diff --git a/README.rst b/README.rst
index 42059881..260081aa 100644
--- a/README.rst
+++ b/README.rst
@@ -30,16 +30,27 @@ Installation
    (You might want to use a different bootstrap python executable
    instead of ``python3`` in this example.)
 
-3. Install the package in the virtual environment using one of the following ways,
-   as you prefer:
+3.  Install the package in the virtual environment,
+    preferably through ``pip`` of your virtual environment::
 
-   - traditional way: ``python setup.py install``
-   - with pip: ``pip install .``
-   - if you plan to do development on the package itself,
-     install it in "development" mode with
-     ``python setup.py develop`` or ``pip install -e .``
+        pip install .
+
+    If you plan to do development on the package itself,
+    install it in "development" mode with::
+
+        pip install -e .
+
+    If plan to process xarray or dask arrays, you probably
+    have the corresponding libraries already installed in your virtual env,
+    and ``openeo_processes`` will handle them appropriately out of the box.
+    You can however also explicitly pull these libraries in as "extra" dependencies
+    when installing ``openeo_processes``.
+    For example with one of the following install commands::
+
+        pip install .[dask]
+        pip install .[xarray]
+        pip install .[dask,xarray]
 
-   (Note that in this step we are using python and pip from the virtual environment.)
 
 4. Optionally run the tests::
 
diff --git a/setup.cfg b/setup.cfg
index 9aeeb9de..80058e61 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -27,14 +27,11 @@ package_dir =
 # Add here dependencies of your project (semicolon-separated), e.g.
 # install_requires = numpy; scipy
 install_requires =
-  dask[array]
   numpy
-  xarray
-  xarray-extras
   pandas
 
 # Add here test requirements (semicolon-separated)
-tests_require = pytest; pytest-cov
+tests_require = pytest; pytest-cov; dask; xarray; xarray-extras
 
 [options.packages.find]
 where = src
@@ -45,6 +42,8 @@ exclude =
 # Add here additional requirements for extra features, to install with:
 # `pip install openeo_processes[PDF]` like:
 # PDF = ReportLab; RXP
+dask = dask[array]
+xarray = xarray; xarray-extras
 
 [test]
 # py.test options when running `python setup.py test`
diff --git a/src/openeo_processes/math.py b/src/openeo_processes/math.py
index a3f97aad..147535df 100644
--- a/src/openeo_processes/math.py
+++ b/src/openeo_processes/math.py
@@ -1,6 +1,10 @@
-import numpy as np
-import xarray_extras as xar_addons
 import builtins
+import numpy as np
+
+try:
+    import xarray_extras as xar_addons
+except ImportError:
+    xar_addons = None
 
 from openeo_processes.utils import process
 from openeo_processes.comparison import is_empty
diff --git a/src/openeo_processes/utils.py b/src/openeo_processes/utils.py
index e9df0852..5828c55e 100644
--- a/src/openeo_processes/utils.py
+++ b/src/openeo_processes/utils.py
@@ -1,6 +1,3 @@
-import xarray
-import dask.array
-import datetime
 import re
 import numpy as np
 from datetime import timezone
@@ -12,14 +9,12 @@ def eval_datatype(data):
     """
     Returns a data type tag depending on the data type of `data`.
     This can be:
-        - "np": `np.ndarray` or list (list are directly converted to numpy arrays)
-        - "xar": `xarray.DataArray`
-        - "dar": `dask.array.core.Array`
-        - "num": `np.integer`, `np.float`, int, float, or str
-        - "dt": `datetime.datetime`
-        - "dict": dict
-        - "fun": callable object
-        - "none": None
+        - "numpy": `nump.ndarray`
+        - "xarray": `xarray.DataArray`
+        - "dask": `dask.array.core.Array`
+        - "int", "float", "dict", "list", "set", "tuple", "NoneType": Python builtins
+        - "datetime": `datetime.datetime`
+        - "function": callable object
 
     Parameters
     ----------
@@ -32,37 +27,14 @@ def eval_datatype(data):
         Data type tag.
 
