The behavior of xarray when assigning values with incompatible dtypes is a bit arbitrary. This is partly due to the behavior of numpy.... numpy 1.20 got a bit cleverer but still seems inconsistent at times... I am not sure what to do about this (and if we should actually be clever here).

1. Direct assignment (dupe of #4612)

```python import xarray as xr import numpy as np

arr = np.array([2])

arr[0] = np.nan

ValueError (since numpy 1.20)

arr[0:1] = np.array([np.nan])

-> array([-9223372036854775808])

da = xr.DataArray([5], dims="x")

da[0] = np.nan

<xarray.DataArray (x: 1)>

array([-9223372036854775808])

Dimensions without coordinates: x

(because this gets converted to da.variable._data[0:1, 0:1] = np.array([np.nan]) (approximately).

da[0] = 1.2345

casts constant_values to int

```

1. Via a numpy function (pad, shift, rolling)

pad

```python da.pad(x=1, constant_values=np.nan)

ValueError: cannot convert float NaN to integer

da.pad(x=1, constant_values=None)

casts da to float

da.pad(x=1, constant_values=1.5)

casts constant_values to int

```

**shift** ```python da.shift(x=1, fill_value=np.nan) # ValueError: cannot convert float NaN to integer # da.shift(x=1, fill_value=None) # None not allowed by shift da.shift(x=1, fill_value=1.5) # casts fill_value to int ``` **rolling** ```python da.rolling(x=1).construct("new_axis", stride=3, fill_value=np.nan) # ValueError: cannot convert float NaN to integer # da.rolling(x=1).construct("new_axis", stride=3, fill_value=None) # None not allowed by rolling da.rolling(x=3).construct("new_axis", stride=3, fill_value=1.5) # casts fill_value to int ```

---

To check:

• What does dask do in these cases?
• What does pandas do?
• What about str dtypes?