Simplify new tests added

* Since int columns will be converted to float if there are unmatched rows, I use `check_dtype=False` but still use nullable int in the assumed correct table.

Author: flcong

tests/integration/fixtures.py

@@ -88,83 +88,45 @@ def datetime_table():
 
 @pytest.fixture
 def user_table_lk():
-    # Link table identified by id and date range (startdate and enddate)
+    # Link table identified by id and startdate
     # Used for query with both equality and inequality conditions
     out = pd.DataFrame(
-        [
-            [0, 0, 2, pd.NA, 110, "a1", 1.1, pd.Timestamp("2001-01-01")],
-            [0, 4, 6, pd.NA, 111, "a2", 1.2, pd.Timestamp("2001-02-01")],
-            [1, 2, 5, pd.NA, 112, "a3", np.nan, pd.Timestamp("2001-03-01")],
-            [1, 4, 6, 13, 113, "a4", np.nan, pd.Timestamp("2001-04-01")],
-            [3, 1, 2, 14, 114, "a5", np.nan, pd.NaT],
-            [3, 2, 3, 15, 115, "a6", 1.6, pd.NaT],
-        ],
-        columns=[
-            "id",
-            "startdate",
-            "enddate",
-            "lk_nullint",
-            "lk_int",
-            "lk_str",
-            "lk_float",
-            "lk_date",
-        ],
+        [[0, 5, 11, 111], [1, 2, pd.NA, 112], [1, 4, 13, 113], [3, 1, 14, 114],],
+        columns=["id", "startdate", "lk_nullint", "lk_int"],
     )
     out["lk_nullint"] = out["lk_nullint"].astype("Int32")
-    out["lk_str"] = out["lk_str"].astype("string")
     return out
 
 
 @pytest.fixture
 def user_table_lk2(user_table_lk):
-    # Link table identified by only date range (startdate and enddate)
+    # Link table identified by startdate only
     # Used for query with inequality conditions
-    return user_table_lk.set_index("id").loc[1].reset_index(drop=True)
+    out = pd.DataFrame(
+        [[2, pd.NA, 112], [4, 13, 113],], columns=["startdate", "lk_nullint", "lk_int"],
+    )
+    out["lk_nullint"] = out["lk_nullint"].astype("Int32")
+    return out
 
 
 @pytest.fixture
 def user_table_ts():
     # A table of time-series data identified by dates
     out = pd.DataFrame(
-        [
-            [3, pd.NA, 221, "b1", 2.1, pd.Timestamp("2002-01-01")],
-            [4, 22, 222, "b2", np.nan, pd.Timestamp("2002-02-01")],
-            [7, 23, 223, "b3", 2.3, pd.NaT],
-        ],
-        columns=["dates", "ts_nullint", "ts_int", "ts_str", "ts_float", "ts_date"],
+        [[1, 21], [3, pd.NA], [7, 23],], columns=["dates", "ts_nullint"],
     )
     out["ts_nullint"] = out["ts_nullint"].astype("Int32")
-    out["ts_str"] = out["ts_str"].astype("string")
     return out
 
 
 @pytest.fixture
 def user_table_pn():
     # A panel table identified by id and dates
     out = pd.DataFrame(
-        [
-            [0, 1, pd.NA, 331, "c1", 3.1, pd.Timestamp("2003-01-01")],
-            [0, 2, pd.NA, 332, "c2", 3.2, pd.Timestamp("2003-02-01")],
-            [0, 3, pd.NA, 333, "c3", 3.3, pd.Timestamp("2003-03-01")],
-            [1, 3, pd.NA, 334, "c4", np.nan, pd.Timestamp("2003-04-01")],
-            [1, 4, 35, 335, "c5", np.nan, pd.Timestamp("2003-05-01")],
-            [2, 1, 36, 336, "c6", np.nan, pd.Timestamp("2003-06-01")],
-            [2, 3, 37, 337, "c7", np.nan, pd.NaT],
-            [3, 2, 38, 338, "c8", 3.8, pd.NaT],
-            [3, 2, 39, 339, "c9", 3.9, pd.NaT],
-        ],
-        columns=[
-            "ids",
-            "dates",
-            "pn_nullint",
-            "pn_int",
-            "pn_str",
-            "pn_float",
-            "pn_date",
-        ],
+        [[0, 1, pd.NA], [1, 5, 32], [2, 1, 33],],
+        columns=["ids", "dates", "pn_nullint"],
     )
     out["pn_nullint"] = out["pn_nullint"].astype("Int32")
-    out["pn_str"] = out["pn_str"].astype("string")
     return out