     """
-    is_list = isinstance(data, list)
-    is_np = isinstance(data, np.ndarray) | is_list
-    is_xar = isinstance(data, xarray.DataArray)
-    is_dar = isinstance(data, dask.array.core.Array)
-    is_num = isinstance(data, (int, float, str, np.integer, np.float))
-    is_datetime = isinstance(data, datetime)
-    is_dict = isinstance(data, dict)
-    is_function = callable(data)
-    is_none = data is None
-
-    if is_np:
-        datatype = "np"
-    elif is_xar:
-        datatype = "xar"
-    elif is_dar:
-        datatype = "dar"
-    elif is_num:
-        datatype = "num"
-    elif is_none:
-        datatype = "none"
-    elif is_datetime:
-        datatype = "dt"
-    elif is_dict:
-        datatype = "dict"
-    elif is_function:
-        datatype = "fun"
+    package = type(data).__module__
+    package_root = package.split(".", 1)[0]
+    if package in ("builtins", "datetime"):
+        return type(data).__name__
+    elif package_root in ("numpy", "xarray", "dask"):
+        return package_root
     else:
-        err_msg = "Data type '{}' is unknown.".format(type(data))
-        raise ValueError(err_msg)
-
-    return datatype
+        return package + '.' + type(data).__name__
 
 
 def process(processor):
@@ -84,29 +56,20 @@ def process(processor):
     def fun_wrapper(*args, **kwargs):
         cls = processor()
 
-        datatypes = []
-        # retrieve data types of input arguments and convert lists to numpy arrays
-        args = list(args)
-        for i, arg in enumerate(args):
-            datatypes.append(eval_datatype(arg))
-            if isinstance(arg, list):
-                args[i] = list2nparray(arg)
-        args = tuple(args)
-
-        # retrieve data types of input keyword arguments and convert lists to numpy arrays
-        for key, kwarg in kwargs.items():
-            datatypes.append(eval_datatype(kwarg))
-            if isinstance(kwarg, list):
-                kwargs[key] = list2nparray(kwarg)
-
-        datatypes = np.array(datatypes)
-        if (datatypes == "np").any():
+        # Convert lists to numpy arrays
+        args = tuple(list2nparray(a) if isinstance(a, list) else a for a in args)
+        kwargs = {k: (list2nparray(v) if isinstance(v, list) else v) for k, v in kwargs.items()}
+
+        # retrieve data types of input (keyword) arguments
+        datatypes = set(eval_datatype(a) for a in args)
+        datatypes.update(eval_datatype(v) for v in kwargs.values())
+        if "numpy" in datatypes:
             cls_fun = getattr(cls, "exec_np")
-        elif (datatypes == "xar").any():
+        elif "xarray" in datatypes:
             cls_fun = getattr(cls, "exec_xar")
-        elif (datatypes == "dar").any():
+        elif "dask" in datatypes:
             cls_fun = getattr(cls, "exec_dar")
-        elif ((datatypes == "num") | (datatypes == "none") | (datatypes == "dt")).all():
+        elif datatypes.issubset({"int", "float", "NoneType", "str", "bool", "datetime"}):
             cls_fun = getattr(cls, "exec_num")
         else:
             raise Exception('Datatype unknown.')
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 00000000..5e4b022d
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,28 @@
+import datetime
+
+import dask.array.core
+import numpy as np
+import pytest
+import xarray
+from openeo_processes.utils import eval_datatype
+
+
+@pytest.mark.parametrize(["data", "expected"], [
+    (None, "NoneType"),
+    (True, "bool"),
+    (False, "bool"),
+    ("123", "str"),
+    (123, "int"),
+    (123.456, "float"),
+    ([1, 2, 3], "list"),
+    ((1, 2, 3), "tuple"),
+    ({1, 2, 3}, "set"),
+    ({1: 2, 3: 4}, "dict"),
+    (lambda x, y: x + y, "function"),
+    (datetime.datetime.now(), "datetime"),
+    (np.array([1, 2, 3]), "numpy"),
+    (xarray.DataArray([1, 2, 3]), "xarray"),
+    (dask.array.core.from_array([1, 2, 3]), "dask"),
+])
+def test_eval_datatype(data, expected):
+    assert eval_datatype(data) == expected