So, a more complete example showing this problem. NetCDF file used in the example : test.nc.zip

````python from netCDF4 import Dataset import xarray as xr import numpy as np import pandas as pd

d = Dataset("test.nc") v = d.variables['var']

print(v)

<class 'netCDF4._netCDF4.Variable'>

int16 var(idx)

_FillValue: 32767

scale_factor: 0.01

unlimited dimensions:

current shape = (2,)

filling on

df_nc = pd.DataFrame(data={'var': v[:]})

print(df_nc)

var

0 21.94

1 27.04

ds = xr.open_dataset("test.nc") df_xr = ds['var'].to_dataframe()

Comparing both dataframes with float32 precision (1e-6)

mask = np.isclose(df_nc['var'], df_xr['var'], rtol=0, atol=1e-6)

print(mask)

[False True]

print(df_xr)

var

idx

0 21.939999

1 27.039999

Changing the type and rounding the xarray dataframe

df_xr2 = df_xr.astype(np.float64).round(int(np.ceil(-np.log10(ds['var'].encoding['scale_factor'])))) mask = np.isclose(df_nc['var'], df_xr2['var'], rtol=0, atol=1e-6)

print(mask)

[ True True]

print(df_xr2)

var

idx

0 21.94

1 27.04

````

As you can see, the problem appears early in the process (not related to the way data are stored in parquet later on) and yes, rounding values does solve it.