Expand benchmarks for dataset insertion and creation

Taken from discussions in pydata#7224 (comment)

Thank you @Illviljan

asv_bench/benchmarks/merge.py

@@ -1,4 +1,5 @@
 import xarray as xr
+import numpy as np
 
 
 class DatasetAddVariable:
@@ -13,6 +14,49 @@ def setup(self, existing_elements):
             d[f"var{i}"] = i
         self.dataset = xr.merge([d])
 
-    def time_variable_insertion(self, existin_elements):
+        d = {
+            "set_2_{i}": i
+            for i in range(existing_elements)
+        }
+        self.dataset2 = xr.merge([d])
+
+    def time_variable_insertion(self, existing_elements):
         dataset = self.dataset
         dataset["new_var"] = 0
+
+    def time_merge_two_datasets(self, existing_elements):
+        xr.merge([self.dataset, self.dataset2])
+
+    def time_dict_of_dataarrays_to_dataset(self, existing_elements):
+        # The idea here is to time how long it takes to go from numpy
+        # and python data types, to a full dataset
+        # See discussion
+        # https://github.com/pydata/xarray/issues/7224#issuecomment-1292216344
+        value = np.array(["0", "b"], dtype=str)
+        time = np.array([0, 1])
+        coords = dict(time=time)
+        data_vars = {
+            "long_variable_name_{i}": xr.DataArray(data=value, coords=coords)
+            for i in range(existing_elements)
+        }
+        xr.Dataset(data_vars=data_vars, coords=coords)
+
+    def time_dict_of_variables_to_dataset(self, existing_elements):
+        value = np.array(["0", "b"], dtype=str)
+        time = np.array([0, 1])
+        coords = dict(time=time)
+        data_vars = {
+            "long_variable_name_{i}": xr.Variable("time", value)
+            for i in range(existing_elements)
+        }
+        xr.Dataset(data_vars=data_vars, coords=coords)
+
+    def time_dict_of_tuples_to_dataset(self, existing_elements):
+        value = np.array(["0", "b"], dtype=str)
+        time = np.array([0, 1])
+        coords = dict(time=time)
+        data_vars = {
+            "long_variable_name_{i}": ("time", value)
+            for i in range(existing_elements)
+        }
+        xr.Dataset(data_vars=data_vars, coords=coords)