371
387343898032    387343898032
406885699892    406885699892
5262665206      5262665206  
544687374       544687374   
6978317806      6978317806  

Whereas on a Windows machine (in PowerShell), we see:

ipdb> ids.astype(int)
id
1818726176      1818726176
1881879486      1881879486
2590366906     -1704600390
284399109       284399109 
299981685       299981685 
370708200       370708200 
387277023371    729966731 
387343898032    796841392 
406885699892   -1136193228
5262665206      967697910 
544687374       544687374 
6978317806     -1611616786

Other than using a sed call to change every astype(int) to astype(np.int64) (which would also require an import numpy as np at the top of every module where currently that doesn't exist), is there a way to do this?

In particular, I was hoping to map int to numpy.int64 somehow in a pandas option or something.

Thank you!

Answer 1

I'm not saying that this is a really good idea, but you can simply redefine int to whatever you want:

import numpy as np

x = 2384351503.0
print(np.array(x).astype(int))
#-2147483648

old_int = int
int = np.int64
print(np.array(x).astype(int))
#2384351503

int = old_int
print(np.array(x).astype(int))
#-2147483648

In the case you described I'd, however, strongly prefer to fix the source code instead of redefining standard data types. It's a one-time effort and any IDE can do it easyly.

Numpy is already implicitely imported by pandas, so it doesn't cost any additional time or resources. If you really want to avoid it (for whatever reason), you can use pd.Int64Dtype.type instead of np.int64 (see source ).