Improve unit tests

Author: vasil-pashov

python/tests/hypothesis/arcticdb/test_resample.py

@@ -11,7 +11,7 @@
 COLUMN_DTYPE = ["float", "int", "uint"]
 ALL_AGGREGATIONS = ["sum", "mean", "min", "max", "first", "last", "count"]
 MIN_DATE = np.datetime64('1969-01-01')
-MAX_DATE = np.datetime64('1973-01-01')
+MAX_DATE = np.datetime64('1971-01-01')
 
 pytestmark = pytest.mark.pipeline
 
@@ -69,8 +69,8 @@ def origin(draw):
     selected_origin = draw(st.sampled_from(["start", "end", "start_day", "end_day", "epoch", "timestamp"]))
     # Hypothesis may generate dates for year > 2200 and some of the arithmetic operation will overflow.
     if selected_origin == "timestamp":
-        min_date = MIN_DATE - np.timedelta64(365, 'D')
-        max_date = MAX_DATE + np.timedelta64(365, 'D')
+        min_date = MIN_DATE - np.timedelta64(5, 'D')
+        max_date = MAX_DATE + np.timedelta64(5, 'D')
         return pd.Timestamp(draw(date(min_date=min_date, max_date=max_date)))
     else:
         return selected_origin
@@ -96,7 +96,7 @@ def offset(draw):
     unit = draw(st.sampled_from(['s', 'min', 'h', None]))
     if unit is None:
         return None
-    count = draw(st.integers(min_value=1, max_value=10_000))
+    count = draw(st.integers(min_value=1, max_value=100))
     result = f"{count}{unit}"
     assume(freq_fits_in_64_bits(count=count, unit=unit))
     return result

python/tests/unit/arcticdb/version_store/test_aggregation.py

@@ -524,14 +524,14 @@ def test_extremum_aggregation_with_missing_aggregation_column(lmdb_version_store
     lib = lmdb_version_store_dynamic_schema_v1
     sym = "sym"
     df1 = pd.DataFrame({"agg_column": np.array([0.0, 0.0], dtype)})
-    df2 = pd.DataFrame({"grouping_column": ["0"]})
-    df3 = pd.DataFrame({"grouping_column": ["00"], "agg_column": np.array([0], dtype)})
+    df2 = pd.DataFrame({"grouping_column": [0]})
+    df3 = pd.DataFrame({"grouping_column": [1], "agg_column": np.array([0], dtype)})
     for df in [df1, df2, df3]:
         lib.append(sym, df)
     q = QueryBuilder()
     q = q.groupby("grouping_column").agg({"agg_column": extremum})
     data = lib.read("sym", query_builder=q).data
     default_value = 0 if dtype == np.int32 else np.nan
-    expected = pd.DataFrame({"agg_column": np.array([default_value, 0], dtype)}, index=["0", "00"])
+    expected = pd.DataFrame({"agg_column": np.array([default_value, 0], dtype)}, index=[0, 1])
     expected.index.name = "grouping_column"
     assert_frame_equal(data, expected)