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https://github.com/pydata/xarray/issues/2304#issuecomment-410807622 https://api.github.com/repos/pydata/xarray/issues/2304 410807622 MDEyOklzc3VlQ29tbWVudDQxMDgwNzYyMg== 1217238 2018-08-06T18:33:06Z 2018-08-06T18:33:06Z MEMBER

Please let us know if converting to float64 explicitly and rounding again does not solve this issue for you.

On Mon, Aug 6, 2018 at 10:47 AM Thomas Zilio notifications@github.com wrote:

To explain the full context and why it became some kind of a problem to us :

We're experimenting with the parquet format (via pyarrow) and we first did something like : netcdf file -> netcdf4 -> pandas -> pyarrow -> pandas (when read later on).

We're now looking at xarray and the the huge ease of access it offers to netcdf like data and we tried something similar : netcdf file -> xarray -> pandas -> pyarrow -> pandas (when read later on).

Our problem appears when we're reading and comparing the data stored with these 2 approches. The difference between the 2 was - sometimes - larger than what expected/acceptable (10e-6 for float32 if I'm not mistaken). We're not constraining any type and letting the system and modules decide how to encode what and in the end we have significantly different values.

There might be something wrong in our process but it originate here with this float32/float64 choice so we thought it might be a problem.

Thanks for taking the time to look into this.

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